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Optimizing Preprocessing and Analysis Pipelines for Single-Subject FMRI. I. Standard Temporal Motion and Physiological Noise Correction Methods

PubMed Central

Churchill, Nathan W.; Oder, Anita; Abdi, Hervé; Tam, Fred; Lee, Wayne; Thomas, Christopher; Ween, Jon E.; Graham, Simon J.; Strother, Stephen C.

2016-01-01

Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747–771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89–95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or “pipeline”) in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was demonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods. PMID:21455942
Embedded sparse representation of fMRI data via group-wise dictionary optimization

NASA Astrophysics Data System (ADS)

Zhu, Dajiang; Lin, Binbin; Faskowitz, Joshua; Ye, Jieping; Thompson, Paul M.

2016-03-01

Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.
The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI.

PubMed

Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

2017-06-15

Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.
Learning and Generalization under Ambiguity: An fMRI Study

PubMed Central

Chumbley, J. R.; Flandin, G.; Bach, D. R.; Daunizeau, J.; Fehr, E.; Dolan, R. J.; Friston, K. J.

2012-01-01

Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence. PMID:22275857
Learning and generalization under ambiguity: an fMRI study.

PubMed

Chumbley, J R; Flandin, G; Bach, D R; Daunizeau, J; Fehr, E; Dolan, R J; Friston, K J

2012-01-01

Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence.
Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field.

PubMed

Moerel, Michelle; De Martino, Federico; Kemper, Valentin G; Schmitter, Sebastian; Vu, An T; Uğurbil, Kâmil; Formisano, Elia; Yacoub, Essa

2018-01-01

Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T 2 * weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T 2 weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T 2 * weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T 2 * weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency preference and selectivity for the GE-EPI dataset, but not for the 3D GRASE dataset. Thus, a T 2 weighted acquisition is recommended if high specificity in tonotopic maps is required. In conclusion, different fMRI acquisitions were better suited for different analyses. It is therefore critical that any sequence parameter optimization considers the eventual intended fMRI analyses and the nature of the neuroscience questions being asked. Copyright © 2017 Elsevier Inc. All rights reserved.
Technical Note: Independent component analysis for quality assurance in functional MRI.

PubMed

Astrakas, Loukas G; Kallistis, Nikolaos S; Kalef-Ezra, John A

2016-02-01

Independent component analysis (ICA) is an established method of analyzing human functional MRI (fMRI) data. Here, an ICA-based fMRI quality control (QC) tool was developed and used. ICA-based fMRI QC tool to be used with a commercial phantom was developed. In an attempt to assess the performance of the tool relative to preexisting alternative tools, it was used seven weeks before and eight weeks after repair of a faulty gradient amplifier of a non-state-of-the-art MRI unit. More specifically, its performance was compared with the AAPM 100 acceptance testing and quality assurance protocol and two fMRI QC protocols, proposed by Freidman et al. ["Report on a multicenter fMRI quality assurance protocol," J. Magn. Reson. Imaging 23, 827-839 (2006)] and Stocker et al. ["Automated quality assurance routines for fMRI data applied to a multicenter study," Hum. Brain Mapp. 25, 237-246 (2005)], respectively. The easily developed and applied ICA-based QC protocol provided fMRI QC indices and maps equally sensitive to fMRI instabilities with the indices and maps of other established protocols. The ICA fMRI QC indices were highly correlated with indices of other fMRI QC protocols and in some cases theoretically related to them. Three or four independent components with slow varying time series are detected under normal conditions. ICA applied on phantom measurements is an easy and efficient tool for fMRI QC. Additionally, it can protect against misinterpretations of artifact components as human brain activations. Evaluating fMRI QC indices in the central region of a phantom is not always the optimal choice.
Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage.

PubMed

Aggarwal, Priya; Shrivastava, Parth; Kabra, Tanay; Gupta, Anubha

2017-03-01

This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l 1 minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. The proposed OptShrink LR + S method yields good qualitative and quantitative results.
Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.
Benevolent sexism alters executive brain responses.

PubMed

Dardenne, Benoit; Dumont, Muriel; Sarlet, Marie; Phillips, Christophe; Balteau, Evelyne; Degueldre, Christian; Luxen, André; Salmon, Eric; Maquet, Pierre; Collette, Fabienne

2013-07-10

Benevolence is widespread in our societies. It is defined as considering a subordinate group nicely but condescendingly, that is, with charity. Deleterious consequences for the target have been reported in the literature. In this experiment, we used functional MRI (fMRI) to identify whether being the target of (sexist) benevolence induces changes in brain activity associated with a working memory task. Participants were confronted by benevolent, hostile, or neutral comments before and while performing a reading span test in an fMRI environment. fMRI data showed that brain regions associated previously with intrusive thought suppression (bilateral, dorsolateral, prefrontal, and anterior cingulate cortex) reacted specifically to benevolent sexism compared with hostile sexism and neutral conditions during the performance of the task. These findings indicate that, despite being subjectively positive, benevolence modifies task-related brain networks by recruiting supplementary areas likely to impede optimal cognitive performance.
Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

PubMed Central

Uğurbil, Kamil; Xu, Junqian; Auerbach, Edward J.; Moeller, Steen; Vu, An; Duarte-Carvajalino, Julio M.; Lenglet, Christophe; Wu, Xiaoping; Schmitter, Sebastian; Van de Moortele, Pierre Francois; Strupp, John; Sapiro, Guillermo; De Martino, Federico; Wang, Dingxin; Harel, Noam; Garwood, Michael; Chen, Liyong; Feinberg, David A.; Smith, Stephen M.; Miller, Karla L.; Sotiropoulos, Stamatios N; Jbabdi, Saad; Andersson, Jesper L; Behrens, Timothy EJ; Glasser, Matthew F.; Van Essen, David; Yacoub, Essa

2013-01-01

The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure. PMID:23702417
Anticipating agoraphobic situations: the neural correlates of panic disorder with agoraphobia.

PubMed

Wittmann, A; Schlagenhauf, F; Guhn, A; Lueken, U; Gaehlsdorf, C; Stoy, M; Bermpohl, F; Fydrich, T; Pfleiderer, B; Bruhn, H; Gerlach, A L; Kircher, T; Straube, B; Wittchen, H-U; Arolt, V; Heinz, A; Ströhle, A

2014-08-01

Panic disorder with agoraphobia is characterized by panic attacks and anxiety in situations where escape might be difficult. However, neuroimaging studies specifically focusing on agoraphobia are rare. Here we used functional magnetic resonance imaging (fMRI) with disorder-specific stimuli to investigate the neural substrates of agoraphobia. We compared the neural activations of 72 patients suffering from panic disorder with agoraphobia with 72 matched healthy control subjects in a 3-T fMRI study. To isolate agoraphobia-specific alterations we tested the effects of the anticipation and perception of an agoraphobia-specific stimulus set. During fMRI, 48 agoraphobia-specific and 48 neutral pictures were randomly presented with and without anticipatory stimulus indicating the content of the subsequent pictures (Westphal paradigm). During the anticipation of agoraphobia-specific pictures, stronger activations were found in the bilateral ventral striatum and left insula in patients compared with controls. There were no group differences during the perception phase of agoraphobia-specific pictures. This study revealed stronger region-specific activations in patients suffering from panic disorder with agoraphobia in anticipation of agoraphobia-specific stimuli. Patients seem to process these stimuli more intensively based on individual salience. Hyperactivation of the ventral striatum and insula when anticipating agoraphobia-specific situations might be a central neurofunctional correlate of agoraphobia. Knowledge about the neural correlates of anticipatory and perceptual processes regarding agoraphobic situations will help to optimize and evaluate treatments, such as exposure therapy, in patients with panic disorder and agoraphobia.
Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

PubMed

Ting, Chee-Ming; Seghouane, Abd-Krim; Khalid, Muhammad Usman; Salleh, Sh-Hussain

2015-09-01

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.
Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI.

PubMed

Ferradal, Silvina L; Eggebrecht, Adam T; Hassanpour, Mahlega; Snyder, Abraham Z; Culver, Joseph P

2014-01-15

Diffuse optical imaging (DOI) is increasingly becoming a valuable neuroimaging tool when fMRI is precluded. Recent developments in high-density diffuse optical tomography (HD-DOT) overcome previous limitations of sparse DOI systems, providing improved image quality and brain specificity. These improvements in instrumentation prompt the need for advancements in both i) realistic forward light modeling for accurate HD-DOT image reconstruction, and ii) spatial normalization for voxel-wise comparisons across subjects. Individualized forward light models derived from subject-specific anatomical images provide the optimal inverse solutions, but such modeling may not be feasible in all situations. In the absence of subject-specific anatomical images, atlas-based head models registered to the subject's head using cranial fiducials provide an alternative solution. In addition, a standard atlas is attractive because it defines a common coordinate space in which to compare results across subjects. The question therefore arises as to whether atlas-based forward light modeling ensures adequate HD-DOT image quality at the individual and group level. Herein, we demonstrate the feasibility of using atlas-based forward light modeling and spatial normalization methods. Both techniques are validated using subject-matched HD-DOT and fMRI data sets for visual evoked responses measured in five healthy adult subjects. HD-DOT reconstructions obtained with the registered atlas anatomy (i.e. atlas DOT) had an average localization error of 2.7mm relative to reconstructions obtained with the subject-specific anatomical images (i.e. subject-MRI DOT), and 6.6mm relative to fMRI data. At the group level, the localization error of atlas DOT reconstruction was 4.2mm relative to subject-MRI DOT reconstruction, and 6.1mm relative to fMRI. These results show that atlas-based image reconstruction provides a viable approach to individual head modeling for HD-DOT when anatomical imaging is not available. Copyright © 2013. Published by Elsevier Inc.
Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

PubMed Central

Jang, Hojin; Plis, Sergey M.; Calhoun, Vince D.; Lee, Jong-Hwan

2016-01-01

Feedforward deep neural networks (DNN), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean ± standard deviation; %) of 6.9 (± 3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4 ± 4.6) and the two-layer network (7.4 ± 4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. PMID:27079534
Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

PubMed

Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

2017-01-15

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. Copyright © 2016 Elsevier Inc. All rights reserved.
Compressed Sensing for fMRI: Feasibility Study on the Acceleration of Non-EPI fMRI at 9.4T

PubMed Central

Kim, Seong-Gi; Ye, Jong Chul

2015-01-01

Conventional functional magnetic resonance imaging (fMRI) technique known as gradient-recalled echo (GRE) echo-planar imaging (EPI) is sensitive to image distortion and degradation caused by local magnetic field inhomogeneity at high magnetic fields. Non-EPI sequences such as spoiled gradient echo and balanced steady-state free precession (bSSFP) have been proposed as an alternative high-resolution fMRI technique; however, the temporal resolution of these sequences is lower than the typically used GRE-EPI fMRI. One potential approach to improve the temporal resolution is to use compressed sensing (CS). In this study, we tested the feasibility of k-t FOCUSS—one of the high performance CS algorithms for dynamic MRI—for non-EPI fMRI at 9.4T using the model of rat somatosensory stimulation. To optimize the performance of CS reconstruction, different sampling patterns and k-t FOCUSS variations were investigated. Experimental results show that an optimized k-t FOCUSS algorithm with acceleration by a factor of 4 works well for non-EPI fMRI at high field under various statistical criteria, which confirms that a combination of CS and a non-EPI sequence may be a good solution for high-resolution fMRI at high fields. PMID:26413503
Improving fMRI reliability in presurgical mapping for brain tumours.

PubMed

Stevens, M Tynan R; Clarke, David B; Stroink, Gerhard; Beyea, Steven D; D'Arcy, Ryan Cn

2016-03-01

Functional MRI (fMRI) is becoming increasingly integrated into clinical practice for presurgical mapping. Current efforts are focused on validating data quality, with reliability being a major factor. In this paper, we demonstrate the utility of a recently developed approach that uses receiver operating characteristic-reliability (ROC-r) to: (1) identify reliable versus unreliable data sets; (2) automatically select processing options to enhance data quality; and (3) automatically select individualised thresholds for activation maps. Presurgical fMRI was conducted in 16 patients undergoing surgical treatment for brain tumours. Within-session test-retest fMRI was conducted, and ROC-reliability of the patient group was compared to a previous healthy control cohort. Individually optimised preprocessing pipelines were determined to improve reliability. Spatial correspondence was assessed by comparing the fMRI results to intraoperative cortical stimulation mapping, in terms of the distance to the nearest active fMRI voxel. The average ROC-r reliability for the patients was 0.58±0.03, as compared to 0.72±0.02 in healthy controls. For the patient group, this increased significantly to 0.65±0.02 by adopting optimised preprocessing pipelines. Co-localisation of the fMRI maps with cortical stimulation was significantly better for more reliable versus less reliable data sets (8.3±0.9 vs 29±3 mm, respectively). We demonstrated ROC-r analysis for identifying reliable fMRI data sets, choosing optimal postprocessing pipelines, and selecting patient-specific thresholds. Data sets with higher reliability also showed closer spatial correspondence to cortical stimulation. ROC-r can thus identify poor fMRI data at time of scanning, allowing for repeat scans when necessary. ROC-r analysis provides optimised and automated fMRI processing for improved presurgical mapping. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Anterior Medial Temporal Lobe Activation during Encoding of Words: FMRI Methods to Optimize Sensitivity

ERIC Educational Resources Information Center

Parsons, Michael W.; Haut, Marc W.; Lemieux, Susan K.; Moran, Maria T.; Leach, Sharon G.

2006-01-01

The existence of a rostrocaudal gradient of medial temporal lobe (MTL) activation during memory encoding has historically received support from positron emission tomography studies, but less so from functional MRI (FMRI) studies. More recently, FMRI studies have demonstrated that characteristics of the stimuli can affect the location of activation…
Optimal experimental designs for fMRI when the model matrix is uncertain.

PubMed

Kao, Ming-Hung; Zhou, Lin

2017-07-15

This study concerns optimal designs for functional magnetic resonance imaging (fMRI) experiments when the model matrix of the statistical model depends on both the selected stimulus sequence (fMRI design), and the subject's uncertain feedback (e.g. answer) to each mental stimulus (e.g. question) presented to her/him. While practically important, this design issue is challenging. This mainly is because that the information matrix cannot be fully determined at the design stage, making it difficult to evaluate the quality of the selected designs. To tackle this challenging issue, we propose an easy-to-use optimality criterion for evaluating the quality of designs, and an efficient approach for obtaining designs optimizing this criterion. Compared with a previously proposed method, our approach requires a much less computing time to achieve designs with high statistical efficiencies. Copyright © 2017 Elsevier Inc. All rights reserved.

Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns

PubMed Central

Santoro, Roberta; Moerel, Michelle; De Martino, Federico; Valente, Giancarlo; Ugurbil, Kamil; Yacoub, Essa; Formisano, Elia

2017-01-01

Ethological views of brain functioning suggest that sound representations and computations in the auditory neural system are optimized finely to process and discriminate behaviorally relevant acoustic features and sounds (e.g., spectrotemporal modulations in the songs of zebra finches). Here, we show that modeling of neural sound representations in terms of frequency-specific spectrotemporal modulations enables accurate and specific reconstruction of real-life sounds from high-resolution functional magnetic resonance imaging (fMRI) response patterns in the human auditory cortex. Region-based analyses indicated that response patterns in separate portions of the auditory cortex are informative of distinctive sets of spectrotemporal modulations. Most relevantly, results revealed that in early auditory regions, and progressively more in surrounding regions, temporal modulations in a range relevant for speech analysis (∼2–4 Hz) were reconstructed more faithfully than other temporal modulations. In early auditory regions, this effect was frequency-dependent and only present for lower frequencies (<∼2 kHz), whereas for higher frequencies, reconstruction accuracy was higher for faster temporal modulations. Further analyses suggested that auditory cortical processing optimized for the fine-grained discrimination of speech and vocal sounds underlies this enhanced reconstruction accuracy. In sum, the present study introduces an approach to embed models of neural sound representations in the analysis of fMRI response patterns. Furthermore, it reveals that, in the human brain, even general purpose and fundamental neural processing mechanisms are shaped by the physical features of real-world stimuli that are most relevant for behavior (i.e., speech, voice). PMID:28420788
A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

PubMed Central

Soares, José M.; Magalhães, Ricardo; Moreira, Pedro S.; Sousa, Alexandre; Ganz, Edward; Sampaio, Adriana; Alves, Victor; Marques, Paulo; Sousa, Nuno

2016-01-01

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community. PMID:27891073
Cortical lamina-dependent blood volume changes in human brain at 7 T.

PubMed

Huber, Laurentius; Goense, Jozien; Kennerley, Aneurin J; Trampel, Robert; Guidi, Maria; Reimer, Enrico; Ivanov, Dimo; Neef, Nicole; Gauthier, Claudine J; Turner, Robert; Möller, Harald E

2015-02-15

Cortical layer-dependent high (sub-millimeter) resolution functional magnetic resonance imaging (fMRI) in human or animal brain can be used to address questions regarding the functioning of cortical circuits, such as the effect of different afferent and efferent connectivities on activity in specific cortical layers. The sensitivity of gradient echo (GE) blood oxygenation level-dependent (BOLD) responses to large draining veins reduces its local specificity and can render the interpretation of the underlying laminar neural activity impossible. The application of the more spatially specific cerebral blood volume (CBV)-based fMRI in humans has been hindered by the low sensitivity of the noninvasive modalities available. Here, a vascular space occupancy (VASO) variant, adapted for use at high field, is further optimized to capture layer-dependent activity changes in human motor cortex at sub-millimeter resolution. Acquired activation maps and cortical profiles show that the VASO signal peaks in gray matter at 0.8-1.6mm depth, and deeper compared to the superficial and vein-dominated GE-BOLD responses. Validation of the VASO signal change versus well-established iron-oxide contrast agent based fMRI methods in animals showed the same cortical profiles of CBV change, after normalization for lamina-dependent baseline CBV. In order to evaluate its potential of revealing small lamina-dependent signal differences due to modulations of the input-output characteristics, layer-dependent VASO responses were investigated in the ipsilateral hemisphere during unilateral finger tapping. Positive activation in ipsilateral primary motor cortex and negative activation in ipsilateral primary sensory cortex were observed. This feature is only visible in high-resolution fMRI where opposing sides of a sulcus can be investigated independently because of a lack of partial volume effects. Based on the results presented here, we conclude that VASO offers good reproducibility, high sensitivity and lower sensitivity than GE-BOLD to changes in larger vessels, making it a valuable tool for layer-dependent fMRI studies in humans. Copyright © 2014 Elsevier Inc. All rights reserved.
Feature-space-based FMRI analysis using the optimal linear transformation.

PubMed

Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

2010-09-01

The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.
Optimizing Complexity Measures for fMRI Data: Algorithm, Artifact, and Sensitivity

PubMed Central

Rubin, Denis; Fekete, Tomer; Mujica-Parodi, Lilianne R.

2013-01-01

Introduction Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization. Methods Here we use both simulated and real data to address two fundamental issues: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi’s estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents. Results Power-spectrum, Higuchi’s fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range. Conclusions Functional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG) for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and sensitivity of complexity estimates. PMID:23700424
Laminar fMRI and computational theories of brain function.

PubMed

Stephan, K E; Petzschner, F H; Kasper, L; Bayer, J; Wellstein, K V; Stefanics, G; Pruessmann, K P; Heinzle, J

2017-11-02

Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing. Copyright © 2017 Elsevier Inc. All rights reserved.
Brain Functional Connectivity in MS: An EEG-NIRS Study

DTIC Science & Technology

2015-10-01

electrical (EEG) and blood volume and blood oxygen-based (NIRS and fMRI ) signals, and to use the results to help optimize blood oxygen level...dependent (BOLD) fMRI analyses of brain activity. Participants will be patients with MS (n=25) and healthy demographically matched controls (n=25) who will...undergo standardized evaluations and imaging using combined EEG-NIRS- fMRI . EEG-NIRS data will be used to construct maps of neurovascular coupling
Integration of Functional Magnetic Resonance Imaging and Magnetoencephalography Functional Maps Into a CyberKnife Planning System: Feasibility Study for Motor Activity Localization and Dose Planning.

PubMed

De Martin, Elena; Duran, Dunja; Ghielmetti, Francesco; Visani, Elisa; Aquino, Domenico; Marchetti, Marcello; Sebastiano, Davide Rossi; Cusumano, Davide; Bruzzone, Maria Grazia; Panzica, Ferruccio; Fariselli, Laura

2017-12-01

Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) provide noninvasive localization of eloquent brain areas for presurgical planning. The aim of this study is the integration of MEG and fMRI maps into a CyberKnife (CK) system to optimize dose planning. Four patients with brain metastases in the motor area underwent functional imaging study of the hand motor cortex before radiosurgery. MEG data were acquired during a visually cued hand motor task. Motor activations were identified also using an fMRI block-designed paradigm. MEG and fMRI maps were then integrated into a CK system and contoured as organs at risk for treatment planning optimization. The integration of fMRI data into the CK system was achieved for all patients by means of a standardized protocol. We also implemented an ad hoc pipeline to convert the MEG signal into a DICOM standard, to make sure that it was readable by our CK treatment planning system. Inclusion of the activation areas into the optimization plan allowed the creation of treatment plans that reduced the irradiation of the motor cortex yet not affecting the brain peripheral dose. The availability of advanced neuroimaging techniques is playing an increasingly important role in radiosurgical planning strategy. We successfully imported MEG and fMRI activations into a CK system. This additional information can improve dose sparing of eloquent areas, allowing a more comprehensive investigation of the related dose-volume constraints that in theory could translate into a gain in tumor local control, and a reduction of neurological complications. Copyright © 2017 Elsevier Inc. All rights reserved.
Detecting functional magnetic resonance imaging activation in white matter: Interhemispheric transfer across the corpus callosum

PubMed Central

Mazerolle, Erin L; D'Arcy, Ryan CN; Beyea, Steven D

2008-01-01

Background It is generally believed that activation in functional magnetic resonance imaging (fMRI) is restricted to gray matter. Despite this, a number of studies have reported white matter activation, particularly when the corpus callosum is targeted using interhemispheric transfer tasks. These findings suggest that fMRI signals may not be neatly confined to gray matter tissue. In the current experiment, 4 T fMRI was employed to evaluate whether it is possible to detect white matter activation. We used an interhemispheric transfer task modelled after neurological studies of callosal disconnection. It was hypothesized that white matter activation could be detected using fMRI. Results Both group and individual data were considered. At liberal statistical thresholds (p < 0.005, uncorrected), group level activation was detected in the isthmus of the corpus callosum. This region connects the superior parietal cortices, which have been implicated previously in interhemispheric transfer. At the individual level, five of the 24 subjects (21%) had activation clusters that were located primarily within the corpus callosum. Consistent with the group results, the clusters of all five subjects were located in posterior callosal regions. The signal time courses for these clusters were comparable to those observed for task related gray matter activation. Conclusion The findings support the idea that, despite the inherent challenges, fMRI activation can be detected in the corpus callosum at the individual level. Future work is needed to determine whether the detection of this activation can be improved by utilizing higher spatial resolution, optimizing acquisition parameters, and analyzing the data with tissue specific models of the hemodynamic response. The ability to detect white matter fMRI activation expands the scope of basic and clinical brain mapping research, and provides a new approach for understanding brain connectivity. PMID:18789154
Synchronized delta oscillations correlate with the resting-state functional MRI signal

PubMed Central

Lu, Hanbing; Zuo, Yantao; Gu, Hong; Waltz, James A.; Zhan, Wang; Scholl, Clara A.; Rea, William; Yang, Yihong; Stein, Elliot A.

2007-01-01

Synchronized low-frequency spontaneous fluctuations of the functional MRI (fMRI) signal have recently been applied to investigate large-scale neuronal networks of the brain in the absence of specific task instructions. However, the underlying neural mechanisms of these fluctuations remain largely unknown. To this end, electrophysiological recordings and resting-state fMRI measurements were conducted in α-chloralose-anesthetized rats. Using a seed-voxel analysis strategy, region-specific, anesthetic dose-dependent fMRI resting-state functional connectivity was detected in bilateral primary somatosensory cortex (S1FL) of the resting brain. Cortical electroencephalographic signals were also recorded from bilateral S1FL; a visual cortex locus served as a control site. Results demonstrate that, unlike the evoked fMRI response that correlates with power changes in the γ bands, the resting-state fMRI signal correlates with the power coherence in low-frequency bands, particularly the δ band. These data indicate that hemodynamic fMRI signal differentially registers specific electrical oscillatory frequency band activity, suggesting that fMRI may be able to distinguish the ongoing from the evoked activity of the brain. PMID:17991778
Test-retest reliability of evoked BOLD signals from a cognitive-emotive fMRI test battery.

PubMed

Plichta, Michael M; Schwarz, Adam J; Grimm, Oliver; Morgen, Katrin; Mier, Daniela; Haddad, Leila; Gerdes, Antje B M; Sauer, Carina; Tost, Heike; Esslinger, Christine; Colman, Peter; Wilson, Frederick; Kirsch, Peter; Meyer-Lindenberg, Andreas

2012-04-15

Even more than in cognitive research applications, moving fMRI to the clinic and the drug development process requires the generation of stable and reliable signal changes. The performance characteristics of the fMRI paradigm constrain experimental power and may require different study designs (e.g., crossover vs. parallel groups), yet fMRI reliability characteristics can be strongly dependent on the nature of the fMRI task. The present study investigated both within-subject and group-level reliability of a combined three-task fMRI battery targeting three systems of wide applicability in clinical and cognitive neuroscience: an emotional (face matching), a motivational (monetary reward anticipation) and a cognitive (n-back working memory) task. A group of 25 young, healthy volunteers were scanned twice on a 3T MRI scanner with a mean test-retest interval of 14.6 days. FMRI reliability was quantified using the intraclass correlation coefficient (ICC) applied at three different levels ranging from a global to a localized and fine spatial scale: (1) reliability of group-level activation maps over the whole brain and within targeted regions of interest (ROIs); (2) within-subject reliability of ROI-mean amplitudes and (3) within-subject reliability of individual voxels in the target ROIs. Results showed robust evoked activation of all three tasks in their respective target regions (emotional task=amygdala; motivational task=ventral striatum; cognitive task=right dorsolateral prefrontal cortex and parietal cortices) with high effect sizes (ES) of ROI-mean summary values (ES=1.11-1.44 for the faces task, 0.96-1.43 for the reward task, 0.83-2.58 for the n-back task). Reliability of group level activation was excellent for all three tasks with ICCs of 0.89-0.98 at the whole brain level and 0.66-0.97 within target ROIs. Within-subject reliability of ROI-mean amplitudes across sessions was fair to good for the reward task (ICCs=0.56-0.62) and, dependent on the particular ROI, also fair-to-good for the n-back task (ICCs=0.44-0.57) but lower for the faces task (ICC=-0.02-0.16). In conclusion, all three tasks are well suited to between-subject designs, including imaging genetics. When specific recommendations are followed, the n-back and reward task are also suited for within-subject designs, including pharmaco-fMRI. The present study provides task-specific fMRI reliability performance measures that will inform the optimal use, powering and design of fMRI studies using comparable tasks. Copyright Â© 2012 Elsevier Inc. All rights reserved.
An Embedded 4-Channel Receive-Only RF Coil Array for fMRI Experiments of the Somatosensory Pathway in Conscious Awake Marmosets at 7T

PubMed Central

Papoti, Daniel; Yen, Cecil Chern-Chyi; Mackel, Julie B.; Merkle, Hellmut; Silva, Afonso C.

2014-01-01

Functional Magnetic Resonance Imaging (fMRI) has established itself as the main research tool in neuroscience and brain cognitive research. The common marmoset (Callithrix jacchus) is a non-human primate model of increasing interest in biomedical research. However, commercial MRI coils for marmosets are not generally available. The present work describes the design and construction of a 4-channel receive-only surface RF coil array with excellent signal-to-noise ratio (SNR) specifically optimized for fMRI experiments in awake marmosets in response to somatosensory stimulation. The array was designed as part of a helmet-based head restraint system used to prevent motion during the scans. High SNR was obtained by building the coil array using a thin and flexible substrate glued to the inner surface of the restraint helmet, so as to minimize the distance between the array elements and the somatosensory cortex. Decoupling between coil elements was achieved by partial geometrical overlapping and by connecting them to home-built low input impedance preamplifiers. In vivo images show excellent coverage of the brain cortical surface with high sensitivity near the somatosensory cortex. Embedding the coil elements within the restraint helmet allowed fMRI data in response to somatosensory stimulation to be collected with high sensitivity and reproducibility in conscious, awake marmosets. PMID:23696219
Distinct hippocampal versus frontoparietal-network contributions to retrieval and memory-guided exploration

PubMed Central

Bridge, Donna J.; Cohen, Neal J.; Voss, Joel L.

2017-01-01

Memory can profoundly influence new learning, presumably because memory optimizes exploration of to-be-learned material. Although hippocampus and frontoparietal networks have been implicated in memory-guided exploration, their specific and interactive roles have not been identified. We examined eye movements during fMRI scanning to identify neural correlates of the influences of memory retrieval on exploration and learning. Following retrieval of one object in a multi-object array, viewing was strategically directed away from the retrieved object toward non-retrieved objects, such that exploration was directed towards to-be-learned content. Retrieved objects later served as optimal reminder cues, indicating that exploration caused memory to become structured around the retrieved content. Hippocampal activity was associated with memory retrieval whereas frontoparietal activity varied with strategic viewing patterns deployed following retrieval, thus providing spatiotemporal dissociation of memory retrieval from memory-guided learning strategies. Time-lagged fMRI connectivity analyses indicated that hippocampal activity predicted frontoparietal activity to a greater extent for a condition in which retrieval guided exploration than for a passive control condition in which exploration was not influenced by retrieval. This demonstrates network-level interaction effects specific to influences of memory on strategic exploration. These findings show how memory guides behavior during learning and demonstrate distinct yet interactive hippocampal-frontoparietal roles in implementing strategic exploration behaviors that determine the fate of evolving memory representations. PMID:28471729
Distinct Hippocampal versus Frontoparietal Network Contributions to Retrieval and Memory-guided Exploration.

PubMed

Bridge, Donna J; Cohen, Neal J; Voss, Joel L

2017-08-01

Memory can profoundly influence new learning, presumably because memory optimizes exploration of to-be-learned material. Although hippocampus and frontoparietal networks have been implicated in memory-guided exploration, their specific and interactive roles have not been identified. We examined eye movements during fMRI scanning to identify neural correlates of the influences of memory retrieval on exploration and learning. After retrieval of one object in a multiobject array, viewing was strategically directed away from the retrieved object toward nonretrieved objects, such that exploration was directed toward to-be-learned content. Retrieved objects later served as optimal reminder cues, indicating that exploration caused memory to become structured around the retrieved content. Hippocampal activity was associated with memory retrieval, whereas frontoparietal activity varied with strategic viewing patterns deployed after retrieval, thus providing spatiotemporal dissociation of memory retrieval from memory-guided learning strategies. Time-lagged fMRI connectivity analyses indicated that hippocampal activity predicted frontoparietal activity to a greater extent for a condition in which retrieval guided exploration occurred than for a passive control condition in which exploration was not influenced by retrieval. This demonstrates network-level interaction effects specific to influences of memory on strategic exploration. These findings show how memory guides behavior during learning and demonstrate distinct yet interactive hippocampal-frontoparietal roles in implementing strategic exploration behaviors that determine the fate of evolving memory representations.
Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

PubMed

Strappini, Francesca; Gilboa, Elad; Pitzalis, Sabrina; Kay, Kendrick; McAvoy, Mark; Nehorai, Arye; Snyder, Abraham Z

2017-03-01

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Spatiotemporal characteristics and vascular sources of neural-specific and -nonspecific fMRI signals at submillimeter columnar resolution

PubMed Central

Moon, Chan Hong; Fukuda, Mitsuhiro; Kim, Seong-Gi

2012-01-01

The neural specificity of hemodynamic-based functional magnetic resonance imaging (fMRI) signals are dependent on both the vascular regulation and the sensitivity of the applied fMRI technique to different types and sizes of blood vessels. In order to examine the specificity of MRI-detectable hemodynamic responses, submillimeter blood oxygenation-level dependent (BOLD) and cerebral blood volume (CBV) fMRI studies were performed in a well-established cat orientation column model at 9.4 Tesla. Neural-nonspecific and -specific signals were separated by comparing the fMRI responses of orthogonal orientation stimuli. The BOLD response was dominantly neural-nonspecific, mostly originating from pial and intracortical emerging veins, and thus was highly correlated with baseline blood volume. Uneven baseline CBV may displace or distort small functional domains in high-resolution BOLD maps. The CBV response in the parenchyma exhibited dual spatiotemporal characteristics, a fast and early neural-nonspecific response (with 4.3-s time constant) and a slightly slower and delayed neural-specific response (with 9.4-s time constant). The nonspecific CBV signal originates from early-responding arteries and arterioles, while the specific CBV response, which is not correlated with baseline blood volume, arises from late-responding microvessels including small pre-capillary arterioles and capillaries. Our data indicate that although the neural specificity of CBV fMRI signals is dependent on stimulation duration, high-resolution functional maps can be obtained from steady-state CBV studies. PMID:22960251
Behavioral and fMRI evidence of the differing cognitive load of domain-specific assessments.

PubMed

Howard, S J; Burianová, H; Ehrich, J; Kervin, L; Calleia, A; Barkus, E; Carmody, J; Humphry, S

2015-06-25

Standards-referenced educational reform has increased the prevalence of standardized testing; however, whether these tests accurately measure students' competencies has been questioned. This may be due to domain-specific assessments placing a differing domain-general cognitive load on test-takers. To investigate this possibility, functional magnetic resonance imaging (fMRI) was used to identify and quantify the neural correlates of performance on current, international standardized methods of spelling assessment. Out-of-scanner testing was used to further examine differences in assessment results. Results provide converging evidence that: (a) the spelling assessments differed in the cognitive load placed on test-takers; (b) performance decreased with increasing cognitive load of the assessment; and (c) brain regions associated with working memory were more highly activated during performance of assessments that were higher in cognitive load. These findings suggest that assessment design should optimize the cognitive load placed on test-takers, to ensure students' results are an accurate reflection of their true levels of competency. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Deficit of state-dependent risk attitude modulation in gambling disorder

PubMed Central

Fujimoto, A; Tsurumi, K; Kawada, R; Murao, T; Takeuchi, H; Murai, T; Takahashi, H

2017-01-01

Gambling disorder (GD) is often considered as a problem of trait-like risk preference. However, the symptoms of GD cannot be fully understood by this trait view. In the present study, we hypothesized that GD patients also had problem with a flexible control of risk attitude (state-dependent strategy optimization), and aimed to investigate the mechanisms underlying abnormal risk-taking of GD. To address this issue, we tested GD patients without comorbidity (GD group: n=21) and age-matched healthy control participants (HC group: n=29) in a multi-step gambling task, in which participants needed to clear ‘block quota' (required units to clear a block, 1000–7000 units) in 20 choices, and conducted a task-functional magnetic resonance imaging (fMRI) experiment. Behavioral analysis indeed revealed a less flexible risk-attitude change in the GD group; the GD group failed to avoid risky choice in a specific quota range (low-quota condition), in which risky strategy was not optimal to solve the quota. Accordingly, fMRI analysis highlighted diminished functioning of the dorsolateral prefrontal cortex (dlPFC), which has been heavily implicated in cognitive flexibility. To our knowledge, the present study provided the first empirical evidence of a deficit of state-dependent strategy optimization in GD. Focusing on flexible control of risk attitude under quota may contribute to a better understanding of the psychopathology of GDs. PMID:28375207
Deficit of state-dependent risk attitude modulation in gambling disorder.

PubMed

Fujimoto, A; Tsurumi, K; Kawada, R; Murao, T; Takeuchi, H; Murai, T; Takahashi, H

2017-04-04

Gambling disorder (GD) is often considered as a problem of trait-like risk preference. However, the symptoms of GD cannot be fully understood by this trait view. In the present study, we hypothesized that GD patients also had problem with a flexible control of risk attitude (state-dependent strategy optimization), and aimed to investigate the mechanisms underlying abnormal risk-taking of GD. To address this issue, we tested GD patients without comorbidity (GD group: n=21) and age-matched healthy control participants (HC group: n=29) in a multi-step gambling task, in which participants needed to clear 'block quota' (required units to clear a block, 1000-7000 units) in 20 choices, and conducted a task-functional magnetic resonance imaging (fMRI) experiment. Behavioral analysis indeed revealed a less flexible risk-attitude change in the GD group; the GD group failed to avoid risky choice in a specific quota range (low-quota condition), in which risky strategy was not optimal to solve the quota. Accordingly, fMRI analysis highlighted diminished functioning of the dorsolateral prefrontal cortex (dlPFC), which has been heavily implicated in cognitive flexibility. To our knowledge, the present study provided the first empirical evidence of a deficit of state-dependent strategy optimization in GD. Focusing on flexible control of risk attitude under quota may contribute to a better understanding of the psychopathology of GDs.
Brain and Behavioral Assessment of Executive Functions for Self-Regulating Levels of Language in Reading Brain.

PubMed

Berninger, Virginia W; Richards, Todd L; Abbott, Robert D

2017-11-01

This brief research report examines brain-behavioral relationships specific to levels of language in the complex reading brain. The first specific aim was to examine prior findings for significant fMRI connectivity from four seeds (left precuneus, left occipital temporal, left supramarginal, left inferior frontal) for each of four levels of language-subword, word (word-specific spelling or affixed words), syntax (with and without homonym foils or affix foils), and multi-sentence text to identify significant fMRI connectivity (a) unique to the lower level of language when compared to the immediately higher adjacent level of language across subword-word, word-syntax, and syntax-text comparisons; and (b) involving a brain region associated with executive functions. The second specific aim was to correlate the magnitude of that connectivity with standard scores on tests of Focused Attention (D-K EFS Color Word Form Inhibition) and Switching Attention (Wolf & Denckla Rapid Automatic Switching). Seven correlations were significant. Focused Attention was significantly correlated with the word level (word-specific spellings of real words) fMRI task in left cingulum from left inferior frontal seed. Switching Attention was significantly correlated with the (a) subword level (grapheme-phoneme correspondence) fMRI task in left and right Cerebellum V from left supramarginal seed; (b) the word level (word-specific spelling) fMRI task in right Cerebellum V from left precuneus seed; (c) the syntax level (with and without homonym foils) fMRI task in right Cerebellum V from left precuneus seed and from left supramarginal seed; and (d) syntax level (with and without affix foils) fMRI task in right Cerebellum V from left precuneus seed. Results are discussed in reference to neuropsychological assessment of supervisory attention (focused and switching) for specific levels of language related to reading acquisition in students with and without language-related specific learning disabilities and self-regulation of the complex reading brain.

Brain and Behavioral Assessment of Executive Functions for Self-Regulating Levels of Language in Reading Brain

PubMed Central

Berninger, Virginia W.; Richards, Todd L.; Abbott, Robert D.

2017-01-01

This brief research report examines brain-behavioral relationships specific to levels of language in the complex reading brain. The first specific aim was to examine prior findings for significant fMRI connectivity from four seeds (left precuneus, left occipital temporal, left supramarginal, left inferior frontal) for each of four levels of language—subword, word (word-specific spelling or affixed words), syntax (with and without homonym foils or affix foils), and multi-sentence text to identify significant fMRI connectivity (a) unique to the lower level of language when compared to the immediately higher adjacent level of language across subword-word, word-syntax, and syntax-text comparisons; and (b) involving a brain region associated with executive functions. The second specific aim was to correlate the magnitude of that connectivity with standard scores on tests of Focused Attention (D-K EFS Color Word Form Inhibition) and Switching Attention (Wolf & Denckla Rapid Automatic Switching). Seven correlations were significant. Focused Attention was significantly correlated with the word level (word-specific spellings of real words) fMRI task in left cingulum from left inferior frontal seed. Switching Attention was significantly correlated with the (a) subword level (grapheme-phoneme correspondence) fMRI task in left and right Cerebellum V from left supramarginal seed; (b) the word level (word-specific spelling) fMRI task in right Cerebellum V from left precuneus seed; (c) the syntax level (with and without homonym foils) fMRI task in right Cerebellum V from left precuneus seed and from left supramarginal seed; and (d) syntax level (with and without affix foils) fMRI task in right Cerebellum V from left precuneus seed. Results are discussed in reference to neuropsychological assessment of supervisory attention (focused and switching) for specific levels of language related to reading acquisition in students with and without language-related specific learning disabilities and self-regulation of the complex reading brain. PMID:29104930
Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.

PubMed

Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru

2010-12-01

The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.
Optimization of Blocked Designs in fMRI Studies

ERIC Educational Resources Information Center

Maus, Barbel; van Breukelen, Gerard J. P.; Goebel, Rainer; Berger, Martijn P. F.

2010-01-01

Blocked designs in functional magnetic resonance imaging (fMRI) are useful to localize functional brain areas. A blocked design consists of different blocks of trials of the same stimulus type and is characterized by three factors: the length of blocks, i.e., number of trials per blocks, the ordering of task and rest blocks, and the time between…
In vivo evaluation of the effect of stimulus distribution on FIR statistical efficiency in event-related fMRI

PubMed Central

Jansma, J Martijn; de Zwart, Jacco A; van Gelderen, Peter; Duyn, Jeff H; Drevets, Wayne C; Furey, Maura L

2013-01-01

Technical developments in MRI have improved signal to noise, allowing use of analysis methods such as Finite impulse response (FIR) of rapid event related functional MRI (er-fMRI). FIR is one of the most informative analysis methods as it determines onset and full shape of the hemodynamic response function (HRF) without any a-priori assumptions. FIR is however vulnerable to multicollinearity, which is directly related to the distribution of stimuli over time. Efficiency can be optimized by simplifying a design, and restricting stimuli distribution to specific sequences, while more design flexibility necessarily reduces efficiency. However, the actual effect of efficiency on fMRI results has never been tested in vivo. Thus, it is currently difficult to make an informed choice between protocol flexibility and statistical efficiency. The main goal of this study was to assign concrete fMRI signal to noise values to the abstract scale of FIR statistical efficiency. Ten subjects repeated a perception task with five random and m-sequence based protocol, with varying but, according to literature, acceptable levels of multicollinearity. Results indicated substantial differences in signal standard deviation, while the level was a function of multicollinearity. Experiment protocols varied up to 55.4% in standard deviation. Results confirm that quality of fMRI in an FIR analysis can significantly and substantially vary with statistical efficiency. Our in vivo measurements can be used to aid in making an informed decision between freedom in protocol design and statistical efficiency. PMID:23473798
A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

PubMed

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.
Functional Neuroimaging of Spike-Wave Seizures

PubMed Central

Motelow, Joshua E.; Blumenfeld, Hal

2013-01-01

Generalized spike-wave seizures are typically brief events associated with dynamic changes in brain physiology, metabolism, and behavior. Functional magnetic resonance imaging (fMRI) provides a relatively high spatio-temporal resolution method for imaging cortical-subcortical network activity during spike-wave seizures. Patients with spike-wave seizures often have episodes of staring and unresponsiveness which interfere with normal behavior. Results from human fMRI studies suggest that spike-wave seizures disrupt specific networks in the thalamus and fronto-parietal association cortex which are critical for normal attentive consciousness. However, the neuronal activity underlying imaging changes seen during fMRI is not well understood, particularly in abnormal conditions such as seizures. Animal models have begun to provide important fundamental insights into the neuronal basis for fMRI changes during spike-wave activity. Work from these models including both fMRI and direct neuronal recordings suggest that, like in humans, specific cortical-subcortical networks are involved in spike-wave, while other regions are spared. Regions showing fMRI increases demonstrate correlated increases in neuronal activity in animal models. The mechanisms of fMRI decreases in spike-wave will require further investigation. A better understanding of the specific brain regions involved in generating spike-wave seizures may help guide efforts to develop targeted therapies aimed at preventing or reversing abnormal excitability in these brain regions, ultimately leading to a cure for this disorder. PMID:18839093
High spatial resolution compressed sensing (HSPARSE) functional MRI.

PubMed

Fang, Zhongnan; Van Le, Nguyen; Choy, ManKin; Lee, Jin Hyung

2016-08-01

To propose a novel compressed sensing (CS) high spatial resolution functional MRI (fMRI) method and demonstrate the advantages and limitations of using CS for high spatial resolution fMRI. A randomly undersampled variable density spiral trajectory enabling an acceleration factor of 5.3 was designed with a balanced steady state free precession sequence to achieve high spatial resolution data acquisition. A modified k-t SPARSE method was then implemented and applied with a strategy to optimize regularization parameters for consistent, high quality CS reconstruction. The proposed method improves spatial resolution by six-fold with 12 to 47% contrast-to-noise ratio (CNR), 33 to 117% F-value improvement and maintains the same temporal resolution. It also achieves high sensitivity of 69 to 99% compared the original ground-truth, small false positive rate of less than 0.05 and low hemodynamic response function distortion across a wide range of CNRs. The proposed method is robust to physiological noise and enables detection of layer-specific activities in vivo, which cannot be resolved using the highest spatial resolution Nyquist acquisition. The proposed method enables high spatial resolution fMRI that can resolve layer-specific brain activity and demonstrates the significant improvement that CS can bring to high spatial resolution fMRI. Magn Reson Med 76:440-455, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.
Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI

PubMed Central

Maneshi, Mona; Vahdat, Shahabeddin; Gotman, Jean; Grova, Christophe

2016-01-01

Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named “shared and specific independent component analysis” (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i.e., components that could be considered “specific” for a group or condition. Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences. We examined SSICA robustness with respect to the number of allowable extracted specific components and between-group orthogonality assumptions. Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-level t-statistics maps based on SSICA results. We also evaluated the consistency and specificity of the extracted specific components by SSICA. The results on realistic simulated and real fMRI data showed that SSICA outperforms the regular group ICA approach in terms of reconstruction and classification performance. We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state fMRI. Our findings in task fMRI show that SSICA confirms results of the general linear model (GLM) analysis and when combined with clustering analysis, it complements GLM findings by providing additional information regarding the reliability and specificity of networks. PMID:27729843
The power of using functional fMRI on small rodents to study brain pharmacology and disease

PubMed Central

Jonckers, Elisabeth; Shah, Disha; Hamaide, Julie; Verhoye, Marleen; Van der Linden, Annemie

2015-01-01

Functional magnetic resonance imaging (fMRI) is an excellent tool to study the effect of pharmacological modulations on brain function in a non-invasive and longitudinal manner. We introduce several blood oxygenation level dependent (BOLD) fMRI techniques, including resting state (rsfMRI), stimulus-evoked (st-fMRI), and pharmacological MRI (phMRI). Respectively, these techniques permit the assessment of functional connectivity during rest as well as brain activation triggered by sensory stimulation and/or a pharmacological challenge. The first part of this review describes the physiological basis of BOLD fMRI and the hemodynamic response on which the MRI contrast is based. Specific emphasis goes to possible effects of anesthesia and the animal’s physiological conditions on neural activity and the hemodynamic response. The second part of this review describes applications of the aforementioned techniques in pharmacologically induced, as well as in traumatic and transgenic disease models and illustrates how multiple fMRI methods can be applied successfully to evaluate different aspects of a specific disorder. For example, fMRI techniques can be used to pinpoint the neural substrate of a disease beyond previously defined hypothesis-driven regions-of-interest. In addition, fMRI techniques allow one to dissect how specific modifications (e.g., treatment, lesion etc.) modulate the functioning of specific brain areas (st-fMRI, phMRI) and how functional connectivity (rsfMRI) between several brain regions is affected, both in acute and extended time frames. Furthermore, fMRI techniques can be used to assess/explore the efficacy of novel treatments in depth, both in fundamental research as well as in preclinical settings. In conclusion, by describing several exemplary studies, we aim to highlight the advantages of functional MRI in exploring the acute and long-term effects of pharmacological substances and/or pathology on brain functioning along with several methodological considerations. PMID:26539115
In vivo evaluation of the effect of stimulus distribution on FIR statistical efficiency in event-related fMRI.

PubMed

Jansma, J Martijn; de Zwart, Jacco A; van Gelderen, Peter; Duyn, Jeff H; Drevets, Wayne C; Furey, Maura L

2013-05-15

Technical developments in MRI have improved signal to noise, allowing use of analysis methods such as Finite impulse response (FIR) of rapid event related functional MRI (er-fMRI). FIR is one of the most informative analysis methods as it determines onset and full shape of the hemodynamic response function (HRF) without any a priori assumptions. FIR is however vulnerable to multicollinearity, which is directly related to the distribution of stimuli over time. Efficiency can be optimized by simplifying a design, and restricting stimuli distribution to specific sequences, while more design flexibility necessarily reduces efficiency. However, the actual effect of efficiency on fMRI results has never been tested in vivo. Thus, it is currently difficult to make an informed choice between protocol flexibility and statistical efficiency. The main goal of this study was to assign concrete fMRI signal to noise values to the abstract scale of FIR statistical efficiency. Ten subjects repeated a perception task with five random and m-sequence based protocol, with varying but, according to literature, acceptable levels of multicollinearity. Results indicated substantial differences in signal standard deviation, while the level was a function of multicollinearity. Experiment protocols varied up to 55.4% in standard deviation. Results confirm that quality of fMRI in an FIR analysis can significantly and substantially vary with statistical efficiency. Our in vivo measurements can be used to aid in making an informed decision between freedom in protocol design and statistical efficiency. Published by Elsevier B.V.
Process and domain specificity in regions engaged for face processing: an fMRI study of perceptual differentiation.

PubMed

Collins, Heather R; Zhu, Xun; Bhatt, Ramesh S; Clark, Jonathan D; Joseph, Jane E

2012-12-01

The degree to which face-specific brain regions are specialized for different kinds of perceptual processing is debated. This study parametrically varied demands on featural, first-order configural, or second-order configural processing of faces and houses in a perceptual matching task to determine the extent to which the process of perceptual differentiation was selective for faces regardless of processing type (domain-specific account), specialized for specific types of perceptual processing regardless of category (process-specific account), engaged in category-optimized processing (i.e., configural face processing or featural house processing), or reflected generalized perceptual differentiation (i.e., differentiation that crosses category and processing type boundaries). ROIs were identified in a separate localizer run or with a similarity regressor in the face-matching runs. The predominant principle accounting for fMRI signal modulation in most regions was generalized perceptual differentiation. Nearly all regions showed perceptual differentiation for both faces and houses for more than one processing type, even if the region was identified as face-preferential in the localizer run. Consistent with process specificity, some regions showed perceptual differentiation for first-order processing of faces and houses (right fusiform face area and occipito-temporal cortex and right lateral occipital complex), but not for featural or second-order processing. Somewhat consistent with domain specificity, the right inferior frontal gyrus showed perceptual differentiation only for faces in the featural matching task. The present findings demonstrate that the majority of regions involved in perceptual differentiation of faces are also involved in differentiation of other visually homogenous categories.
Process- and Domain-Specificity in Regions Engaged for Face Processing: An fMRI Study of Perceptual Differentiation

PubMed Central

Collins, Heather R.; Zhu, Xun; Bhatt, Ramesh S.; Clark, Jonathan D.; Joseph, Jane E.

2015-01-01

The degree to which face-specific brain regions are specialized for different kinds of perceptual processing is debated. The present study parametrically varied demands on featural, first-order configural or second-order configural processing of faces and houses in a perceptual matching task to determine the extent to which the process of perceptual differentiation was selective for faces regardless of processing type (domain-specific account), specialized for specific types of perceptual processing regardless of category (process-specific account), engaged in category-optimized processing (i.e., configural face processing or featural house processing) or reflected generalized perceptual differentiation (i.e. differentiation that crosses category and processing type boundaries). Regions of interest were identified in a separate localizer run or with a similarity regressor in the face-matching runs. The predominant principle accounting for fMRI signal modulation in most regions was generalized perceptual differentiation. Nearly all regions showed perceptual differentiation for both faces and houses for more than one processing type, even if the region was identified as face-preferential in the localizer run. Consistent with process-specificity, some regions showed perceptual differentiation for first-order processing of faces and houses (right fusiform face area and occipito-temporal cortex, and right lateral occipital complex), but not for featural or second-order processing. Somewhat consistent with domain-specificity, the right inferior frontal gyrus showed perceptual differentiation only for faces in the featural matching task. The present findings demonstrate that the majority of regions involved in perceptual differentiation of faces are also involved in differentiation of other visually homogenous categories. PMID:22849402
Estimating neural response functions from fMRI

PubMed Central

Kumar, Sukhbinder; Penny, William

2014-01-01

This paper proposes a methodology for estimating Neural Response Functions (NRFs) from fMRI data. These NRFs describe non-linear relationships between experimental stimuli and neuronal population responses. The method is based on a two-stage model comprising an NRF and a Hemodynamic Response Function (HRF) that are simultaneously fitted to fMRI data using a Bayesian optimization algorithm. This algorithm also produces a model evidence score, providing a formal model comparison method for evaluating alternative NRFs. The HRF is characterized using previously established “Balloon” and BOLD signal models. We illustrate the method with two example applications based on fMRI studies of the auditory system. In the first, we estimate the time constants of repetition suppression and facilitation, and in the second we estimate the parameters of population receptive fields in a tonotopic mapping study. PMID:24847246
Advances in fMRI Real-Time Neurofeedback.

PubMed

Watanabe, Takeo; Sasaki, Yuka; Shibata, Kazuhisa; Kawato, Mitsuo

2017-12-01

Functional magnetic resonance imaging (fMRI) neurofeedback is a type of biofeedback in which real-time online fMRI signals are used to self-regulate brain function. Since its advent in 2003 significant progress has been made in fMRI neurofeedback techniques. Specifically, the use of implicit protocols, external rewards, multivariate analysis, and connectivity analysis has allowed neuroscientists to explore a possible causal involvement of modified brain activity in modified behavior. These techniques have also been integrated into groundbreaking new neurofeedback technologies, specifically decoded neurofeedback (DecNef) and functional connectivity-based neurofeedback (FCNef). By modulating neural activity and behavior, DecNef and FCNef have substantially advanced both basic and clinical research. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
Passive fMRI mapping of language function for pediatric epilepsy surgical planning: validation using Wada, ECS, and FMAER.

PubMed

Suarez, Ralph O; Taimouri, Vahid; Boyer, Katrina; Vega, Clemente; Rotenberg, Alexander; Madsen, Joseph R; Loddenkemper, Tobias; Duffy, Frank H; Prabhu, Sanjay P; Warfield, Simon K

2014-12-01

In this study we validate passive language fMRI protocols designed for clinical application in pediatric epilepsy surgical planning as they do not require overt participation from patients. We introduced a set of quality checks that assess reliability of noninvasive fMRI mappings utilized for clinical purposes. We initially compared two fMRI language mapping paradigms, one active in nature (requiring participation from the patient) and the other passive in nature (requiring no participation from the patient). Group-level analysis in a healthy control cohort demonstrated similar activation of the putative language centers of the brain in the inferior frontal (IFG) and temporoparietal (TPG) regions. Additionally, we showed that passive language fMRI produced more left-lateralized activation in TPG (LI=+0.45) compared to the active task; with similarly robust left-lateralized IFG (LI=+0.24) activations using the passive task. We validated our recommended fMRI mapping protocols in a cohort of 15 pediatric epilepsy patients by direct comparison against the invasive clinical gold-standards. We found that language-specific TPG activation by fMRI agreed to within 9.2mm to subdural localizations by invasive functional mapping in the same patients, and language dominance by fMRI agreed with Wada test results at 80% congruency in TPG and 73% congruency in IFG. Lastly, we tested the recommended passive language fMRI protocols in a cohort of very young patients and confirmed reliable language-specific activation patterns in that challenging cohort. We concluded that language activation maps can be reliably achieved using the passive language fMRI protocols we proposed even in very young (average 7.5 years old) or sedated pediatric epilepsy patients. Copyright © 2014 Elsevier B.V. All rights reserved.
Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

PubMed

Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia

2014-08-01

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches). Copyright © 2014. Published by Elsevier Inc.
The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI

PubMed Central

Lorenz, Romy; Monti, Ricardo Pio; Violante, Inês R.; Anagnostopoulos, Christoforos; Faisal, Aldo A.; Montana, Giovanni; Leech, Robert

2016-01-01

Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals. PMID:26804778
The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI.

PubMed

Lorenz, Romy; Monti, Ricardo Pio; Violante, Inês R; Anagnostopoulos, Christoforos; Faisal, Aldo A; Montana, Giovanni; Leech, Robert

2016-04-01

Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
Characterization and Reduction of Cardiac- and Respiratory-Induced Noise as a Function of the Sampling Rate (TR) in fMRI

PubMed Central

Cordes, Dietmar; Nandy, Rajesh R.; Schafer, Scott; Wager, Tor D.

2014-01-01

It has recently been shown that both high-frequency and low-frequency cardiac and respiratory noise sources exist throughout the entire brain and can cause significant signal changes in fMRI data. It is also known that the brainstem, basal forebrain and spinal cord area are problematic for fMRI because of the magnitude of cardiac-induced pulsations at these locations. In this study, the physiological noise contributions in the lower brain areas (covering the brainstem and adjacent regions) are investigated and a novel method is presented for computing both low-frequency and high-frequency physiological regressors accurately for each subject. In particular, using a novel optimization algorithm that penalizes curvature (i.e. the second derivative) of the physiological hemodynamic response functions, the cardiac -and respiratory-related response functions are computed. The physiological noise variance is determined for each voxel and the frequency-aliasing property of the high-frequency cardiac waveform as a function of the repetition time (TR) is investigated. It is shown that for the brainstem and other brain areas associated with large pulsations of the cardiac rate, the temporal SNR associated with the low-frequency range of the BOLD response has maxima at subject-specific TRs. At these values, the high-frequency aliased cardiac rate can be eliminated by digital filtering without affecting the BOLD-related signal. PMID:24355483
Optimization of flow-sensitive alternating inversion recovery (FAIR) for perfusion functional MRI of rodent brain.

PubMed

Nasrallah, Fatima A; Lee, Eugene L Q; Chuang, Kai-Hsiang

2012-11-01

Arterial spin labeling (ASL) MRI provides a noninvasive method to image perfusion, and has been applied to map neural activation in the brain. Although pulsed labeling methods have been widely used in humans, continuous ASL with a dedicated neck labeling coil is still the preferred method in rodent brain functional MRI (fMRI) to maximize the sensitivity and allow multislice acquisition. However, the additional hardware is not readily available and hence its application is limited. In this study, flow-sensitive alternating inversion recovery (FAIR) pulsed ASL was optimized for fMRI of rat brain. A practical challenge of FAIR is the suboptimal global inversion by the transmit coil of limited dimensions, which results in low effective labeling. By using a large volume transmit coil and proper positioning to optimize the body coverage, the perfusion signal was increased by 38.3% compared with positioning the brain at the isocenter. An additional 53.3% gain in signal was achieved using optimized repetition and inversion times compared with a long TR. Under electrical stimulation to the forepaws, a perfusion activation signal change of 63.7 ± 6.3% can be reliably detected in the primary somatosensory cortices using single slice or multislice echo planar imaging at 9.4 T. This demonstrates the potential of using pulsed ASL for multislice perfusion fMRI in functional and pharmacological applications in rat brain. Copyright © 2012 John Wiley & Sons, Ltd.

Neural signatures of experience-based improvements in deterministic decision-making.

PubMed

Tremel, Joshua J; Laurent, Patryk A; Wolk, David A; Wheeler, Mark E; Fiez, Julie A

2016-12-15

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. Copyright © 2016 Elsevier B.V. All rights reserved.
Neural signatures of experience-based improvements in deterministic decision-making

PubMed Central

Tremel, Joshua J.; Laurent, Patryk A.; Wolk, David A.; Wheeler, Mark E.; Fiez, Julie A.

2016-01-01

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. PMID:27523644
Moment-to-Moment BOLD Signal Variability Reflects Regional Changes in Neural Flexibility across the Lifespan.

PubMed

Nomi, Jason S; Bolt, Taylor S; Ezie, C E Chiemeka; Uddin, Lucina Q; Heller, Aaron S

2017-05-31

Variability of neuronal responses is thought to underlie flexible and optimal brain function. Because previous work investigating BOLD signal variability has been conducted within task-based fMRI contexts on adults and older individuals, very little is currently known regarding regional changes in spontaneous BOLD signal variability in the human brain across the lifespan. The current study used resting-state fMRI data from a large sample of male and female human participants covering a wide age range (6-85 years) across two different fMRI acquisition parameters (TR = 0.645 and 1.4 s). Variability in brain regions including a key node of the salience network (anterior insula) increased linearly across the lifespan across datasets. In contrast, variability in most other large-scale networks decreased linearly over the lifespan. These results demonstrate unique lifespan trajectories of BOLD variability related to specific regions of the brain and add to a growing literature demonstrating the importance of identifying normative trajectories of functional brain maturation. SIGNIFICANCE STATEMENT Although brain signal variability has traditionally been considered a source of unwanted noise, recent work demonstrates that variability in brain signals during task performance is related to brain maturation in old age as well as individual differences in behavioral performance. The current results demonstrate that intrinsic fluctuations in resting-state variability exhibit unique maturation trajectories in specific brain regions and systems, particularly those supporting salience detection. These results have implications for investigations of brain development and aging, as well as interpretations of brain function underlying behavioral changes across the lifespan. Copyright © 2017 the authors 0270-6474/17/375539-10$15.00/0.
Preliminary pilot fMRI study of neuropostural optimization with a noninvasive asymmetric radioelectric brain stimulation protocol in functional dysmetria

PubMed Central

Mura, Marco; Castagna, Alessandro; Fontani, Vania; Rinaldi, Salvatore

2012-01-01

Purpose This study assessed changes in functional dysmetria (FD) and in brain activation observable by functional magnetic resonance imaging (fMRI) during a leg flexion-extension motor task following brain stimulation with a single radioelectric asymmetric conveyer (REAC) pulse, according to the precisely defined neuropostural optimization (NPO) protocol. Population and methods Ten healthy volunteers were assessed using fMRI conducted during a simple motor task before and immediately after delivery of a single REAC-NPO pulse. The motor task consisted of a flexion-extension movement of the legs with the knees bent. FD signs and brain activation patterns were compared before and after REAC-NPO. Results A single 250-millisecond REAC-NPO treatment alleviated FD, as evidenced by patellar asymmetry during a sit-up motion, and modulated activity patterns in the brain, particularly in the cerebellum, during the performance of the motor task. Conclusion Activity in brain areas involved in motor control and coordination, including the cerebellum, is altered by administration of a REAC-NPO treatment and this effect is accompanied by an alleviation of FD. PMID:22536071
Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

PubMed

Glasser, Matthew F; Coalson, Timothy S; Bijsterbosch, Janine D; Harrison, Samuel J; Harms, Michael P; Anticevic, Alan; Van Essen, David C; Smith, Stephen M

2018-06-02

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread "global" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary-to do or not to do global signal regression-given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a "best of both worlds" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data. Copyright © 2018 Elsevier Inc. All rights reserved.
An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI

PubMed Central

Churchill, Nathan W.; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C.

2015-01-01

BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets. PMID:26161667
A Window into the Brain: Advances in Psychiatric fMRI

PubMed Central

Zhan, Xiaoyan

2015-01-01

Functional magnetic resonance imaging (fMRI) plays a key role in modern psychiatric research. It provides a means to assay differences in brain systems that underlie psychiatric illness, treatment response, and properties of brain structure and function that convey risk factor for mental diseases. Here we review recent advances in fMRI methods in general use and progress made in understanding the neural basis of mental illness. Drawing on concepts and findings from psychiatric fMRI, we propose that mental illness may not be associated with abnormalities in specific local regions but rather corresponds to variation in the overall organization of functional communication throughout the brain network. Future research may need to integrate neuroimaging information drawn from different analysis methods and delineate spatial and temporal patterns of brain responses that are specific to certain types of psychiatric disorders. PMID:26413531
Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059
The role of fMRI in cognitive neuroscience: where do we stand?

PubMed

Poldrack, Russell A

2008-04-01

Functional magnetic resonance imaging (fMRI) has quickly become the most prominent tool in cognitive neuroscience. In this article, I outline some of the limits on the kinds of inferences that can be supported by fMRI, focusing particularly on reverse inference, in which the engagement of specific mental processes is inferred from patterns of brain activation. Although this form of inference is weak, newly developed methods from the field of machine learning offer the potential to formalize and strengthen reverse inferences. I conclude by discussing the increasing presence of fMRI results in the popular media and the ethical implications of the increasing predictive power of fMRI.
Functional versus effector-specific organization of the human posterior parietal cortex: revisited

PubMed Central

Leone, Frank T. M.; Medendorp, W. Pieter

2016-01-01

It has been proposed that the posterior parietal cortex (PPC) is characterized by an effector-specific organization. However, strikingly similar functional MRI (fMRI) activation patterns have been found in the PPC for hand and foot movements. Because the fMRI signal is related to average neuronal activity, similar activation levels may result either from effector-unspecific neurons or from intermingled subsets of effector-specific neurons within a voxel. We distinguished between these possibilities using fMRI repetition suppression (RS). Participants made delayed, goal-directed eye, hand, and foot movements to visual targets. In each trial, the instructed effector was identical or different to that of the previous trial. RS effects indicated an attenuation of the fMRI signal in repeat trials. The caudal PPC was active during the delay but did not show RS, suggesting that its planning activity was effector independent. Hand and foot-specific RS effects were evident in the anterior superior parietal lobule (SPL), extending to the premotor cortex, with limb overlap in the anterior SPL. Connectivity analysis suggested information flow between the caudal PPC to limb-specific anterior SPL regions and between the limb-unspecific anterior SPL toward limb-specific motor regions. These results underline that both function and effector specificity should be integrated into a concept of PPC action representation not only on a regional but also on a fine-grained, subvoxel level. PMID:27466132
An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Calhoun, Vince D.

2013-01-01

Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398
Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals.

PubMed

Erdoğan, Sinem B; Tong, Yunjie; Hocke, Lia M; Lindsey, Kimberly P; deB Frederick, Blaise

2016-01-01

Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, "dynamic global signal regression" (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional "static" global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.
Optimization of Contrast Detection Power with Probabilistic Behavioral Information

PubMed Central

Cordes, Dietmar; Herzmann, Grit; Nandy, Rajesh; Curran, Tim

2012-01-01

Recent progress in the experimental design for event-related fMRI experiments made it possible to find the optimal stimulus sequence for maximum contrast detection power using a genetic algorithm. In this study, a novel algorithm is proposed for optimization of contrast detection power by including probabilistic behavioral information, based on pilot data, in the genetic algorithm. As a particular application, a recognition memory task is studied and the design matrix optimized for contrasts involving the familiarity of individual items (pictures of objects) and the recollection of qualitative information associated with the items (left/right orientation). Optimization of contrast efficiency is a complicated issue whenever subjects’ responses are not deterministic but probabilistic. Contrast efficiencies are not predictable unless behavioral responses are included in the design optimization. However, available software for design optimization does not include options for probabilistic behavioral constraints. If the anticipated behavioral responses are included in the optimization algorithm, the design is optimal for the assumed behavioral responses, and the resulting contrast efficiency is greater than what either a block design or a random design can achieve. Furthermore, improvements of contrast detection power depend strongly on the behavioral probabilities, the perceived randomness, and the contrast of interest. The present genetic algorithm can be applied to any case in which fMRI contrasts are dependent on probabilistic responses that can be estimated from pilot data. PMID:22326984
Functional Laterality of Task-Evoked Activation in Sensorimotor Cortex of Preterm Infants: An Optimized 3 T fMRI Study Employing a Customized Neonatal Head Coil.

PubMed

Scheef, Lukas; Nordmeyer-Massner, Jurek A; Smith-Collins, Adam Pr; Müller, Nicole; Stegmann-Woessner, Gaby; Jankowski, Jacob; Gieseke, Jürgen; Born, Mark; Seitz, Hermann; Bartmann, Peter; Schild, Hans H; Pruessmann, Klaas P; Heep, Axel; Boecker, Henning

2017-01-01

Functional magnetic resonance imaging (fMRI) in neonates has been introduced as a non-invasive method for studying sensorimotor processing in the developing brain. However, previous neonatal studies have delivered conflicting results regarding localization, lateralization, and directionality of blood oxygenation level dependent (BOLD) responses in sensorimotor cortex (SMC). Amongst the confounding factors in interpreting neonatal fMRI studies include the use of standard adult MR-coils providing insufficient signal to noise, and liberal statistical thresholds, compromising clinical interpretation at the single subject level. Here, we employed a custom-designed neonatal MR-coil adapted and optimized to the head size of a newborn in order to improve robustness, reliability and validity of neonatal sensorimotor fMRI. Thirteen preterm infants with a median gestational age of 26 weeks were scanned at term-corrected age using a prototype 8-channel neonatal head coil at 3T (Achieva, Philips, Best, NL). Sensorimotor stimulation was elicited by passive extension/flexion of the elbow at 1 Hz in a block design. Analysis of temporal signal to noise ratio (tSNR) was performed on the whole brain and the SMC, and was compared to data acquired with an 'adult' 8 channel head coil published previously. Task-evoked activation was determined by single-subject SPM8 analyses, thresholded at p < 0.05, whole-brain FWE-corrected. Using a custom-designed neonatal MR-coil, we found significant positive BOLD responses in contralateral SMC after unilateral passive sensorimotor stimulation in all neonates (analyses restricted to artifact-free data sets = 8/13). Improved imaging characteristics of the neonatal MR-coil were evidenced by additional phantom and in vivo tSNR measurements: phantom studies revealed a 240% global increase in tSNR; in vivo studies revealed a 73% global and a 55% local (SMC) increase in tSNR, as compared to the 'adult' MR-coil. Our findings strengthen the importance of using optimized coil settings for neonatal fMRI, yielding robust and reproducible SMC activation at the single subject level. We conclude that functional lateralization of SMC activation, as found in children and adults, is already present in the newborn period.
Approach to functional magnetic resonance imaging of language based on models of language organization.

PubMed

McGraw, P; Mathews, V P; Wang, Y; Phillips, M D

2001-05-01

Functional MR imaging (fMRI) has been a useful tool in the evaluation of language both in normal individuals and patient populations. The purpose of this article is to use various models of language as a framework to review fMRI studies. Specifically, fMRI language studies are subdivided into the following categories: word generation or fluency, passive listening, orthography, phonology, semantics, and syntax.
Fixation-related FMRI analysis in the domain of reading research: using self-paced eye movements as markers for hemodynamic brain responses during visual letter string processing.

PubMed

Richlan, Fabio; Gagl, Benjamin; Hawelka, Stefan; Braun, Mario; Schurz, Matthias; Kronbichler, Martin; Hutzler, Florian

2014-10-01

The present study investigated the feasibility of using self-paced eye movements during reading (measured by an eye tracker) as markers for calculating hemodynamic brain responses measured by functional magnetic resonance imaging (fMRI). Specifically, we were interested in whether the fixation-related fMRI analysis approach was sensitive enough to detect activation differences between reading material (words and pseudowords) and nonreading material (line and unfamiliar Hebrew strings). Reliable reading-related activation was identified in left hemisphere superior temporal, middle temporal, and occipito-temporal regions including the visual word form area (VWFA). The results of the present study are encouraging insofar as fixation-related analysis could be used in future fMRI studies to clarify some of the inconsistent findings in the literature regarding the VWFA. Our study is the first step in investigating specific visual word recognition processes during self-paced natural sentence reading via simultaneous eye tracking and fMRI, thus aiming at an ecologically valid measurement of reading processes. We provided the proof of concept and methodological framework for the analysis of fixation-related fMRI activation in the domain of reading research. © The Author 2013. Published by Oxford University Press.
Real-time fMRI: a tool for local brain regulation.

PubMed

Caria, Andrea; Sitaram, Ranganatha; Birbaumer, Niels

2012-10-01

Real-time fMRI permits simultaneous measurement and observation of brain activity during an ongoing task. One of the most challenging applications of real-time fMRI in neuroscientific and clinical research is the possibility of acquiring volitional control of localized brain activity using real-time fMRI-based neurofeedback protocols. Real-time fMRI allows the experimenter to noninvasively manipulate brain activity as an independent variable to observe the effects on behavior. Real-time fMRI neurofeedback studies demonstrated that learned control of the local brain activity leads to specific changes in behavior. Here, the authors describe the implementation and application of real-time fMRI with particular emphasis on the self-regulation of local brain activity and the investigation of brain-function relationships. Real-time fMRI represents a promising new approach to cognitive neuroscience that could complement traditional neuroimaging techniques by providing more causal insights into the functional role of circumscribed brain regions in behavior.
The insula is not specifically involved in disgust processing: an fMRI study.

PubMed

Schienle, A; Stark, R; Walter, B; Blecker, C; Ott, U; Kirsch, P; Sammer, G; Vaitl, D

2002-11-15

fMRI studies have shown that the perception of facial disgust expressions specifically activates the insula. The present fMRI study investigated whether this structure is also involved in the processing of visual stimuli depicting non-mimic disgust elicitors compared to fear-inducing and neutral scenes. Twelve female subjects were scanned while viewing alternating blocks of 40 disgust-inducing, 40 fear-inducing and 40 affectively neutral pictures, shown for 1.5 s each. Afterwards, affective ratings were assessed. The disgust pictures, rated as highly repulsive, induced activation in the insula, the amygdala, the orbitofrontal and occipito-temporal cortex. Since during the fear condition the insula was also involved, our findings do not fit the idea of the insula as a specific disgust processor.
Investigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of merit.

PubMed

Ngan, Shing-Chung; Hu, Xiaoping; Khong, Pek-Lan

2011-03-01

We propose a method for preprocessing event-related functional magnetic resonance imaging (fMRI) data that can lead to enhancement of template-free activation detection. The method is based on using a figure of merit to guide the wavelet shrinkage of a given fMRI data set. Several previous studies have demonstrated that in the root-mean-square error setting, wavelet shrinkage can improve the signal-to-noise ratio of fMRI time courses. However, preprocessing fMRI data in the root-mean-square error setting does not necessarily lead to enhancement of template-free activation detection. Motivated by this observation, in this paper, we move to the detection setting and investigate the possibility of using wavelet shrinkage to enhance template-free activation detection of fMRI data. The main ingredients of our method are (i) forward wavelet transform of the voxel time courses, (ii) shrinking the resulting wavelet coefficients as directed by an appropriate figure of merit, (iii) inverse wavelet transform of the shrunk data, and (iv) submitting these preprocessed time courses to a given activation detection algorithm. Two figures of merit are developed in the paper, and two other figures of merit adapted from the literature are described. Receiver-operating characteristic analyses with simulated fMRI data showed quantitative evidence that data preprocessing as guided by the figures of merit developed in the paper can yield improved detectability of the template-free measures. We also demonstrate the application of our methodology on an experimental fMRI data set. The proposed method is useful for enhancing template-free activation detection in event-related fMRI data. It is of significant interest to extend the present framework to produce comprehensive, adaptive and fully automated preprocessing of fMRI data optimally suited for subsequent data analysis steps. Copyright © 2010 Elsevier B.V. All rights reserved.
A new paradigm (Westphal-Paradigm) to study the neural correlates of panic disorder with agoraphobia.

PubMed

Wittmann, A; Schlagenhauf, F; John, T; Guhn, A; Rehbein, H; Siegmund, A; Stoy, M; Held, D; Schulz, I; Fehm, L; Fydrich, T; Heinz, A; Bruhn, H; Ströhle, A

2011-04-01

Agoraphobia (with and without panic disorder) is a highly prevalent and disabling anxiety disorder. Its neural complexity can be characterized by specific cues in fMRI studies. Therefore, we developed a fMRI paradigm with agoraphobia-specific stimuli. Pictures of potential agoraphobic situations were generated. Twenty-six patients, suffering from panic disorder and agoraphobia, and 22 healthy controls rated the pictures with respect to arousal, valence, and agoraphobia-related anxiety. The 96 pictures, which discriminated best between groups were chosen, split into two parallel sets and supplemented with matched neutral pictures from the International Affective Picture System. Reliability, criterion, and construct validity of the picture set were determined in a second sample (44 patients, 28 controls). The resulting event-related "Westphal-Paradigm" with cued and uncued pictures was tested in a fMRI pilot study with 16 patients. Internal consistency of the sets was very high; parallelism was given. Positive correlations of picture ratings with Mobility Inventory and Hamilton anxiety scores support construct validity. FMRI data revealed activations in areas associated with the fear circuit including amygdala, insula, and hippocampal areas. Psychometric properties of the Westphal-Paradigm meet necessary quality requirements for further scientific use. The paradigm reliably produces behavioral and fMRI patterns in response to agoraphobia-specific stimuli. To our knowledge, it is the first fMRI paradigm with these properties. This paradigm can be used to further characterize the functional neuroanatomy of panic disorder and agoraphobia and might be useful to contribute data to the differentiation of panic disorder and agoraphobia as related, but conceptually different clinical disorders.

A Space Affine Matching Approach to fMRI Time Series Analysis.

PubMed

Chen, Liang; Zhang, Weishi; Liu, Hongbo; Feng, Shigang; Chen, C L Philip; Wang, Huili

2016-07-01

For fMRI time series analysis, an important challenge is to overcome the potential delay between hemodynamic response signal and cognitive stimuli signal, namely the same frequency but different phase (SFDP) problem. In this paper, a novel space affine matching feature is presented by introducing the time domain and frequency domain features. The time domain feature is used to discern different stimuli, while the frequency domain feature to eliminate the delay. And then we propose a space affine matching (SAM) algorithm to match fMRI time series by our affine feature, in which a normal vector is estimated using gradient descent to explore the time series matching optimally. The experimental results illustrate that the SAM algorithm is insensitive to the delay between the hemodynamic response signal and the cognitive stimuli signal. Our approach significantly outperforms GLM method while there exists the delay. The approach can help us solve the SFDP problem in fMRI time series matching and thus of great promise to reveal brain dynamics.
Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area.

PubMed

Fang, S; Liang, J; Qian, T; Wang, Y; Liu, X; Fan, X; Li, S; Wang, Y; Jiang, T

2017-10-01

The accuracy of preoperative blood oxygen level-dependent fMRI remains controversial. This study assessed the association between the anatomic location of a tumor and the accuracy of fMRI-based motor function mapping in diffuse lower-grade gliomas. Thirty-five patients with lower-grade gliomas involving motor areas underwent preoperative blood oxygen level-dependent fMRI scans with grasping tasks and received intraoperative direct cortical stimulation. Patients were classified into an overlapping group and a nonoverlapping group, depending on the extent to which blood oxygen level-dependent fMRI and direct cortical stimulation results concurred. Tumor location was quantitatively measured, including the shortest distance from the tumor to the hand knob and the deviation distance of the midpoint of the hand knob in the lesion hemisphere relative to the midline compared with the normal contralateral hemisphere. A 4-mm shortest distance from the tumor to the hand knob value was identified as optimal for differentiating the overlapping and nonoverlapping group with the receiver operating characteristic curve (sensitivity, 84.6%; specificity, 77.8%). The shortest distances from the tumor to the hand knob of ≤4 mm were associated with inaccurate fMRI-based localizations of the hand motor cortex. The shortest distances from the tumor to the hand knob were larger ( P = .002), and the deviation distances for the midpoint of the hand knob in the lesion hemisphere were smaller ( P = .003) in the overlapping group than in the nonoverlapping group. This study suggests that the shortest distance from the tumor to the hand knob and the deviation distance for the midpoint of the hand knob on the lesion hemisphere are predictive of the accuracy of blood oxygen level-dependent fMRI results. Smaller shortest distances from the tumor to the hand knob and larger deviation distances for the midpoint of hand knob on the lesion hemisphere are associated with less accuracy of motor cortex localization with blood oxygen level-dependent fMRI. Preoperative fMRI data for surgical planning should be used cautiously when the shortest distance from the tumor to the hand knob is ≤4 mm, especially for lower-grade gliomas anterior to the central sulcus. © 2017 by American Journal of Neuroradiology.
A Putative Multiple-Demand System in the Macaque Brain.

PubMed

Mitchell, Daniel J; Bell, Andrew H; Buckley, Mark J; Mitchell, Anna S; Sallet, Jerome; Duncan, John

2016-08-17

In humans, cognitively demanding tasks of many types recruit common frontoparietal brain areas. Pervasive activation of this "multiple-demand" (MD) network suggests a core function in supporting goal-oriented behavior. A similar network might therefore be predicted in nonhuman primates that readily perform similar tasks after training. However, an MD network in nonhuman primates has not been described. Single-cell recordings from macaque frontal and parietal cortex show some similar properties to human MD fMRI responses (e.g., adaptive coding of task-relevant information). Invasive recordings, however, come from limited prespecified locations, so they do not delineate a macaque homolog of the MD system and their positioning could benefit from knowledge of where MD foci lie. Challenges of scanning behaving animals mean that few macaque fMRI studies specifically contrast levels of cognitive demand, so we sought to identify a macaque counterpart to the human MD system using fMRI connectivity in 35 rhesus macaques. Putative macaque MD regions, mapped from frontoparietal MD regions defined in humans, were found to be functionally connected under anesthesia. To further refine these regions, an iterative process was used to maximize their connectivity cross-validated across animals. Finally, whole-brain connectivity analyses identified voxels that were robustly connected to MD regions, revealing seven clusters across frontoparietal and insular cortex comparable to human MD regions and one unexpected cluster in the lateral fissure. The proposed macaque MD regions can be used to guide future electrophysiological investigation of MD neural coding and in task-based fMRI to test predictions of similar functional properties to human MD cortex. In humans, a frontoparietal "multiple-demand" (MD) brain network is recruited during a wide range of cognitively demanding tasks. Because this suggests a fundamental function, one might expect a similar network to exist in nonhuman primates, but this remains controversial. Here, we sought to identify a macaque counterpart to the human MD system using fMRI connectivity. Putative macaque MD regions were functionally connected under anesthesia and were further refined by iterative optimization. The result is a network including lateral frontal, dorsomedial frontal, and insular and inferior parietal regions closely similar to the human counterpart. The proposed macaque MD regions can be useful in guiding electrophysiological recordings or in task-based fMRI to test predictions of similar functional properties to human MD cortex. Copyright © 2016 Mitchell et al.
Test-Retest Reliability of fMRI Brain Activity during Memory Encoding

PubMed Central

Brandt, David J.; Sommer, Jens; Krach, Sören; Bedenbender, Johannes; Kircher, Tilo; Paulus, Frieder M.; Jansen, Andreas

2013-01-01

The mechanisms underlying hemispheric specialization of memory are not completely understood. Functional magnetic resonance imaging (fMRI) can be used to develop and test models of hemispheric specialization. In particular for memory tasks however, the interpretation of fMRI results is often hampered by the low reliability of the data. In the present study we therefore analyzed the test-retest reliability of fMRI brain activation related to an implicit memory encoding task, with a particular focus on brain activity of the medial temporal lobe (MTL). Fifteen healthy subjects were scanned with fMRI on two sessions (average retest interval 35 days) using a commonly applied novelty encoding paradigm contrasting known and unknown stimuli. To assess brain lateralization, we used three different stimuli classes that differed in their verbalizability (words, scenes, fractals). Test-retest reliability of fMRI brain activation was assessed by an intraclass-correlation coefficient (ICC), describing the stability of inter-individual differences in the brain activation magnitude over time. We found as expected a left-lateralized brain activation network for the words paradigm, a bilateral network for the scenes paradigm, and predominantly right-hemispheric brain activation for the fractals paradigm. Although these networks were consistently activated in both sessions on the group level, across-subject reliabilities were only poor to fair (ICCs ≤ 0.45). Overall, the highest ICC values were obtained for the scenes paradigm, but only in strongly activated brain regions. In particular the reliability of brain activity of the MTL was poor for all paradigms. In conclusion, for novelty encoding paradigms the interpretation of fMRI results on a single subject level is hampered by its low reliability. More studies are needed to optimize the retest reliability of fMRI activation for memory tasks. PMID:24367338
Optimized Design and Analysis of Sparse-Sampling fMRI Experiments

PubMed Central

Perrachione, Tyler K.; Ghosh, Satrajit S.

2013-01-01

Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase the number of samples and improve statistical power. PMID:23616742
Optimized design and analysis of sparse-sampling FMRI experiments.

PubMed

Perrachione, Tyler K; Ghosh, Satrajit S

2013-01-01

Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase the number of samples and improve statistical power.
Recovery of directed intracortical connectivity from fMRI data

NASA Astrophysics Data System (ADS)

Gilson, Matthieu; Ritter, Petra; Deco, Gustavo

2016-06-01

The brain exhibits complex spatio-temporal patterns of activity. In particular, its baseline activity at rest has a specific structure: imaging techniques (e.g., fMRI, EEG and MEG) show that cortical areas experience correlated fluctuations, which is referred to as functional connectivity (FC). The present study relies on our recently developed model in which intracortical white-matter connections shape noise-driven fluctuations to reproduce FC observed in experimental data (here fMRI BOLD signal). Here noise has a functional role and represents the variability of neural activity. The model also incorporates anatomical information obtained using diffusion tensor imaging (DTI), which estimates the density of white-matter fibers (structural connectivity, SC). After optimization to match empirical FC, the model provides an estimation of the efficacies of these fibers, which we call effective connectivity (EC). EC differs from SC, as EC not only accounts for the density of neural fibers, but also the concentration of synapses formed at their end, the type of neurotransmitters associated and the excitability of target neural populations. In summary, the model combines anatomical SC and activity FC to evaluate what drives the neural dynamics, embodied in EC. EC can then be analyzed using graph theory to understand how it generates FC and to seek for functional communities among cortical areas (parcellation of 68 areas). We find that intracortical connections are not symmetric, which affects the dynamic range of cortical activity (i.e., variety of states it can exhibit).
Individual Functional ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

PubMed Central

Li, Kaiming; Guo, Lei; Zhu, Dajiang; Hu, Xintao; Han, Junwei; Liu, Tianming

2013-01-01

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain. PMID:22281931
Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals.

PubMed

Kim, Seong-Gi; Ogawa, Seiji

2012-07-01

After its discovery in 1990, blood oxygenation level-dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) has been widely used to map brain activation in humans and animals. Since fMRI relies on signal changes induced by neural activity, its signal source can be complex and is also dependent on imaging parameters and techniques. In this review, we identify and describe the origins of BOLD fMRI signals, including the topics of (1) effects of spin density, volume fraction, inflow, perfusion, and susceptibility as potential contributors to BOLD fMRI, (2) intravascular and extravascular contributions to conventional gradient-echo and spin-echo BOLD fMRI, (3) spatial specificity of hemodynamic-based fMRI related to vascular architecture and intrinsic hemodynamic responses, (4) BOLD signal contributions from functional changes in cerebral blood flow (CBF), cerebral blood volume (CBV), and cerebral metabolic rate of O(2) utilization (CMRO(2)), (5) dynamic responses of BOLD, CBF, CMRO(2), and arterial and venous CBV, (6) potential sources of initial BOLD dips, poststimulus BOLD undershoots, and prolonged negative BOLD fMRI signals, (7) dependence of stimulus-evoked BOLD signals on baseline physiology, and (8) basis of resting-state BOLD fluctuations. These discussions are highly relevant to interpreting BOLD fMRI signals as physiological means.
Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals

PubMed Central

Kim, Seong-Gi; Ogawa, Seiji

2012-01-01

After its discovery in 1990, blood oxygenation level-dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) has been widely used to map brain activation in humans and animals. Since fMRI relies on signal changes induced by neural activity, its signal source can be complex and is also dependent on imaging parameters and techniques. In this review, we identify and describe the origins of BOLD fMRI signals, including the topics of (1) effects of spin density, volume fraction, inflow, perfusion, and susceptibility as potential contributors to BOLD fMRI, (2) intravascular and extravascular contributions to conventional gradient-echo and spin-echo BOLD fMRI, (3) spatial specificity of hemodynamic-based fMRI related to vascular architecture and intrinsic hemodynamic responses, (4) BOLD signal contributions from functional changes in cerebral blood flow (CBF), cerebral blood volume (CBV), and cerebral metabolic rate of O2 utilization (CMRO2), (5) dynamic responses of BOLD, CBF, CMRO2, and arterial and venous CBV, (6) potential sources of initial BOLD dips, poststimulus BOLD undershoots, and prolonged negative BOLD fMRI signals, (7) dependence of stimulus-evoked BOLD signals on baseline physiology, and (8) basis of resting-state BOLD fluctuations. These discussions are highly relevant to interpreting BOLD fMRI signals as physiological means. PMID:22395207
HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).

PubMed

Makkie, Milad; Zhao, Shijie; Jiang, Xi; Lv, Jinglei; Zhao, Yu; Ge, Bao; Li, Xiang; Han, Junwei; Liu, Tianming

Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI 'big data.' Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed. To address this challenge, in this work, we introduce our newly developed informatics platform, namely, 'HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).' HELPNI implements our recently developed computational framework of sparse representation of whole-brain fMRI signals which is called holistic atlases of functional networks and interactions (HAFNI) for fMRI data analysis. HELPNI provides integrated solutions to archive and process large-scale fMRI data automatically and structurally, to extract and visualize meaningful results information from raw fMRI data, and to share open-access processed and raw data with other collaborators through web. We tested the proposed HELPNI platform using publicly available 1000 Functional Connectomes dataset including over 1200 subjects. We identified consistent and meaningful functional brain networks across individuals and populations based on resting state fMRI (rsfMRI) big data. Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the data and associated results much faster.
HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).

PubMed

Makkie, Milad; Zhao, Shijie; Jiang, Xi; Lv, Jinglei; Zhao, Yu; Ge, Bao; Li, Xiang; Han, Junwei; Liu, Tianming

2015-12-01

Tremendous efforts have thus been devoted on the establishment of functional MRI informatics systems that recruit a comprehensive collection of statistical/computational approaches for fMRI data analysis. However, the state-of-the-art fMRI informatics systems are especially designed for specific fMRI sessions or studies of which the data size is not really big, and thus has difficulty in handling fMRI 'big data.' Given the size of fMRI data are growing explosively recently due to the advancement of neuroimaging technologies, an effective and efficient fMRI informatics system which can process and analyze fMRI big data is much needed. To address this challenge, in this work, we introduce our newly developed informatics platform, namely, 'HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI).' HELPNI implements our recently developed computational framework of sparse representation of whole-brain fMRI signals which is called holistic atlases of functional networks and interactions (HAFNI) for fMRI data analysis. HELPNI provides integrated solutions to archive and process large-scale fMRI data automatically and structurally, to extract and visualize meaningful results information from raw fMRI data, and to share open-access processed and raw data with other collaborators through web. We tested the proposed HELPNI platform using publicly available 1000 Functional Connectomes dataset including over 1200 subjects. We identified consistent and meaningful functional brain networks across individuals and populations based on resting state fMRI (rsfMRI) big data. Using efficient sampling module, the experimental results demonstrate that our HELPNI system has superior performance than other systems for large-scale fMRI data in terms of processing and storing the data and associated results much faster.
Determination of hemispheric language dominance in the surgical epilepsy patient: diagnostic properties of functional magnetic resonance imaging.

PubMed

Spritzer, Scott D; Hoerth, Matthew T; Zimmerman, Richard S; Shmookler, Aaron; Hoffman-Snyder, Charlene R; Wellik, Kay E; Demaerschalk, Bart M; Wingerchuk, Dean M

2012-09-01

Presurgical evaluation for refractory epilepsy typically includes assessment of cognitive and language functions. The reference standard for determination of hemispheric language dominance has been the intracarotid amobarbital test (IAT) but functional magnetic resonance imaging (fMRI) is increasingly used. To critically assess current evidence regarding the diagnostic properties of fMRI in comparison with the IAT for determination of hemispheric language dominance. The objective was addressed through the development of a structured critically appraised topic. This included a clinical scenario, structured question, literature search strategy, critical appraisal, results, evidence summary, commentary, and bottom-line conclusions. Participants included consultant and resident neurologists, a medical librarian, clinical epidemiologists, and content experts in the fields of epilepsy and neurosurgery. A systematic review and meta-analysis that compared the sensitivity and specificity of fMRI to IAT-determined language lateralization was selected for critical appraisal. The review included data from 23 articles (n=442); study methodology varied widely. fMRI was 83.5% sensitive and 88.1% specific for detection of hemispheric language dominance. There are insufficient data to support routine use of fMRI for the purpose of determining hemispheric language dominance in patients with intractable epilepsy. Larger, well-designed studies of fMRI for language and other cognitive outcomes as part of the presurgical and postsurgical evaluation of epilepsy patients are necessary.
An EEG Finger-Print of fMRI deep regional activation.

PubMed

Meir-Hasson, Yehudit; Kinreich, Sivan; Podlipsky, Ilana; Hendler, Talma; Intrator, Nathan

2014-11-15

This work introduces a general framework for producing an EEG Finger-Print (EFP) which can be used to predict specific brain activity as measured by fMRI at a given deep region. This new approach allows for improved EEG spatial resolution based on simultaneous fMRI activity measurements. Advanced signal processing and machine learning methods were applied on EEG data acquired simultaneously with fMRI during relaxation training guided by on-line continuous feedback on changing alpha/theta EEG measure. We focused on demonstrating improved EEG prediction of activation in sub-cortical regions such as the amygdala. Our analysis shows that a ridge regression model that is based on time/frequency representation of EEG data from a single electrode, can predict the amygdala related activity significantly better than a traditional theta/alpha activity sampled from the best electrode and about 1/3 of the times, significantly better than a linear combination of frequencies with a pre-defined delay. The far-reaching goal of our approach is to be able to reduce the need for fMRI scanning for probing specific sub-cortical regions such as the amygdala as the basis for brain-training procedures. On the other hand, activity in those regions can be characterized with higher temporal resolution than is obtained by fMRI alone thus revealing additional information about their processing mode. Copyright © 2013 Elsevier Inc. All rights reserved.
Augmenting intraoperative MRI with preoperative fMRI and DTI by biomechanical simulation of brain deformation

NASA Astrophysics Data System (ADS)

Warfield, Simon K.; Talos, Florin; Kemper, Corey; Cosman, Eric; Tei, Alida; Ferrant, Matthieu; Macq, Benoit M. M.; Wells, William M., III; Black, Peter M.; Jolesz, Ferenc A.; Kikinis, Ron

2003-05-01

The key challenge facing the neurosurgeon during neurosurgery is to be able to remove from the brain as much tumor tissue as possible while preserving healthy tissue and minimizing the disruption of critical anatomical structures. The purpose of this work was to demonstrate the use of biomechanical simulation of brain deformation to project preoperative fMRI and DTI data into the coordinate system of the patient brain deformed during neurosurgery. This projection enhances the visualization of relevant critical structures available to the neurosurgeon. Our approach to tracking brain changes during neurosurgery has been previously described. We applied this procedure to warp preoperative fMRI and DTI to match intraoperative MRI. We constructed visualizations of preoperative fMRI and DTI, and intraoperative MRI showing a close correspondence between the matched data. We have previously demonstrated our biomechanical simulation of brain deformation can be executed entirely during neurosurgery. We previously used a generic atlas as a substitute for patient specific data. Here we report the successful alignment of patient-specific DTI and fMRI preoperative data into the intraoperative configuration of the patient's brain. This can significantly enhance the information available to the neurosurgeon.
Comparison of glomerular activity patterns by fMRI and wide-field calcium imaging: implications for principles underlying odor mapping

PubMed Central

Sanganahalli, Basavaraju G.; Rebello, Michelle R.; Herman, Peter; Papademetris, Xenophon; Shepherd, Gordon M.; Verhagen, Justus V.; Hyder, Fahmeed

2015-01-01

Functional imaging signals arise from distinct metabolic and hemodynamic events at the neuropil, but how these processes are influenced by pre- and post-synaptic activities need to be understood for quantitative interpretation of stimulus-evoked mapping data. The olfactory bulb (OB) glomeruli, spherical neuropil regions with well-defined neuronal circuitry, can provide insights into this issue. Optical calcium-sensitive fluorescent dye imaging (OICa2+) reflects dynamics of pre-synaptic input to glomeruli, whereas high-resolution functional magnetic resonance imaging (fMRI) using deoxyhemoglobin contrast reveals neuropil function within the glomerular layer where both pre- and post-synaptic activities contribute. We imaged odor-specific activity patterns of the dorsal OB in the same anesthetized rats with fMRI and OICa2+ and then co-registered the respective maps to compare patterns in the same space. Maps by each modality were very reproducible as trial-to-trial patterns for a given odor, overlapping by ~80%. Maps evoked by ethyl butyrate and methyl valerate for a given modality overlapped by ~80%, suggesting activation of similar dorsal glomerular networks by these odors. Comparison of maps generated by both methods for a given odor showed ~70% overlap, indicating similar odor-specific maps by each method. These results suggest that odor-specific glomerular patterns by high-resolution fMRI primarily tracks pre-synaptic input to the OB. Thus combining OICa2+ and fMRI lays the framework for studies of OB processing over a range of spatiotemporal scales, where OICa2+ can feature the fast dynamics of dorsal glomerular clusters and fMRI can map the entire glomerular sheet in the OB. PMID:26631819
fMRI brain response during sentence reading comprehension in children with benign epilepsy with centro-temporal spikes.

PubMed

Malfait, D; Tucholka, A; Mendizabal, S; Tremblay, J; Poulin, C; Oskoui, M; Srour, M; Carmant, L; Major, P; Lippé, S

2015-11-01

Children with benign epilepsy with centro-temporal spikes (BECTS) often have language problems. Abnormal epileptic activity is found in central and temporal brain regions, which are involved in reading and semantic and syntactic comprehension. Using functional magnetic resonance imaging (fMRI), we examined reading networks in BECTS children with a new sentence reading comprehension task involving semantic and syntactic processing. Fifteen children with BECTS (age=11y 1m ± 16 m; 12 boys) and 18 healthy controls (age=11 y 8m ± 20 m; 11 boys) performed an fMRI reading comprehension task in which they read a pair of syntactically complex sentences and decided whether the target sentence (the second sentence in the pair) was true or false with respect to the first sentence. All children also underwent an exhaustive neuropsychological assessment. We demonstrated weaknesses in several cognitive domains in BECTS children. During the sentence reading fMRI task, left inferior frontal regions and bilateral temporal areas were activated in BECTS children and healthy controls. However, additional brain regions such as the left hippocampus and precuneus were activated in BECTS children. Moreover, specific activation was found in the left caudate and putamen in BECTS children but not in healthy controls. Cognitive results and accuracy during the fMRI task were associated with specific brain activation patterns. BECTS children recruited a wider network to perform the fMRI sentence reading comprehension task, with specific activation in the left dorsal striatum. BECTS cognitive performance differently predicted functional activation in frontal and temporal regions compared to controls, suggesting differences in brain network organisation that contribute to reading comprehension. Crown Copyright © 2015. Published by Elsevier B.V. All rights reserved.
Integration of fMRI, NIROT and ERP for studies of human brain function.

PubMed

Gore, John C; Horovitz, Silvina G; Cannistraci, Christopher J; Skudlarski, Pavel

2006-05-01

Different methods of assessing human brain function possess specific advantages and disadvantages compared to others, but it is believed that combining different approaches will provide greater information than can be obtained from each alone. For example, functional magnetic resonance imaging (fMRI) has good spatial resolution but poor temporal resolution, whereas the converse is true for electrophysiological recordings (event-related potentials or ERPs). In this review of recent work, we highlight a novel approach to combining these modalities in a manner designed to increase information on the origins and locations of the generators of specific ERPs and the relationship between fMRI and ERP signals. Near infrared imaging techniques have also been studied as alternatives to fMRI and can be readily integrated with simultaneous electrophysiological recordings. Each of these modalities may in principle be also used in so-called steady-state acquisitions in which the correlational structure of signals from the brain may be analyzed to provide new insights into brain function.
Using fMRI to study reward processing in humans: past, present, and future

PubMed Central

Wang, Kainan S.; Smith, David V.

2016-01-01

Functional magnetic resonance imaging (fMRI) is a noninvasive tool used to probe cognitive and affective processes. Although fMRI provides indirect measures of neural activity, the advent of fMRI has allowed for 1) the corroboration of significant animal findings in the human brain, and 2) the expansion of models to include more common human attributes that inform behavior. In this review, we briefly consider the neural basis of the blood oxygenation level dependent signal to set up a discussion of how fMRI studies have applied it in examining cognitive models in humans and the promise of using fMRI to advance such models. Specifically, we illustrate the contribution that fMRI has made to the study of reward processing, focusing on the role of the striatum in encoding reward-related learning signals that drive anticipatory and consummatory behaviors. For instance, we discuss how fMRI can be used to link neural signals (e.g., striatal responses to rewards) to individual differences in behavior and traits. While this functional segregation approach has been constructive to our understanding of reward-related functions, many fMRI studies have also benefitted from a functional integration approach that takes into account how interconnected regions (e.g., corticostriatal circuits) contribute to reward processing. We contend that future work using fMRI will profit from using a multimodal approach, such as combining fMRI with noninvasive brain stimulation tools (e.g., transcranial electrical stimulation), that can identify causal mechanisms underlying reward processing. Consequently, advancements in implementing fMRI will promise new translational opportunities to inform our understanding of psychopathologies. PMID:26740530
Tensorial extensions of independent component analysis for multisubject FMRI analysis.

PubMed

Beckmann, C F; Smith, S M

2005-03-01

We discuss model-free analysis of multisubject or multisession FMRI data by extending the single-session probabilistic independent component analysis model (PICA; Beckmann and Smith, 2004. IEEE Trans. on Medical Imaging, 23 (2) 137-152) to higher dimensions. This results in a three-way decomposition that represents the different signals and artefacts present in the data in terms of their temporal, spatial, and subject-dependent variations. The technique is derived from and compared with parallel factor analysis (PARAFAC; Harshman and Lundy, 1984. In Research methods for multimode data analysis, chapter 5, pages 122-215. Praeger, New York). Using simulated data as well as data from multisession and multisubject FMRI studies we demonstrate that the tensor PICA approach is able to efficiently and accurately extract signals of interest in the spatial, temporal, and subject/session domain. The final decompositions improve upon PARAFAC results in terms of greater accuracy, reduced interference between the different estimated sources (reduced cross-talk), robustness (against deviations of the data from modeling assumptions and against overfitting), and computational speed. On real FMRI 'activation' data, the tensor PICA approach is able to extract plausible activation maps, time courses, and session/subject modes as well as provide a rich description of additional processes of interest such as image artefacts or secondary activation patterns. The resulting data decomposition gives simple and useful representations of multisubject/multisession FMRI data that can aid the interpretation and optimization of group FMRI studies beyond what can be achieved using model-based analysis techniques.

EEG-Informed fMRI Reveals a Disturbed Gamma-Band–Specific Network in Subjects at High Risk for Psychosis

PubMed Central

Leicht, Gregor; Vauth, Sebastian; Polomac, Nenad; Andreou, Christina; Rauh, Jonas; Mußmann, Marius; Karow, Anne; Mulert, Christoph

2016-01-01

Objectives. Abnormalities of oscillatory gamma activity are supposed to reflect a core pathophysiological mechanism underlying cognitive disturbances in schizophrenia. The auditory evoked gamma-band response (aeGBR) is known to be reduced across all stages of the disease. The present study aimed to elucidate alterations of an aeGBR-specific network mediated by gamma oscillations in the high-risk state of psychosis (HRP) by means of functional magnetic resonance imaging (fMRI) informed by electroencephalography (EEG). Methods. EEG and fMRI were simultaneously recorded from 27 HRP individuals and 26 healthy controls (HC) during performance of a cognitively demanding auditory reaction task. We used single trial coupling of the aeGBR with the corresponding blood oxygen level depending response (EEG-informed fMRI). Results. A gamma-band–specific network was significantly lower active in HRP subjects compared with HC (random effects analysis, P < .01, Bonferroni-corrected for multiple comparisons) accompanied by a worse task performance. This network involved the bilateral auditory cortices, the thalamus and frontal brain regions including the anterior cingulate cortex, as well as the bilateral dorsolateral prefrontal cortex. Conclusions. For the first time we report a reduced activation of an aeGBR-specific network in HRP subjects brought forward by EEG-informed fMRI. Because the HRP reflects the clinical risk for conversion to psychotic disorders including schizophrenia and the aeGBR has repeatedly been shown to be altered in patients with schizophrenia the results of our study point towards a potential applicability of aeGBR disturbances as a marker for the prediction of transition of HRP subjects to schizophrenia. PMID:26163477
EEG-Informed fMRI Reveals a Disturbed Gamma-Band-Specific Network in Subjects at High Risk for Psychosis.

PubMed

Leicht, Gregor; Vauth, Sebastian; Polomac, Nenad; Andreou, Christina; Rauh, Jonas; Mußmann, Marius; Karow, Anne; Mulert, Christoph

2016-01-01

Abnormalities of oscillatory gamma activity are supposed to reflect a core pathophysiological mechanism underlying cognitive disturbances in schizophrenia. The auditory evoked gamma-band response (aeGBR) is known to be reduced across all stages of the disease. The present study aimed to elucidate alterations of an aeGBR-specific network mediated by gamma oscillations in the high-risk state of psychosis (HRP) by means of functional magnetic resonance imaging (fMRI) informed by electroencephalography (EEG). EEG and fMRI were simultaneously recorded from 27 HRP individuals and 26 healthy controls (HC) during performance of a cognitively demanding auditory reaction task. We used single trial coupling of the aeGBR with the corresponding blood oxygen level depending response (EEG-informed fMRI). A gamma-band-specific network was significantly lower active in HRP subjects compared with HC (random effects analysis, P < .01, Bonferroni-corrected for multiple comparisons) accompanied by a worse task performance. This network involved the bilateral auditory cortices, the thalamus and frontal brain regions including the anterior cingulate cortex, as well as the bilateral dorsolateral prefrontal cortex. For the first time we report a reduced activation of an aeGBR-specific network in HRP subjects brought forward by EEG-informed fMRI. Because the HRP reflects the clinical risk for conversion to psychotic disorders including schizophrenia and the aeGBR has repeatedly been shown to be altered in patients with schizophrenia the results of our study point towards a potential applicability of aeGBR disturbances as a marker for the prediction of transition of HRP subjects to schizophrenia. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.
Impairment of preoperative language mapping by lesion location: a functional magnetic resonance imaging, navigated transcranial magnetic stimulation, and direct cortical stimulation study.

PubMed

Ille, Sebastian; Sollmann, Nico; Hauck, Theresa; Maurer, Stefanie; Tanigawa, Noriko; Obermueller, Thomas; Negwer, Chiara; Droese, Doris; Boeckh-Behrens, Tobias; Meyer, Bernhard; Ringel, Florian; Krieg, Sandro M

2015-08-01

Language mapping by repetitive navigated transcranial magnetic stimulation (rTMS) is increasingly used and has already replaced functional MRI (fMRI) in some institutions for preoperative mapping of neurosurgical patients. Yet some factors affect the concordance of both methods with direct cortical stimulation (DCS), most likely by lesions affecting cortical oxygenation levels. Therefore, the impairment of the accuracy of rTMS and fMRI was analyzed and compared with DCS during awake surgery in patients with intraparenchymal lesions. Language mapping was performed by DCS, rTMS, and fMRI using an object-naming task in 27 patients with left-sided perisylvian lesions, and the induced language errors of each method were assigned to the cortical parcellation system. Subsequently, the receiver operating characteristics were calculated for rTMS and fMRI and compared with DCS as ground truth for regions with (w/) and without (w/o) the lesion in the mapped regions. The w/ subgroup revealed a sensitivity of 100% (w/o 100%), a specificity of 8% (w/o 5%), a positive predictive value of 34% (w/o: 53%), and a negative predictive value (NPV) of 100% (w/o: 100%) for the comparison of rTMS versus DCS. Findings for the comparison of fMRI versus DCS within the w/ subgroup revealed a sensitivity of 32% (w/o: 62%), a specificity of 88% (w/o: 60%), a positive predictive value of 56% (w/o: 62%), and a NPV of 73% (w/o: 60%). Although strengths and weaknesses exist for both rTMS and fMRI, the results show that rTMS is less affected by a brain lesion than fMRI, especially when performing mapping of language-negative cortical regions based on sensitivity and NPV.
A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.
CortiQ-based Real-Time Functional Mapping for Epilepsy Surgery.

PubMed

Kapeller, Christoph; Korostenskaja, Milena; Prueckl, Robert; Chen, Po-Ching; Lee, Ki Heyeong; Westerveld, Michael; Salinas, Christine M; Cook, Jane C; Baumgartner, James E; Guger, Christoph

2015-06-01

To evaluate the use of the cortiQ-based mapping system (g.tec medication engineering GmbH, Austria) for real-time functional mapping (RTFM) and to compare it to results from electrical cortical stimulation mapping (ESM) and functional magnetic resonance imaging (fMRI). Electrocorticographic activity was recorded in 3 male patients with intractable epilepsy by using cortiQ mapping system and analyzed in real time. Activation related to motor, sensory, and receptive language tasks was determined by evaluating the power of the high gamma frequency band (60-170 Hz). The sensitivity and specificity of RTFM were tested against ESM and fMRI results. "Next-neighbor" approach demonstrated [sensitivity/specificity %] (1) RTFM against ESM: 100.00/79.70 for hand motor; 100.00/73.87 for hand sensory; -/87 for language (it was not identified by the ESM); (2) RTFM against fMRI: 100.00/84.4 for hand motor; 66.70/85.35 for hand sensory; and 87.85/77.70 for language. The results of the quantitative "next-neighbor" RTFM evaluation were concordant to those from ESM and fMRI. The RTFM correlates well with localization of hand motor function provided by ESM and fMRI, which may offer added localization in the operating room and guidance for extraoperative ESM mapping. Real-time functional mapping correlates with fMRI language activation when ESM findings are negative. It has fewer limitations than ESM and greater flexibility in activation paradigms and measuring responses.
Single-shot ADC imaging for fMRI.

PubMed

Song, Allen W; Guo, Hua; Truong, Trong-Kha

2007-02-01

It has been suggested that apparent diffusion coefficient (ADC) contrast can be sensitive to cerebral blood flow (CBF) changes during brain activation. However, current ADC imaging techniques have an inherently low temporal resolution due to the requirement of multiple acquisitions with different b-factors, as well as potential confounds from cross talk between the deoxyhemoglobin-induced background gradients and the externally applied diffusion-weighting gradients. In this report a new method is proposed and implemented that addresses these two limitations. Specifically, a single-shot pulse sequence that sequentially acquires one gradient-echo (GRE) and two diffusion-weighted spin-echo (SE) images was developed. In addition, the diffusion-weighting gradient waveform was numerically optimized to null the cross terms with the deoxyhemoglobin-induced background gradients to fully isolate the effect of diffusion weighting from that of oxygenation-level changes. The experimental results show that this new single-shot method can acquire ADC maps with sufficient signal-to-noise ratio (SNR), and establish its practical utility in functional MRI (fMRI) to complement the blood oxygenation level-dependent (BOLD) technique and provide differential sensitivity for different vasculatures to better localize neural activity originating from the small vessels. Copyright (c) 2007 Wiley-Liss, Inc.
Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.
Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI.

PubMed

Chu, Shu-Hsien; Parhi, Keshab K; Lenglet, Christophe

2018-03-16

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation.
Pre-Surgical Integration of fMRI and DTI of the Sensorimotor System in Transcortical Resection of a High-Grade Insular Astrocytoma

PubMed Central

Ekstrand, Chelsea L.; Mickleborough, Marla J. S.; Fourney, Daryl R.; Gould, Layla A.; Lorentz, Eric J.; Ellchuk, Tasha; Borowsky, Ron W.

2016-01-01

Herein we report on a patient with a WHO Grade III astrocytoma in the right insular region in close proximity to the internal capsule who underwent a right frontotemporal craniotomy. Total gross resection of insular gliomas remains surgically challenging based on the possibility of damage to the corticospinal tracts. However, maximizing the extent of resection has been shown to decrease future adverse outcomes. Thus, the goal of such surgeries should focus on maximizing extent of resection while minimizing possible adverse outcomes. In this case, pre-surgical planning included integration of functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), to localize motor and sensory pathways. Novel fMRI tasks were individually developed for the patient to maximize both somatosensory and motor activation simultaneously in areas in close proximity to the tumor. Information obtained was used to optimize resection trajectory and extent, facilitating gross total resection of the astrocytoma. Across all three motor-sensory tasks administered, fMRI revealed an area of interest just superior and lateral to the astrocytoma. Further, DTI analyses showed displacement of the corona radiata around the superior dorsal surface of the astrocytoma, extending in the direction of the activation found using fMRI. Taking into account these results, a transcortical superior temporal gyrus surgical approach was chosen in order to avoid the area of interest identified by fMRI and DTI. Total gross resection was achieved and minor post-surgical motor and sensory deficits were temporary. This case highlights the utility of comprehensive pre-surgical planning, including fMRI and DTI, to maximize surgical outcomes on a case-by-case basis. PMID:27013996
Studying the neural bases of prism adaptation using fMRI: A technical and design challenge.

PubMed

Bultitude, Janet H; Farnè, Alessandro; Salemme, Romeo; Ibarrola, Danielle; Urquizar, Christian; O'Shea, Jacinta; Luauté, Jacques

2017-12-01

Prism adaptation induces rapid recalibration of visuomotor coordination. The neural mechanisms of prism adaptation have come under scrutiny since the observations that the technique can alleviate hemispatial neglect following stroke, and can alter spatial cognition in healthy controls. Relative to non-imaging behavioral studies, fMRI investigations of prism adaptation face several challenges arising from the confined physical environment of the scanner and the supine position of the participants. Any researcher who wishes to administer prism adaptation in an fMRI environment must adjust their procedures enough to enable the experiment to be performed, but not so much that the behavioral task departs too much from true prism adaptation. Furthermore, the specific temporal dynamics of behavioral components of prism adaptation present additional challenges for measuring their neural correlates. We developed a system for measuring the key features of prism adaptation behavior within an fMRI environment. To validate our configuration, we present behavioral (pointing) and head movement data from 11 right-hemisphere lesioned patients and 17 older controls who underwent sham and real prism adaptation in an MRI scanner. Most participants could adapt to prismatic displacement with minimal head movements, and the procedure was well tolerated. We propose recommendations for fMRI studies of prism adaptation based on the design-specific constraints and our results.
fMRI identifies chronotype-specific brain activation associated with attention to motion--why we need to know when subjects go to bed.

PubMed

Reske, Martina; Rosenberg, Jessica; Plapp, Sabrina; Kellermann, Thilo; Shah, N Jon

2015-05-01

Human cognition relies on attentional capacities which, among others, are influenced by factors like tiredness or mood. Based on their inherent preferences in sleep and wakefulness, individuals can be classified as specific "chronotypes". The present study investigated how early, intermediate and late chronotypes (EC, IC, LC) differ neurally on an attention-to-motion task. Twelve EC, 18 IC and 17 LC were included into the study. While undergoing functional magnetic resonance imaging (fMRI) scans, subjects looked at vertical bars in an attention-to-motion task. In the STATIONARY condition, subjects focused on a central fixation cross. During Fix-MOVING and Attend-MOVING, bars were moving horizontally. Only during the Attend-MOVING, subjects were required to attend to changes in the velocity of bars and indicate those by button presses. A two-way repeated measures ANOVA probed group by attentional load effects. The dorsolateral prefrontal cortex (DLPFC), insula and anterior cingulate cortex showed group by attention specific activations. Specifically, EC and LC presented attenuated DLPFC activation under high attentional load (Attend-MOVING), while EC showed less anterior insula activation than IC. LC compared to IC exhibited attenuation of superior parietal cortex. Our study reveals that individual sleep preferences are associated with characteristic brain activation in areas crucial for attention and bodily awareness. These results imply that considering sleep preferences in neuroimaging studies is crucial when administering cognitive tasks. Our study also has socio-economic implications. Task performance in non-optimal times of the day (e.g. shift workers), may result in cognitive impairments leading to e.g. increased error rates and slower reaction times. Copyright © 2015 Elsevier Inc. All rights reserved.
A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models.

PubMed

Asaad, Mazen; Lee, Jin Hyung

2018-05-18

Alzheimer's disease is a leading healthcare challenge facing our society today. Functional magnetic resonance imaging (fMRI) of the brain has played an important role in our efforts to understand how Alzheimer's disease alters brain function. Using fMRI in animal models of Alzheimer's disease has the potential to provide us with a more comprehensive understanding of the observations made in human clinical fMRI studies. However, using fMRI in animal models of Alzheimer's disease presents some unique challenges. Here, we highlight some of these challenges and discuss potential solutions for researchers interested in performing fMRI in animal models. First, we briefly summarize our current understanding of Alzheimer's disease from a mechanistic standpoint. We then overview the wide array of animal models available for studying this disease and how to choose the most appropriate model to study, depending on which aspects of the condition researchers seek to investigate. Finally, we discuss the contributions of fMRI to our understanding of Alzheimer's disease and the issues to consider when designing fMRI studies for animal models, such as differences in brain activity based on anesthetic choice and ways to interrogate more specific questions in rodents beyond those that can be addressed in humans. The goal of this article is to provide information on the utility of fMRI, and approaches to consider when using fMRI, for studies of Alzheimer's disease in animal models. © 2018. Published by The Company of Biologists Ltd.
A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models

PubMed Central

Asaad, Mazen

2018-01-01

ABSTRACT Alzheimer's disease is a leading healthcare challenge facing our society today. Functional magnetic resonance imaging (fMRI) of the brain has played an important role in our efforts to understand how Alzheimer's disease alters brain function. Using fMRI in animal models of Alzheimer's disease has the potential to provide us with a more comprehensive understanding of the observations made in human clinical fMRI studies. However, using fMRI in animal models of Alzheimer's disease presents some unique challenges. Here, we highlight some of these challenges and discuss potential solutions for researchers interested in performing fMRI in animal models. First, we briefly summarize our current understanding of Alzheimer's disease from a mechanistic standpoint. We then overview the wide array of animal models available for studying this disease and how to choose the most appropriate model to study, depending on which aspects of the condition researchers seek to investigate. Finally, we discuss the contributions of fMRI to our understanding of Alzheimer's disease and the issues to consider when designing fMRI studies for animal models, such as differences in brain activity based on anesthetic choice and ways to interrogate more specific questions in rodents beyond those that can be addressed in humans. The goal of this article is to provide information on the utility of fMRI, and approaches to consider when using fMRI, for studies of Alzheimer's disease in animal models. PMID:29784664
Differential fMRI Activation Patterns to Noxious Heat and Tactile Stimuli in the Primate Spinal Cord

PubMed Central

Yang, Pai-Feng; Wang, Feng

2015-01-01

Mesoscale local functional organizations of the primate spinal cord are largely unknown. Using high-resolution fMRI at 9.4 T, we identified distinct interhorn and intersegment fMRI activation patterns to tactile versus nociceptive heat stimulation of digits in lightly anesthetized monkeys. Within a spinal segment, 8 Hz vibrotactile stimuli elicited predominantly fMRI activations in the middle part of ipsilateral dorsal horn (iDH), along with significantly weaker activations in ipsilateral (iVH) and contralateral (cVH) ventral horns. In contrast, nociceptive heat stimuli evoked widespread strong activations in the superficial part of iDH, as well as in iVH and contralateral dorsal (cDH) horns. As controls, only weak signal fluctuations were detected in the white matter. The iDH responded most strongly to both tactile and heat stimuli, whereas the cVH and cDH responded selectively to tactile versus nociceptive heat, respectively. Across spinal segments, iDH activations were detected in three consecutive segments in both tactile and heat conditions. Heat responses, however, were more extensive along the cord, with strong activations in iVH and cDH in two consecutive segments. Subsequent subunit B of cholera toxin tracer histology confirmed that the spinal segments showing fMRI activations indeed received afferent inputs from the stimulated digits. Comparisons of the fMRI signal time courses in early somatosensory area 3b and iDH revealed very similar hemodynamic stimulus–response functions. In summary, we identified with fMRI distinct segmental networks for the processing of tactile and nociceptive heat stimuli in the cervical spinal cord of nonhuman primates. SIGNIFICANCE STATEMENT This is the first fMRI demonstration of distinct intrasegmental and intersegmental nociceptive heat and touch processing circuits in the spinal cord of nonhuman primates. This study provides novel insights into the local functional organizations of the primate spinal cord for pain and touch, information that will be valuable for designing and optimizing therapeutic interventions for chronic pain management. PMID:26203144
Analysis strategies for high-resolution UHF-fMRI data.

PubMed

Polimeni, Jonathan R; Renvall, Ville; Zaretskaya, Natalia; Fischl, Bruce

2018-03-01

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential. Copyright © 2017 Elsevier Inc. All rights reserved.
Visual Detection and Identification Are Not the Same: Evidence from Psychophysics and fMRI

ERIC Educational Resources Information Center

Straube, Sirko; Fahle, Manfred

2011-01-01

Sometimes object detection as opposed to identification is sufficient to initiate the appropriate action. To explore the neural origin of behavioural differences between the two tasks, we combine psychophysical measurements and fMRI, specifically contrasting shape detection versus identification of a figure. This figure consisted of Gabor elements…
Event-Related fMRI of Category Learning: Differences in Classification and Feedback Networks

ERIC Educational Resources Information Center

Little, Deborah M.; Shin, Silvia S.; Sisco, Shannon M.; Thulborn, Keith R.

2006-01-01

Eighteen healthy young adults underwent event-related (ER) functional magnetic resonance imaging (fMRI) of the brain while performing a visual category learning task. The specific category learning task required subjects to extract the rules that guide classification of quasi-random patterns of dots into categories. Following each classification…
Impact of preoperative functional magnetic resonance imaging during awake craniotomy procedures for intraoperative guidance and complication avoidance.

PubMed

Trinh, Victoria T; Fahim, Daniel K; Maldaun, Marcos V C; Shah, Komal; McCutcheon, Ian E; Rao, Ganesh; Lang, Frederick; Weinberg, Jeffrey; Sawaya, Raymond; Suki, Dima; Prabhu, Sujit S

2014-01-01

We wanted to study the role of functional MRI (fMRI) in preventing neurological injury in awake craniotomy patients as this has not been previously studied. To examine the role of fMRI as an intraoperative adjunct during awake craniotomy procedures. Preoperative fMRI was carried out routinely in 214 patients undergoing awake craniotomy with direct cortical stimulation (DCS). In 40% of our cases (n = 85) fMRI was utilized for the intraoperative localization of the eloquent cortex. In the other 129 cases significant noise distortion, poor task performance and nonspecific BOLD activation precluded the surgeon from using the fMRI data. Compared with DCS, fMRI had a sensitivity and specificity, respectively, of 91 and 64% in Broca's area, 93 and 18% in Wernicke's area and 100 and 100% in motor areas. A new intraoperative neurological deficit during subcortical dissection was predictive of a worsened deficit following surgery (p < 0.001). The use of fMRI for intraoperative localization was, however, not significant in preventing worsened neurological deficits, both in the immediate postoperative period (p = 1.00) and at the 3-month follow-up (p = 0.42). The routine use of fMRI was not useful in identifying language sites as performed and, more importantly, practiced tasks failed to prevent neurological deficits following awake craniotomy procedures. © 2014 S. Karger AG, Basel.
EEG-Informed fMRI: A Review of Data Analysis Methods

PubMed Central

Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

2018-01-01

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634
An evaluation of independent component analyses with an application to resting-state fMRI

PubMed Central

Matteson, David S.; Ruppert, David; Eloyan, Ani; Caffo, Brian S.

2013-01-01

Summary We examine differences between independent component analyses (ICAs) arising from different as-sumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods–whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method. PMID:24350655

Cortical Coupling Reflects Bayesian Belief Updating in the Deployment of Spatial Attention.

PubMed

Vossel, Simone; Mathys, Christoph; Stephan, Klaas E; Friston, Karl J

2015-08-19

The deployment of visuospatial attention and the programming of saccades are governed by the inferred likelihood of events. In the present study, we combined computational modeling of psychophysical data with fMRI to characterize the computational and neural mechanisms underlying this flexible attentional control. Sixteen healthy human subjects performed a modified version of Posner's location-cueing paradigm in which the percentage of cue validity varied in time and the targets required saccadic responses. Trialwise estimates of the certainty (precision) of the prediction that the target would appear at the cued location were derived from a hierarchical Bayesian model fitted to individual trialwise saccadic response speeds. Trial-specific model parameters then entered analyses of fMRI data as parametric regressors. Moreover, dynamic causal modeling (DCM) was performed to identify the most likely functional architecture of the attentional reorienting network and its modulation by (Bayes-optimal) precision-dependent attention. While the frontal eye fields (FEFs), intraparietal sulcus, and temporoparietal junction (TPJ) of both hemispheres showed higher activity on invalid relative to valid trials, reorienting responses in right FEF, TPJ, and the putamen were significantly modulated by precision-dependent attention. Our DCM results suggested that the precision of predictability underlies the attentional modulation of the coupling of TPJ with FEF and the putamen. Our results shed new light on the computational architecture and neuronal network dynamics underlying the context-sensitive deployment of visuospatial attention. Spatial attention and its neural correlates in the human brain have been studied extensively with the help of fMRI and cueing paradigms in which the location of targets is pre-cued on a trial-by-trial basis. One aspect that has so far been neglected concerns the question of how the brain forms attentional expectancies when no a priori probability information is available but needs to be inferred from observations. This study elucidates the computational and neural mechanisms under which probabilistic inference governs attentional deployment. Our results show that Bayesian belief updating explains changes in cortical connectivity; in that directional influences from the temporoparietal junction on the frontal eye fields and the putamen were modulated by (Bayes-optimal) updates. Copyright © 2015 Vossel et al.
ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks.

PubMed

Kozák, Lajos R; van Graan, Louis André; Chaudhary, Umair J; Szabó, Ádám György; Lemieux, Louis

2017-12-01

Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of 'engagement' of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Practice guideline summary: Use of fMRI in the presurgical evaluation of patients with epilepsy

PubMed Central

Szaflarski, Jerzy P.; Gloss, David; Binder, Jeffrey R.; Gaillard, William D.; Golby, Alexandra J.; Holland, Scott K.; Ojemann, Jeffrey; Spencer, David C.; Swanson, Sara J.; French, Jacqueline A.; Theodore, William H.

2017-01-01

Objective: To assess the diagnostic accuracy and prognostic value of functional MRI (fMRI) in determining lateralization and predicting postsurgical language and memory outcomes. Methods: An 11-member panel evaluated and rated available evidence according to the 2004 American Academy of Neurology process. At least 2 panelists reviewed the full text of 172 articles and selected 37 for data extraction. Case reports, reports with <15 cases, meta-analyses, and editorials were excluded. Results and recommendations: The use of fMRI may be considered an option for lateralizing language functions in place of intracarotid amobarbital procedure (IAP) in patients with medial temporal lobe epilepsy (MTLE; Level C), temporal epilepsy in general (Level C), or extratemporal epilepsy (Level C). For patients with temporal neocortical epilepsy or temporal tumors, the evidence is insufficient (Level U). fMRI may be considered to predict postsurgical language deficits after anterior temporal lobe resection (Level C). The use of fMRI may be considered for lateralizing memory functions in place of IAP in patients with MTLE (Level C) but is of unclear utility in other epilepsy types (Level U). fMRI of verbal memory or language encoding should be considered for predicting verbal memory outcome (Level B). fMRI using nonverbal memory encoding may be considered for predicting visuospatial memory outcomes (Level C). Presurgical fMRI could be an adequate alternative to IAP memory testing for predicting verbal memory outcome (Level C). Clinicians should carefully advise patients of the risks and benefits of fMRI vs IAP during discussions concerning choice of specific modality in each case. PMID:28077494
Functional Magnetic Resonance Imaging for Preoperative Planning in Brain Tumour Surgery.

PubMed

Lau, Jonathan C; Kosteniuk, Suzanne E; Bihari, Frank; Megyesi, Joseph F

2017-01-01

Functional magnetic resonance imaging (fMRI) is being increasingly used for the preoperative evaluation of patients with brain tumours. The study is a retrospective chart review investigating the use of clinical fMRI from 2002 through 2013 in the preoperative evaluation of brain tumour patients. Baseline demographic and clinical data were collected. The specific fMRI protocols used for each patient were recorded. Sixty patients were identified over the 12-year period. The tumour types most commonly investigated were high-grade glioma (World Health Organization grade III or IV), low-grade glioma (World Health Organization grade II), and meningioma. Most common presenting symptoms were seizures (69.6%), language deficits (23.2%), and headache (19.6%). There was a predominance of left hemispheric lesions investigated with fMRI (76.8% vs 23.2% for right). The most commonly involved lobes were frontal (64.3%), temporal (33.9%), parietal (21.4%), and insular (7.1%). The most common fMRI paradigms were language (83.9%), motor (75.0%), sensory (16.1%), and memory (10.7%). The majority of patients ultimately underwent a craniotomy (75.0%), whereas smaller groups underwent stereotactic biopsy (8.9%) and nonsurgical management (16.1%). Time from request for fMRI to actual fMRI acquisition was 3.1±2.3 weeks. Time from fMRI acquisition to intervention was 4.9±5.5 weeks. We have characterized patient demographics in a retrospective single-surgeon cohort undergoing preoperative clinical fMRI at a Canadian centre. Our experience suggests an acceptable wait time from scan request to scan completion/analysis and from scan to intervention.
Decoding Information in the Human Hippocampus: A User's Guide

ERIC Educational Resources Information Center

Chadwick, Martin J.; Bonnici, Heidi M.; Maguire, Eleanor A.

2012-01-01

Multi-voxel pattern analysis (MVPA), or "decoding", of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded,…
An fMRI Study of the Social Competition in Healthy Subjects

ERIC Educational Resources Information Center

Polosan, M.; Baciu, M.; Cousin, E.; Perrone, M.; Pichat, C.; Bougerol, T.

2011-01-01

Social interaction requires the ability to infer another person's mental state (Theory of Mind, ToM) and also executive functions. This fMRI study aimed to identify the cerebral correlates activated by ToM during a specific social interaction, the human-human competition. In this framework, we tested a conflict resolution task (Stroop) adapted to…
Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

NASA Astrophysics Data System (ADS)

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.
A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.
Dominance of layer-specific microvessel dilation in contrast-enhanced high-resolution fMRI: Comparison between hemodynamic spread and vascular architecture with CLARITY.

PubMed

Poplawsky, Alexander John; Fukuda, Mitsuhiro; Kang, Bok-Man; Kim, Jae Hwan; Suh, Minah; Kim, Seong-Gi

2017-08-16

Contrast-enhanced cerebral blood volume-weighted (CBVw) fMRI response peaks are specific to the layer of evoked synaptic activity (Poplawsky et al., 2015), but the spatial resolution limit of CBVw fMRI is unknown. In this study, we measured the laminar spread of the CBVw fMRI evoked response in the external plexiform layer (EPL, 265 ± 65 μm anatomical thickness, mean ± SD, n = 30 locations from 5 rats) of the rat olfactory bulb during electrical stimulation of the lateral olfactory tract and examined its potential vascular source. First, we obtained the evoked CBVw fMRI responses with a 55 × 55 μm 2 in-plane resolution and a 500-μm thickness at 9.4 T, and found that the fMRI signal peaked predominantly in the inner half of EPL (136 ± 54 μm anatomical thickness). The mean full-width at half-maximum of these fMRI peaks was 347 ± 102 μm and the functional spread was approximately 100 or 200 μm when the effects of the laminar thicknesses of EPL or inner EPL were removed, respectively. Second, we visualized the vascular architecture of EPL from a different rat using a Clear Lipid-exchanged Anatomically Rigid Imaging/immunostaining-compatible Tissue hYdrogel (CLARITY)-based tissue preparation method and confocal microscopy. Microvascular segments with an outer diameter of <11 μm accounted for 64.3% of the total vascular volume within EPL and had a mean segment length of 55 ± 40 μm (n = 472). Additionally, vessels that crossed the EPL border had a mean segment length outside of EPL equal to 73 ± 61 μm (n = 28), which is comparable to half of the functional spread (50-100 μm). Therefore, we conclude that dilation of these microvessels, including capillaries, likely dominate the CBVw fMRI response and that the biological limit of the fMRI spatial resolution is approximately the average length of 1-2 microvessel segments, which may be sufficient for examining sublaminar circuits. Copyright © 2017 Elsevier Inc. All rights reserved.
Incidental Memory Encoding Assessed with Signal Detection Theory and Functional Magnetic Resonance Imaging (fMRI).

PubMed

Clemens, Benjamin; Regenbogen, Christina; Koch, Kathrin; Backes, Volker; Romanczuk-Seiferth, Nina; Pauly, Katharina; Shah, N Jon; Schneider, Frank; Habel, Ute; Kellermann, Thilo

2015-01-01

In functional magnetic resonance imaging (fMRI) studies that apply a "subsequent memory" approach, successful encoding is indicated by increased fMRI activity during the encoding phase for hits vs. misses, in areas underlying memory encoding such as the hippocampal formation. Signal-detection theory (SDT) can be used to analyze memory-related fMRI activity as a function of the participant's memory trace strength (d(')). The goal of the present study was to use SDT to examine the relationship between fMRI activity during incidental encoding and participants' recognition performance. To implement a new approach, post-experimental group assignment into High- or Low Performers (HP or LP) was based on 29 healthy participants' recognition performance, assessed with SDT. The analyses focused on the interaction between the factors group (HP vs. LP) and recognition performance (hits vs. misses). A whole-brain analysis revealed increased activation for HP vs. LP during incidental encoding for remembered vs. forgotten items (hits > misses) in the insula/temporo-parietal junction (TPJ) and the fusiform gyrus (FFG). Parameter estimates in these regions exhibited a significant positive correlation with d('). As these brain regions are highly relevant for salience detection (insula), stimulus-driven attention (TPJ), and content-specific processing of mnemonic stimuli (FFG), we suggest that HPs' elevated memory performance was associated with enhanced attentional and content-specific sensory processing during the encoding phase. We provide first correlative evidence that encoding-related activity in content-specific sensory areas and content-independent attention and salience detection areas influences memory performance in a task with incidental encoding of facial stimuli. Based on our findings, we discuss whether the aforementioned group differences in brain activity during incidental encoding might constitute the basis of general differences in memory performance between HP and LP.
Functional magnetic resonance imaging in clinical practice: State of the art and science.

PubMed

Barras, Christen D; Asadi, Hamed; Baldeweg, Torsten; Mancini, Laura; Yousry, Tarek A; Bisdas, Sotirios

2016-11-01

Functional magnetic resonance imaging (fMRI) has become a mainstream neuroimaging modality in the assessment of patients being evaluated for brain tumour and epilepsy surgeries. Thus, it is important for doctors in primary care settings to be well acquainted with the present and potential future applications, as well as limitations, of this modality. The objective of this article is to introduce the theoretical principles and state-of-the-art clinical applications of fMRI in brain tumour and epilepsy surgery, with a focus on the implications for clinical primary care. fMRI enables non-invasive functional mapping of specific cortical tasks (eg motor, language, memory-based, visual), revealing information about functional localisation, anatomical variation in cortical function, and disease effects and adaptations, including the fascinating phenomenon of brain plasticity. fMRI is currently ordered by specialist neurologists and neurosurgeons for the purposes of pre-surgical assessment, and within the context of an experienced multidisciplinary team to prepare, conduct and interpret the scan. With an increasing number of patients undergoing fMRI, general practitioners can expect questions about the current and emerging role of fMRI in clinical care from these patients and their families.
Impacts of simultaneous multislice acquisition on sensitivity and specificity in fMRI.

PubMed

Risk, Benjamin B; Kociuba, Mary C; Rowe, Daniel B

2018-05-15

Simultaneous multislice (SMS) imaging can be used to decrease the time between acquisition of fMRI volumes, which can increase sensitivity by facilitating the removal of higher-frequency artifacts and boosting effective sample size. The technique requires an additional processing step in which the slices are separated, or unaliased, to recover the whole brain volume. However, this may result in signal "leakage" between aliased locations, i.e., slice "leakage," and lead to spurious activation (decreased specificity). SMS can also lead to noise amplification, which can reduce the benefits of decreased repetition time. In this study, we evaluate the original slice-GRAPPA (no leak block) reconstruction algorithm and acceleration factor (AF = 8) used in the fMRI data in the young adult Human Connectome Project (HCP). We also evaluate split slice-GRAPPA (leak block), which can reduce slice leakage. We use simulations to disentangle higher test statistics into true positives (sensitivity) and false positives (decreased specificity). Slice leakage was greatly decreased by split slice-GRAPPA. Noise amplification was decreased by using moderate acceleration factors (AF = 4). We examined slice leakage in unprocessed fMRI motor task data from the HCP. When data were smoothed, we found evidence of slice leakage in some, but not all, subjects. We also found evidence of SMS noise amplification in unprocessed task and processed resting-state HCP data. Copyright © 2018 Elsevier Inc. All rights reserved.
A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging.

PubMed

Hong Kai Yap; Kamaldin, Nazir; Jeong Hoon Lim; Nasrallah, Fatima A; Goh, James Cho Hong; Chen-Hua Yeow

2017-06-01

In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically. Our results show that the device did not cause artifacts to fMRI images during hand rehabilitation and task-specific exercises. This study demonstrated the possibility of using fMRI and MR-compatible soft wearable robotic device to study brain activities and motor performances during hand rehabilitation, and to unravel the functional effects of rehabilitation robotics on brain stimulation.
Review of thalamocortical resting-state fMRI studies in schizophrenia

PubMed Central

Giraldo-Chica, Monica; Woodward, Neil D.

2017-01-01

Brain circuitry underlying cognition, emotion, and perception is abnormal in schizophrenia. There is considerable evidence that the neuropathology of schizophrenia includes the thalamus, a key hub of cortical-subcortical circuitry and an important regulator of cortical activity. However, the thalamus is a heterogeneous structure composed of several nuclei with distinct inputs and cortical connections. Limitations of conventional neuroimaging methods and conflicting findings from post-mortem investigations have made it difficult to determine if thalamic pathology in schizophrenia is widespread or limited to specific thalamocortical circuits. Resting-state fMRI has proven invaluable for understanding the large-scale functional organization of the brain and investigating neural circuitry relevant to psychiatric disorders. This article summarizes resting-state fMRI investigations of thalamocortical functional connectivity in schizophrenia. Particular attention is paid to the course, diagnostic specificity, and clinical correlates of thalamocortical network dysfunction. PMID:27531067
Can functional magnetic resonance imaging studies help with the optimization of health messaging for lifestyle behavior change? A systematic review.

PubMed

Whelan, Maxine E; Morgan, Paul S; Sherar, Lauren B; Orme, Mark W; Esliger, Dale W

2017-06-01

Unhealthy behaviors, including smoking, poor nutrition, excessive alcohol consumption, physical inactivity and sedentary lifestyles, are global risk factors for non-communicable diseases and premature death. Functional magnetic resonance imaging (fMRI) offers a unique approach to optimize health messages by examining how the brain responds to information relating to health. Our aim was to systematically review fMRI studies that have investigated variations in brain activation in response to health messages relating to (i) smoking; (ii) alcohol consumption; (iii) physical activity; (iv) diet; and (v) sedentary behavior. The electronic databases used were Medline/PubMed, Web of Science (Core Collection), PsychINFO, SPORTDiscuss, Cochrane Library and Open Grey. Studies were included if they investigated subjects aged ≥10years and were published before January 2017. Of the 13,836 studies identified in the database search, 18 studies (smoking k=15; diet k=2; physical activity/sedentary behavior k=1) were included in the review. The prefrontal cortex was activated in seven (47%) of the smoking-related studies and the physical activity study. Results suggest that activation of the ventromedial, dorsolateral and medial prefrontal cortex regions were predictive of subsequent behavior change following exposure to aversive anti-smoking stimuli. Studies investigating the neurological responses to anti-smoking material were most abundant. Of note, the prefrontal cortex and amygdala were most commonly activated in response to health messages across lifestyle behaviors. The review highlights an important disparity between research focusing on different lifestyle behaviors. Insights from smoking literature suggest fMRI may help to optimize health messaging in relation to other lifestyle behaviors. Copyright © 2017 Elsevier Inc. All rights reserved.
Efficacy of navigation may be influenced by retrosplenial cortex-mediated learning of landmark stability.

PubMed

Auger, Stephen D; Zeidman, Peter; Maguire, Eleanor A

2017-09-01

Human beings differ considerably in their ability to orient and navigate within the environment, but it has been difficult to determine specific causes of these individual differences. Permanent, stable landmarks are thought to be crucial for building a mental representation of an environment. Poor, compared to good, navigators have been shown to have difficulty identifying permanent landmarks, with a concomitant reduction in functional MRI (fMRI) activity in the retrosplenial cortex. However, a clear association between navigation ability and the learning of permanent landmarks has not been established. Here we tested for such a link. We had participants learn a virtual reality environment by repeatedly moving through it during fMRI scanning. The environment contained landmarks of which participants had no prior experience, some of which remained fixed in their locations while others changed position each time they were seen. After the fMRI learning phase, we divided participants into good and poor navigators based on their ability to find their way in the environment. The groups were closely matched on a range of cognitive and structural brain measures. Examination of the learning phase during scanning revealed that, while good and poor navigators learned to recognise the environment's landmarks at a similar rate, poor navigators were impaired at registering whether landmarks were stable or transient, and this was associated with reduced engagement of the retrosplenial cortex. Moreover, a mediation analysis showed that there was a significant effect of landmark permanence learning on navigation performance mediated through retrosplenial cortex activity. We conclude that a diminished ability to process landmark permanence may be a contributory factor to sub-optimal navigation, and could be related to the level of retrosplenial cortex engagement. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Sequencing bilateral and unilateral task-oriented training versus task oriented training alone to improve arm function in individuals with chronic stroke.

PubMed

McCombe Waller, Sandy; Whitall, Jill; Jenkins, Toye; Magder, Laurence S; Hanley, Daniel F; Goldberg, Andrew; Luft, Andreas R

2014-12-14

Recovering useful hand function after stroke is a major scientific challenge for patients with limited motor recovery. We hypothesized that sequential training beginning with proximal bilateral followed by unilateral task oriented training is superior to time-matched unilateral training alone. Proximal bilateral training could optimally prepare the motor system to respond to the more challenging task-oriented training. Twenty-six participants with moderate severity hemiparesis Intervention: PARTICIPANTS received either 6-weeks of bilateral proximal training followed sequentially by 6-weeks unilateral task-oriented training (COMBO) or 12-weeks of unilateral task-oriented training alone (SAEBO). A subset of 8 COMB0 and 9 SAEBO participants underwent three functional magnetic resonance imaging (fMRI) scans of hand and elbow movement every 6 weeks. Fugl-Meyer Upper extremity scale, Modified Wolf Motor Function Test, University of Maryland Arm Questionnaire for Stroke, Motor cortex activation (fMRI). The COMBO group demonstrated significantly greater gains between baseline and 12-weeks over all outcome measures (p = .018 based on a MANOVA test) and specifically in the Modified Wolf Motor Function test (time). Both groups demonstrated within-group gains on the Fugl-Meyer Upper Extremity test (impairment) and University of Maryland Arm Questionnaire for Stroke (functional use). fMRI subset analyses showed motor cortex (primary and premotor) activation during hand movement was significantly increased by sequential combination training but not by task-oriented training alone. Sequentially combining a proximal bilateral before a unilateral task-oriented training may be an effective way to facilitate gains in arm and hand function in those with moderate to severe paresis post-stroke compared to unilateral task oriented training alone.
Understanding neuromotor strategy during functional upper extremity tasks using symbolic dynamics.

PubMed

Nathan, Dominic E; Guastello, Stephen J; Prost, Robert W; Jeutter, Dean C

2012-01-01

The ability to model and quantify brain activation patterns that pertain to natural neuromotor strategy of the upper extremities during functional task performance is critical to the development of therapeutic interventions such as neuroprosthetic devices. The mechanisms of information flow, activation sequence and patterns, and the interaction between anatomical regions of the brain that are specific to movement planning, intention and execution of voluntary upper extremity motor tasks were investigated here. This paper presents a novel method using symbolic dynamics (orbital decomposition) and nonlinear dynamic tools of entropy, self-organization and chaos to describe the underlying structure of activation shifts in regions of the brain that are involved with the cognitive aspects of functional upper extremity task performance. Several questions were addressed: (a) How is it possible to distinguish deterministic or causal patterns of activity in brain fMRI from those that are really random or non-contributory to the neuromotor control process? (b) Can the complexity of activation patterns over time be quantified? (c) What are the optimal ways of organizing fMRI data to preserve patterns of activation, activation levels, and extract meaningful temporal patterns as they evolve over time? Analysis was performed using data from a custom developed time resolved fMRI paradigm involving human subjects (N=18) who performed functional upper extremity motor tasks with varying time delays between the onset of intention and onset of actual movements. The results indicate that there is structure in the data that can be quantified through entropy and dimensional complexity metrics and statistical inference, and furthermore, orbital decomposition is sensitive in capturing the transition of states that correlate with the cognitive aspects of functional task performance.
Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition.

PubMed

Wirsich, Jonathan; Bénar, Christian; Ranjeva, Jean-Philippe; Descoins, Médéric; Soulier, Elisabeth; Le Troter, Arnaud; Confort-Gouny, Sylviane; Liégeois-Chauvel, Catherine; Guye, Maxime

2014-10-15

Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe. Copyright © 2014 Elsevier Inc. All rights reserved.
Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data.

PubMed

Røge, Rasmus E; Madsen, Kristoffer H; Schmidt, Mikkel N; Mørup, Morten

2017-10-01

Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.
Brain correlates of autonomic modulation: combining heart rate variability with fMRI.

PubMed

Napadow, Vitaly; Dhond, Rupali; Conti, Giulia; Makris, Nikos; Brown, Emery N; Barbieri, Riccardo

2008-08-01

The central autonomic network (CAN) has been described in animal models but has been difficult to elucidate in humans. Potential confounds include physiological noise artifacts affecting brainstem neuroimaging data, and difficulty in deriving non-invasive continuous assessments of autonomic modulation. We have developed and implemented a new method which relates cardiac-gated fMRI timeseries with continuous-time heart rate variability (HRV) to estimate central autonomic processing. As many autonomic structures of interest are in brain regions strongly affected by cardiogenic pulsatility, we chose to cardiac-gate our fMRI acquisition to increase sensitivity. Cardiac-gating introduces T1-variability, which was corrected by transforming fMRI data to a fixed TR using a previously published method [Guimaraes, A.R., Melcher, J.R., et al., 1998. Imaging subcortical auditory activity in humans. Hum. Brain Mapp. 6(1), 33-41]. The electrocardiogram was analyzed with a novel point process adaptive-filter algorithm for computation of the high-frequency (HF) index, reflecting the time-varying dynamics of efferent cardiovagal modulation. Central command of cardiovagal outflow was inferred by using the resample HF timeseries as a regressor to the fMRI data. A grip task was used to perturb the autonomic nervous system. Our combined HRV-fMRI approach demonstrated HF correlation with fMRI activity in the hypothalamus, cerebellum, parabrachial nucleus/locus ceruleus, periaqueductal gray, amygdala, hippocampus, thalamus, and dorsomedial/dorsolateral prefrontal, posterior insular, and middle temporal cortices. While some regions consistent with central cardiovagal control in animal models gave corroborative evidence for our methodology, other mostly higher cortical or limbic-related brain regions may be unique to humans. Our approach should be optimized and applied to study the human brain correlates of autonomic modulation for various stimuli in both physiological and pathological states.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang Jenghwa; Kowalski, Alex; Hou, Bob

The purpose of this work was to study the feasibility of incorporating functional magnetic resonance imaging (fMRI) information for intensity modulated radiotherapy (IMRT) treatment planning of brain tumors. Three glioma patients were retrospectively replanned for radiotherapy (RT) with additional fMRI information. The fMRI of each patient was acquired using a bilateral finger-tapping paradigm with a gradient echo EPI (Echo Planer Imaging) sequence. The fMRI data were processed using the Analysis of Functional Neuroimaging (AFNI) software package for determining activation volumes, and the volumes were fused with the simulation computed tomography (CT) scan. The actived pixels in left and right primarymore » motor cortexes (PMCs) were contoured as critical structures for IMRT planning. The goal of replanning was to minimize the RT dose to the activation volumes in the PMC regions, while maintaining a similar coverage to the planning target volume (PTV) and keeping critical structures within accepted dose tolerance. Dose-volume histograms of the treatment plans with and without considering the fMRI information were compared. Beam angles adjustment or additional beams were needed for 2 cases to meet the planning criteria. Mean dose to the contralateral and ipsilateral PMC was significantly reduced by 66% and 55%, respectively, for 1 patient. For the other 2 patients, mean dose to contralateral PMC region was lowered by 73% and 69%. In general, IMRT optimization can reduce the RT dose to the PMC regions without compromising the PTV coverage or sparing of other critical organs. In conclusion, it is feasible to incorporate the fMRI information into the RT treatment planning. IMRT planning allows a significant reduction in RT dose to the PMC regions, especially if the region does not lie within the PTV.« less
Evaluation of preprocessing steps to compensate for magnetic field distortions due to body movements in BOLD fMRI

PubMed Central

Barry, Robert L.; Williams, Joy M.; Klassen, L. Martyn; Gallivan, Jason P.; Culham, Jody C.

2009-01-01

Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is currently the dominant technique for non-invasive investigation of brain functions. One of the challenges with BOLD fMRI, particularly at high fields, is compensation for the effects of spatiotemporally varying magnetic field inhomogeneities (ΔB0) caused by normal subject respiration, and in some studies, movement of the subject during the scan to perform tasks related to the functional paradigm. The presence of ΔB0 during data acquisition distorts reconstructed images and introduces extraneous fluctuations in the fMRI time series that decrease the BOLD contrast-to-noise ratio. Optimization of the fMRI data-processing pipeline to compensate for geometric distortions is of paramount importance to ensure high quality of fMRI data. To investigate ΔB0 caused by subject movement, echo-planar imaging scans were collected with and without concurrent motion of a phantom arm. The phantom arm was constructed and moved by the experimenter to emulate forearm motions while subjects remained still and observed a visual stimulation paradigm. These data were then subjected to eight different combinations of preprocessing steps. The best preprocessing pipeline included navigator correction, a complex phase regressor, and spatial smoothing. The synergy between navigator correction and phase regression reduced geometric distortions better than either step in isolation, and preconditioned the data to make them more amenable to the benefits of spatial smoothing. The combination of these steps provided a 10% increase in t-statistics compared to only navigator correction and spatial smoothing, and reduced the noise and false activations in regions where no legitimate effects would occur. PMID:19695810
Comparison of multi-subject ICA methods for analysis of fMRI data

PubMed Central

Erhardt, Erik Barry; Rachakonda, Srinivas; Bedrick, Edward; Allen, Elena; Adali, Tülay; Calhoun, Vince D.

2010-01-01

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation. PMID:21162045
High Field fMRI Reveals Thalamocortical Integration of Segregated Cognitive and Emotional Processing in Mediodorsal and Intralaminar Thalamic Nuclei

PubMed Central

Metzger, C. D.; Eckert, U.; Steiner, J.; Sartorius, A.; Buchmann, J. E.; Stadler, J.; Tempelmann, C.; Speck, O.; Bogerts, B.; Abler, B.; Walter, M.

2010-01-01

Thalamocortical loops, connecting functionally segregated, higher order cortical regions, and basal ganglia, have been proposed not only for well described motor and sensory regions, but also for limbic and prefrontal areas relevant for affective and cognitive processes. These functions are, however, more specific to humans, rendering most invasive neuroanatomical approaches impossible and interspecies translations difficult. In contrast, non-invasive imaging of functional neuroanatomy using fMRI allows for the development of elaborate task paradigms capable of testing the specific functionalities proposed for these circuits. Until recently, spatial resolution largely limited the anatomical definition of functional clusters at the level of distinct thalamic nuclei. Since their anatomical distinction seems crucial not only for the segregation of cognitive and limbic loops but also for the detection of their functional interaction during cognitive–emotional integration, we applied high resolution fMRI on 7 Tesla. Using an event-related design, we could isolate thalamic effects for preceding attention as well as experience of erotic stimuli. We could demonstrate specific thalamic effects of general emotional arousal in mediodorsal nucleus and effects specific to preceding attention and expectancy in intralaminar centromedian/parafascicular complex. These thalamic effects were paralleled by specific coactivations in the head of caudate nucleus as well as segregated portions of rostral or caudal cingulate cortex and anterior insula supporting distinct thalamo–striato–cortical loops. In addition to predescribed effects of sexual arousal in hypothalamus and ventral striatum, high resolution fMRI could extent this network to paraventricular thalamus encompassing laterodorsal and parataenial nuclei. We could lend evidence to segregated subcortical loops which integrate cognitive and emotional aspects of basic human behavior such as sexual processing. PMID:21088699
Distortion products in auditory fMRI research: Measurements and solutions.

PubMed

Norman-Haignere, Sam; McDermott, Josh H

2016-04-01

Nonlinearities in the cochlea can introduce audio frequencies that are not present in the sound signal entering the ear. Known as distortion products (DPs), these added frequencies complicate the interpretation of auditory experiments. Sound production systems also introduce distortion via nonlinearities, a particular concern for fMRI research because the Sensimetrics earphones widely used for sound presentation are less linear than most high-end audio devices (due to design constraints). Here we describe the acoustic and neural effects of cochlear and earphone distortion in the context of fMRI studies of pitch perception, and discuss how their effects can be minimized with appropriate stimuli and masking noise. The amplitude of cochlear and Sensimetrics earphone DPs were measured for a large collection of harmonic stimuli to assess effects of level, frequency, and waveform amplitude. Cochlear DP amplitudes were highly sensitive to the absolute frequency of the DP, and were most prominent at frequencies below 300 Hz. Cochlear DPs could thus be effectively masked by low-frequency noise, as expected. Earphone DP amplitudes, in contrast, were highly sensitive to both stimulus and DP frequency (due to prominent resonances in the earphone's transfer function), and their levels grew more rapidly with increasing stimulus level than did cochlear DP amplitudes. As a result, earphone DP amplitudes often exceeded those of cochlear DPs. Using fMRI, we found that earphone DPs had a substantial effect on the response of pitch-sensitive cortical regions. In contrast, cochlear DPs had a small effect on cortical fMRI responses that did not reach statistical significance, consistent with their lower amplitudes. Based on these findings, we designed a set of pitch stimuli optimized for identifying pitch-responsive brain regions using fMRI. These stimuli robustly drive pitch-responsive brain regions while producing minimal cochlear and earphone distortion, and will hopefully aid fMRI researchers in avoiding distortion confounds. Copyright © 2016 Elsevier Inc. All rights reserved.
Distortion Products in Auditory fMRI Research: Measurements and Solutions

PubMed Central

Norman-Haignere, Sam; McDermott, Josh H.

2016-01-01

Nonlinearities in the cochlea can introduce audio frequencies that are not present in the sound signal entering the ear. Known as distortion products (DPs), these added frequencies complicate the interpretation of auditory experiments. Sound production systems also introduce distortion via nonlinearities, a particular concern for fMRI research because the Sensimetrics earphones widely used for sound presentation are less linear than most high-end audio devices (due to design constraints). Here we describe the acoustic and neural effects of cochlear and earphone distortion in the context of fMRI studies of pitch perception, and discuss how their effects can be minimized with appropriate stimuli and masking noise. The amplitude of cochlear and Sensimetrics earphone DPs were measured for a large collection of harmonic stimuli to assess effects of level, frequency, and waveform amplitude. Cochlear DP amplitudes were highly sensitive to the absolute frequency of the DP, and were most prominent at frequencies below 300 Hz. Cochlear DPs could thus be effectively masked by low-frequency noise, as expected. Earphone DP amplitudes, in contrast, were highly sensitive to both stimulus and DP frequency (due to prominent resonances in the earphone’s transfer function), and their levels grew more rapidly with increasing stimulus level than did cochlear DP amplitudes. As a result, earphone DP amplitudes often exceeded those of cochlear DPs. Using fMRI, we found that earphone DPs had a substantial effect on the response of pitch-sensitive cortical regions. In contrast, cochlear DPs had a small effect on cortical fMRI responses that did not reach statistical significance, consistent with their lower amplitudes. Based on these findings, we designed a set of pitch stimuli optimized for identifying pitch-responsive brain regions using fMRI. These stimuli robustly drive pitch-responsive brain regions while producing minimal cochlear and earphone distortion, and will hopefully aid fMRI researchers in avoiding distortion confounds. PMID:26827809
fMRI Validation of fNIRS Measurements During a Naturalistic Task

PubMed Central

Noah, J. Adam; Ono, Yumie; Nomoto, Yasunori; Shimada, Sotaro; Tachibana, Atsumichi; Zhang, Xian; Bronner, Shaw; Hirsch, Joy

2015-01-01

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson’s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning. PMID:26132365
Practice guideline summary: Use of fMRI in the presurgical evaluation of patients with epilepsy: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology.

PubMed

Szaflarski, Jerzy P; Gloss, David; Binder, Jeffrey R; Gaillard, William D; Golby, Alexandra J; Holland, Scott K; Ojemann, Jeffrey; Spencer, David C; Swanson, Sara J; French, Jacqueline A; Theodore, William H

2017-01-24

To assess the diagnostic accuracy and prognostic value of functional MRI (fMRI) in determining lateralization and predicting postsurgical language and memory outcomes. An 11-member panel evaluated and rated available evidence according to the 2004 American Academy of Neurology process. At least 2 panelists reviewed the full text of 172 articles and selected 37 for data extraction. Case reports, reports with <15 cases, meta-analyses, and editorials were excluded. The use of fMRI may be considered an option for lateralizing language functions in place of intracarotid amobarbital procedure (IAP) in patients with medial temporal lobe epilepsy (MTLE; Level C), temporal epilepsy in general (Level C), or extratemporal epilepsy (Level C). For patients with temporal neocortical epilepsy or temporal tumors, the evidence is insufficient (Level U). fMRI may be considered to predict postsurgical language deficits after anterior temporal lobe resection (Level C). The use of fMRI may be considered for lateralizing memory functions in place of IAP in patients with MTLE (Level C) but is of unclear utility in other epilepsy types (Level U). fMRI of verbal memory or language encoding should be considered for predicting verbal memory outcome (Level B). fMRI using nonverbal memory encoding may be considered for predicting visuospatial memory outcomes (Level C). Presurgical fMRI could be an adequate alternative to IAP memory testing for predicting verbal memory outcome (Level C). Clinicians should carefully advise patients of the risks and benefits of fMRI vs IAP during discussions concerning choice of specific modality in each case. © 2017 American Academy of Neurology.
Altered functional connectivity of the amygdaloid input nuclei in adolescents and young adults with autism spectrum disorder: a resting state fMRI study.

PubMed

Rausch, Annika; Zhang, Wei; Haak, Koen V; Mennes, Maarten; Hermans, Erno J; van Oort, Erik; van Wingen, Guido; Beckmann, Christian F; Buitelaar, Jan K; Groen, Wouter B

2016-01-01

Amygdala dysfunction is hypothesized to underlie the social deficits observed in autism spectrum disorders (ASD). However, the neurobiological basis of this hypothesis is underspecified because it is unknown whether ASD relates to abnormalities of the amygdaloid input or output nuclei. Here, we investigated the functional connectivity of the amygdaloid social-perceptual input nuclei and emotion-regulation output nuclei in ASD versus controls. We collected resting state functional magnetic resonance imaging (fMRI) data, tailored to provide optimal sensitivity in the amygdala as well as the neocortex, in 20 adolescents and young adults with ASD and 25 matched controls. We performed a regular correlation analysis between the entire amygdala (EA) and the whole brain and used a partial correlation analysis to investigate whole-brain functional connectivity uniquely related to each of the amygdaloid subregions. Between-group comparison of regular EA correlations showed significantly reduced connectivity in visuospatial and superior parietal areas in ASD compared to controls. Partial correlation analysis revealed that this effect was driven by the left superficial and right laterobasal input subregions, but not the centromedial output nuclei. These results indicate reduced connectivity of specifically the amygdaloid sensory input channels in ASD, suggesting that abnormal amygdalo-cortical connectivity can be traced down to the socio-perceptual pathways.
How native-like can you possibly get: fMRI evidence for processing accent

PubMed Central

Ghazi-Saidi, Ladan; Dash, Tanya; Ansaldo, Ana I.

2015-01-01

Introduction: If ever attained, adopting native-like accent is achieved late in the learning process. Resemblance between L2 and mother tongue can facilitate L2 learning. In particular, cognates (phonologically and semantically similar words across languages), offer the opportunity to examine the issue of foreign accent in quite a unique manner. Methods: Twelve Spanish speaking (L1) adults learnt French (L2) cognates and practiced their native-like pronunciation by means of a computerized method. After consolidation, they were tested on L1 and L2 oral picture- naming during fMRI scanning. Results and Discussion: The results of the present study show that there is a specific impact of accent on brain activation, even if L2 words are cognates, and belong to a pair of closely related languages. Results point that the insula is a key component of accent processing, which is in line with reports from patients with foreign accent syndrome following damage to the insula (e.g., Katz et al., 2012; Moreno-Torres et al., 2013; Tomasino et al., 2013), and healthy L2 learners (Chee et al., 2004). Thus, the left insula has been consistently related to the integration of attentional and working memory abilities, together with fine-tuning of motor programming to achieve optimal articulation. PMID:26578931
Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process.

PubMed

Yao, Shengnan; Zeng, Weiming; Wang, Nizhuan; Chen, Lei

2013-07-01

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA. Copyright © 2013 Elsevier Inc. All rights reserved.
A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

PubMed

Wang, Nizhuan; Chang, Chunqi; Zeng, Weiming; Shi, Yuhu; Yan, Hongjie

2017-01-01

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.
Investigating the Role of Hypothalamic Tumor Involvement in Sleep and Cognitive Outcomes Among Children Treated for Craniopharyngioma

PubMed Central

Conklin, Heather M.; Scoggins, Matthew A.; Ashford, Jason M.; Merchant, Thomas E.; Mandrell, Belinda N.; Ogg, Robert J.; Curtis, Elizabeth; Wise, Merrill S.; Indelicato, Daniel J.; Crabtree, Valerie M.

2016-01-01

Objective: Despite excellent survival prognosis, children treated for craniopharyngioma experience significant morbidity. We examined the role of hypothalamic involvement (HI) in excessive daytime sleepiness (EDS) and attention regulation in children enrolled on a Phase II trial of limited surgery and proton therapy. Methods: Participants completed a sleep evaluation (N = 62) and a continuous performance test (CPT) during functional magnetic resonance imaging (fMRI; n = 29) prior to proton therapy. Results: EDS was identified in 76% of the patients and was significantly related to increased HI extent (p = .04). There was no relationship between CPT performance during fMRI and HI or EDS. Visual examination of group composite fMRI images revealed greater spatial extent of activation in frontal cortical regions in patients with EDS, consistent with a compensatory activation hypothesis. Conclusion: Routine screening for sleep problems during therapy is indicated for children with craniopharyngioma, to optimize the timing of interventions and reduce long-term morbidity. PMID:27189690
Ranking and averaging independent component analysis by reproducibility (RAICAR).

PubMed

Yang, Zhi; LaConte, Stephen; Weng, Xuchu; Hu, Xiaoping

2008-06-01

Independent component analysis (ICA) is a data-driven approach that has exhibited great utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations, however, do not provide the number and relative importance of the resulting components. In addition, ICA algorithms utilizing gradient-based optimization give decompositions that are dependent on initialization values, which can lead to dramatically different results. In this work, a new method, RAICAR (Ranking and Averaging Independent Component Analysis by Reproducibility), is introduced to address these issues for spatial ICA applied to fMRI. RAICAR utilizes repeated ICA realizations and relies on the reproducibility between them to rank and select components. Different realizations are aligned based on correlations, leading to aligned components. Each component is ranked and thresholded based on between-realization correlations. Furthermore, different realizations of each aligned component are selectively averaged to generate the final estimate of the given component. Reliability and accuracy of this method are demonstrated with both simulated and experimental fMRI data. Copyright 2007 Wiley-Liss, Inc.
Mapping lexical-semantic networks and determining hemispheric language dominance: Do task design, sex, age, and language performance make a difference?

PubMed

Chang, Yu-Hsuan A; Javadi, Sogol S; Bahrami, Naeim; Uttarwar, Vedang S; Reyes, Anny; McDonald, Carrie R

2018-04-01

Blocked and event-related fMRI designs are both commonly used to localize language networks and determine hemispheric dominance in research and clinical settings. We compared activation profiles on a semantic monitoring task using one of the two designs in a total of 43 healthy individual to determine whether task design or subject-specific factors (i.e., age, sex, or language performance) influence activation patterns. We found high concordance between the two designs within core language regions, including the inferior frontal, posterior temporal, and basal temporal region. However, differences emerged within inferior parietal cortex. Subject-specific factors did not influence activation patterns, nor did they interact with task design. These results suggest that despite high concordance within perisylvian regions that are robust to subject-specific factors, methodological differences between blocked and event-related designs may contribute to parietal activations. These findings provide important information for researchers incorporating fMRI results into meta-analytic studies, as well as for clinicians using fMRI to guide pre-surgical planning. Copyright © 2018 Elsevier Inc. All rights reserved.
Improved sensitivity and specificity for resting state and task fMRI with multiband multi-echo EPI compared to multi-echo EPI at 7 T.

PubMed

Boyacioğlu, Rasim; Schulz, Jenni; Koopmans, Peter J; Barth, Markus; Norris, David G

2015-10-01

A multiband multi-echo (MBME) sequence is implemented and compared to a matched standard multi-echo (ME) protocol to investigate the potential improvement in sensitivity and spatial specificity at 7 T for resting state and task fMRI. ME acquisition is attractive because BOLD sensitivity is less affected by variation in T2*, and because of the potential for separating BOLD and non-BOLD signal components. MBME further reduces TR thus increasing the potential reduction in physiological noise. In this study we used FSL-FIX to clean ME and MBME resting state and task fMRI data (both 3.5mm isotropic). After noise correction, the detection of resting state networks improves with more non-artifactual independent components being observed. Additional activation clusters for task data are discovered for MBME data (increased sensitivity) whereas existing clusters become more localized for resting state (improved spatial specificity). The results obtained indicate that MBME is superior to ME at high field strengths. Copyright © 2015 Elsevier Inc. All rights reserved.
A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

PubMed Central

Sherwood, Matthew S.; Diller, Emily E.; Ey, Elizabeth; Ganapathy, Subhashini; Nelson, Jeremy T.; Parker, Jason G.

2017-01-01

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain. PMID:28872110
A Protocol for the Administration of Real-Time fMRI Neurofeedback Training.

PubMed

Sherwood, Matthew S; Diller, Emily E; Ey, Elizabeth; Ganapathy, Subhashini; Nelson, Jeremy T; Parker, Jason G

2017-08-24

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain.

Who gets afraid in the MRI-scanner? Neurogenetics of state-anxiety changes during an fMRI experiment.

PubMed

Mutschler, Isabella; Wieckhorst, Birgit; Meyer, Andrea H; Schweizer, Tina; Klarhöfer, Markus; Wilhelm, Frank H; Seifritz, Erich; Ball, Tonio

2014-11-07

Experiments using functional magnetic resonance imaging (fMRI) play a fundamental role in affective neuroscience. When placed in an MR scanner, some volunteers feel safe and relaxed in this situation, while others experience uneasiness and fear. Little is known about the basis and consequences of such inter-individually different responses to the general experimental fMRI setting. In this study emotional stimuli were presented during fMRI and subjects' state-anxiety was assessed at the onset and end of the experiment while they were within the scanner. We show that Val/Val but neither Met/Met nor Val/Met carriers of the catechol-O-methyltransferase (COMT) Val(158)Met polymorphism-a prime candidate for anxiety vulnerability-became significantly more anxious during the fMRI experiment (N=97 females: 24 Val/Val, 51 Val/Met, and 22 Met/Met). Met carriers demonstrated brain responses with increased stability over time in the right parietal cortex and significantly better cognitive performances likely mediated by lower levels of anxiety. Val/Val, Val/Met and Met/Met did not significantly differ in state-anxiety at the beginning of the experiment. The exposure of a control group (N=56 females) to the same experiment outside the scanner did not cause a significant increase in state-anxiety, suggesting that the increase we observe in the fMRI experiment may be specific to the fMRI setting. Our findings reveal that genetics may play an important role in shaping inter-individual different emotional, cognitive and neuronal responses during fMRI experiments. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

PubMed Central

Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

2015-01-01

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. PMID:25972586
Modeling fMRI signals can provide insights into neural processing in the cerebral cortex.

PubMed

Vanni, Simo; Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

2015-08-01

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. Copyright © 2015 the American Physiological Society.
Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal.

PubMed

Wen, Haiguang; Liu, Zhongming

2016-06-01

Spontaneous activity observed with resting-state fMRI is used widely to uncover the brain's intrinsic functional networks in health and disease. Although many networks appear modular and specific, global and nonspecific fMRI fluctuations also exist and both pose a challenge and present an opportunity for characterizing and understanding brain networks. Here, we used a multimodal approach to investigate the neural correlates to the global fMRI signal in the resting state. Like fMRI, resting-state power fluctuations of broadband and arrhythmic, or scale-free, macaque electrocorticography and human magnetoencephalography activity were correlated globally. The power fluctuations of scale-free human electroencephalography (EEG) were coupled with the global component of simultaneously acquired resting-state fMRI, with the global hemodynamic change lagging the broadband spectral change of EEG by ∼5 s. The levels of global and nonspecific fluctuation and synchronization in scale-free population activity also varied across and depended on arousal states. Together, these results suggest that the neural origin of global resting-state fMRI activity is the broadband power fluctuation in scale-free population activity observable with macroscopic electrical or magnetic recordings. Moreover, the global fluctuation in neurophysiological and hemodynamic activity is likely modulated through diffuse neuromodulation pathways that govern arousal states and vigilance levels. This study provides new insights into the neural origin of resting-state fMRI. Results demonstrate that the broadband power fluctuation of scale-free electrophysiology is globally synchronized and directly coupled with the global component of spontaneous fMRI signals, in contrast to modularly synchronized fluctuations in oscillatory neural activity. These findings lead to a new hypothesis that scale-free and oscillatory neural processes account for global and modular patterns of functional connectivity observed with resting-state fMRI, respectively. Copyright © 2016 the authors 0270-6474/16/366030-11$15.00/0.
Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.

PubMed

Hojjati, Seyed Hani; Ebrahimzadeh, Ata; Khazaee, Ali; Babajani-Feremi, Abbas

2017-04-15

We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI). Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features. Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD. To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC. Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion. Copyright © 2017 Elsevier B.V. All rights reserved.
FMRI Brain Activation in a Finnish Family with Specific Language Impairment Compared with a Normal Control Group

ERIC Educational Resources Information Center

Hugdahl, Kenneth; Gundersen, Hilde; Brekke, Cecilie; Thomsen, Tormod; Rimol, Lars Morten; Ersland, Lars; Niemi, Jussi

2004-01-01

The aim of the present study was to investigate differences in brain activation in a family with SLI as compared to intact individuals with normally developed language during processing of language stimuli. Functional magnetic resonance imaging (fMRI) was used to monitor changes in neuronal activation in temporal and frontal lobe areas in 5…
Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

PubMed

Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

2016-01-01

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.
Maintenance and Representation of Mind Wandering during Resting-State fMRI.

PubMed

Chou, Ying-Hui; Sundman, Mark; Whitson, Heather E; Gaur, Pooja; Chu, Mei-Lan; Weingarten, Carol P; Madden, David J; Wang, Lihong; Kirste, Imke; Joliot, Marc; Diaz, Michele T; Li, Yi-Ju; Song, Allen W; Chen, Nan-Kuei

2017-01-12

Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.
Functional magnetic resonance imaging: basic principles and application in the neurosciences.

PubMed

Labbé Atenas, T; Ciampi Díaz, E; Cruz Quiroga, J P; Uribe Arancibia, S; Cárcamo Rodríguez, C

2018-03-12

Functional magnetic resonance imaging (fMRI) is an advanced tool for the study of brain functions in healthy subjects and in neuropsychiatric patients. This tool makes it possible to identify and locate specific phenomena related to neuronal metabolism and activity. Starting with the detection of changes in the blood supply to a region that participates in a function, more complex approaches have been developed to study the dynamics of neuronal networks. Studies examining the brain at rest or involved in different tasks have provided evidence related to the onset, development, and/or response to treatment in various diseases. The diversity of the possible artifacts associated with image registration as well as the complexity of the analytical experimental designs has generated abundant debate about the technique behind fMRI. This article aims to introduce readers to the fundamentals underlying fMRI, to explain how fMRI studies are interpreted, and to discuss fMRI's contributions to the study of the mechanisms underlying diverse diseases of the nervous system. Copyright © 2018 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.
Anterior temporal face patches: a meta-analysis and empirical study

PubMed Central

Von Der Heide, Rebecca J.; Skipper, Laura M.; Olson, Ingrid R.

2013-01-01

Evidence suggests the anterior temporal lobe (ATL) plays an important role in person identification and memory. In humans, neuroimaging studies of person memory report consistent activations in the ATL to famous and personally familiar faces and studies of patients report resection or damage of the ATL causes an associative prosopagnosia in which face perception is intact but face memory is compromised. In addition, high-resolution fMRI studies of non-human primates and electrophysiological studies of humans also suggest regions of the ventral ATL are sensitive to novel faces. The current study extends previous findings by investigating whether similar subregions in the dorsal, ventral, lateral, or polar aspects of the ATL are sensitive to personally familiar, famous, and novel faces. We present the results of two studies of person memory: a meta-analysis of existing fMRI studies and an empirical fMRI study using optimized imaging parameters. Both studies showed left-lateralized ATL activations to familiar individuals while novel faces activated the right ATL. Activations to famous faces were quite ventral, similar to what has been reported in previous high-resolution fMRI studies of non-human primates. These findings suggest that face memory-sensitive patches in the human ATL are in the ventral/polar ATL. PMID:23378834
Effects of citalopram and escitalopram on fMRI response to affective stimuli in healthy volunteers selected by serotonin transporter genotype.

PubMed

Henry, Michael E; Lauriat, Tara L; Lowen, Steven B; Churchill, Jeffrey H; Hodgkinson, Colin A; Goldman, David; Renshaw, Perry F

2013-09-30

This study was designed to assess whether functional magnetic resonance imaging (fMRI) following antidepressant administration (pharmaco-fMRI) is sufficiently sensitive to detect differences in patterns of activation between enantiomers of the same compound. Healthy adult males (n=11) participated in a randomized, double-blind, cross-over trial with three medication periods during which they received citalopram (racemic mixture), escitalopram (S-citalopram alone), or placebo for 2 weeks. All participants had high expression serotonin transporter genotypes. An fMRI scan that included passive viewing of overt and covert affective faces and affective words was performed after each medication period. Activation in response to overt faces was greater following escitalopram than following citalopram in the right insula, thalamus, and putamen when the faces were compared with a fixation stimulus. For the rapid covert presentation, a greater response was observed in the left middle temporal gyrus in the happy versus fearful contrast following escitalopram than following citalopram. Thus, the combination of genomics and fMRI was successful in discriminating between two very similar drugs. However, the pattern of activation observed suggests that further studies are indicated to understand how to optimally combine the two techniques. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Effects of citalopram and escitalopram on fMRI response to affective stimuli in healthy volunteers selected by serotonin transporter genotype

PubMed Central

Henry, Michael E.; Lauriat, Tara L.; Lowen, Steven B.; Churchill, Jeffrey H.; Hodgkinson, Colin A.; Goldman, David; Renshaw, Perry F.

2015-01-01

This study was designed to assess whether functional magnetic resonance imaging (fMRI) following antidepressant administration (pharmaco-fMRI) is sufficiently sensitive to detect differences in patterns of activation between enantiomers of the same compound. Healthy adult males (n = 11) participated in a randomized, double-blind, cross-over trial with three medication periods during which they received citalopram (racemic mixture), escitalopram (S-citalopram alone), or placebo for 2 weeks. All participants had high expression serotonin transporter genotypes. An fMRI scan that included passive viewing of overt and covert affective faces and affective words was performed after each medication period. Activation in response to overt faces was greater following escitalopram than following citalopram in the right insula, thalamus, and putamen when the faces were compared with a fixation stimulus. For the rapid covert presentation, a greater response was observed in the left middle temporal gyrus in the happy versus fearful contrast following escitalopram than following citalopram. Thus, the combination of genomics and fMRI was successful in discriminating between two very similar drugs. However, the pattern of activation observed suggests that further studies are indicated to understand how to optimally combine the two techniques. PMID:23845563
Electromyography as a recording system for eyeblink conditioning with functional magnetic resonance imaging.

PubMed

Knuttinen, M-G; Parrish, T B; Weiss, C; LaBar, K S; Gitelman, D R; Power, J M; Mesulam, M-M; Disterhoft, J F

2002-10-01

This study was designed to develop a suitable method of recording eyeblink responses while conducting functional magnetic resonance imaging (fMRI). Given the complexity of this behavioral setup outside of the magnet, this study sought to adapt and further optimize an approach to eyeblink conditioning that would be suitable for conducting event-related fMRI experiments. This method involved the acquisition of electromyographic (EMG) signals from the orbicularis oculi of the right eye, which were subsequently amplified and converted into an optical signal outside of the head coil. This optical signal was converted back into an electrical signal once outside the magnet room. Electromyography (EMG)-detected eyeblinks were used to measure responses in a delay eyeblink conditioning paradigm. Our results indicate that: (1) electromyography is a sensitive method for the detection of eyeblinks during fMRI; (2) minimal interactions or artifacts of the EMG signal were created from the magnetic resonance pulse sequence; and (3) no electromyography-related artifacts were detected in the magnetic resonance images. Furthermore, an analysis of the functional data showed areas of activation that have previously been shown in positron emission tomography studies of human eyeblink conditioning. Our results support the strength of this behavioral setup as a suitable method to be used in association with fMRI.
A wavelet-based statistical analysis of FMRI data: I. motivation and data distribution modeling.

PubMed

Dinov, Ivo D; Boscardin, John W; Mega, Michael S; Sowell, Elizabeth L; Toga, Arthur W

2005-01-01

We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli in young, elderly, and demented subjects.
Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks.

PubMed

Hyatt, Christopher J; Calhoun, Vince D; Pearlson, Godfrey D; Assaf, Michal

2015-08-01

The ability to attribute mental states to others, or "mentalizing," is posited to involve specific subnetworks within the overall default mode network (DMN), but this question needs clarification. To determine which default mode (DM) subnetworks are engaged by mentalizing processes, we assessed task-related recruitment of DM subnetworks. Spatial independent component analysis (sICA) applied to fMRI data using relatively high-order model (75 components). Healthy participants (n = 53, ages 17-60) performed two fMRI tasks: an interactive game involving mentalizing (Domino), a semantic memory task (SORT), and a resting state fMRI scan. sICA of the two tasks split the DMN into 10 subnetworks located in three core regions: medial prefrontal cortex (mPFC; five subnetworks), posterior cingulate/precuneus (PCC/PrC; three subnetworks), and bilateral temporoparietal junction (TPJ). Mentalizing events increased recruitment in five of 10 DM subnetworks, located in all three core DMN regions. In addition, three of these five DM subnetworks, one dmPFC subnetwork, one PCC/PrC subnetwork, and the right TPJ subnetwork, showed reduced recruitment by semantic memory task events. The opposing modulation by the two tasks suggests that these three DM subnetworks are specifically engaged in mentalizing. Our findings, therefore, suggest the unique involvement of mentalizing processes in only three of 10 DM subnetworks, and support the importance of the dmPFC, PCC/PrC, and right TPJ in mentalizing as described in prior studies. © 2015 Wiley Periodicals, Inc.
Reliability-Weighted Integration of Audiovisual Signals Can Be Modulated by Top-down Attention

PubMed Central

Noppeney, Uta

2018-01-01

Abstract Behaviorally, it is well established that human observers integrate signals near-optimally weighted in proportion to their reliabilities as predicted by maximum likelihood estimation. Yet, despite abundant behavioral evidence, it is unclear how the human brain accomplishes this feat. In a spatial ventriloquist paradigm, participants were presented with auditory, visual, and audiovisual signals and reported the location of the auditory or the visual signal. Combining psychophysics, multivariate functional MRI (fMRI) decoding, and models of maximum likelihood estimation (MLE), we characterized the computational operations underlying audiovisual integration at distinct cortical levels. We estimated observers’ behavioral weights by fitting psychometric functions to participants’ localization responses. Likewise, we estimated the neural weights by fitting neurometric functions to spatial locations decoded from regional fMRI activation patterns. Our results demonstrate that low-level auditory and visual areas encode predominantly the spatial location of the signal component of a region’s preferred auditory (or visual) modality. By contrast, intraparietal sulcus forms spatial representations by integrating auditory and visual signals weighted by their reliabilities. Critically, the neural and behavioral weights and the variance of the spatial representations depended not only on the sensory reliabilities as predicted by the MLE model but also on participants’ modality-specific attention and report (i.e., visual vs. auditory). These results suggest that audiovisual integration is not exclusively determined by bottom-up sensory reliabilities. Instead, modality-specific attention and report can flexibly modulate how intraparietal sulcus integrates sensory signals into spatial representations to guide behavioral responses (e.g., localization and orienting). PMID:29527567
Subtle In-Scanner Motion Biases Automated Measurement of Brain Anatomy From In Vivo MRI

PubMed Central

Alexander-Bloch, Aaron; Clasen, Liv; Stockman, Michael; Ronan, Lisa; Lalonde, Francois; Giedd, Jay; Raznahan, Armin

2016-01-01

While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in-scanner motion on morphological analysis of structural MRI is relatively under-studied. Even among “good quality” structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects’ tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in-scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend-level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non-overlapping sets of structural MRI scans, convergent evidence showed that in-scanner motion—even at levels which do not manifest in visible motion artifact—can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non-sedated humans. PMID:27004471
Determination of hemispheric dominance with mental rotation using functional transcranial Doppler sonography and FMRI.

PubMed

Hattemer, Katja; Plate, Annika; Heverhagen, Johannes T; Haag, Anja; Keil, Boris; Klein, Karl Martin; Hermsen, Anke; Oertel, Wolfgang H; Hamer, Hajo M; Rosenow, Felix; Knake, Susanne

2011-01-01

the aim of this study was to investigate specific activation patterns and potential gender differences during mental rotation and to investigate whether functional magnetic resonance imaging (fMRI) and functional transcranial Doppler sonography (fTCD) lateralize hemispheric dominance concordantly. regional brain activation and hemispheric dominance during mental rotation (cube perspective test) were investigated in 10 female and 10 male healthy subjects using fMRI and fTCD. significant activation was found in the superior parietal lobe, at the parieto-occipital border, in the middle and superior frontal gyrus bilaterally, and the right inferior frontal gyrus using fMRI. Men showed a stronger lateralization to the right hemisphere during fMRI and a tendency toward stronger right-hemispheric activation during fTCD. Furthermore, more activation in frontal and parieto-occipital regions of the right hemisphere was observed using fMRI. Hemispheric dominance for mental rotation determined by the 2 methods correlated well (P= .008), but did not show concordant results in every single subject. the neural basis of mental rotation depends on a widespread bilateral network. Hemispheric dominance for mental rotation determined by fMRI and fTCD, though correlating well, is not always concordant. Hemispheric lateralization of complex cortical functions such as spatial rotation therefore should be investigated using multimodal imaging approaches, especially if used clinically as a tool for the presurgical evaluation of patients undergoing neurosurgery. Copyright © 2009 by the American Society of Neuroimaging.
Altered spinal cord activity during sexual stimulation in women with SCI: a pilot fMRI study.

PubMed

Alexander, Marcalee; Kozyrev, Natalie; Figley, Chase R; Richards, J Scott

2017-01-01

The objective of this study was to assess the feasibility of the use of functional magnetic resonance imaging (fMRI) to evaluate the spinal activation during sexual response of the thoracic, lumbar and sacral spinal cord. This is a laboratory-based pilot study in human females at a University-based medical center in the United States. In three healthy spinal cord injury (SCI) females, spinal cord activations during sexual audiovisual stimulation (alone), genital self-stimulation (alone) and simultaneous audiovisual and genital self-stimulation (combined) were assessed and then compared with each subjects' remaining sensory and motor function. Spinal fMRI responses of the intermediolateral columns were found during audiovisual stimulation in both subjects with incomplete injuries, but they were not observed in the subject with a complete injury. Moreover, sacral responses to combined stimulation differed greatly between the subjects with complete and incomplete injuries. These results not only provide the first in vivo documentation of spinal fMRI responses associated with sexual arousal in women with SCIs, but also suggest that spinal cord fMRI is capable of distinguishing between injury subtypes. Therefore, although there are certain limitations associated with fMRI during sexual stimulation (for example, movement artifacts, an artificially controlled environment and so), these findings demonstrate the potential utility of incorporating spinal cord fMRI in future research to evaluate the impact of specific patterns of SCI on sexual responses and/or the effects of treatment.
Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Stimulus-specific suppression preserves information in auditory short-term memory.

PubMed

Linke, Annika C; Vicente-Grabovetsky, Alejandro; Cusack, Rhodri

2011-08-02

Philosophers and scientists have puzzled for millennia over how perceptual information is stored in short-term memory. Some have suggested that early sensory representations are involved, but their precise role has remained unclear. The current study asks whether auditory cortex shows sustained frequency-specific activation while sounds are maintained in short-term memory using high-resolution functional MRI (fMRI). Investigating short-term memory representations within regions of human auditory cortex with fMRI has been difficult because of their small size and high anatomical variability between subjects. However, we overcame these constraints by using multivoxel pattern analysis. It clearly revealed frequency-specific activity during the encoding phase of a change detection task, and the degree of this frequency-specific activation was positively related to performance in the task. Although the sounds had to be maintained in memory, activity in auditory cortex was significantly suppressed. Strikingly, patterns of activity in this maintenance period correlated negatively with the patterns evoked by the same frequencies during encoding. Furthermore, individuals who used a rehearsal strategy to remember the sounds showed reduced frequency-specific suppression during the maintenance period. Although negative activations are often disregarded in fMRI research, our findings imply that decreases in blood oxygenation level-dependent response carry important stimulus-specific information and can be related to cognitive processes. We hypothesize that, during auditory change detection, frequency-specific suppression protects short-term memory representations from being overwritten by inhibiting the encoding of interfering sounds.
FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network

PubMed Central

Qin, Wei; Tian, Jie; Bai, Lijun; Pan, Xiaohong; Yang, Lin; Chen, Peng; Dai, Jianping; Ai, Lin; Zhao, Baixiao; Gong, Qiyong; Wang, Wei; von Deneen, Karen M; Liu, Yijun

2008-01-01

Background Recently, increasing evidence has indicated that the primary acupuncture effects are mediated by the central nervous system. However, specific brain networks underpinning these effects remain unclear. Results In the present study using fMRI, we employed a within-condition interregional covariance analysis method to investigate functional connectivity of brain networks involved in acupuncture. The fMRI experiment was performed before, during and after acupuncture manipulations on healthy volunteers at an acupuncture point, which was previously implicated in a neural pathway for pain modulation. We first identified significant fMRI signal changes during acupuncture stimulation in the left amygdala, which was subsequently selected as a functional reference for connectivity analyses. Our results have demonstrated that there is a brain network associated with the amygdala during a resting condition. This network encompasses the brain structures that are implicated in both pain sensation and pain modulation. We also found that such a pain-related network could be modulated by both verum acupuncture and sham acupuncture. Furthermore, compared with a sham acupuncture, the verum acupuncture induced a higher level of correlations among the amygdala-associated network. Conclusion Our findings indicate that acupuncture may change this amygdala-specific brain network into a functional state that underlies pain perception and pain modulation. PMID:19014532
Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach

PubMed Central

Lindquist, Martin A.; Xu, Yuting; Nebel, Mary Beth; Caffo, Brain S.

2014-01-01

To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-windows technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data. PMID:24993894
Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

PubMed Central

Murta, Teresa; Leite, Marco; Carmichael, David W; Figueiredo, Patrícia; Lemieux, Louis

2015-01-01

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG–fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological–haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG–fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG–fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG–fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations. PMID:25277370
Joint brain connectivity estimation from diffusion and functional MRI data

NASA Astrophysics Data System (ADS)

Chu, Shu-Hsien; Lenglet, Christophe; Parhi, Keshab K.

2015-03-01

Estimating brain wiring patterns is critical to better understand the brain organization and function. Anatomical brain connectivity models axonal pathways, while the functional brain connectivity characterizes the statistical dependencies and correlation between the activities of various brain regions. The synchronization of brain activity can be inferred through the variation of blood-oxygen-level dependent (BOLD) signal from functional MRI (fMRI) and the neural connections can be estimated using tractography from diffusion MRI (dMRI). Functional connections between brain regions are supported by anatomical connections, and the synchronization of brain activities arises through sharing of information in the form of electro-chemical signals on axon pathways. Jointly modeling fMRI and dMRI data may improve the accuracy in constructing anatomical connectivity as well as functional connectivity. Such an approach may lead to novel multimodal biomarkers potentially able to better capture functional and anatomical connectivity variations. We present a novel brain network model which jointly models the dMRI and fMRI data to improve the anatomical connectivity estimation and extract the anatomical subnetworks associated with specific functional modes by constraining the anatomical connections as structural supports to the functional connections. The key idea is similar to a multi-commodity flow optimization problem that minimizes the cost or maximizes the efficiency for flow configuration and simultaneously fulfills the supply-demand constraint for each commodity. In the proposed network, the nodes represent the grey matter (GM) regions providing brain functionality, and the links represent white matter (WM) fiber bundles connecting those regions and delivering information. The commodities can be thought of as the information corresponding to brain activity patterns as obtained for instance by independent component analysis (ICA) of fMRI data. The concept of information flow is introduced and used to model the propagation of information between GM areas through WM fiber bundles. The link capacity, i.e., ability to transfer information, is characterized by the relative strength of fiber bundles, e.g., fiber count gathered from the tractography of dMRI data. The node information demand is considered to be proportional to the correlation between neural activity at various cortical areas involved in a particular functional mode (e.g. visual, motor, etc.). These two properties lead to the link capacity and node demand constraints in the proposed model. Moreover, the information flow of a link cannot exceed the demand from either end node. This is captured by the feasibility constraints. Two different cost functions are considered in the optimization formulation in this paper. The first cost function, the reciprocal of fiber strength represents the unit cost for information passing through the link. In the second cost function, a min-max (minimizing the maximal link load) approach is used to balance the usage of each link. Optimizing the first cost function selects the pathway with strongest fiber strength for information propagation. In the second case, the optimization procedure finds all the possible propagation pathways and allocates the flow proportionally to their strength. Additionally, a penalty term is incorporated with both the cost functions to capture the possible missing and weak anatomical connections. With this set of constraints and the proposed cost functions, solving the network optimization problem recovers missing and weak anatomical connections supported by the functional information and provides the functional-associated anatomical subnetworks. Feasibility is demonstrated using realistic diffusion and functional MRI phantom data. It is shown that the proposed model recovers the maximum number of true connections, with fewest number of false connections when compared with the connectivity derived from a joint probabilistic model using the expectation-maximization (EM) algorithm presented in a prior work. We also apply the proposed method to data provided by the Human Connectome Project (HCP).
OdorMapComparer: an application for quantitative analyses and comparisons of fMRI brain odor maps.

PubMed

Liu, Nian; Xu, Fuqiang; Miller, Perry L; Shepherd, Gordon M

2007-01-01

Brain odor maps are reconstructed flat images that describe the spatial activity patterns in the glomerular layer of the olfactory bulbs in animals exposed to different odor stimuli. We have developed a software application, OdorMapComparer, to carry out quantitative analyses and comparisons of the fMRI odor maps. This application is an open-source window program that first loads two odor map images being compared. It allows image transformations including scaling, flipping, rotating, and warping so that the two images can be appropriately aligned to each other. It performs simple subtraction, addition, and average of signals in the two images. It also provides comparative statistics including the normalized correlation (NC) and spatial correlation coefficient. Experimental studies showed that the rodent fMRI odor maps for aliphatic aldehydes displayed spatial activity patterns that are similar in gross outlines but somewhat different in specific subregions. Analyses with OdorMapComparer indicate that the similarity between odor maps decreases with increasing difference in the length of carbon chains. For example, the map of butanal is more closely related to that of pentanal (with a NC = 0.617) than to that of octanal (NC = 0.082), which is consistent with animal behavioral studies. The study also indicates that fMRI odor maps are statistically odor-specific and repeatable across both the intra- and intersubject trials. OdorMapComparer thus provides a tool for quantitative, statistical analyses and comparisons of fMRI odor maps in a fashion that is integrated with the overall odor mapping techniques.
SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.

PubMed

Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan

2017-09-01

With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group. Copyright © 2017 Elsevier B.V. All rights reserved.
Brain activity changes in cognitive networks in relapsing-remitting multiple sclerosis - insights from a longitudinal FMRI study.

PubMed

Loitfelder, Marisa; Fazekas, Franz; Koschutnig, Karl; Fuchs, Siegrid; Petrovic, Katja; Ropele, Stefan; Pichler, Alexander; Jehna, Margit; Langkammer, Christian; Schmidt, Reinhold; Neuper, Christa; Enzinger, Christian

2014-01-01

Extrapolations from previous cross-sectional fMRI studies suggest cerebral functional changes with progression of Multiple Sclerosis (MS), but longitudinal studies are scarce. We assessed brain activation changes over time in MS patients using a cognitive fMRI paradigm and examined correlations with clinical and cognitive status and brain morphology. 13 MS patients and 15 healthy controls (HC) underwent MRI including fMRI (go/no-go task), neurological and neuropsychological exams at baseline (BL) and follow-up (FU; minimum 12, median 20 months). We assessed estimates of and changes in fMRI activation, total brain and subcortical grey matter volumes, cortical thickness, and T2-lesion load. Bland-Altman (BA) plots served to assess fMRI signal variability. Cognitive and disability levels remained largely stable in the patients. With the fMRI task, both at BL and FU, patients compared to HC showed increased activation in the insular cortex, precuneus, cerebellum, posterior cingulate cortex, and occipital cortex. At BL, patients vs. HC also had lower caudate nucleus, thalamus and putamen volumes. Over time, patients (but not HC) demonstrated fMRI activity increments in the left inferior parietal lobule. These correlated with worse single-digit-modality test (SDMT) performance. BA-plots attested to reproducibility of the fMRI task. In the patients, the right caudate nucleus decreased in volume which again correlated with worsening SDMT performance. Given preserved cognitive performance, the increased activation at BL in the patients may be viewed as largely adaptive. In contrast, the negative correlation with SDMT performance suggests increasing parietal activation over time to be maladaptive. Several areas with purported relevance for cognition showed decreased volumes at BL and right caudate nucleus volume decline correlated with decreasing SDMT performance. This highlights the dynamics of functional changes and the strategic importance of specific brain areas for cognitive processes in MS.
Trouble at rest: how correlation patterns and group differences become distorted after global signal regression.

PubMed

Saad, Ziad S; Gotts, Stephen J; Murphy, Kevin; Chen, Gang; Jo, Hang Joon; Martin, Alex; Cox, Robert W

2012-01-01

Resting-state functional magnetic resonance imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but is not easy to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences; global signal regression (GSReg), the practice of projecting out signal averaged over the entire brain, can change resting-state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although Murphy et al. in 2009 demonstrated that GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem that GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter interregional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true interregional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network's regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.
Functional and Neuroanatomical Specificity of Episodic Memory Dysfunction in Schizophrenia: An fMRI study of the Relational and Item-Specific Encoding Task

PubMed Central

Ragland, J. Daniel; Ranganath, Charan; Harms, Michael P.; Barch, Deanna M.; Gold, James M.; Layher, Evan; Lesh, Tyler A.; MacDonald, Angus W.; Niendam, Tara A.; Phillips, Joshua; Silverstein, Steven M.; Yonelinas, Andrew P.; Carter, Cameron S.

2015-01-01

Importance Individuals with schizophrenia (SZ) can encode item-specific information to support familiarity-based recognition, but are disproportionately impaired encoding inter-item relationships (relational encoding) and recollecting information. The Relational and Item-Specific Encoding (RiSE) paradigm has been used to disentangle these encoding and retrieval processes, which may be dependent on specific medial temporal lobe (MTL) and prefrontal cortex (PFC) subregions. Functional imaging during RiSE task performance could help to specify dysfunctional neural circuits in SZ that can be targeted for interventions to improve memory and functioning in the illness. Objectives To use functional magnetic resonance imaging (fMRI) to test the hypothesis that SZ disproportionately affects MTL and PFC subregions during relational encoding and retrieval, relative to item-specific memory processes. Imaging results from healthy comparison subjects (HC) will also be used to establish neural construct validity for RiSE. Design, Setting, and Participants This multi-site, case-control, cross-sectional fMRI study was conducted at five CNTRACS sites. The final sample included 52 clinically stable outpatients with SZ, and 57 demographically matched HC. Main Outcomes and Measures Behavioral performance speed and accuracy (d’) on item recognition and associative recognition tasks. Voxelwise statistical parametric maps for a priori MTL and PFC regions of interest (ROI), testing activation differences between relational and item-specific memory during encoding and retrieval. Results Item recognition was disproportionately impaired in SZ patients relative to controls following relational encoding. The differential deficit was accompanied by reduced dorsolateral prefrontal cortex (DLPFC) activation during relational encoding in SZ, relative to HC. Retrieval success (hits > misses) was associated with hippocampal (HI) activation in HC during relational item recognition and associative recognition conditions, and HI activation was specifically reduced in SZ for recognition of relational but not item-specific information. Conclusions In this unique, multi-site fMRI study, HC results supported RiSE construct validity by revealing expected memory effects in PFC and MTL subregions during encoding and retrieval. Comparison of SZ and HC revealed disproportionate memory deficits in SZ for relational versus item-specific information, accompanied by regionally and functionally specific deficits in DLPFC and HI activation. PMID:26200928
Learning-dependent plasticity with and without training in the human brain.

PubMed

Zhang, Jiaxiang; Kourtzi, Zoe

2010-07-27

Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.
Improving language mapping in clinical fMRI through assessment of grammar.

PubMed

Połczyńska, Monika; Japardi, Kevin; Curtiss, Susan; Moody, Teena; Benjamin, Christopher; Cho, Andrew; Vigil, Celia; Kuhn, Taylor; Jones, Michael; Bookheimer, Susan

2017-01-01

Brain surgery in the language dominant hemisphere remains challenging due to unintended post-surgical language deficits, despite using pre-surgical functional magnetic resonance (fMRI) and intraoperative cortical stimulation. Moreover, patients are often recommended not to undergo surgery if the accompanying risk to language appears to be too high. While standard fMRI language mapping protocols may have relatively good predictive value at the group level, they remain sub-optimal on an individual level. The standard tests used typically assess lexico-semantic aspects of language, and they do not accurately reflect the complexity of language either in comprehension or production at the sentence level. Among patients who had left hemisphere language dominance we assessed which tests are best at activating language areas in the brain. We compared grammar tests (items testing word order in actives and passives, wh -subject and object questions, relativized subject and object clauses and past tense marking) with standard tests (object naming, auditory and visual responsive naming), using pre-operative fMRI. Twenty-five surgical candidates (13 females) participated in this study. Sixteen patients presented with a brain tumor, and nine with epilepsy. All participants underwent two pre-operative fMRI protocols: one including CYCLE-N grammar tests (items testing word order in actives and passives, wh-subject and object questions, relativized subject and object clauses and past tense marking); and a second one with standard fMRI tests (object naming, auditory and visual responsive naming). fMRI activations during performance in both protocols were compared at the group level, as well as in individual candidates. The grammar tests generated more volume of activation in the left hemisphere (left/right angular gyrus, right anterior/posterior superior temporal gyrus) and identified additional language regions not shown by the standard tests (e.g., left anterior/posterior supramarginal gyrus). The standard tests produced more activation in left BA 47. Ten participants had more robust activations in the left hemisphere in the grammar tests and two in the standard tests. The grammar tests also elicited substantial activations in the right hemisphere and thus turned out to be superior at identifying both right and left hemisphere contribution to language processing. The grammar tests may be an important addition to the standard pre-operative fMRI testing.
fMRI reliability: influences of task and experimental design.

PubMed

Bennett, Craig M; Miller, Michael B

2013-12-01

As scientists, it is imperative that we understand not only the power of our research tools to yield results, but also their ability to obtain similar results over time. This study is an investigation into how common decisions made during the design and analysis of a functional magnetic resonance imaging (fMRI) study can influence the reliability of the statistical results. To that end, we gathered back-to-back test-retest fMRI data during an experiment involving multiple cognitive tasks (episodic recognition and two-back working memory) and multiple fMRI experimental designs (block, event-related genetic sequence, and event-related m-sequence). Using these data, we were able to investigate the relative influences of task, design, statistical contrast (task vs. rest, target vs. nontarget), and statistical thresholding (unthresholded, thresholded) on fMRI reliability, as measured by the intraclass correlation (ICC) coefficient. We also utilized data from a second study to investigate test-retest reliability after an extended, six-month interval. We found that all of the factors above were statistically significant, but that they had varying levels of influence on the observed ICC values. We also found that these factors could interact, increasing or decreasing the relative reliability of certain Task × Design combinations. The results suggest that fMRI reliability is a complex construct whose value may be increased or decreased by specific combinations of factors.
A computerized tablet with visual feedback of hand position for functional magnetic resonance imaging

PubMed Central

Karimpoor, Mahta; Tam, Fred; Strother, Stephen C.; Fischer, Corinne E.; Schweizer, Tom A.; Graham, Simon J.

2015-01-01

Neuropsychological tests behavioral tasks that very commonly involve handwriting and drawing are widely used in the clinic to detect abnormal brain function. Functional magnetic resonance imaging (fMRI) may be useful in increasing the specificity of such tests. However, performing complex pen-and-paper tests during fMRI involves engineering challenges. Previously, we developed an fMRI-compatible, computerized tablet system to address this issue. However, the tablet did not include visual feedback of hand position (VFHP), a human factors component that may be important for fMRI of certain patient populations. A real-time system was thus developed to provide VFHP and integrated with the tablet in an augmented reality display. The effectiveness of the system was initially tested in young healthy adults who performed various handwriting tasks in front of a computer display with and without VFHP. Pilot fMRI of writing tasks were performed by two representative individuals with and without VFHP. Quantitative analysis of the behavioral results indicated improved writing performance with VFHP. The pilot fMRI results suggest that writing with VFHP requires less neural resources compared to the without VFHP condition, to maintain similar behavior. Thus, the tablet system with VFHP is recommended for future fMRI studies involving patients with impaired brain function and where ecologically valid behavior is important. PMID:25859201
Functional magnetic resonance imaging in chronic ischaemic stroke.

PubMed

Lake, Evelyn M R; Bazzigaluppi, Paolo; Stefanovic, Bojana

2016-10-05

Ischaemic stroke is the leading cause of adult disability worldwide. Effective rehabilitation is hindered by uncertainty surrounding the underlying mechanisms that govern long-term ischaemic injury progression. Despite its potential as a sensitive non-invasive in vivo marker of brain function that may aid in the development of new treatments, blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has found limited application in the clinical research on chronic stage stroke progression. Stroke affects each of the physiological parameters underlying the BOLD contrast, markedly complicating the interpretation of BOLD fMRI data. This review summarizes current progress on application of BOLD fMRI in the chronic stage of ischaemic injury progression and discusses means by which more information may be gained from such BOLD fMRI measurements. Concomitant measurements of vascular reactivity, neuronal activity and metabolism in preclinical models of stroke are reviewed along with illustrative examples of post-ischaemic evolution in neuronal, glial and vascular function. The realization of the BOLD fMRI potential to propel stroke research is predicated on the carefully designed preclinical research establishing an ischaemia-specific quantitative model of BOLD signal contrast to provide the framework for interpretation of fMRI findings in clinical populations.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. © 2016 The Author(s).
Implementations of clinical functional magnetic resonance imaging using character-based paradigms for the prediction of Chinese language dominance.

PubMed

Liu, Ho-Ling; Wu, Chien-Te; Chen, Jian-Chuan; Hsu, Yuan-Yu; Wai, Yau-Yau; Wan, Yung-Liang

2003-01-01

Recently, functional MRI (fMRI) using word generation (WG) tasks has been shown to be effective for mapping the Chinese language-related brain areas. In clinical applications, however, patients' performance cannot be easily monitored during WG tasks. In this study, we evaluated the feasibility of a word choice (WC) paradigm in the clinical setting and compared the results with those from WG tasks. Intrasubject comparisons of fMRI with both WG and WC paradigms were performed on six normal human subjects and two tumor patients. Subject responses in the WC paradigm, based on semantic judgments, were recorded. Activation strength, extent, and laterality were evaluated and compared. Our results showed that fMRI with the WC paradigm evoked weaker neuronal activation than that with the WG paradigm in Chinese language-related brain areas. It was sufficient to reveal language laterality for clinical use, however. In addition, it resulted in less nonlanguage-specific brain activation. Results from the patient data demonstrated strong evidence for the necessity of incorporating response monitoring during fMRI studies, which suggested that fMRI with the WC paradigm is more appropriate to be implemented for the prediction of Chinese language dominance in clinical environments.
Causal mapping of emotion networks in the human brain: Framework and initial findings.

PubMed

Dubois, Julien; Oya, Hiroyuki; Tyszka, J Michael; Howard, Matthew; Eberhardt, Frederick; Adolphs, Ralph

2017-11-13

Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI. Copyright © 2017 Elsevier Ltd. All rights reserved.
Hypercapnic evaluation of vascular reactivity in healthy aging and acute stroke via functional MRI.

PubMed

Raut, Ryan V; Nair, Veena A; Sattin, Justin A; Prabhakaran, Vivek

2016-01-01

Functional MRI (fMRI) is well-established for the study of brain function in healthy populations, although its clinical application has proven more challenging. Specifically, cerebrovascular reactivity (CVR), which allows the assessment of the vascular response that serves as the basis for fMRI, has been shown to be reduced in healthy aging as well as in a range of diseases, including chronic stroke. However, the timing of when this occurs relative to the stroke event is unclear. We used a breath-hold fMRI task to evaluate CVR across gray matter in a group of acute stroke patients (< 10 days from stroke; N = 22) to address this question. These estimates were compared with those from both age-matched (N = 22) and younger (N = 22) healthy controls. As expected, young controls had the greatest mean CVR, as indicated by magnitude and extent of fMRI activation; however, stroke patients did not differ from age-matched controls. Moreover, the ipsilesional and contralesional hemispheres of stroke patients did not differ with respect to any of these measures. These findings suggest that fMRI remains a valid tool within the first few days of a stroke, particularly for group fMRI studies in which findings are compared with healthy subjects of similar age. However, given the relatively high variability in CVR observed in our stroke sample, caution is warranted when interpreting fMRI data from individual patients or a small cohort. We conclude that a breath-hold task can be a useful addition to functional imaging protocols for stroke patients.
Material specific lateralization of medial temporal lobe function: An fMRI investigation.

PubMed

Dalton, Marshall A; Hornberger, Michael; Piguet, Olivier

2016-03-01

The theory of material specific lateralization of memory function posits that left and right MTL regions are asymmetrically involved in mnemonic processing of verbal and nonverbal material respectively. Lesion and functional imaging (fMRI) studies provide robust evidence for a left MTL asymmetry in the verbal memory domain. Evidence for a right MTL/nonverbal asymmetry is not as robust. A handful of fMRI studies have investigated this issue but have generally utilised nonverbal stimuli which are amenable to semantic elaboration. This fMRI study aimed to investigate the neural correlates of recognition memory processing in 20 healthy young adults (mean age = 26 years) for verbal stimuli and nonverbal stimuli that were specifically designed to minimize verbalisation. Analyses revealed that the neural correlates of recognition memory processing for verbal and nonverbal stimuli were differentiable and asymmetrically recruited the left and right MTL respectively. The right perirhinal cortex and hippocampus were preferentially involved in successful recognition memory of items devoid of semantic information. In contrast, the left anterior hippocampus was preferentially involved in successful recognition memory of stimuli which contained semantic meaning. These results suggest that the left MTL is preferentially involved in mnemonic processing of verbal/semantic information. In contrast, the right MTL is preferentially involved in visual/non-semantic mnemonic processing. We propose that during development, the left MTL becomes specialised for verbal mnemonic processing due to its proximity with left lateralised cortical language processing areas while visual/non-semantic mnemonic processing gets 'crowded out' to become predominantly, but not completely, the domain of the right MTL. © 2015 Wiley Periodicals, Inc.
Hallucination- and speech-specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task-based fMRI data: An fBIRN study.

PubMed

Lavigne, Katie M; Woodward, Todd S

2018-04-01

Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation. © 2017 Wiley Periodicals, Inc.

Alteration of brain activation patterns in nonallergic rhinitis patients using functional magnetic resonance imaging before and after treatment with intranasal azelastine.

PubMed

Bernstein, Jonathan A; Hastings, Lloyd; Boespflug, Erin L; Allendorfer, Jane B; Lamy, Martine; Eliassen, James C

2011-06-01

Although nonallergic rhinitis (NAR) patients tend to be more sensitive to chemical/olfactory stimuli, a suprathreshold olfactory response or the presence of specific olfactory receptor genes do not explain why their symptoms are triggered by such exposures. To investigate differential neurogenic responses to azelastine in NAR patients, using functional magnetic resonance imaging (fMRI) in response to specific olfactory triggers. A longitudinal study design on 12 subjects with a physician diagnosis of NAR previously demonstrated to be clinically responsive to intranasal azelastine (Astelin) was performed. Subjects underwent fMRI during exposure to unpleasant (hickory smoke) and pleasant (vanilla) odorants while off and then on azelastine for 2 weeks. The olfactory fMRI paradigm consisted of a visually triggered sniff every 21 seconds with synchronized delivery of a 4 second pulse of odorant. Each odorant was presented 18 times over 4-6-minute fMRI runs. Continuous fresh air was presented to wash out each odorant after presentation. Nonallergic rhinitis patients exhibited increased blood flow to several regions of the brain in response to both pleasant and unpleasant odorants, specifically in odor-sensitive regions, while off intranasal azelastine. Treatment with intranasal azelastine significantly attenuated blood flow to regions of the brain relevant to either olfactory sensation or sensory processing in response to these odorants compared with fresh air. The general reduction compared with increase in brain activation in NAR patients on versus off azelastine suggests that a possible effect of this medication may be reduction of brain responses to odorants. Copyright © 2011. Published by Elsevier Inc.
Pediatric functional magnetic resonance neuroimaging: tactics for encouraging task compliance.

PubMed

Schlund, Michael W; Cataldo, Michael F; Siegle, Greg J; Ladouceur, Cecile D; Silk, Jennifer S; Forbes, Erika E; McFarland, Ashley; Iyengar, Satish; Dahl, Ronald E; Ryan, Neal D

2011-05-06

Neuroimaging technology has afforded advances in our understanding of normal and pathological brain function and development in children and adolescents. However, noncompliance involving the inability to remain in the magnetic resonance imaging (MRI) scanner to complete tasks is one common and significant problem. Task noncompliance is an especially significant problem in pediatric functional magnetic resonance imaging (fMRI) research because increases in noncompliance produces a greater risk that a study sample will not be representative of the study population. In this preliminary investigation, we describe the development and application of an approach for increasing the number of fMRI tasks children complete during neuroimaging. Twenty-eight healthy children ages 9-13 years participated. Generalization of the approach was examined in additional fMRI and event-related potential investigations with children at risk for depression, children with anxiety and children with depression (N=120). Essential features of the approach include a preference assessment for identifying multiple individualized rewards, increasing reinforcement rates during imaging by pairing tasks with chosen rewards and presenting a visual 'road map' listing tasks, rewards and current progress. Our results showing a higher percentage of fMRI task completion by healthy children provides proof of concept data for the recommended tactics. Additional support was provided by results showing our approach generalized to several additional fMRI and event-related potential investigations and clinical populations. We proposed that some forms of task noncompliance may emerge from less than optimal reward protocols. While our findings may not directly support the effectiveness of the multiple reward compliance protocol, increased attention to how rewards are selected and delivered may aid cooperation with completing fMRI tasks. The proposed approach contributes to the pediatric neuroimaging literature by providing a useful way to conceptualize and measure task noncompliance and by providing simple cost effective tactics for improving the effectiveness of common reward-based protocols.
Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment.

PubMed

Oblak, Ethan F; Lewis-Peacock, Jarrod A; Sulzer, James S

2017-07-01

Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different self-regulation mechanisms prefer different feedback timings, and that these factors can be effectively explored and optimized via simulation prior to deployment in the MRI scanner.
Pediatric functional magnetic resonance neuroimaging: tactics for encouraging task compliance

PubMed Central

2011-01-01

Background Neuroimaging technology has afforded advances in our understanding of normal and pathological brain function and development in children and adolescents. However, noncompliance involving the inability to remain in the magnetic resonance imaging (MRI) scanner to complete tasks is one common and significant problem. Task noncompliance is an especially significant problem in pediatric functional magnetic resonance imaging (fMRI) research because increases in noncompliance produces a greater risk that a study sample will not be representative of the study population. Method In this preliminary investigation, we describe the development and application of an approach for increasing the number of fMRI tasks children complete during neuroimaging. Twenty-eight healthy children ages 9-13 years participated. Generalization of the approach was examined in additional fMRI and event-related potential investigations with children at risk for depression, children with anxiety and children with depression (N = 120). Essential features of the approach include a preference assessment for identifying multiple individualized rewards, increasing reinforcement rates during imaging by pairing tasks with chosen rewards and presenting a visual 'road map' listing tasks, rewards and current progress. Results Our results showing a higher percentage of fMRI task completion by healthy children provides proof of concept data for the recommended tactics. Additional support was provided by results showing our approach generalized to several additional fMRI and event-related potential investigations and clinical populations. Discussion We proposed that some forms of task noncompliance may emerge from less than optimal reward protocols. While our findings may not directly support the effectiveness of the multiple reward compliance protocol, increased attention to how rewards are selected and delivered may aid cooperation with completing fMRI tasks Conclusion The proposed approach contributes to the pediatric neuroimaging literature by providing a useful way to conceptualize and measure task noncompliance and by providing simple cost effective tactics for improving the effectiveness of common reward-based protocols. PMID:21548928
Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment

PubMed Central

Sulzer, James S.

2017-01-01

Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different self-regulation mechanisms prefer different feedback timings, and that these factors can be effectively explored and optimized via simulation prior to deployment in the MRI scanner. PMID:28753639
BOLDSync: a MATLAB-based toolbox for synchronized stimulus presentation in functional MRI.

PubMed

Joshi, Jitesh; Saharan, Sumiti; Mandal, Pravat K

2014-02-15

Precise and synchronized presentation of paradigm stimuli in functional magnetic resonance imaging (fMRI) is central to obtaining accurate information about brain regions involved in a specific task. In this manuscript, we present a new MATLAB-based toolbox, BOLDSync, for synchronized stimulus presentation in fMRI. BOLDSync provides a user friendly platform for design and presentation of visual, audio, as well as multimodal audio-visual (AV) stimuli in functional imaging experiments. We present simulation experiments that demonstrate the millisecond synchronization accuracy of BOLDSync, and also illustrate the functionalities of BOLDSync through application to an AV fMRI study. BOLDSync gains an advantage over other available proprietary and open-source toolboxes by offering a user friendly and accessible interface that affords both precision in stimulus presentation and versatility across various types of stimulus designs and system setups. BOLDSync is a reliable, efficient, and versatile solution for synchronized stimulus presentation in fMRI study. Copyright © 2013 Elsevier B.V. All rights reserved.
A behavioural and neural evaluation of prospective decision-making under risk

PubMed Central

Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J.

2010-01-01

Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single choice contexts there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal pre-determined strategy, irrespective of the particular order in which options are presented. An alternative model involves continuously re-evaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of re-evaluating decision utilities, where available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously-acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes. PMID:20980595
Subject-level reliability analysis of fast fMRI with application to epilepsy.

PubMed

Hao, Yongfu; Khoo, Hui Ming; von Ellenrieder, Nicolas; Gotman, Jean

2017-07-01

Recent studies have applied the new magnetic resonance encephalography (MREG) sequence to the study of interictal epileptic discharges (IEDs) in the electroencephalogram (EEG) of epileptic patients. However, there are no criteria to quantitatively evaluate different processing methods, to properly use the new sequence. We evaluated different processing steps of this new sequence under the common generalized linear model (GLM) framework by assessing the reliability of results. A bootstrap sampling technique was first used to generate multiple replicated data sets; a GLM with different processing steps was then applied to obtain activation maps, and the reliability of these maps was assessed. We applied our analysis in an event-related GLM related to IEDs. A higher reliability was achieved by using a GLM with head motion confound regressor with 24 components rather than the usual 6, with an autoregressive model of order 5 and with a canonical hemodynamic response function (HRF) rather than variable latency or patient-specific HRFs. Comparison of activation with IED field also favored the canonical HRF, consistent with the reliability analysis. The reliability analysis helps to optimize the processing methods for this fast fMRI sequence, in a context in which we do not know the ground truth of activation areas. Magn Reson Med 78:370-382, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.
A behavioral and neural evaluation of prospective decision-making under risk.

PubMed

Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J

2010-10-27

Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single-choice contexts, there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal predetermined strategy, regardless of the particular order in which options are presented. An alternative model involves continuously reevaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of reevaluating decision utilities, in which available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance, and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes.
Optimizing real time fMRI neurofeedback for therapeutic discovery and development

PubMed Central

Stoeckel, L.E.; Garrison, K.A.; Ghosh, S.; Wighton, P.; Hanlon, C.A.; Gilman, J.M.; Greer, S.; Turk-Browne, N.B.; deBettencourt, M.T.; Scheinost, D.; Craddock, C.; Thompson, T.; Calderon, V.; Bauer, C.C.; George, M.; Breiter, H.C.; Whitfield-Gabrieli, S.; Gabrieli, J.D.; LaConte, S.M.; Hirshberg, L.; Brewer, J.A.; Hampson, M.; Van Der Kouwe, A.; Mackey, S.; Evins, A.E.

2014-01-01

While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain–behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders. PMID:25161891
Heuristic and optimal policy computations in the human brain during sequential decision-making.

PubMed

Korn, Christoph W; Bach, Dominik R

2018-01-23

Optimal decisions across extended time horizons require value calculations over multiple probabilistic future states. Humans may circumvent such complex computations by resorting to easy-to-compute heuristics that approximate optimal solutions. To probe the potential interplay between heuristic and optimal computations, we develop a novel sequential decision-making task, framed as virtual foraging in which participants have to avoid virtual starvation. Rewards depend only on final outcomes over five-trial blocks, necessitating planning over five sequential decisions and probabilistic outcomes. Here, we report model comparisons demonstrating that participants primarily rely on the best available heuristic but also use the normatively optimal policy. FMRI signals in medial prefrontal cortex (MPFC) relate to heuristic and optimal policies and associated choice uncertainties. Crucially, reaction times and dorsal MPFC activity scale with discrepancies between heuristic and optimal policies. Thus, sequential decision-making in humans may emerge from integration between heuristic and optimal policies, implemented by controllers in MPFC.
Role of the parahippocampal cortex in memory for the configuration but not the identity of objects: converging evidence from patients with selective thermal lesions and fMRI.

PubMed

Bohbot, Véronique D; Allen, John J B; Dagher, Alain; Dumoulin, Serge O; Evans, Alan C; Petrides, Michael; Kalina, Miroslav; Stepankova, Katerina; Nadel, Lynn

2015-01-01

The parahippocampal cortex and hippocampus are brain structures known to be involved in memory. However, the unique contribution of the parahippocampal cortex remains unclear. The current study investigates memory for object identity and memory of the configuration of objects in patients with small thermo-coagulation lesions to the hippocampus or the parahippocampal cortex. Results showed that in contrast to control participants and patients with damage to the hippocampus leaving the parahippocampal cortex intact, patients with lesions that included the right parahippocampal cortex (RPH) were severely impaired on a task that required learning the spatial configuration of objects on a computer screen; these patients, however, were not impaired at learning the identity of objects. Conversely, we found that patients with lesions to the right hippocampus (RH) or left hippocampus (LH), sparing the parahippocampal cortex, performed just as well as the control participants. Furthermore, they were not impaired on the object identity task. In the functional Magnetic Resonance Imaging (fMRI) experiment, healthy young adults performed the same tasks. Consistent with the findings of the lesion study, the fMRI results showed significant activity in the RPH in the memory for the spatial configuration condition, but not memory for object identity. Furthermore, the pattern of fMRI activity measured in the baseline control conditions decreased specifically in the parahippocampal cortex as a result of the experimental task, providing evidence for task specific repetition suppression. In summary, while our previous studies demonstrated that the hippocampus is critical to the construction of a cognitive map, both the lesion and fMRI studies have shown an involvement of the RPH for learning spatial configurations of objects but not object identity, and that this takes place independent of the hippocampus.
Testing assumptions on prefrontal transcranial direct current stimulation: Comparison of electrode montages using multimodal fMRI.

PubMed

Wörsching, Jana; Padberg, Frank; Goerigk, Stephan; Heinz, Irmgard; Bauer, Christine; Plewnia, Christian; Hasan, Alkomiet; Ertl-Wagner, Birgit; Keeser, Daniel

2018-05-04

Transcranial direct current stimulation (tDCS) of the prefrontal cortex (PFC) has been widely applied in cognitive neurosciences and advocated as a therapeutic intervention, e.g. in major depressive disorder. Although several targets and protocols have been suggested, comparative studies of tDCS parameters, particularly electrode montages and their cortical targets, are still lacking. This study investigated a priori hypotheses on specific effects of prefrontal-tDCS montages by using multimodal functional magnetic resonance imaging (fMRI) in healthy participants. 28 healthy male participants underwent three common active-tDCS montages and sham tDCS in a pseudo-randomized order, comprising a total of 112 tDCS-fMRI sessions. Active tDCS was applied at 2 mA for 20 min. Before and after tDCS, a resting-state fMRI (RS fMRI) was recorded, followed by a task fMRI with a delayed-response working-memory (DWM) task for assessing cognitive control over emotionally negative or neutral distractors. After tDCS with a cathode-F3/anode-F4 montage, RS-fMRI connectivity decreased in a medial part of the left PFC. Also, after the same stimulation condition, regional brain activity during DWM retrieval decreased more in this area after negative than after neutral distraction, and responses to the DWM task were faster, independent of distractor type. The current study does not confirm our a priori hypotheses on direction and localization of polarity-dependent tDCS effects using common bipolar electrode montages over PFC regions, but it provides evidence for montage-specific effects on multimodal neurophysiological and behavioral outcome measures. Systematic research on the actual targets and the respective dose-response relationships of prefrontal tDCS is warranted. Copyright © 2018 Elsevier Inc. All rights reserved.
Distinct iEEG activity patterns in temporal-limbic and prefrontal sites induced by emotional intentionality

PubMed Central

Singer, Neomi; Podlipsky, Ilana; Esposito, Fabrizio; Okon-Singer, Hadas; Andelman, Fani; Kipervasser, Svetlana; Neufeld, Miri Y.; Goebel, Rainer; Fried, Itzhak; Hendler, Talma

2015-01-01

Our emotions tend to be directed towards someone or something. Such emotional intentionality calls for the integration between two streams of information; abstract hedonic value and its associated concrete content. In a previous functional magnetic resonance imaging (fMRI) study we found that the combination of these two streams, as modeled by short emotional music excerpts and neutral film clips, was associated with synergistic activation in both temporal-limbic (TL) and ventral-lateral PFC (vLPFC) regions. This additive effect implies the integration of domain-specific ‘affective’ and ‘cognitive’ processes. Yet, the low temporal resolution of the fMRI limits the characterization of such cross-domain integration. To this end, we complemented the fMRI data with intracranial electroencephalogram (iEEG) recordings from twelve patients with intractable epilepsy. As expected, the additive fMRI activation in the amygdala and vLPFC was associated with distinct spatio-temporal iEEG patterns among electrodes situated within the vicinity of the fMRI activation foci. On the one hand, TL channels exhibited a transient (0–500 msec) increase in gamma power (61–69 Hz), possibly reflecting initial relevance detection or hedonic value tagging. On the other hand, vLPFC channels showed sustained (1–12 sec) suppression of low frequency power (2.3–24 Hz), possibly mediating changes in gating, enabling an on-going readiness for content-based processing of emotionally tagged signals. Moreover, an additive effect in delta-gamma phase-amplitude coupling (PAC) was found among the TL channels, possibly reflecting the integration between distinct domain specific processes. Together, this study provides a multi-faceted neurophysiological signature for computations that possibly underlie emotional intentionality in humans. PMID:25288171
Distinct iEEG activity patterns in temporal-limbic and prefrontal sites induced by emotional intentionality.

PubMed

Singer, Neomi; Podlipsky, Ilana; Esposito, Fabrizio; Okon-Singer, Hadas; Andelman, Fani; Kipervasser, Svetlana; Neufeld, Miri Y; Goebel, Rainer; Fried, Itzhak; Hendler, Talma

2014-11-01

Our emotions tend to be directed towards someone or something. Such emotional intentionality calls for the integration between two streams of information; abstract hedonic value and its associated concrete content. In a previous functional magnetic resonance imaging (fMRI) study we found that the combination of these two streams, as modeled by short emotional music excerpts and neutral film clips, was associated with synergistic activation in both temporal-limbic (TL) and ventral-lateral PFC (vLPFC) regions. This additive effect implies the integration of domain-specific 'affective' and 'cognitive' processes. Yet, the low temporal resolution of the fMRI limits the characterization of such cross-domain integration. To this end, we complemented the fMRI data with intracranial electroencephalogram (iEEG) recordings from twelve patients with intractable epilepsy. As expected, the additive fMRI activation in the amygdala and vLPFC was associated with distinct spatio-temporal iEEG patterns among electrodes situated within the vicinity of the fMRI activation foci. On the one hand, TL channels exhibited a transient (0-500 msec) increase in gamma power (61-69 Hz), possibly reflecting initial relevance detection or hedonic value tagging. On the other hand, vLPFC channels showed sustained (1-12 sec) suppression of low frequency power (2.3-24 Hz), possibly mediating changes in gating, enabling an on-going readiness for content-based processing of emotionally tagged signals. Moreover, an additive effect in delta-gamma phase-amplitude coupling (PAC) was found among the TL channels, possibly reflecting the integration between distinct domain specific processes. Together, this study provides a multi-faceted neurophysiological signature for computations that possibly underlie emotional intentionality in humans. Copyright © 2014 Elsevier Ltd. All rights reserved.
Hippocampal Sharp-Wave Ripples Influence Selective Activation of the Default Mode Network

PubMed Central

Kaplan, Raphael; Adhikari, Mohit H.; Hindriks, Rikkert; Mantini, Dante; Murayama, Yusuke; Logothetis, Nikos K.; Deco, Gustavo

2016-01-01

Summary The default mode network (DMN) is a commonly observed resting-state network (RSN) that includes medial temporal, parietal, and prefrontal regions involved in episodic memory [1, 2, 3]. The behavioral relevance of endogenous DMN activity remains elusive, despite an emerging literature correlating resting fMRI fluctuations with memory performance [4, 5]—particularly in DMN regions [6, 7, 8]. Mechanistic support for the DMN’s role in memory consolidation might come from investigation of large deflections (sharp-waves) in the hippocampal local field potential that co-occur with high-frequency (>80 Hz) oscillations called ripples—both during sleep [9, 10] and awake deliberative periods [11, 12, 13]. Ripples are ideally suited for memory consolidation [14, 15], since the reactivation of hippocampal place cell ensembles occurs during ripples [16, 17, 18, 19]. Moreover, the number of ripples after learning predicts subsequent memory performance in rodents [20, 21, 22] and humans [23], whereas electrical stimulation of the hippocampus after learning interferes with memory consolidation [24, 25, 26]. A recent study in macaques showed diffuse fMRI neocortical activation and subcortical deactivation specifically after ripples [27]. Yet it is unclear whether ripples and other hippocampal neural events influence endogenous fluctuations in specific RSNs—like the DMN—unitarily. Here, we examine fMRI datasets from anesthetized monkeys with simultaneous hippocampal electrophysiology recordings, where we observe a dramatic increase in the DMN fMRI signal following ripples, but not following other hippocampal electrophysiological events. Crucially, we find increases in ongoing DMN activity after ripples, but not in other RSNs. Our results relate endogenous DMN fluctuations to hippocampal ripples, thereby linking network-level resting fMRI fluctuations with behaviorally relevant circuit-level neural dynamics. PMID:26898464
Mapping the MRI voxel volume in which thermal noise matches physiological noise--implications for fMRI.

PubMed

Bodurka, J; Ye, F; Petridou, N; Murphy, K; Bandettini, P A

2007-01-15

This work addresses the choice of the imaging voxel volume in blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). Noise of physiological origin that is present in the voxel time course is a prohibitive factor in the detection of small activation-induced BOLD signal changes. If the physiological noise contribution dominates over the temporal fluctuation contribution in the imaging voxel, further increases in the voxel signal-to-noise ratio (SNR) will have diminished corresponding increases in temporal signal-to-noise (TSNR), resulting in reduced corresponding increases in the ability to detect activation induced signal changes. On the other hand, if the thermal and system noise dominate (suggesting a relatively low SNR) further decreases in SNR can prohibit detection of activation-induced signal changes. Here we have proposed and called the "suggested" voxel volume for fMRI the volume where thermal plus system-related and physiological noise variances are equal. Based on this condition we have created maps of fMRI suggested voxel volume from our experimental data at 3T, since this value will spatially vary depending on the contribution of physiologic noise in each voxel. Based on our fast EPI segmentation technique we have found that for gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) brain compartments the mean suggested cubical voxel volume is: (1.8 mm)3, (2.1 mm)3 and (1.4 mm)3, respectively. Serendipitously, (1.8 mm)3 cubical voxel volume for GM approximately matches the cortical thickness, thus optimizing BOLD contrast by minimizing partial volume averaging. The introduced suggested fMRI voxel volume can be a useful parameter for choice of imaging volume for functional studies.
An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks.

PubMed

Muhei-aldin, Othman; VanSwearingen, Jessie; Karim, Helmet; Huppert, Theodore; Sparto, Patrick J; Erickson, Kirk I; Sejdić, Ervin

2014-04-30

Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition. Most of the current literature does not examine stationarity prior to processing. The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data. Copyright © 2014 Elsevier B.V. All rights reserved.
Functional MRI mapping of visual function and selective attention for performance assessment and presurgical planning using conjunctive visual search.

PubMed

Parker, Jason G; Zalusky, Eric J; Kirbas, Cemil

2014-03-01

Accurate mapping of visual function and selective attention using fMRI is important in the study of human performance as well as in presurgical treatment planning of lesions in or near visual centers of the brain. Conjunctive visual search (CVS) is a useful tool for mapping visual function during fMRI because of its greater activation extent compared with high-capacity parallel search processes. The purpose of this work was to develop and evaluate a CVS that was capable of generating consistent activation in the basic and higher level visual areas of the brain by using a high number of distractors as well as an optimized contrast condition. Images from 10 healthy volunteers were analyzed and brain regions of greatest activation and deactivation were determined using a nonbiased decomposition of the results at the hemisphere, lobe, and gyrus levels. The results were quantified in terms of activation and deactivation extent and mean z-statistic. The proposed CVS was found to generate robust activation of the occipital lobe, as well as regions in the middle frontal gyrus associated with coordinating eye movements and in regions of the insula associated with task-level control and focal attention. As expected, the task demonstrated deactivation patterns commonly implicated in the default-mode network. Further deactivation was noted in the posterior region of the cerebellum, most likely associated with the formation of optimal search strategy. We believe the task will be useful in studies of visual and selective attention in the neuroscience community as well as in mapping visual function in clinical fMRI.
Role of semantic paradigms for optimization of language mapping in clinical FMRI studies.

PubMed

Zacà, D; Jarso, S; Pillai, J J

2013-10-01

The optimal paradigm choice for language mapping in clinical fMRI studies is challenging due to the variability in activation among different paradigms, the contribution to activation of cognitive processes other than language, and the difficulties in monitoring patient performance. In this study, we compared language localization and lateralization between 2 commonly used clinical language paradigms and 3 newly designed dual-choice semantic paradigms to define a streamlined and adequate language-mapping protocol. Twelve healthy volunteers performed 5 language paradigms: Silent Word Generation, Sentence Completion, Visual Antonym Pair, Auditory Antonym Pair, and Noun-Verb Association. Group analysis was performed to assess statistically significant differences in fMRI percentage signal change and lateralization index among these paradigms in 5 ROIs: inferior frontal gyrus, superior frontal gyrus, middle frontal gyrus for expressive language activation, middle temporal gyrus, and superior temporal gyrus for receptive language activation. In the expressive ROIs, Silent Word Generation was the most robust and best lateralizing paradigm (greater percentage signal change and lateralization index than semantic paradigms at P < .01 and P < .05 levels, respectively). In the receptive region of interest, Sentence Completion and Noun-Verb Association were the most robust activators (greater percentage signal change than other paradigms, P < .01). All except Auditory Antonym Pair were good lateralizing tasks (the lateralization index was significantly lower than other paradigms, P < .05). The combination of Silent Word Generation and ≥1 visual semantic paradigm, such as Sentence Completion and Noun-Verb Association, is adequate to determine language localization and lateralization; Noun-Verb Association has the additional advantage of objective monitoring of patient performance.

Processing of Intentional and Automatic Number Magnitudes in Children Born Prematurely: Evidence From fMRI

PubMed Central

Klein, Elise; Moeller, Korbinian; Kiechl-Kohlendorfer, Ursula; Kremser, Christian; Starke, Marc; Cohen Kadosh, Roi; Pupp-Peglow, Ulrike; Schocke, Michael; Kaufmann, Liane

2014-01-01

This study examined the neural correlates of intentional and automatic number processing (indexed by number comparison and physical Stroop task, respectively) in 6- and 7-year-old children born prematurely. Behavioral results revealed significant numerical distance and size congruity effects. Imaging results disclosed (1) largely overlapping fronto-parietal activation for intentional and automatic number processing, (2) a frontal to parietal shift of activation upon considering the risk factors gestational age and birth weight, and (3) a task-specific link between math proficiency and functional magnetic resonance imaging (fMRI) signal within distinct regions of the parietal lobes—indicating commonalities but also specificities of intentional and automatic number processing. PMID:25090014
Tracking Cognitive Change over 24 Weeks with Longitudinal Functional Magnetic Resonance Imaging in Alzheimer's Disease

PubMed Central

McLaren, Donald G.; Sreenivasan, Aishwarya; Diamond, Eli L.; Mitchell, Meghan B.; Van Dijk, Koene R.A.; DeLuca, Amy N.; O’Brien, Jacqueline L.; Rentz, Dorene M.; Sperling, Reisa A.; Atri, Alireza

2012-01-01

Background Previous studies have revealed that functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD) signal in specific brain regions correlates with cross-sectional performance on standardized clinical trial measures in Alzheimer's disease (AD); however, the relationship between longitudinal change in fMRI-BOLD signal and neuropsychological performance remains unknown. Objective: To identify changes in regional fMRI-BOLD activity that tracks change in neuropsychological performance in mild AD dementia over 6 months. Methods Twenty-four subjects (mean age 71.6) with mild AD dementia (mean Mini Mental State Examination 21.7, Global Clinical Dementia Rating 1.0) on stable donepezil dosing participated in two task-related fMRI sessions consisting of a face-name paired associative encoding memory paradigm 24 weeks apart during a randomized placebo-controlled pharmaco-fMRI drug study. Regression analysis was used to identify regions where the change in fMRI activity for Novel > Repeated stimulus contrast was associated with the change scores on postscan memory tests and the Free and Cued Selective Reminding Test (FCSRT). Results Correlations between changes in postscan memory accuracy and changes in fMRI activity were observed in regions including the angular gyrus, parahippocampal gyrus, inferior frontal gyrus and cerebellum. Correlations between changes in FCSRT-free recall and changes in fMRI were observed in regions including the inferior parietal lobule, precuneus, hippocampus and parahippocampal gyrus. Conclusion Changes in encoding-related fMRI activity in regions implicated in mnemonic networks correlated with changes in psychometric measures of episodic memory retrieval performed outside the scanner. These exploratory results support the potential of fMRI activity to track cognitive change and detect signals of short-term pharmacologic effect in early-phase AD studies. PMID:22456451
Tracking cognitive change over 24 weeks with longitudinal functional magnetic resonance imaging in Alzheimer's disease.

PubMed

McLaren, Donald G; Sreenivasan, Aishwarya; Diamond, Eli L; Mitchell, Meghan B; Van Dijk, Koene R A; Deluca, Amy N; O'Brien, Jacqueline L; Rentz, Dorene M; Sperling, Reisa A; Atri, Alireza

2012-01-01

Previous studies have revealed that functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD) signal in specific brain regions correlates with cross-sectional performance on standardized clinical trial measures in Alzheimer's disease (AD); however, the relationship between longitudinal change in fMRI-BOLD signal and neuropsychological performance remains unknown. To identify changes in regional fMRI-BOLD activity that tracks change in neuropsychological performance in mild AD dementia over 6 months. Twenty-four subjects (mean age 71.6) with mild AD dementia (mean Mini Mental State Examination 21.7, Global Clinical Dementia Rating 1.0) on stable donepezil dosing participated in two task-related fMRI sessions consisting of a face-name paired associative encoding memory paradigm 24 weeks apart during a randomized placebo-controlled pharmaco-fMRI drug study. Regression analysis was used to identify regions where the change in fMRI activity for Novel > Repeated stimulus contrast was associated with the change scores on postscan memory tests and the Free and Cued Selective Reminding Test (FCSRT). Correlations between changes in postscan memory accuracy and changes in fMRI activity were observed in regions including the angular gyrus, parahippocampal gyrus, inferior frontal gyrus and cerebellum. Correlations between changes in FCSRT-free recall and changes in fMRI were observed in regions including the inferior parietal lobule, precuneus, hippocampus and parahippocampal gyrus. Changes in encoding-related fMRI activity in regions implicated in mnemonic networks correlated with changes in psychometric measures of episodic memory retrieval performed outside the scanner. These exploratory results support the potential of fMRI activity to track cognitive change and detect signals of short-term pharmacologic effect in early-phase AD studies. Copyright © 2012 S. Karger AG, Basel.
Mapping (and modeling) physiological movements during EEG-fMRI recordings: the added value of the video acquired simultaneously.

PubMed

Ruggieri, Andrea; Vaudano, Anna Elisabetta; Benuzzi, Francesca; Serafini, Marco; Gessaroli, Giuliana; Farinelli, Valentina; Nichelli, Paolo Frigio; Meletti, Stefano

2015-01-15

During resting-state EEG-fMRI studies in epilepsy, patients' spontaneous head-face movements occur frequently. We tested the usefulness of synchronous video recording to identify and model the fMRI changes associated with non-epileptic movements to improve sensitivity and specificity of fMRI maps related to interictal epileptiform discharges (IED). Categorization of different facial/cranial movements during EEG-fMRI was obtained for 38 patients [with benign epilepsy with centro-temporal spikes (BECTS, n=16); with idiopathic generalized epilepsy (IGE, n=17); focal symptomatic/cryptogenic epilepsy (n=5)]. We compared at single subject- and at group-level the IED-related fMRI maps obtained with and without additional regressors related to spontaneous movements. As secondary aim, we considered facial movements as events of interest to test the usefulness of video information to obtain fMRI maps of the following face movements: swallowing, mouth-tongue movements, and blinking. Video information substantially improved the identification and classification of the artifacts with respect to the EEG observation alone (mean gain of 28 events per exam). Inclusion of physiological activities as additional regressors in the GLM model demonstrated an increased Z-score and number of voxels of the global maxima and/or new BOLD clusters in around three quarters of the patients. Video-related fMRI maps for swallowing, mouth-tongue movements, and blinking were comparable to the ones obtained in previous task-based fMRI studies. Video acquisition during EEG-fMRI is a useful source of information. Modeling physiological movements in EEG-fMRI studies for epilepsy will lead to more informative IED-related fMRI maps in different epileptic conditions. Copyright © 2014 Elsevier B.V. All rights reserved.
Performance of Blind Source Separation Algorithms for FMRI Analysis using a Group ICA Method

PubMed Central

Correa, Nicolle; Adali, Tülay; Calhoun, Vince D.

2007-01-01

Independent component analysis (ICA) is a popular blind source separation (BSS) technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist, however the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely information maximization, maximization of non-gaussianity, joint diagonalization of cross-cumulant matrices, and second-order correlation based methods when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study the variability among different ICA algorithms and propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA, and JADE all yield reliable results; each having their strengths in specific areas. EVD, an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for the iterative ICA algorithms, it is important to investigate the variability of the estimates from different runs. We test the consistency of the iterative algorithms, Infomax and FastICA, by running the algorithm a number of times with different initializations and note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis. PMID:17540281
Performance of blind source separation algorithms for fMRI analysis using a group ICA method.

PubMed

Correa, Nicolle; Adali, Tülay; Calhoun, Vince D

2007-06-01

Independent component analysis (ICA) is a popular blind source separation technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist; however, the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely, information maximization, maximization of non-Gaussianity, joint diagonalization of cross-cumulant matrices and second-order correlation-based methods, when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study variability among different ICA algorithms, and we propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA and joint approximate diagonalization of eigenmatrices (JADE) all yield reliable results, with each having its strengths in specific areas. Eigenvalue decomposition (EVD), an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for iterative ICA algorithms, it is important to investigate the variability of estimates from different runs. We test the consistency of the iterative algorithms Infomax and FastICA by running the algorithm a number of times with different initializations, and we note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis.
The temporal change in the cortical activations due to salty and sweet tastes in humans: fMRI and time-intensity sensory evaluation.

PubMed

Nakamura, Yuko; Goto, Tazuko K; Tokumori, Kenji; Yoshiura, Takashi; Kobayashi, Koji; Nakamura, Yasuhiko; Honda, Hiroshi; Ninomiya, Yuzo; Yoshiura, Kazunori

2012-04-18

It remains unclear how the cerebral cortex of humans perceives taste temporally, and whether or not such objective data about the brain show a correlation with the current widely used conventional methods of taste-intensity sensory evaluation. The aim of this study was to investigate the difference in the time-intensity profile between salty and sweet tastes in the human brain. The time-intensity profiles of functional MRI (fMRI) data of the human taste cortex were analyzed using finite impulse response analysis for a direct interpretation in terms of the peristimulus time signal. Also, time-intensity sensory evaluations for tastes were performed under the same condition as fMRI to confirm the reliability of the temporal profile in the fMRI data. The time-intensity profile for the brain activations due to a salty taste changed more rapidly than those due to a sweet taste in the human brain cortex and was also similar to the time-intensity sensory evaluation, confirming the reliability of the temporal profile of the fMRI data. In conclusion, the time-intensity profile using finite impulse response analysis for fMRI data showed that there was a temporal difference in the neural responses between salty and sweet tastes over a given period of time. This indicates that there might be taste-specific temporal profiles of activations in the human brain.
Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task

PubMed Central

Ciuciu, P.; Varoquaux, G.; Abry, P.; Sadaghiani, S.; Kleinschmidt, A.

2012-01-01

Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (e.g., artifacts), at a broader spatial scale than the voxel one, using a multivariate approach, namely the Multi-Subject Dictionary Learning (MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes. PMID:22715328
A Model of Emotion Management for U.S. Army Leaders

DTIC Science & Technology

2010-12-01

study . The Leadership Quarterly, 13, 601-614. Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. E. (2002). Rethinking feelings: An fMRI ...adaptability, innovation) 3) Motivation (achievement drive, commitment to group/organization, initiative, optimism) 4) Empathy (understanding...regard, emotional self-awareness, assertiveness, independence, self-actualization) 2) Interpersonal ( empathy , social responsibility, establishing
Recent progress and outstanding issues in motion correction in resting state fMRI

PubMed Central

Power, Jonathan D; Schlaggar, Bradley L; Petersen, Steven E

2014-01-01

The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI. In 2011, three groups reported that small head movements produced spurious but structured noise in brain scans, causing distance-dependent changes in signal correlations. This finding has prompted both methods development and the re-examination of prior findings with more stringent motion correction. Since 2011, over a dozen papers have been published specifically on motion artifact in resting state fMRI. We will attempt to distill these papers to their most essential content. We will point out some aspects of motion artifact that are easily or often overlooked. Throughout the review, we will highlight gaps in current knowledge and avenues for future research. PMID:25462692
Autogenic training alters cerebral activation patterns in fMRI.

PubMed

Schlamann, Marc; Naglatzki, Ryan; de Greiff, Armin; Forsting, Michael; Gizewski, Elke R

2010-10-01

Cerebral activation patterns during the first three auto-suggestive phases of autogenic training (AT) were investigated in relation to perceived experiences. Nineteen volunteers trained in AT and 19 controls were studied with fMRI during the first steps of autogenic training. FMRI revealed activation of the left postcentral areas during AT in those with experience in AT, which also correlated with the level of AT experience. Activation of prefrontal and insular cortex was significantly higher in the group with experience in AT while insular activation was correlated with number years of simple relaxation exercises. Specific activation in subjects experienced in AT may represent a training effect. Furthermore, the correlation of insular activation suggests that these subjects are different from untrained subjects in emotional processing or self-awareness.
Revealing the functional neuroanatomy of intrinsic alertness using fMRI: methodological peculiarities.

PubMed

Clemens, Benjamin; Zvyagintsev, Mikhail; Sack, Alexander T; Sack, Alexander; Heinecke, Armin; Willmes, Klaus; Sturm, Walter

2011-01-01

Clinical observations and neuroimaging data revealed a right-hemisphere fronto-parietal-thalamic-brainstem network for intrinsic alertness, and additional left fronto-parietal activity during phasic alertness. The primary objective of this fMRI study was to map the functional neuroanatomy of intrinsic alertness as precisely as possible in healthy participants, using a novel assessment paradigm already employed in clinical settings. Both the paradigm and the experimental design were optimized to specifically assess intrinsic alertness, while at the same time controlling for sensory-motor processing. The present results suggest that the processing of intrinsic alertness is accompanied by increased activity within the brainstem, thalamus, anterior cingulate gyrus, right insula, and right parietal cortex. Additionally, we found increased activation in the left hemisphere around the middle frontal gyrus (BA 9), the insula, the supplementary motor area, and the cerebellum. Our results further suggest that rather minute aspects of the experimental design may induce aspects of phasic alertness, which in turn might lead to additional brain activation in left-frontal areas not normally involved in intrinsic alertness. Accordingly, left BA 9 activation may be related to co-activation of the phasic alertness network due to the switch between rest and task conditions functioning as an external warning cue triggering the phasic alertness network. Furthermore, activation of the intrinsic alertness network during fixation blocks due to enhanced expectancy shortly before the switch to the task block might, when subtracted from the task block, lead to diminished activation in the typical right hemisphere intrinsic alertness network. Thus, we cautiously suggest that--as a methodological artifact--left frontal activations might show up due to phasic alertness involvement and intrinsic alertness activations might be weakened due to contrasting with fixation blocks, when assessing the functional neuroanatomy of intrinsic alertness with a block design in fMRI studies.
Revealing the Functional Neuroanatomy of Intrinsic Alertness Using fMRI: Methodological Peculiarities

PubMed Central

Clemens, Benjamin; Zvyagintsev, Mikhail; Sack, Alexander; Heinecke, Armin; Willmes, Klaus; Sturm, Walter

2011-01-01

Clinical observations and neuroimaging data revealed a right-hemisphere fronto-parietal-thalamic-brainstem network for intrinsic alertness, and additional left fronto-parietal activity during phasic alertness. The primary objective of this fMRI study was to map the functional neuroanatomy of intrinsic alertness as precisely as possible in healthy participants, using a novel assessment paradigm already employed in clinical settings. Both the paradigm and the experimental design were optimized to specifically assess intrinsic alertness, while at the same time controlling for sensory-motor processing. The present results suggest that the processing of intrinsic alertness is accompanied by increased activity within the brainstem, thalamus, anterior cingulate gyrus, right insula, and right parietal cortex. Additionally, we found increased activation in the left hemisphere around the middle frontal gyrus (BA 9), the insula, the supplementary motor area, and the cerebellum. Our results further suggest that rather minute aspects of the experimental design may induce aspects of phasic alertness, which in turn might lead to additional brain activation in left-frontal areas not normally involved in intrinsic alertness. Accordingly, left BA 9 activation may be related to co-activation of the phasic alertness network due to the switch between rest and task conditions functioning as an external warning cue triggering the phasic alertness network. Furthermore, activation of the intrinsic alertness network during fixation blocks due to enhanced expectancy shortly before the switch to the task block might, when subtracted from the task block, lead to diminished activation in the typical right hemisphere intrinsic alertness network. Thus, we cautiously suggest that – as a methodological artifact – left frontal activations might show up due to phasic alertness involvement and intrinsic alertness activations might be weakened due to contrasting with fixation blocks, when assessing the functional neuroanatomy of intrinsic alertness with a block design in fMRI studies. PMID:21984928
The representation of order information in auditory-verbal short-term memory.

PubMed

Kalm, Kristjan; Norris, Dennis

2014-05-14

Here we investigate how order information is represented in auditory-verbal short-term memory (STM). We used fMRI and a serial recall task to dissociate neural activity patterns representing the phonological properties of the items stored in STM from the patterns representing their order. For this purpose, we analyzed fMRI activity patterns elicited by different item sets and different orderings of those items. These fMRI activity patterns were compared with the predictions made by positional and chaining models of serial order. The positional models encode associations between items and their positions in a sequence, whereas the chaining models encode associations between successive items and retain no position information. We show that a set of brain areas in the postero-dorsal stream of auditory processing store associations between items and order as predicted by a positional model. The chaining model of order representation generates a different pattern similarity prediction, which was shown to be inconsistent with the fMRI data. Our results thus favor a neural model of order representation that stores item codes, position codes, and the mapping between them. This study provides the first fMRI evidence for a specific model of order representation in the human brain. Copyright © 2014 the authors 0270-6474/14/346879-08$15.00/0.
Upsampling to 400-ms Resolution for Assessing Effective Connectivity in Functional Magnetic Resonance Imaging Data with Granger Causality

PubMed Central

Kerr, Deborah L.; Nitschke, Jack B.

2013-01-01

Abstract Granger causality analysis of functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent signal data allows one to infer the direction and magnitude of influence that brain regions exert on one another. We employed a method for upsampling the time resolution of fMRI data that does not require additional interpolation beyond the interpolation that is regularly used for slice-timing correction. The mathematics for this new method are provided, and simulations demonstrate its viability. Using fMRI, 17 snake phobics and 19 healthy controls viewed snake, disgust, and neutral fish video clips preceded by anticipatory cues. Multivariate Granger causality models at the native 2-sec resolution and at the upsampled 400-ms resolution assessed directional associations of fMRI data among 13 anatomical regions of interest identified in prior research on anxiety and emotion. Superior sensitivity was observed for the 400-ms model, both for connectivity within each group and for group differences in connectivity. Context-dependent analyses for the 400-ms multivariate Granger causality model revealed the specific trial types showing group differences in connectivity. This is the first demonstration of effective connectivity of fMRI data using a method for achieving 400-ms resolution without sacrificing accuracy available at 2-sec resolution. PMID:23134194
Differential sensory fMRI signatures in autism and schizophrenia: Analysis of amplitude and trial-to-trial variability.

PubMed

Haigh, Sarah M; Gupta, Akshat; Barb, Scott M; Glass, Summer A F; Minshew, Nancy J; Dinstein, Ilan; Heeger, David J; Eack, Shaun M; Behrmann, Marlene

2016-08-01

Autism and schizophrenia share multiple phenotypic and genotypic markers, and there is ongoing debate regarding the relationship of these two disorders. To examine whether cortical dynamics are similar across these disorders, we directly compared fMRI responses to visual, somatosensory and auditory stimuli in adults with autism (N=15), with schizophrenia (N=15), and matched controls (N=15). All participants completed a one-back letter detection task presented at fixation (to control attention) while task-irrelevant sensory stimulation was delivered to the different modalities. We focused specifically on the response amplitudes and the variability in sensory fMRI responses of the two groups, given the evidence of greater trial-to-trial variability in adults with autism. Both autism and schizophrenia individuals showed weaker signal-to-noise ratios (SNR) in sensory-evoked responses compared to controls (d>0.42), but for different reasons. For the autism group, the fMRI response amplitudes were indistinguishable from controls but were more variable trial-to-trial (d=0.47). For the schizophrenia group, response amplitudes were smaller compared to autism (d=0.44) and control groups (d=0.74), but were not significantly more variable (d<0.29). These differential group profiles suggest (1) that greater trial-to-trial variability in cortical responses may be specific to autism and is not a defining characteristic of schizophrenia, and (2) that blunted response amplitudes may be characteristic of schizophrenia. The relationship between the amplitude and the variability of cortical activity might serve as a specific signature differentiating these neurodevelopmental disorders. Identifying the neural basis of these responses and their relationship to the underlying genetic bases may substantially enlighten the understanding of both disorders. Copyright © 2016 Elsevier B.V. All rights reserved.
Cholinergic blockade under working memory demands encountered by increased rehearsal strategies: evidence from fMRI in healthy subjects.

PubMed

Voss, Bianca; Thienel, Renate; Reske, Martina; Kellermann, Thilo; Sheldrick, Abigail J; Halfter, Sarah; Radenbach, Katrin; Shah, Nadim J; Habel, Ute; Kircher, Tilo T J

2012-06-01

The connection between cholinergic transmission and cognitive performance has been established in behavioural studies. The specific contribution of the muscarinic receptor system on cognitive performance and brain activation, however, has not been evaluated satisfyingly. To investigate the specific contribution of the muscarinic transmission on neural correlates of working memory, we examined the effects of scopolamine, an antagonist of the muscarinic receptors, using functional magnetic resonance imaging (fMRI). Fifteen healthy male, non-smoking subjects performed a fMRI scanning session following the application of scopolamine (0.4 mg, i.v.) or saline in a placebo-controlled, repeated measure, pseudo-randomized, single-blind design. Working memory was probed using an n-back task. Compared to placebo, challenging the cholinergic transmission with scopolamine resulted in hypoactivations in parietal, occipital and cerebellar areas and hyperactivations in frontal and prefrontal areas. These alterations are interpreted as compensatory strategies used to account for downregulation due to muscarinic acetylcholine blockade in parietal and cerebral storage systems by increased activation in frontal and prefrontal areas related to working memory rehearsal. Our results further underline the importance of cholinergic transmission to working memory performance and determine the specific contribution of muscarinic transmission on cerebral activation associated with executive functioning.
Assessment of biofeedback rehabilitation in post-stroke patients combining fMRI and gait analysis: a case study

PubMed Central

2014-01-01

Background The ability to walk independently is a primary goal for rehabilitation after stroke. Gait analysis provides a great amount of valuable information, while functional magnetic resonance imaging (fMRI) offers a powerful approach to define networks involved in motor control. The present study reports a new methodology based on both fMRI and gait analysis outcomes in order to investigate the ability of fMRI to reflect the phases of motor learning before/after electromyographic biofeedback treatment: the preliminary fMRI results of a post stroke subject’s brain activation, during passive and active ankle dorsal/plantarflexion, before and after biofeedback (BFB) rehabilitation are reported and their correlation with gait analysis data investigated. Methods A control subject and a post-stroke patient with chronic hemiparesis were studied. Functional magnetic resonance images were acquired during a block-design protocol on both subjects while performing passive and active ankle dorsal/plantarflexion. fMRI and gait analysis were assessed on the patient before and after electromyographic biofeedback rehabilitation treatment during gait activities. Lower limb three-dimensional kinematics, kinetics and surface electromyography were evaluated. Correlation between fMRI and gait analysis categorical variables was assessed: agreement/disagreement was assigned to each variable if the value was in/outside the normative range (gait analysis), or for presence of normal/diffuse/no activation of motor area (fMRI). Results Altered fMRI activity was found on the post-stroke patient before biofeedback rehabilitation with respect to the control one. Meanwhile the patient showed a diffuse, but more limited brain activation after treatment (less voxels). The post-stroke gait data showed a trend towards the normal range: speed, stride length, ankle power, and ankle positive work increased. Preliminary correlation analysis revealed that consistent changes were observed both for the fMRI data, and the gait analysis data after treatment (R > 0.89): this could be related to the possible effects BFB might have on the central as well as on the peripheral nervous system. Conclusions Our findings showed that this methodology allows evaluation of the relationship between alterations in gait and brain activation of a post-stroke patient. Such methodology, if applied on a larger sample subjects, could provide information about the specific motor area involved in a rehabilitation treatment. PMID:24716475
Manipulating motor performance and memory through real-time fMRI neurofeedback.

PubMed

Scharnowski, Frank; Veit, Ralf; Zopf, Regine; Studer, Petra; Bock, Simon; Diedrichsen, Jörn; Goebel, Rainer; Mathiak, Klaus; Birbaumer, Niels; Weiskopf, Nikolaus

2015-05-01

Task performance depends on ongoing brain activity which can be influenced by attention, arousal, or motivation. However, such modulating factors of cognitive efficiency are unspecific, can be difficult to control, and are not suitable to facilitate neural processing in a regionally specific manner. Here, we non-pharmacologically manipulated regionally specific brain activity using technically sophisticated real-time fMRI neurofeedback. This was accomplished by training participants to simultaneously control ongoing brain activity in circumscribed motor and memory-related brain areas, namely the supplementary motor area and the parahippocampal cortex. We found that learned voluntary control over these functionally distinct brain areas caused functionally specific behavioral effects, i.e. shortening of motor reaction times and specific interference with memory encoding. The neurofeedback approach goes beyond improving cognitive efficiency by unspecific psychological factors such as attention, arousal, or motivation. It allows for directly manipulating sustained activity of task-relevant brain regions in order to yield specific behavioral or cognitive effects. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.
Manipulating motor performance and memory through real-time fMRI neurofeedback

PubMed Central

Scharnowski, Frank; Veit, Ralf; Zopf, Regine; Studer, Petra; Bock, Simon; Diedrichsen, Jörn; Goebel, Rainer; Mathiak, Klaus; Birbaumer, Niels; Weiskopf, Nikolaus

2015-01-01

Task performance depends on ongoing brain activity which can be influenced by attention, arousal, or motivation. However, such modulating factors of cognitive efficiency are unspecific, can be difficult to control, and are not suitable to facilitate neural processing in a regionally specific manner. Here, we non-pharmacologically manipulated regionally specific brain activity using technically sophisticated real-time fMRI neurofeedback. This was accomplished by training participants to simultaneously control ongoing brain activity in circumscribed motor and memory-related brain areas, namely the supplementary motor area and the parahippocampal cortex. We found that learned voluntary control over these functionally distinct brain areas caused functionally specific behavioral effects, i.e. shortening of motor reaction times and specific interference with memory encoding. The neurofeedback approach goes beyond improving cognitive efficiency by unspecific psychological factors such as attention, arousal, or motivation. It allows for directly manipulating sustained activity of task-relevant brain regions in order to yield specific behavioral or cognitive effects. PMID:25796342

Optimizing the general linear model for functional near-infrared spectroscopy: an adaptive hemodynamic response function approach

PubMed Central

Uga, Minako; Dan, Ippeita; Sano, Toshifumi; Dan, Haruka; Watanabe, Eiju

2014-01-01

Abstract. An increasing number of functional near-infrared spectroscopy (fNIRS) studies utilize a general linear model (GLM) approach, which serves as a standard statistical method for functional magnetic resonance imaging (fMRI) data analysis. While fMRI solely measures the blood oxygen level dependent (BOLD) signal, fNIRS measures the changes of oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) signals at a temporal resolution severalfold higher. This suggests the necessity of adjusting the temporal parameters of a GLM for fNIRS signals. Thus, we devised a GLM-based method utilizing an adaptive hemodynamic response function (HRF). We sought the optimum temporal parameters to best explain the observed time series data during verbal fluency and naming tasks. The peak delay of the HRF was systematically changed to achieve the best-fit model for the observed oxy- and deoxy-Hb time series data. The optimized peak delay showed different values for each Hb signal and task. When the optimized peak delays were adopted, the deoxy-Hb data yielded comparable activations with similar statistical power and spatial patterns to oxy-Hb data. The adaptive HRF method could suitably explain the behaviors of both Hb parameters during tasks with the different cognitive loads during a time course, and thus would serve as an objective method to fully utilize the temporal structures of all fNIRS data. PMID:26157973
Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity.

PubMed

Thompson, Garth John; Pan, Wen-Ju; Magnuson, Matthew Evan; Jaeger, Dieter; Keilholz, Shella Dawn

2014-01-01

Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders. © 2013.
Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity

PubMed Central

Thompson, Garth John; Pan, Wen-Ju; Magnuson, Matthew Evan; Jaeger, Dieter; Keilholz, Shella Dawn

2013-01-01

Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders. PMID:24071524
Real-Time fMRI Pattern Decoding and Neurofeedback Using FRIEND: An FSL-Integrated BCI Toolbox

PubMed Central

Sato, João R.; Basilio, Rodrigo; Paiva, Fernando F.; Garrido, Griselda J.; Bramati, Ivanei E.; Bado, Patricia; Tovar-Moll, Fernanda; Zahn, Roland; Moll, Jorge

2013-01-01

The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available. PMID:24312569
Assessing Effects of Prenatal Alcohol Exposure Using Group-wise Sparse Representation of FMRI Data

PubMed Central

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Zhao, Shijie; Zhang, Tuo; Hu, Xintao; Han, Junwei; Guo, Lei; Li, Zhihao; Coles, Claire; Hu, Xiaoping; Liu, Tianming

2015-01-01

Task-based fMRI activation mapping has been widely used in clinical neuroscience in order to assess different functional activity patterns in conditions such as prenatal alcohol exposure (PAE) affected brains and healthy controls. In this paper, we propose a novel, alternative approach of group-wise sparse representation of the fMRI data of multiple groups of subjects (healthy control, exposed non-dysmorphic PAE and exposed dysmorphic PAE) and assess the systematic functional activity differences among these three populations. Specifically, a common time series signal dictionary is learned from the aggregated fMRI signals of all three groups of subjects, and then the weight coefficient matrices (named statistical coefficient map (SCM)) associated with each common dictionary were statistically assessed for each group separately. Through inter-group comparisons based on the correspondence established by the common dictionary, our experimental results have demonstrated that the group-wise sparse coding strategy and the SCM can effectively reveal a collection of brain networks/regions that were affected by different levels of severity of PAE. PMID:26195294
Mapping white-matter functional organization at rest and during naturalistic visual perception.

PubMed

Marussich, Lauren; Lu, Kun-Han; Wen, Haiguang; Liu, Zhongming

2017-02-01

Despite the wide applications of functional magnetic resonance imaging (fMRI) to mapping brain activation and connectivity in cortical gray matter, it has rarely been utilized to study white-matter functions. In this study, we investigated the spatiotemporal characteristics of fMRI data within the white matter acquired from humans both in the resting state and while watching a naturalistic movie. By using independent component analysis and hierarchical clustering, resting-state fMRI data in the white matter were de-noised and decomposed into spatially independent components, which were further assembled into hierarchically organized axonal fiber bundles. Interestingly, such components were partly reorganized during natural vision. Relative to resting state, the visual task specifically induced a stronger degree of temporal coherence within the optic radiations, as well as significant correlations between the optic radiations and multiple cortical visual networks. Therefore, fMRI contains rich functional information about the activity and connectivity within white matter at rest and during tasks, challenging the conventional practice of taking white-matter signals as noise or artifacts. Copyright © 2016 Elsevier Inc. All rights reserved.
Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex.

PubMed

Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhao, Shijie; Zhang, Shu; Zhang, Wei; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2018-06-01

Various studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on functional magnetic resonance imaging (fMRI) has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored. Second, the SOPFNs are assessed within individual subjects, while group-wise consistency of the SOPFNs is largely unknown. To address the two issues, we propose a novel computational framework of group-wise sparse representation of whole-brain fMRI temporal segments to assess the temporal dynamic spatial patterns of SOPFNs that are consistent across different subjects. Experimental results based on the recently publicly released Human Connectome Project grayordinate task fMRI data demonstrate that meaningful SOPFNs exhibiting dynamic spatial patterns across different time periods are effectively and robustly identified based on the reconstructed concurrent functional networks via the proposed framework. Specifically, those SOPFNs locate significantly more on gyral regions than on sulcal regions across different time periods. These results reveal novel functional architecture of cortical gyri and sulci. Moreover, these results help better understand functional dynamics mechanisms of cerebral cortex in the future.
Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations.

PubMed

Zhang, Shu; Li, Xiang; Lv, Jinglei; Jiang, Xi; Guo, Lei; Liu, Tianming

2016-03-01

A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, which was then factorized into a subject-specific dictionary matrix and a weight coefficient matrix for sparse representation. In the second stage, all of the dictionary matrices from both tfMRI/rsfMRI data across multiple subjects were composed into another big data-matrix, which was further sparsely represented by a cross-subjects common dictionary and a weight matrix. This framework has been applied on the recently publicly released Human Connectome Project (HCP) fMRI data and experimental results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100% classification accuracy. Moreover, our methods and results can be meaningfully interpreted, e.g., the well-known default mode network (DMN) activities can be recovered from the very noisy and heterogeneous aggregated big-data of tfMRI and rsfMRI signals across all subjects in HCP Q1 release.
Investigating common coding of observed and executed actions in the monkey brain using cross-modal multi-variate fMRI classification.

PubMed

Fiave, Prosper Agbesi; Sharma, Saloni; Jastorff, Jan; Nelissen, Koen

2018-05-19

Mirror neurons are generally described as a neural substrate hosting shared representations of actions, by simulating or 'mirroring' the actions of others onto the observer's own motor system. Since single neuron recordings are rarely feasible in humans, it has been argued that cross-modal multi-variate pattern analysis (MVPA) of non-invasive fMRI data is a suitable technique to investigate common coding of observed and executed actions, allowing researchers to infer the presence of mirror neurons in the human brain. In an effort to close the gap between monkey electrophysiology and human fMRI data with respect to the mirror neuron system, here we tested this proposal for the first time in the monkey. Rhesus monkeys either performed reach-and-grasp or reach-and-touch motor acts with their right hand in the dark or observed videos of human actors performing similar motor acts. Unimodal decoding showed that both executed or observed motor acts could be decoded from numerous brain regions. Specific portions of rostral parietal, premotor and motor cortices, previously shown to house mirror neurons, in addition to somatosensory regions, yielded significant asymmetric action-specific cross-modal decoding. These results validate the use of cross-modal multi-variate fMRI analyses to probe the representations of own and others' actions in the primate brain and support the proposed mapping of others' actions onto the observer's own motor cortices. Copyright © 2018 Elsevier Inc. All rights reserved.
Investigating the Role of Hypothalamic Tumor Involvement in Sleep and Cognitive Outcomes Among Children Treated for Craniopharyngioma.

PubMed

Jacola, Lisa M; Conklin, Heather M; Scoggins, Matthew A; Ashford, Jason M; Merchant, Thomas E; Mandrell, Belinda N; Ogg, Robert J; Curtis, Elizabeth; Wise, Merrill S; Indelicato, Daniel J; Crabtree, Valerie M

2016-07-01

Despite excellent survival prognosis, children treated for craniopharyngioma experience significant morbidity. We examined the role of hypothalamic involvement (HI) in excessive daytime sleepiness (EDS) and attention regulation in children enrolled on a Phase II trial of limited surgery and proton therapy. Participants completed a sleep evaluation (N = 62) and a continuous performance test (CPT) during functional magnetic resonance imaging (fMRI; n = 29) prior to proton therapy. EDS was identified in 76% of the patients and was significantly related to increased HI extent (p = .04). There was no relationship between CPT performance during fMRI and HI or EDS. Visual examination of group composite fMRI images revealed greater spatial extent of activation in frontal cortical regions in patients with EDS, consistent with a compensatory activation hypothesis. Routine screening for sleep problems during therapy is indicated for children with craniopharyngioma, to optimize the timing of interventions and reduce long-term morbidity. © The Author 2016. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

PubMed Central

Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang

2017-01-01

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926
Sparse network-based models for patient classification using fMRI

PubMed Central

Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

2015-01-01

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459
Altered resting-state effective connectivity of fronto-parietal motor control systems on the primary motor network following stroke

PubMed Central

Inman, Cory S.; James, G. Andrew; Hamann, Stephan; Rajendra, Justin K.; Pagnoni, Giuseppe; Butler, Andrew J.

2011-01-01

Previous brain imaging work suggests that stroke alters the effective connectivity (the influence neural regions exert upon each other) of motor execution networks. The present study examines the intrinsic effective connectivity of top-down motor control in stroke survivors (n=13) relative to healthy participants (n=12). Stroke survivors exhibited significant deficits in motor function, as assessed by the Fugl-Meyer Motor Assessment. We used structural equation modeling (SEM) of resting-state fMRI data to investigate the relationship between motor deficits and the intrinsic effective connectivity between brain regions involved in motor control and motor execution. An exploratory adaptation of SEM determined the optimal model of motor execution effective connectivity in healthy participants, and confirmatory SEM assessed stroke survivors’ fit to that model. We observed alterations in spontaneous resting-state effective connectivity from fronto-parietal guidance systems to the motor network in stroke survivors. More specifically, diminished connectivity was found in connections from the superior parietal cortex to primary motor cortex and supplementary motor cortex. Furthermore, the paths demonstrated large individual variance in stroke survivors but less variance in healthy participants. These findings suggest that characterizing the deficits in resting-state connectivity of top-down processes in stroke survivors may help optimize cognitive and physical rehabilitation therapies by individually targeting specific neural pathway. PMID:21839174
Combined noninvasive language mapping by navigated transcranial magnetic stimulation and functional MRI and its comparison with direct cortical stimulation.

PubMed

Ille, Sebastian; Sollmann, Nico; Hauck, Theresa; Maurer, Stefanie; Tanigawa, Noriko; Obermueller, Thomas; Negwer, Chiara; Droese, Doris; Zimmer, Claus; Meyer, Bernhard; Ringel, Florian; Krieg, Sandro M

2015-07-01

Repetitive navigated transcranial magnetic stimulation (rTMS) is now increasingly used for preoperative language mapping in patients with lesions in language-related areas of the brain. Yet its correlation with intraoperative direct cortical stimulation (DCS) has to be improved. To increase rTMS's specificity and positive predictive value, the authors aim to provide thresholds for rTMS's positive language areas. Moreover, they propose a protocol for combining rTMS with functional MRI (fMRI) to combine the strength of both methods. The authors performed multimodal language mapping in 35 patients with left-sided perisylvian lesions by using rTMS, fMRI, and DCS. The rTMS mappings were conducted with a picture-to-trigger interval (PTI, time between stimulus presentation and stimulation onset) of either 0 or 300 msec. The error rates (ERs; that is, the number of errors per number of stimulations) were calculated for each region of the cortical parcellation system (CPS). Subsequently, the rTMS mappings were analyzed through different error rate thresholds (ERT; that is, the ER at which a CPS region was defined as language positive in terms of rTMS), and the 2-out-of-3 rule (a stimulation site was defined as language positive in terms of rTMS if at least 2 out of 3 stimulations caused an error). As a second step, the authors combined the results of fMRI and rTMS in a predefined protocol of combined noninvasive mapping. To validate this noninvasive protocol, they correlated its results to DCS during awake surgery. The analysis by different rTMS ERTs obtained the highest correlation regarding sensitivity and a low rate of false positives for the ERTs of 15%, 20%, 25%, and the 2-out-of-3 rule. However, when comparing the combined fMRI and rTMS results with DCS, the authors observed an overall specificity of 83%, a positive predictive value of 51%, a sensitivity of 98%, and a negative predictive value of 95%. In comparison with fMRI, rTMS is a more sensitive but less specific tool for preoperative language mapping than DCS. Moreover, rTMS is most reliable when using ERTs of 15%, 20%, 25%, or the 2-out-of-3 rule and a PTI of 0 msec. Furthermore, the combination of fMRI and rTMS leads to a higher correlation to DCS than both techniques alone, and the presented protocols for combined noninvasive language mapping might play a supportive role in the language-mapping assessment prior to the gold-standard intraoperative DCS.
Simultaneous GCaMP6-based fiber photometry and fMRI in rats.

PubMed

Liang, Zhifeng; Ma, Yuncong; Watson, Glenn D R; Zhang, Nanyin

2017-09-01

Understanding the relationship between neural and vascular signals is essential for interpretation of functional MRI (fMRI) results with respect to underlying neuronal activity. Simultaneously measuring neural activity using electrophysiology with fMRI has been highly valuable in elucidating the neural basis of the blood oxygenation-level dependent (BOLD) signal. However, this approach is also technically challenging due to the electromagnetic interference that is observed in electrophysiological recordings during MRI scanning. Recording optical correlates of neural activity, such as calcium signals, avoids this issue, and has opened a new avenue to simultaneously acquire neural and BOLD signals. The present study is the first to demonstrate the feasibility of simultaneously and repeatedly acquiring calcium and BOLD signals in animals using a genetically encoded calcium indicator, GCaMP6. This approach was validated with a visual stimulation experiment, during which robust increases of both calcium and BOLD signals in the superior colliculus were observed. In addition, repeated measurement in the same animal demonstrated reproducible calcium and BOLD responses to the same stimuli. Taken together, simultaneous GCaMP6-based fiber photometry and fMRI recording presents a novel, artifact-free approach to simultaneously measuring neural and fMRI signals. Furthermore, given the cell-type specificity of GCaMP6, this approach has the potential to mechanistically dissect the contributions of individual neuron populations to BOLD signal, and ultimately reveal its underlying neural mechanisms. The current study established the method for simultaneous GCaMP6-based fiber photometry and fMRI in rats. Copyright © 2017 Elsevier B.V. All rights reserved.
Recent progress and outstanding issues in motion correction in resting state fMRI.

PubMed

Power, Jonathan D; Schlaggar, Bradley L; Petersen, Steven E

2015-01-15

The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI. In 2011, three groups reported that small head movements produced spurious but structured noise in brain scans, causing distance-dependent changes in signal correlations. This finding has prompted both methods development and the re-examination of prior findings with more stringent motion correction. Since 2011, over a dozen papers have been published specifically on motion artifact in resting state fMRI. We will attempt to distill these papers to their most essential content. We will point out some aspects of motion artifact that are easily or often overlooked. Throughout the review, we will highlight gaps in current knowledge and avenues for future research. Copyright © 2014 Elsevier Inc. All rights reserved.
Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI.

PubMed

Colas, Jaron T; Pauli, Wolfgang M; Larsen, Tobias; Tyszka, J Michael; O'Doherty, John P

2017-10-01

Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Pantelis, Evaggelos, E-mail: vpantelis@phys.uoa.g; Medical Physics Laboratory, Medical School, University of Athens, Athens; Papadakis, Nikolaos

Purpose: To study the efficacy of the integration of functional magnetic resonance imaging (fMRI) and diffusion tensor imaging tractography data into stereotactic radiosurgery clinical practice. Methods and Materials: fMRI and tractography data sets were acquired and fused with corresponding anatomical MR and computed tomography images of patients with arteriovenous malformation (AVM), astrocytoma, brain metastasis, or hemangioma and referred for stereotactic radiosurgery. The acquired data sets were imported into a CyberKnife stereotactic radiosurgery system and used to delineate the target, organs at risk, and nearby functional structures and fiber tracts. Treatment plans with and without the incorporation of the functional structuresmore » and the fiber tracts into the optimization process were developed and compared. Results: The nearby functional structures and fiber tracts could receive doses of >50% of the maximum dose if they were excluded from the planning process. In the AVM case, the doses received by the Broadmann-17 structure and the optic tract were reduced to 700 cGy from 1,400 cGy and to 1,200 cGy from 2,000 cGy, respectively, upon inclusion into the optimization process. In the metastasis case, the motor cortex received 850 cGy instead of 1,400 cGy; and in the hemangioma case, the pyramidal tracts received 780 cGy instead of 990 cGy. In the astrocytoma case, the dose to the motor cortex bordering the lesion was reduced to 1,900 cGy from 2,100 cGy, and therefore, the biologically equivalent dose in three fractions was delivered instead. Conclusions: Functional structures and fiber tracts could receive high doses if they were not considered during treatment planning. With the aid of fMRI and tractography images, they can be delineated and spared.« less
Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI

PubMed Central

Pauli, Wolfgang M.; Larsen, Tobias; Tyszka, J. Michael; O’Doherty, John P.

2017-01-01

Prediction-error signals consistent with formal models of “reinforcement learning” (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models—namely, “actor/critic” models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning. PMID:29049406
Functional MRI mapping of visual function and selective attention for performance assessment and presurgical planning using conjunctive visual search

PubMed Central

Parker, Jason G; Zalusky, Eric J; Kirbas, Cemil

2014-01-01

Background Accurate mapping of visual function and selective attention using fMRI is important in the study of human performance as well as in presurgical treatment planning of lesions in or near visual centers of the brain. Conjunctive visual search (CVS) is a useful tool for mapping visual function during fMRI because of its greater activation extent compared with high-capacity parallel search processes. Aims The purpose of this work was to develop and evaluate a CVS that was capable of generating consistent activation in the basic and higher level visual areas of the brain by using a high number of distractors as well as an optimized contrast condition. Materials and methods Images from 10 healthy volunteers were analyzed and brain regions of greatest activation and deactivation were determined using a nonbiased decomposition of the results at the hemisphere, lobe, and gyrus levels. The results were quantified in terms of activation and deactivation extent and mean z-statistic. Results The proposed CVS was found to generate robust activation of the occipital lobe, as well as regions in the middle frontal gyrus associated with coordinating eye movements and in regions of the insula associated with task-level control and focal attention. As expected, the task demonstrated deactivation patterns commonly implicated in the default-mode network. Further deactivation was noted in the posterior region of the cerebellum, most likely associated with the formation of optimal search strategy. Conclusion We believe the task will be useful in studies of visual and selective attention in the neuroscience community as well as in mapping visual function in clinical fMRI. PMID:24683515

Specific cerebral activation due to visual erotic stimuli in male-to-female transsexuals compared with male and female controls: an fMRI study.

PubMed

Gizewski, Elke R; Krause, Eva; Schlamann, Marc; Happich, Friederike; Ladd, Mark E; Forsting, Michael; Senf, Wolfgang

2009-02-01

Transsexuals harbor the strong feeling of having been born to the wrong sex. There is a continuing controversial discussion of whether or not transsexualism has a biological representation. Differences between males and females in terms of functional imaging during erotic stimuli have been previously described, revealing gender-specific results. Therefore, we postulated that male-to-female (MTF) transsexuals may show specific cerebral activation differing from their biological gender. Cerebral activation patterns during viewing of erotic film excerpts in functional magnetic resonance imaging (fMRI). Twelve male and 12 female heterosexual volunteers and 12 MTF transsexuals before any treatment viewed erotic film excerpts during fMRI. Additionally, subjective rating of sexual arousal was assessed. Statistics were performed using the Statistical Parametric Mapping software. Significantly enhanced activation for men compared with women was revealed in brain areas involved in erotic processing, i.e., the thalamus, the amygdala, and the orbitofrontal and insular cortex, whereas no specific activation for women was found. When comparing MTF transsexuals with male volunteers, activation patterns similar to female volunteers being compared with male volunteers were revealed. Sexual arousal was assessed using standard rating scales and did not differ significantly for the three groups. We revealed a cerebral activation pattern in MTF transsexuals compared with male controls similar to female controls compared with male controls during viewing of erotic stimuli, indicating a tendency of female-like cerebral processing in transsexualism.
Implicit sequence-specific motor learning after sub-cortical stroke is associated with increased prefrontal brain activations: An fMRI study

PubMed Central

Meehan, Sean K.; Randhawa, Bubblepreet; Wessel, Brenda; Boyd, Lara A.

2010-01-01

Implicit motor learning is preserved after stroke, but how the brain compensates for damage to facilitate learning is unclear. We used a random effects analysis to determine how stroke alters patterns of brain activity during implicit sequence-specific motor learning as compared to general improvements in motor control. Nine healthy participants and 9 individuals with chronic, right focal sub-cortical stroke performed a continuous joystick-based tracking task during an initial fMRI session, over 5 days of practice, and a retention test during a separate fMRI session. Sequence-specific implicit motor learning was differentiated from general improvements in motor control by comparing tracking performance on a novel, repeated tracking sequences during early practice and again at the retention test. Both groups demonstrated implicit sequence-specific motor learning at the retention test, yet substantial differences were apparent. At retention, healthy control participants demonstrated increased BOLD response in left dorsal premotor cortex (BA 6) but decreased BOLD response left dorsolateral prefrontal cortex (DLPFC; BA 9) during repeated sequence tracking. In contrast, at retention individuals with stroke did not show this reduction in DLPFC during repeated tracking. Instead implicit sequence-specific motor learning and general improvements in motor control were associated with increased BOLD response in the left middle frontal gyrus BA 8, regardless of sequence type after stroke. These data emphasize the potential importance of a prefrontal-based attentional network for implicit motor learning after stroke. The present study is the first to highlight the importance of the prefrontal cortex for implicit sequence-specific motor learning after stroke. PMID:20725908
Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.
Brain activity underlying auditory perceptual learning during short period training: simultaneous fMRI and EEG recording

PubMed Central

2013-01-01

Background There is an accumulating body of evidence indicating that neuronal functional specificity to basic sensory stimulation is mutable and subject to experience. Although fMRI experiments have investigated changes in brain activity after relative to before perceptual learning, brain activity during perceptual learning has not been explored. This work investigated brain activity related to auditory frequency discrimination learning using a variational Bayesian approach for source localization, during simultaneous EEG and fMRI recording. We investigated whether the practice effects are determined solely by activity in stimulus-driven mechanisms or whether high-level attentional mechanisms, which are linked to the perceptual task, control the learning process. Results The results of fMRI analyses revealed significant attention and learning related activity in left and right superior temporal gyrus STG as well as the left inferior frontal gyrus IFG. Current source localization of simultaneously recorded EEG data was estimated using a variational Bayesian method. Analysis of current localized to the left inferior frontal gyrus and the right superior temporal gyrus revealed gamma band activity correlated with behavioral performance. Conclusions Rapid improvement in task performance is accompanied by plastic changes in the sensory cortex as well as superior areas gated by selective attention. Together the fMRI and EEG results suggest that gamma band activity in the right STG and left IFG plays an important role during perceptual learning. PMID:23316957
A validation of event-related FMRI comparisons between users of cocaine, nicotine, or cannabis and control subjects.

PubMed

Murphy, Kevin; Dixon, Veronica; LaGrave, Kathleen; Kaufman, Jacqueline; Risinger, Robert; Bloom, Alan; Garavan, Hugh

2006-07-01

Noninvasive brain imaging techniques are a powerful tool for researching the effects of drug abuse on brain activation measures. However, because many drugs have direct vascular effects, the validity of techniques that depend on blood flow measures as a reflection of neuronal activity may be called into question. This may be of particular concern in event-related functional magnetic resonance imaging (fMRI), where current analytic techniques search for a specific shape in the hemodynamic response to neuronal activity. To investigate possible alterations in task-related activation as a result of drug abuse, fMRI scans were conducted on subjects in four groups as they performed a simple event-related finger-tapping task: users of cocaine, nicotine, or cannabis and control subjects. Activation measures, as determined by two different analytic methods, did not differ between the groups. A comparison between an intravenous saline and an intravenous cocaine condition in cocaine users found a similar null result. Further in-depth analyses of the shape of the hemodynamic responses in each group also showed no differences. This study demonstrates that drug groups may be compared with control subjects using event-related fMRI without the need for any post hoc procedures to correct for possible drug-induced cardiovascular alterations. Thus, fMRI activation differences reported between these drug groups can be more confidently interpreted as reflecting neuronal differences.
Large enhancement of perfusion contribution on fMRI signal

PubMed Central

Wang, Xiao; Zhu, Xiao-Hong; Zhang, Yi; Chen, Wei

2012-01-01

The perfusion contribution to the total functional magnetic resonance imaging (fMRI) signal was investigated using a rat model with mild hypercapnia at 9.4 T, and human subjects with visual stimulation at 4 T. It was found that the total fMRI signal change could be approximated as a linear superposition of ‘true' blood oxygenation level-dependent (BOLD; T2/T2*) effect and the blood flow-related (T1) effect. The latter effect was significantly enhanced by using short repetition time and large radiofrequency pulse flip angle and became comparable to the ‘true' BOLD signal in response to a mild hypercapnia in the rat brain, resulting in an improved contrast-to-noise ratio (CNR). Bipolar diffusion gradients suppressed the intravascular signals but had no significant effect on the flow-related signal. Similar results of enhanced fMRI signal were observed in the human study. The overall results suggest that the observed flow-related signal enhancement is likely originated from perfusion, and this enhancement can improve CNR and the spatial specificity for mapping brain activity and physiology changes. The nature of mixed BOLD and perfusion-related contributions in the total fMRI signal also has implication on BOLD quantification, in particular, the BOLD calibration model commonly used to estimate the change of cerebral metabolic rate of oxygen. PMID:22395206
Use of Preoperative Functional MRI to Predict Verbal Memory Decline After Temporal Lobe Epilepsy Surgery

PubMed Central

Binder, Jeffrey R.; Sabsevitz, David S.; Swanson, Sara J.; Hammeke, Thomas A.; Raghavan, Manoj; Mueller, Wade M.

2010-01-01

Purpose Verbal memory decline is a frequent complication of left anterior temporal lobectomy (L-ATL). The goal of this study was to determine whether preoperative language mapping using functional magnetic resonance imaging (fMRI) is useful for predicting which patients are likely to experience verbal memory decline after L-ATL. Methods Sixty L-ATL patients underwent preoperative language mapping with fMRI, preoperative intracarotid amobarbital (Wada) testing for language and memory lateralization, and pre- and postoperative neuropsychological testing. Demographic, historical, neuropsychological, and imaging variables were examined for their ability to predict pre- to postoperative memory change. Results Verbal memory decline occurred in over 30% of patients. Good preoperative performance, late age at onset of epilepsy, left dominance on fMRI, and left dominance on the Wada test were each predictive of memory decline. Preoperative performance and age at onset together accounted for roughly 50% of the variance in memory outcome (p < .001), and fMRI explained an additional 10% of this variance (p ≤ .003). Neither Wada memory asymmetry nor Wada language asymmetry added additional predictive power beyond these noninvasive measures. Discussion Preoperative fMRI is useful for identifying patients at high risk for verbal memory decline prior to L-ATL surgery. Lateralization of language is correlated with lateralization of verbal memory, whereas Wada memory testing is either insufficiently reliable or insufficiently material-specific to accurately localize verbal memory processes. PMID:18435753
Correlates of figure-ground segregation in fMRI.

PubMed

Skiera, G; Petersen, D; Skalej, M; Fahle, M

2000-01-01

We investigated which correlates of figure-ground-segregation can be detected by means of functional magnetic resonance imaging (fMRI). Five subjects were scanned with a Siemens Vision 1.5 T system. Motion, colour, and luminance-defined checkerboards were presented with alternating control conditions containing one of the two features of the checkerboard. We find a segregation-specific activation in V1 for all subjects and all stimuli and conclude that neural mechanisms exist as early as in the primary visual cortex that are sensitive to figure-ground segregation.
Functional Magnetic Resonance Imaging (fMRI) Neurofeedback: Implementations and Applications

PubMed Central

DEWIPUTRI, Wan Ilma; AUER, Tibor

2013-01-01

Neurofeedback (NFB) allows subjects to learn how to volitionally influence the neuronal activation in the brain by employing real-time neural activity as feedback. NFB has already been performed with electroencephalography (EEG) since the 1970s. Functional MRI (fMRI), offering a higher spatial resolution, has further increased the spatial specificity. In this paper, we briefly outline the general principles behind NFB, the implementation of fMRI-NFB studies, the feasibility of fMRI-NFB, and the application of NFB as a supplementary therapy tool. PMID:24643368
Oxytocin's neurochemical effects in the medial prefrontal cortex underlie recovery of task-specific brain activity in autism: a randomized controlled trial.

PubMed

Aoki, Y; Watanabe, T; Abe, O; Kuwabara, H; Yahata, N; Takano, Y; Iwashiro, N; Natsubori, T; Takao, H; Kawakubo, Y; Kasai, K; Yamasue, H

2015-04-01

The neuropeptide oxytocin may be an effective therapeutic strategy for the currently untreatable social and communication deficits associated with autism. Our recent paper reported that oxytocin mitigated autistic behavioral deficits through the restoration of activity in the ventromedial prefrontal cortex (vmPFC), as demonstrated with functional magnetic resonance imaging (fMRI) during a socio-communication task. However, it is unknown whether oxytocin exhibited effects at the neuronal level, which was outside of the specific task examined. In the same randomized, double-blind, placebo-controlled, within-subject cross-over clinical trial in which a single dose of intranasal oxytocin (24 IU) was administered to 40 men with high-functioning autism spectrum disorder (UMIN000002241/000004393), we measured N-acetylaspartate (NAA) levels, a marker for neuronal energy demand, in the vmPFC using (1)H-magnetic resonance spectroscopy ((1)H-MRS). The differences in the NAA levels between the oxytocin and placebo sessions were associated with oxytocin-induced fMRI signal changes in the vmPFC. The oxytocin-induced increases in the fMRI signal could be predicted by the NAA differences between the oxytocin and placebo sessions (P=0.002), an effect that remained after controlling for variability in the time between the fMRI and (1)H-MRS scans (P=0.006) and the order of administration of oxytocin and placebo (P=0.001). Furthermore, path analysis showed that the NAA differences in the vmPFC triggered increases in the task-dependent fMRI signals in the vmPFC, which consequently led to improvements in the socio-communication difficulties associated with autism. The present study suggests that the beneficial effects of oxytocin are not limited to the autistic behavior elicited by our psychological task, but may generalize to other autistic behavioral problems associated with the vmPFC.
Oxytocin's neurochemical effects in the medial prefrontal cortex underlie recovery of task-specific brain activity in autism: a randomized controlled trial

PubMed Central

Aoki, Y; Watanabe, T; Abe, O; Kuwabara, H; Yahata, N; Takano, Y; Iwashiro, N; Natsubori, T; Takao, H; Kawakubo, Y; Kasai, K; Yamasue, H

2015-01-01

The neuropeptide oxytocin may be an effective therapeutic strategy for the currently untreatable social and communication deficits associated with autism. Our recent paper reported that oxytocin mitigated autistic behavioral deficits through the restoration of activity in the ventromedial prefrontal cortex (vmPFC), as demonstrated with functional magnetic resonance imaging (fMRI) during a socio-communication task. However, it is unknown whether oxytocin exhibited effects at the neuronal level, which was outside of the specific task examined. In the same randomized, double-blind, placebo-controlled, within-subject cross-over clinical trial in which a single dose of intranasal oxytocin (24 IU) was administered to 40 men with high-functioning autism spectrum disorder (UMIN000002241/000004393), we measured N-acetylaspartate (NAA) levels, a marker for neuronal energy demand, in the vmPFC using 1H-magnetic resonance spectroscopy (1H-MRS). The differences in the NAA levels between the oxytocin and placebo sessions were associated with oxytocin-induced fMRI signal changes in the vmPFC. The oxytocin-induced increases in the fMRI signal could be predicted by the NAA differences between the oxytocin and placebo sessions (P=0.002), an effect that remained after controlling for variability in the time between the fMRI and 1H-MRS scans (P=0.006) and the order of administration of oxytocin and placebo (P=0.001). Furthermore, path analysis showed that the NAA differences in the vmPFC triggered increases in the task-dependent fMRI signals in the vmPFC, which consequently led to improvements in the socio-communication difficulties associated with autism. The present study suggests that the beneficial effects of oxytocin are not limited to the autistic behavior elicited by our psychological task, but may generalize to other autistic behavioral problems associated with the vmPFC. PMID:25070538
Neuroimaging of decoding and language comprehension in young very low birth weight (VLBW) adolescents: Indications for compensatory mechanisms.

PubMed

van Ettinger-Veenstra, Helene; Widén, Carin; Engström, Maria; Karlsson, Thomas; Leijon, Ingemar; Nelson, Nina

2017-01-01

In preterm children with very low birth weight (VLBW ≤ 1500 g), reading problems are often observed. Reading comprehension is dependent on word decoding and language comprehension. We investigated neural activation-within brain regions important for reading-related to components of reading comprehension in young VLBW adolescents in direct comparison to normal birth weight (NBW) term-born peers, with the use of functional magnetic resonance imaging (fMRI). We hypothesized that the decoding mechanisms will be affected by VLBW, and expect to see increased neural activity for VLBW which may be modulated by task performance and cognitive ability. The study investigated 13 (11 included in fMRI) young adolescents (ages 12 to 14 years) born preterm with VLBW and in 13 NBW controls (ages 12-14 years) for performance on the Block Design and Vocabulary subtests of the Wechsler Intelligence Scale for Children; and for semantic, orthographic, and phonological processing during an fMRI paradigm. The VLBW group showed increased phonological activation in left inferior frontal gyrus, decreased orthographic activation in right supramarginal gyrus, and decreased semantic activation in left inferior frontal gyrus. Block Design was related to altered right-hemispheric activation, and VLBW showed lower WISC Block Design scores. Left angular gyrus showed activation increase specific for VLBW with high accuracy on the semantic test. Young VLBW adolescents showed no accuracy and reaction time performance differences on our fMRI language tasks, but they did exhibit altered neural activation during these tasks. This altered activation for VLBW was observed as increased activation during phonological decoding, and as mainly decreased activation during orthographic and semantic processing. Correlations of neural activation with accuracy on the semantic fMRI task and with decreased WISC Block Design performance were specific for the VLBW group. Together, results suggest compensatory mechanisms by recruiting additional brain regions upon altered neural development of decoding for VLBW.
Role of the parahippocampal cortex in memory for the configuration but not the identity of objects: converging evidence from patients with selective thermal lesions and fMRI

PubMed Central

Bohbot, Véronique D.; Allen, John J. B.; Dagher, Alain; Dumoulin, Serge O.; Evans, Alan C.; Petrides, Michael; Kalina, Miroslav; Stepankova, Katerina; Nadel, Lynn

2015-01-01

The parahippocampal cortex and hippocampus are brain structures known to be involved in memory. However, the unique contribution of the parahippocampal cortex remains unclear. The current study investigates memory for object identity and memory of the configuration of objects in patients with small thermo-coagulation lesions to the hippocampus or the parahippocampal cortex. Results showed that in contrast to control participants and patients with damage to the hippocampus leaving the parahippocampal cortex intact, patients with lesions that included the right parahippocampal cortex (RPH) were severely impaired on a task that required learning the spatial configuration of objects on a computer screen; these patients, however, were not impaired at learning the identity of objects. Conversely, we found that patients with lesions to the right hippocampus (RH) or left hippocampus (LH), sparing the parahippocampal cortex, performed just as well as the control participants. Furthermore, they were not impaired on the object identity task. In the functional Magnetic Resonance Imaging (fMRI) experiment, healthy young adults performed the same tasks. Consistent with the findings of the lesion study, the fMRI results showed significant activity in the RPH in the memory for the spatial configuration condition, but not memory for object identity. Furthermore, the pattern of fMRI activity measured in the baseline control conditions decreased specifically in the parahippocampal cortex as a result of the experimental task, providing evidence for task specific repetition suppression. In summary, while our previous studies demonstrated that the hippocampus is critical to the construction of a cognitive map, both the lesion and fMRI studies have shown an involvement of the RPH for learning spatial configurations of objects but not object identity, and that this takes place independent of the hippocampus. PMID:26283949
Structural Brain Atlases: Design, Rationale, and Applications in Normal and Pathological Cohorts

PubMed Central

Mandal, Pravat K.; Mahajan, Rashima; Dinov, Ivo D.

2015-01-01

Structural magnetic resonance imaging (MRI) provides anatomical information about the brain in healthy as well as in diseased conditions. On the other hand, functional MRI (fMRI) provides information on the brain activity during performance of a specific task. Analysis of fMRI data requires the registration of the data to a reference brain template in order to identify the activated brain regions. Brain templates also find application in other neuroimaging modalities, such as diffusion tensor imaging and multi-voxel spectroscopy. Further, there are certain differences (e.g., brain shape and size) in the brains of populations of different origin and during diseased conditions like in Alzheimer’s disease (AD), population and disease-specific brain templates may be considered crucial for accurate registration and subsequent analysis of fMRI as well as other neuroimaging data. This manuscript provides a comprehensive review of the history, construction and application of brain atlases. A chronological outline of the development of brain template design, starting from the Talairach and Tournoux atlas to the Chinese brain template (to date), along with their respective detailed construction protocols provides the backdrop to this manuscript. The manuscript also provides the automated workflow-based protocol for designing a population-specific brain atlas from structural MRI data using LONI Pipeline graphical workflow environment. We conclude by discussing the scope of brain templates as a research tool and their application in various neuroimaging modalities. PMID:22647262
Topiramate and its effect on fMRI of language in patients with right or left temporal lobe epilepsy.

PubMed

Szaflarski, Jerzy P; Allendorfer, Jane B

2012-05-01

Topiramate (TPM) is well recognized for its negative effects on cognition, language performance and lateralization results on the intracarotid amobarbital procedure (IAP). But, the effects of TPM on functional MRI (fMRI) of language and the fMRI signals are less clear. Functional MRI is increasingly used for presurgical evaluation of epilepsy patients in place of IAP for language lateralization. Thus, the goal of this study was to assess the effects of TPM on fMRI signals. In this study, we included 8 patients with right temporal lobe epilepsy (RTLE) and 8 with left temporal lobe epilepsy (LTLE) taking TPM (+TPM). Matched to them for age, handedness and side of seizure onset were 8 patients with RTLE and 8 with LTLE not taking TPM (-TPM). Matched for age and handedness to the patients with TLE were 32 healthy controls. The fMRI paradigm involved semantic decision/tone decision task (in-scanner behavioral data were collected). All epilepsy patients received a standard neuropsychological language battery. One sample t-tests were performed within each group to assess task-specific activations. Functional MRI data random-effects analysis was performed to determine significant group activation differences and to assess the effect of TPM dose on task activation. Direct group comparisons of fMRI, language and demographic data between patients with R/L TLE +TPM vs. -TPM and the analysis of the effects of TPM on blood oxygenation level-dependent (BOLD) signal were performed. Groups were matched for age, handedness and, within the R/L TLE groups, for the age of epilepsy onset/duration and the number of AEDs/TPM dose. The in-scanner language performance of patients was worse when compared to healthy controls - all p<0.044. While all groups showed fMRI activation typical for this task, regression analyses comparing L/R TLE +TPM vs. -TPM showed significant fMRI signal differences between groups (increases in left cingulate gyrus and decreases in left superior temporal gyrus in the patients with LTLE +TPM; increases in the right BA 10 and left visual cortex and decreases in the left BA 47 in +TPM RTLE). Further, TPM dose showed positive relationship with activation in the basal ganglia and negative associations with activation in anterior cingulate and posterior visual cortex. Thus, TPM appears to have a different effect on fMRI language distribution in patients with R/L TLE and a dose-dependent effect on fMRI signals. These findings may, in part, explain the negative effects of TPM on cognition and language performance and support the notion that TPM may affect the results of language fMRI lateralization/localization. Copyright © 2012 Elsevier Inc. All rights reserved.
Cerebellar tDCS Modulates Neural Circuits during Semantic Prediction: A Combined tDCS-fMRI Study.

PubMed

D'Mello, Anila M; Turkeltaub, Peter E; Stoodley, Catherine J

2017-02-08

It has been proposed that the cerebellum acquires internal models of mental processes that enable prediction, allowing for the optimization of behavior. In language, semantic prediction speeds speech production and comprehension. Right cerebellar lobules VI and VII (including Crus I/II) are engaged during a variety of language processes and are functionally connected with cerebral cortical language networks. Further, right posterolateral cerebellar neuromodulation modifies behavior during predictive language processing. These data are consistent with a role for the cerebellum in semantic processing and semantic prediction. We combined transcranial direct current stimulation (tDCS) and fMRI to assess the behavioral and neural consequences of cerebellar tDCS during a sentence completion task. Task-based and resting-state fMRI data were acquired in healthy human adults ( n = 32; μ = 23.1 years) both before and after 20 min of 1.5 mA anodal ( n = 18) or sham ( n = 14) tDCS applied to the right posterolateral cerebellum. In the sentence completion task, the first four words of the sentence modulated the predictability of the final target word. In some sentences, the preceding context strongly predicted the target word, whereas other sentences were nonpredictive. Completion of predictive sentences increased activation in right Crus I/II of the cerebellum. Relative to sham tDCS, anodal tDCS increased activation in right Crus I/II during semantic prediction and enhanced resting-state functional connectivity between hubs of the reading/language networks. These results are consistent with a role for the right posterolateral cerebellum beyond motor aspects of language, and suggest that cerebellar internal models of linguistic stimuli support semantic prediction. SIGNIFICANCE STATEMENT Cerebellar involvement in language tasks and language networks is now well established, yet the specific cerebellar contribution to language processing remains unclear. It is thought that the cerebellum acquires internal models of mental processes that enable prediction, allowing for the optimization of behavior. Here we combined neuroimaging and neuromodulation to provide evidence that the cerebellum is specifically involved in semantic prediction during sentence processing. We found that activation within right Crus I/II was enhanced when semantic predictions were made, and we show that modulation of this region with transcranial direct current stimulation alters both activation patterns and functional connectivity within whole-brain language networks. For the first time, these data show that cerebellar neuromodulation impacts activation patterns specifically during predictive language processing. Copyright © 2017 the authors 0270-6474/17/371604-10$15.00/0.
Lag threads organize the brain’s intrinsic activity

PubMed Central

Mitra, Anish; Snyder, Abraham Z.; Blazey, Tyler; Raichle, Marcus E.

2015-01-01

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals. PMID:25825720
Tract-based Spatial Statistics and fMRI Analysis in Patients with Small Cell Lung Cancer before Prophylactic Cranial Irradiation

NASA Astrophysics Data System (ADS)

Benezi, S.; Bromis, K.; Karavasilis, E.; Karanasiou, I. S.; Koutsoupidou, M.; Matsopoulos, G.; Ventouras, E.; Uzunoglu, N.; Kouloulias, V.; Papathanasiou, M.; Foteineas, A.; Efstathopoulos, E.; Kelekis, N.; Kelekis, D.

2015-09-01

Prophylactic cranial irradiation (PCI) is known to increase life expectancy to a significant degree in Small Cell Lung Cancer (SCLC) patients. The overall scope of this research is to investigate changes in structural and functional connectivity between SCLC patients and controls before and after PCI treatment. In the current study specifically we use diffusion tensor imaging (DTI) and functional Magnetic Resonance (fMRI) to identify potential alterations in white matter structure and brain function respectively, in SCLC patients before PCI compared to healthy participants. The results in DTI analysis have showed lower fractional anisotropy (FA) and higher eigenvalues in white matter regions in the patient group. Similarly, in fMRI analysis a lower level of activation in the primary somatosensory cortex was reported. The results presented herein are subject to further investigation with larger patient and control groups.
NIRS-SPM: statistical parametric mapping for near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Tak, Sungho; Jang, Kwang Eun; Jung, Jinwook; Jang, Jaeduck; Jeong, Yong; Ye, Jong Chul

2008-02-01

Even though there exists a powerful statistical parametric mapping (SPM) tool for fMRI, similar public domain tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM statistically analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous recording NIRS signal with fMRI, the spatial mapping between fMRI image and real coordinate in 3-D digitizer is estimated using Horn's algorithm. These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools.
Identifying fMRI Model Violations with Lagrange Multiplier Tests

PubMed Central

Cassidy, Ben; Long, Christopher J; Rae, Caroline; Solo, Victor

2013-01-01

The standard modeling framework in Functional Magnetic Resonance Imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialised software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis. Using Lagrange Multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as non-linearity, non-stationarity and validity of the common Double Gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples. PMID:22542665

The inferior, anterior temporal lobes and semantic memory clarified: novel evidence from distortion-corrected fMRI.

PubMed

Visser, M; Embleton, K V; Jefferies, E; Parker, G J; Ralph, M A Lambon

2010-05-01

The neural basis of semantic memory generates considerable debate. Semantic dementia results from bilateral anterior temporal lobe (ATL) atrophy and gives rise to a highly specific impairment of semantic memory, suggesting that this region is a critical neural substrate for semantic processing. Recent rTMS experiments with neurologically-intact participants also indicate that the ATL are a necessary substrate for semantic memory. Exactly which regions within the ATL are important for semantic memory are difficult to detect from these methods (because the damage in SD covers a large part of the ATL). Functional neuroimaging might provide important clues about which specific areas exhibit activation that correlates with normal semantic performance. Neuroimaging studies, however, have not consistently found anterior temporal lobe activation in semantic tasks. A recent meta-analysis indicates that this inconsistency may be due to a collection of technical limitations associated with previous studies, including a reduced field-of-view and magnetic susceptibility artefacts associated with standard gradient echo fMRI. We conducted an fMRI study of semantic memory using a combination of techniques which improve sensitivity to ATL activations whilst preserving whole-brain coverage. As expected from SD patients and ATL rTMS experiments, this method revealed bilateral temporal activation extending from the inferior temporal lobe along the fusiform gyrus to the anterior temporal regions, bilaterally. We suggest that the inferior, anterior temporal lobe region makes a crucial contribution to semantic cognition and utilising this version of fMRI will enable further research on the semantic role of the ATL. 2010 Elsevier Ltd. All rights reserved.
Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies

PubMed Central

Bernal-Casas, David; Fang, Zhongnan; Lee, Jin Hyung

2017-01-01

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods’ performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data. PMID:27993672
There's more than one way to scan a cat: imaging cat auditory cortex with high-field fMRI using continuous or sparse sampling.

PubMed

Hall, Amee J; Brown, Trecia A; Grahn, Jessica A; Gati, Joseph S; Nixon, Pam L; Hughes, Sarah M; Menon, Ravi S; Lomber, Stephen G

2014-03-15

When conducting auditory investigations using functional magnetic resonance imaging (fMRI), there are inherent potential confounds that need to be considered. Traditional continuous fMRI acquisition methods produce sounds >90 dB which compete with stimuli or produce neural activation masking evoked activity. Sparse scanning methods insert a period of reduced MRI-related noise, between image acquisitions, in which a stimulus can be presented without competition. In this study, we compared sparse and continuous scanning methods to identify the optimal approach to investigate acoustically evoked cortical, thalamic and midbrain activity in the cat. Using a 7 T magnet, we presented broadband noise, 10 kHz tones, or 0.5 kHz tones in a block design, interleaved with blocks in which no stimulus was presented. Continuous scanning resulted in larger clusters of activation and more peak voxels within the auditory cortex. However, no significant activation was observed within the thalamus. Also, there was no significant difference found, between continuous or sparse scanning, in activations of midbrain structures. Higher magnitude activations were identified in auditory cortex compared to the midbrain using both continuous and sparse scanning. These results indicate that continuous scanning is the preferred method for investigations of auditory cortex in the cat using fMRI. Also, choice of method for future investigations of midbrain activity should be driven by other experimental factors, such as stimulus intensity and task performance during scanning. Copyright © 2014 Elsevier B.V. All rights reserved.
A Comparison of Five FMRI Protocols for Mapping Speech Comprehension Systems

PubMed Central

Binder, Jeffrey R.; Swanson, Sara J.; Hammeke, Thomas A.; Sabsevitz, David S.

2008-01-01

Aims Many fMRI protocols for localizing speech comprehension have been described, but there has been little quantitative comparison of these methods. We compared five such protocols in terms of areas activated, extent of activation, and lateralization. Methods FMRI BOLD signals were measured in 26 healthy adults during passive listening and active tasks using words and tones. Contrasts were designed to identify speech perception and semantic processing systems. Activation extent and lateralization were quantified by counting activated voxels in each hemisphere for each participant. Results Passive listening to words produced bilateral superior temporal activation. After controlling for pre-linguistic auditory processing, only a small area in the left superior temporal sulcus responded selectively to speech. Active tasks engaged an extensive, bilateral attention and executive processing network. Optimal results (consistent activation and strongly lateralized pattern) were obtained by contrasting an active semantic decision task with a tone decision task. There was striking similarity between the network of brain regions activated by the semantic task and the network of brain regions that showed task-induced deactivation, suggesting that semantic processing occurs during the resting state. Conclusions FMRI protocols for mapping speech comprehension systems differ dramatically in pattern, extent, and lateralization of activation. Brain regions involved in semantic processing were identified only when an active, non-linguistic task was used as a baseline, supporting the notion that semantic processing occurs whenever attentional resources are not controlled. Identification of these lexical-semantic regions is particularly important for predicting language outcome in patients undergoing temporal lobe surgery. PMID:18513352
Neural reactivity to visual food stimuli is reduced in some areas of the brain during evening hours compared to morning hours: an fMRI study in women.

PubMed

Masterson, Travis D; Kirwan, C Brock; Davidson, Lance E; LeCheminant, James D

2016-03-01

The extent that neural responsiveness to visual food stimuli is influenced by time of day is not well examined. Using a crossover design, 15 healthy women were scanned using fMRI while presented with low- and high-energy pictures of food, once in the morning (6:30-8:30 am) and once in the evening (5:00-7:00 pm). Diets were identical on both days of the fMRI scans and were verified using weighed food records. Visual analog scales were used to record subjective perception of hunger and preoccupation with food prior to each fMRI scan. Six areas of the brain showed lower activation in the evening to both high- and low-energy foods, including structures in reward pathways (P < 0.05). Nine brain regions showed significantly higher activation for high-energy foods compared to low-energy foods (P < 0.05). High-energy food stimuli tended to produce greater fMRI responses than low-energy food stimuli in specific areas of the brain, regardless of time of day. However, evening scans showed a lower response to both low- and high-energy food pictures in some areas of the brain. Subjectively, participants reported no difference in hunger by time of day (F = 1.84, P = 0.19), but reported they could eat more (F = 4.83, P = 0.04) and were more preoccupied with thoughts of food (F = 5.51, P = 0.03) in the evening compared to the morning. These data underscore the role that time of day may have on neural responses to food stimuli. These results may also have clinical implications for fMRI measurement in order to prevent a time of day bias.
A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125
Network modelling methods for FMRI.

PubMed

Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

2011-01-15

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
Fine tuning breath-hold-based cerebrovascular reactivity analysis models.

PubMed

van Niftrik, Christiaan Hendrik Bas; Piccirelli, Marco; Bozinov, Oliver; Pangalu, Athina; Valavanis, Antonios; Regli, Luca; Fierstra, Jorn

2016-02-01

We elaborate on existing analysis methods for breath-hold (BH)-derived cerebrovascular reactivity (CVR) measurements and describe novel insights and models toward more exact CVR interpretation. Five blood-oxygen-level-dependent (BOLD) fMRI datasets of neurovascular patients with unilateral hemispheric hemodynamic impairment were used to test various BH CVR analysis methods. Temporal lag (phase), percent BOLD signal change (CVR), and explained variance (coherence) maps were calculated using three different sine models and two novel "Optimal Signal" model-free methods based on the unaffected hemisphere and the sagittal sinus fMRI signal time series, respectively. All models showed significant differences in CVR and coherence between the affected-hemodynamic impaired-and unaffected hemisphere. Voxel-wise phase determination significantly increases CVR (0.60 ± 0.18 vs. 0.82 ± 0.27; P < 0.05). Incorporating different durations of breath hold and resting period in one sine model (two-task) did increase coherence in the unaffected hemisphere, as well as eliminating negative phase commonly obtained by one-task frequency models. The novel model-free "optimal signal" methods both explained the BOLD MR data similar to the two task sine model. Our CVR analysis demonstrates an improved CVR and coherence after implementation of voxel-wise phase and frequency adjustment. The novel "optimal signal" methods provide a robust and feasible alternative to the sine models, as both are model-free and independent of compliance. Here, the sagittal sinus model may be advantageous, as it is independent of hemispheric CVR impairment.
Exploiting Complexity Information for Brain Activation Detection

PubMed Central

Zhang, Yan; Liang, Jiali; Lin, Qiang; Hu, Zhenghui

2016-01-01

We present a complexity-based approach for the analysis of fMRI time series, in which sample entropy (SampEn) is introduced as a quantification of the voxel complexity. Under this hypothesis the voxel complexity could be modulated in pertinent cognitive tasks, and it changes through experimental paradigms. We calculate the complexity of sequential fMRI data for each voxel in two distinct experimental paradigms and use a nonparametric statistical strategy, the Wilcoxon signed rank test, to evaluate the difference in complexity between them. The results are compared with the well known general linear model based Statistical Parametric Mapping package (SPM12), where a decided difference has been observed. This is because SampEn method detects brain complexity changes in two experiments of different conditions and the data-driven method SampEn evaluates just the complexity of specific sequential fMRI data. Also, the larger and smaller SampEn values correspond to different meanings, and the neutral-blank design produces higher predictability than threat-neutral. Complexity information can be considered as a complementary method to the existing fMRI analysis strategies, and it may help improving the understanding of human brain functions from a different perspective. PMID:27045838
Modality Specific Cerebro-Cerebellar Activations in Verbal Working Memory: An fMRI Study

PubMed Central

Kirschen, Matthew P.; Chen, S. H. Annabel; Desmond, John E.

2010-01-01

Verbal working memory (VWM) engages frontal and temporal/parietal circuits subserving the phonological loop, as well as, superior and inferior cerebellar regions which have projections from these neocortical areas. Different cerebro-cerebellar circuits may be engaged for integrating aurally- and visually-presented information for VWM. The present fMRI study investigated load (2, 4, or 6 letters) and modality (auditory and visual) dependent cerebro-cerebellar VWM activation using a Sternberg task. FMRI revealed modality-independent activations in left frontal (BA 6/9/44), insular, cingulate (BA 32), and bilateral inferior parietal/supramarginal (BA 40) regions, as well as in bilateral superior (HVI) and right inferior (HVIII) cerebellar regions. Visual presentation evoked prominent activations in right superior (HVI/CrusI) cerebellum, bilateral occipital (BA19) and left parietal (BA7/40) cortex while auditory presentation showed robust activations predominately in bilateral temporal regions (BA21/22). In the cerebellum, we noted a visual to auditory emphasis of function progressing from superior to inferior and from lateral to medial regions. These results extend our previous findings of fMRI activation in cerebro-cerebellar networks during VWM, and demonstrate both modality dependent commonalities and differences in activations with increasing memory load. PMID:20714061
Modality specific cerebro-cerebellar activations in verbal working memory: an fMRI study.

PubMed

Kirschen, Matthew P; Chen, S H Annabel; Desmond, John E

2010-01-01

Verbal working memory (VWM) engages frontal and temporal/parietal circuits subserving the phonological loop, as well as, superior and inferior cerebellar regions which have projections from these neocortical areas. Different cerebro-cerebellar circuits may be engaged for integrating aurally- and visually-presented information for VWM. The present fMRI study investigated load (2, 4, or 6 letters) and modality (auditory and visual) dependent cerebro-cerebellar VWM activation using a Sternberg task. FMRI revealed modality-independent activations in left frontal (BA 6/9/44), insular, cingulate (BA 32), and bilateral inferior parietal/supramarginal (BA 40) regions, as well as in bilateral superior (HVI) and right inferior (HVIII) cerebellar regions. Visual presentation evoked prominent activations in right superior (HVI/CrusI) cerebellum, bilateral occipital (BA19) and left parietal (BA7/40) cortex while auditory presentation showed robust activations predominantly in bilateral temporal regions (BA21/22). In the cerebellum, we noted a visual to auditory emphasis of function progressing from superior to inferior and from lateral to medial regions. These results extend our previous findings of fMRI activation in cerebro-cerebellar networks during VWM, and demonstrate both modality dependent commonalities and differences in activations with increasing memory load.
IMAGING OF BRAIN FUNCTION BASED ON THE ANALYSIS OF FUNCTIONAL CONNECTIVITY - IMAGING ANALYSIS OF BRAIN FUNCTION BY FMRI AFTER ACUPUNCTURE AT LR3 IN HEALTHY INDIVIDUALS.

PubMed

Zheng, Yu; Wang, Yuying; Lan, Yujun; Qu, Xiaodong; Lin, Kelin; Zhang, Jiping; Qu, Shanshan; Wang, Yanjie; Tang, Chunzhi; Huang, Yong

2016-01-01

This Study observed the relevant brain areas activated by acupuncture at the Taichong acupoint (LR3) and analyzed the functional connectivity among brain areas using resting state functional magnetic resonance imaging (fMRI) to explore the acupoint specificity of the Taichong acupoint. A total of 45 healthy subjects were randomly divided into the Taichong (LR3) group, sham acupuncture group and sham acupoint group. Subjects received resting state fMRI before acupuncture, after true (sham) acupuncture in each group. Analysis of changes in connectivity among the brain areas was performed using the brain functional connectivity method. The right cerebrum temporal lobe was selected as the seed point to analyze the functional connectivity. It had a functional connectivity with right cerebrum superior frontal gyrus, limbic lobe cingulate gyrus and left cerebrum inferior temporal gyrus (BA 37), inferior parietal lobule compared by before vs. after acupuncture at LR3, and right cerebrum sub-lobar insula and left cerebrum middle frontal gyrus, medial frontal gyrus compared by true vs. sham acupuncture at LR3, and right cerebrum occipital lobe cuneus, occipital lobe sub-gyral, parietal lobe precuneus and left cerebellum anterior lobe culmen by acupuncture at LR3 vs. sham acupoint. Acupuncture at LR3 mainly specifically activated the brain functional network that participates in visual function, associative function, and emotion cognition, which are similar to the features on LR3 in tradition Chinese medicine. These brain areas constituted a neural network structure with specific functions that had specific reference values for the interpretation of the acupoint specificity of the Taichong acupoint.
Differential involvement of cortical and cerebellar areas using dominant and nondominant hands: An FMRI study

PubMed Central

Pardini, Matteo; Samson, Rebecca S.; D'Angelo, Egidio; Friston, Karl J.; Toosy, Ahmed T.; Gandini Wheeler‐Kingshott, Claudia A.M.

2015-01-01

Abstract Motor fMRI studies, comparing dominant (DH) and nondominant (NDH) hand activations have reported mixed findings, especially for the extent of ipsilateral (IL) activations and their relationship with task complexity. To date, no study has directly compared DH and NDH activations using an event‐related visually guided dynamic power‐grip paradigm with parametric (three) forces (GF) in healthy right‐handed subjects. We implemented a hierarchical statistical approach aimed to: (i) identify the main effect networks engaged when using either hand; (ii) characterise DH/NDH responses at different GFs; (iii) assess contralateral (CL)/IL‐specific and hemisphere‐specific activations. Beyond confirming previously reported results, this study demonstrated that increasing GF has an effect on motor response that is contextualised also by the use of DH or NDH. Linear analysis revealed increased activations in sensorimotor areas, with additional increased recruitments of subcortical and cerebellar areas when using the NDH. When looking at CL/IL‐specific activations, CL sensorimotor areas and IL cerebellum were activated with both hands. When performing the task with the NDH, several areas were also recruited including the CL cerebellum. Finally, there were hand‐side‐independent activations of nonmotor‐specific areas in the right and left hemispheres, with the right hemisphere being involved more extensively in sensori‐motor integration through associative areas while the left hemisphere showing greater activation at higher GF. This study shows that the functional networks subtending DH/NDH power‐grip visuomotor functions are qualitatively and quantitatively distinct and this should be taken into consideration when performing fMRI studies, particularly when planning interventions in patients with specific impairments. Hum Brain Mapp 36:5079–5100, 2015. © 2015 Wiley Periodicals, Inc. PMID:26415818
Search for Patterns of Functional Specificity in the Brain: A Nonparametric Hierarchical Bayesian Model for Group fMRI Data

PubMed Central

Sridharan, Ramesh; Vul, Edward; Hsieh, Po-Jang; Kanwisher, Nancy; Golland, Polina

2012-01-01

Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. The method does not require spatial alignment of functional images from different subjects. The algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to learn the patterns of functional specificity shared across the group, which we call functional systems, and estimate the number of these systems. Inference based on our model enables automatic discovery and characterization of dominant and consistent functional systems. We apply the method to data from a visual fMRI study comprised of 69 distinct stimulus images. The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. Among systems found by our method, we identify new areas that are deactivated by face stimuli. In empirical comparisons with perviously proposed exploratory methods, our results appear superior in capturing the structure in the space of visual categories of stimuli. PMID:21884803
Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task.

PubMed

Perronnet, Lorraine; Lécuyer, Anatole; Mano, Marsel; Bannier, Elise; Lotte, Fabien; Clerc, Maureen; Barillot, Christian

2017-01-01

Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D). Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.
Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

PubMed Central

Perronnet, Lorraine; Lécuyer, Anatole; Mano, Marsel; Bannier, Elise; Lotte, Fabien; Clerc, Maureen; Barillot, Christian

2017-01-01

Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D). Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback. PMID:28473762
Effectiveness of four different clinical fMRI paradigms for preoperative regional determination of language lateralization in patients with brain tumors.

PubMed

Zacà, Domenico; Nickerson, Joshua P; Deib, Gerard; Pillai, Jay J

2012-09-01

Blood oxygen level-dependent functional magnetic resonance imaging (fMRI) has demonstrated its capability to provide comparable results to gold standard intracarotid sodium amobarbital (Wada) testing for preoperative determination of language hemispheric dominance. However, thus far, no consensus has been established regarding which fMRI paradigms are the most effective for the determination of hemispheric language lateralization in specific categories of patients and specific regions of interest (ROIs). Forty-one brain tumor patients who performed four different language tasks-rhyming (R), silent word generation (SWG) sentence completion, and sentence listening comprehension (LC)-for presurgical language mapping by fMRI were included in this study. A statistical threshold-independent lateralization index (LI) was calculated and compared among the paradigms in four different ROIs for language activation: functional Broca's (BA) and Wernicke's areas (WA) as well as larger anatomically defined expressive (EA) and receptive (RA) areas. The two expressive paradigms evaluated in this study are very good lateralizing tasks in expressive language areas; specifically, a significantly higher mean LI value was noted for SWG (0.36 ± 0.25) compared to LC (0.16 ± 0.24, p = 0.009) and for R (0.40 ± 0.22) compared to LC (0.16 ± 0.24, p = 0.001) in BA. SWG LI (0.28 ± 0.19) was higher than LC LI (0.12 ± 0.16, p = 0.01) also in EA. No significant differences in LI were found among these paradigms in WA or RA. SWG and R are sufficient for the determination of lateralization in expressive language areas, whereas new semantic or receptive paradigms need to be designed for an improved assessment of lateralization in receptive language areas.
Identifying musical pieces from fMRI data using encoding and decoding models.

PubMed

Hoefle, Sebastian; Engel, Annerose; Basilio, Rodrigo; Alluri, Vinoo; Toiviainen, Petri; Cagy, Maurício; Moll, Jorge

2018-02-02

Encoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that first mapped musical features in auditory cortices and then decoded novel musical pieces. We then probed the influence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We further showed that Shannon entropy is a driving factor, boosting accuracy up to 95% for music with highest information content. These findings provide key insights for future decoding and reconstruction algorithms and open new venues for possible clinical applications.
The Human Connectome Project: A data acquisition perspective

PubMed Central

Van Essen, D.C.; Ugurbil, K.; Auerbach, E.; Barch, D.; Behrens, T.E.J.; Bucholz, R.; Chang, A.; Chen, L.; Corbetta, M.; Curtiss, S.W.; Della Penna, S.; Feinberg, D.; Glasser, M.F.; Harel, N.; Heath, A.C.; Larson-Prior, L.; Marcus, D.; Michalareas, G.; Moeller, S.; Oostenveld, R.; Petersen, S.E.; Prior, F.; Schlaggar, B.L.; Smith, S.M.; Snyder, A.Z.; Xu, J.; Yacoub, E.

2012-01-01

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences. PMID:22366334
The association between cortisol and the BOLD response in male adolescents undergoing fMRI.

PubMed

Keulers, Esther H H; Stiers, Peter; Nicolson, Nancy A; Jolles, Jelle

2015-02-19

MRI participation has been shown to induce subjective and neuroendocrine stress reactions. A recent aging study showed that cortisol levels during fMRI have an age-dependent effect on cognitive performance and brain functioning. The present study examined whether this age-specific influence of cortisol on behavioral and brain activation levels also applies to adolescence. Salivary cortisol as well as subjective experienced anxiety were assessed during the practice session, at home, and before, during and after the fMRI session in young versus old male adolescents. Cortisol levels were enhanced pre-imaging relative to during and post-imaging in both age groups, suggesting anticipatory stress and anxiety. Overall, a negative correlation was found between cortisol output during the fMRI experiment and brain activation magnitude during performance of a gambling task. In young but not in old adolescents, higher cortisol output was related to stronger deactivation of clusters in the anterior and posterior cingulate cortex. In old but not in young adolescents, a negative correlation was found between cortisol and activation in the inferior parietal and in the superior frontal cortex. In sum, cortisol increased the deactivation of several brain areas, although the location of the affected areas in the brain was age-dependent. The present findings suggest that cortisol output during fMRI should be considered as confounder and integrated in analyzing developmental changes in brain activation during adolescence. Copyright © 2014 Elsevier B.V. All rights reserved.

Combining task-evoked and spontaneous activity to improve pre-operative brain mapping with fMRI

PubMed Central

Fox, Michael D.; Qian, Tianyi; Madsen, Joseph R.; Wang, Danhong; Li, Meiling; Ge, Manling; Zuo, Huan-cong; Groppe, David M.; Mehta, Ashesh D.; Hong, Bo; Liu, Hesheng

2016-01-01

Noninvasive localization of brain function is used to understand and treat neurological disease, exemplified by pre-operative fMRI mapping prior to neurosurgical intervention. The principal approach for generating these maps relies on brain responses evoked by a task and, despite known limitations, has dominated clinical practice for over 20 years. Recently, pre-operative fMRI mapping based on correlations in spontaneous brain activity has been demonstrated, however this approach has its own limitations and has not seen widespread clinical use. Here we show that spontaneous and task-based mapping can be performed together using the same pre-operative fMRI data, provide complimentary information relevant for functional localization, and can be combined to improve identification of eloquent motor cortex. Accuracy, sensitivity, and specificity of our approach are quantified through comparison with electrical cortical stimulation mapping in eight patients with intractable epilepsy. Broad applicability and reproducibility of our approach is demonstrated through prospective replication in an independent dataset of six patients from a different center. In both cohorts and every individual patient, we see a significant improvement in signal to noise and mapping accuracy independent of threshold, quantified using receiver operating characteristic curves. Collectively, our results suggest that modifying the processing of fMRI data to incorporate both task-based and spontaneous activity significantly improves functional localization in pre-operative patients. Because this method requires no additional scan time or modification to conventional pre-operative data acquisition protocols it could have widespread utility. PMID:26408860
Rapid geodesic mapping of brain functional connectivity: implementation of a dedicated co-processor in a field-programmable gate array (FPGA) and application to resting state functional MRI.

PubMed

Minati, Ludovico; Cercignani, Mara; Chan, Dennis

2013-10-01

Graph theory-based analyses of brain network topology can be used to model the spatiotemporal correlations in neural activity detected through fMRI, and such approaches have wide-ranging potential, from detection of alterations in preclinical Alzheimer's disease through to command identification in brain-machine interfaces. However, due to prohibitive computational costs, graph-based analyses to date have principally focused on measuring connection density rather than mapping the topological architecture in full by exhaustive shortest-path determination. This paper outlines a solution to this problem through parallel implementation of Dijkstra's algorithm in programmable logic. The processor design is optimized for large, sparse graphs and provided in full as synthesizable VHDL code. An acceleration factor between 15 and 18 is obtained on a representative resting-state fMRI dataset, and maps of Euclidean path length reveal the anticipated heterogeneous cortical involvement in long-range integrative processing. These results enable high-resolution geodesic connectivity mapping for resting-state fMRI in patient populations and real-time geodesic mapping to support identification of imagined actions for fMRI-based brain-machine interfaces. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.
Complementary fMRI and EEG evidence for more efficient neural processing of rhythmic vs. unpredictably timed sounds

PubMed Central

van Atteveldt, Nienke; Musacchia, Gabriella; Zion-Golumbic, Elana; Sehatpour, Pejman; Javitt, Daniel C.; Schroeder, Charles

2015-01-01

The brain’s fascinating ability to adapt its internal neural dynamics to the temporal structure of the sensory environment is becoming increasingly clear. It is thought to be metabolically beneficial to align ongoing oscillatory activity to the relevant inputs in a predictable stream, so that they will enter at optimal processing phases of the spontaneously occurring rhythmic excitability fluctuations. However, some contexts have a more predictable temporal structure than others. Here, we tested the hypothesis that the processing of rhythmic sounds is more efficient than the processing of irregularly timed sounds. To do this, we simultaneously measured functional magnetic resonance imaging (fMRI) and electro-encephalograms (EEG) while participants detected oddball target sounds in alternating blocks of rhythmic (e.g., with equal inter-stimulus intervals) or random (e.g., with randomly varied inter-stimulus intervals) tone sequences. Behaviorally, participants detected target sounds faster and more accurately when embedded in rhythmic streams. The fMRI response in the auditory cortex was stronger during random compared to random tone sequence processing. Simultaneously recorded N1 responses showed larger peak amplitudes and longer latencies for tones in the random (vs. the rhythmic) streams. These results reveal complementary evidence for more efficient neural and perceptual processing during temporally predictable sensory contexts. PMID:26579044
Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.
Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics

PubMed Central

Tong, Yunxia; Chen, Qiang; Nichols, Thomas E.; Rasetti, Roberta; Callicott, Joseph H.; Berman, Karen F.; Weinberger, Daniel R.; Mattay, Venkata S.

2016-01-01

A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others. PMID:26974435
The Anterior Insula Tracks Behavioral Entropy during an Interpersonal Competitive Game

PubMed Central

Matsumoto, Madoka; Matsumoto, Kenji; Omori, Takashi

2015-01-01

In competitive situations, individuals need to adjust their behavioral strategy dynamically in response to their opponent’s behavior. In the present study, we investigated the neural basis of how individuals adjust their strategy during a simple, competitive game of matching pennies. We used entropy as a behavioral index of randomness in decision-making, because maximizing randomness is thought to be an optimal strategy in the game, according to game theory. While undergoing functional magnetic resonance imaging (fMRI), subjects played matching pennies with either a human or computer opponent in each block, although in reality they played the game with the same computer algorithm under both conditions. The winning rate of each block was also manipulated. Both the opponent (human or computer), and the winning rate, independently affected subjects’ block-wise entropy during the game. The fMRI results revealed that activity in the bilateral anterior insula was positively correlated with subjects’ (not opponent’s) behavioral entropy during the game, which indicates that during an interpersonal competitive game, the anterior insula tracked how uncertain subjects’ behavior was, rather than how uncertain subjects felt their opponent's behavior was. Our results suggest that intuitive or automatic processes based on somatic markers may be a key to optimally adjusting behavioral strategies in competitive situations. PMID:26039634
Spatio-temporal Granger causality: a new framework

PubMed Central

Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A.; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

2015-01-01

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924
Oxytocin, brain physiology, and functional connectivity: a review of intranasal oxytocin fMRI studies.

PubMed

Bethlehem, Richard A I; van Honk, Jack; Auyeung, Bonnie; Baron-Cohen, Simon

2013-07-01

In recent years the neuropeptide oxytocin (OT) has become one of the most studied peptides of the human neuroendocrine system. Research has shown widespread behavioural effects and numerous potential therapeutic benefits. However, little is known about how OT triggers these effects in the brain. Here, we discuss some of the physiological properties of OT in the human brain including the long half-life of neuropeptides, the diffuse projections of OT throughout the brain and interactions with other systems such as the dopaminergic system. These properties indicate that OT acts without clear spatial and temporal specificity. Therefore, it is likely to have widespread effects on the brain's intrinsic functioning. Additionally, we review studies that have used functional magnetic resonance imaging (fMRI) concurrently with OT administration. These studies reveal a specific set of 'social' brain regions that are likely to be the strongest targets for OT's potential to influence human behaviour. On the basis of the fMRI literature and the physiological properties of the neuropeptide, we argue that OT has the potential to not only modulate activity in a set of specific brain regions, but also the functional connectivity between these regions. In light of the increasing knowledge of the behavioural effects of OT in humans, studies of the effects of OT administration on brain function can contribute to our understanding of the neural networks in the social brain. Copyright © 2012 Elsevier Ltd. All rights reserved.
Abnormal processing of deontological guilt in obsessive-compulsive disorder.

PubMed

Basile, Barbara; Mancini, Francesco; Macaluso, Emiliano; Caltagirone, Carlo; Bozzali, Marco

2014-07-01

Guilt plays a significant role in the occurrence and maintenance of obsessive-compulsive disorder (OCD). Two major types of guilt have been identified: one deriving from the transgression of a moral rule (deontological guilt DG), another (altruistic guilt AG), relying on the assumption of having compromised a personal altruistic goal. Clinical evidence suggests that OCD patients are particularly sensitive to DG, but not AG. In this functional magnetic resonance imaging (fMRI) study, we investigated brain response of OCD patients while processing DG and AG stimuli. A previously validated fMRI paradigm was used to selectively evoke DG and AG, and anger and sadness, as control emotions in 13 OCD patients and 19 healthy controls. Patients' behavioral results showed a prominent attitude to experience guilt, compared to controls, while accomplishing task. fMRI results revealed that patients have reduced activation in the anterior cingulate (ACC) and frontal gyrus when experiencing guilt, regardless of its specific type (DG or AG). When separately considering each type of guilt (against each of its control), patients showed decreased activation in the ACC, the insula and the precuneus, for DG. No significant differences were observed between groups when processing AG, anger or sad stimuli. This study provides evidence for an abnormal processing of guilt, and specifically DG, in OCD patients. We suggest that decreased activation may reflect patients' cerebral efficiency, which derives from their frequent exposure to guilty feelings ("neural efficiency hypothesis"). In conclusion, our study confirms a selective abnormal processing of guilt, and specifically DG, in OCD.
Brain mechanisms of successful recognition through retrieval of semantic context.

PubMed

Flegal, Kristin E; Marín-Gutiérrez, Alejandro; Ragland, J Daniel; Ranganath, Charan

2014-08-01

Episodic memory is associated with the encoding and retrieval of context information and with a subjective sense of reexperiencing past events. The neural correlates of episodic retrieval have been extensively studied using fMRI, leading to the identification of a "general recollection network" including medial temporal, parietal, and prefrontal regions. However, in these studies, it is difficult to disentangle the effects of context retrieval from recollection. In this study, we used fMRI to determine the extent to which the recruitment of regions in the recollection network is contingent on context reinstatement. Participants were scanned during a cued recognition test for target words from encoded sentences. Studied target words were preceded by either a cue word studied in the same sentence (thus congruent with encoding context) or a cue word studied in a different sentence (thus incongruent with encoding context). Converging fMRI results from independently defined ROIs and whole-brain analysis showed regional specificity in the recollection network. Activity in hippocampus and parahippocampal cortex was specifically increased during successful retrieval following congruent context cues, whereas parietal and prefrontal components of the general recollection network were associated with confident retrieval irrespective of contextual congruency. Our findings implicate medial temporal regions in the retrieval of semantic context, contributing to, but dissociable from, recollective experience.
The specificity of neural responses to music and their relation to voice processing: an fMRI-adaptation study.

PubMed

Armony, Jorge L; Aubé, William; Angulo-Perkins, Arafat; Peretz, Isabelle; Concha, Luis

2015-04-23

Several studies have identified, using functional magnetic resonance imaging (fMRI), a region within the superior temporal gyrus that preferentially responds to musical stimuli. However, in most cases, significant responses to other complex stimuli, particularly human voice, were also observed. Thus, it remains unknown if the same neurons respond to both stimulus types, albeit with different strengths, or whether the responses observed with fMRI are generated by distinct, overlapping neural populations. To address this question, we conducted an fMRI experiment in which short music excerpts and human vocalizations were presented in a pseudo-random order. Critically, we performed an adaptation-based analysis in which responses to the stimuli were analyzed taking into account the category of the preceding stimulus. Our results confirm the presence of a region in the anterior STG that responds more strongly to music than voice. Moreover, we found a music-specific adaptation effect in this area, consistent with the existence of music-preferred neurons. Lack of differences between musicians and non-musicians argues against an expertise effect. These findings provide further support for neural separability between music and speech within the temporal lobe. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Specificity of Esthetic Experience for Artworks: An fMRI Study

PubMed Central

Di Dio, Cinzia; Canessa, Nicola; Cappa, Stefano F.; Rizzolatti, Giacomo

2011-01-01

In a previous functional magnetic resonance imaging (fMRI) study, where we investigated the neural correlates of esthetic experience, we found that observing canonical sculptures, relative to sculptures whose proportions had been modified, produced the activation of a network that included the lateral occipital gyrus, precuneus, prefrontal areas, and, most interestingly, the right anterior insula. We interpreted this latter activation as the neural signature underpinning hedonic response during esthetic experience. With the aim of exploring whether this specific hedonic response is also present during the observation of non-art biological stimuli, in the present fMRI study we compared the activations associated with viewing masterpieces of classical sculpture with those produced by the observation of pictures of young athletes. The two stimulus-categories were matched on various factors, including body postures, proportion, and expressed dynamism. The stimuli were presented in two conditions: observation and esthetic judgment. The two stimulus-categories produced a rather similar global activation pattern. Direct comparisons between sculpture and real-body images revealed, however, relevant differences, among which the activation of right antero-dorsal insula during sculptures viewing only. Along with our previous data, this finding suggests that the hedonic state associated with activation of right dorsal anterior insula underpins esthetic experience for artworks. PMID:22121344
Real-time functional magnetic resonance imaging neurofeedback in motor neurorehabilitation.

PubMed

Linden, David E J; Turner, Duncan L

2016-08-01

Recent developments in functional magnetic resonance imaging (fMRI) have catalyzed a new field of translational neuroscience. Using fMRI to monitor the aspects of task-related changes in neural activation or brain connectivity, investigators can offer feedback of simple or complex neural signals/patterns back to the participant on a quasireal-time basis [real-time-fMRI-based neurofeedback (rt-fMRI-NF)]. Here, we introduce some background methodology of the new developments in this field and give a perspective on how they may be used in neurorehabilitation in the future. The development of rt-fMRI-NF has been used to promote self-regulation of activity in several brain regions and networks. In addition, and unlike other noninvasive techniques, rt-fMRI-NF can access specific subcortical regions and in principle any region that can be monitored using fMRI including the cerebellum, brainstem and spinal cord. In Parkinson's disease and stroke, rt-fMRI-NF has been demonstrated to alter neural activity after the self-regulation training was completed and to modify specific behaviours. Future exploitation of rt-fMRI-NF could be used to induce neuroplasticity in brain networks that are involved in certain neurological conditions. However, currently, the use of rt-fMRI-NF in randomized, controlled clinical trials is in its infancy.
Functional imaging reveals rapid reorganization of cortical activity after parietal inactivation in monkeys

PubMed Central

Wilke, Melanie; Kagan, Igor; Andersen, Richard A.

2012-01-01

Impairments of spatial awareness and decision making occur frequently as a consequence of parietal lesions. Here we used event-related functional MRI (fMRI) in monkeys to investigate rapid reorganization of spatial networks during reversible pharmacological inactivation of the lateral intraparietal area (LIP), which plays a role in the selection of eye movement targets. We measured fMRI activity in control and inactivation sessions while monkeys performed memory saccades to either instructed or autonomously chosen spatial locations. Inactivation caused a reduction of contralesional choices. Inactivation effects on fMRI activity were anatomically and functionally specific and mainly consisted of: (i) activity reduction in the upper bank of the superior temporal sulcus (temporal parietal occipital area) for single contralesional targets, especially in the inactivated hemisphere; and (ii) activity increase accompanying contralesional choices between bilateral targets in several frontal and parieto-temporal areas in both hemispheres. There was no overactivation for ipsilesional targets or choices in the intact hemisphere. Task-specific effects of LIP inactivation on blood oxygen level-dependent activity in the temporal parietal occipital area underline the importance of the superior temporal sulcus for spatial processing. Furthermore, our results agree only partially with the influential interhemispheric competition model of spatial neglect and suggest an additional component of interhemispheric cooperation in the compensation of neglect deficits. PMID:22562793
Neural correlates of the emotional Stroop task in panic disorder patients: an event-related fMRI study.

PubMed

Dresler, Thomas; Hindi Attar, Catherine; Spitzer, Carsten; Löwe, Bernd; Deckert, Jürgen; Büchel, Christian; Ehlis, Ann-Christine; Fallgatter, Andreas J

2012-12-01

Although being a standard tool to assess interference effects of disorder-specific words in clinical samples, the neural underpinnings of the emotional Stroop task are still not well understood and have hardly been investigated in experimental case-control studies. We therefore used functional magnetic resonance imaging (fMRI) to examine the attentional bias toward panic-related words in panic disorder (PD) patients and healthy controls. Twenty PD patients (with or without agoraphobia) and 23 healthy controls matched for age and gender performed an event-related emotional Stroop task with panic-related and neutral words while undergoing 3 Tesla fMRI. On the behavioral level, PD patients showed a significant emotional Stroop effect, i.e. color-naming of panic-related words was prolonged compared to neutral words. This effect was not observed in the control group. PD patients further differed from controls on the neural level in showing increased BOLD activity in the left inferior frontal gyrus in response to panic-related relative to neutral words. PD patients showed the expected attentional bias, i.e. an altered processing of disorder-specific stimuli. This emotional Stroop effect was paralleled by increased activation in the left prefrontal cortex which may indicate altered processing of emotional stimulus material. Copyright © 2012 Elsevier Ltd. All rights reserved.
Processing concrete words: fMRI evidence against a specific right-hemisphere involvement.

PubMed

Fiebach, Christian J; Friederici, Angela D

2004-01-01

Behavioral, patient, and electrophysiological studies have been taken as support for the assumption that processing of abstract words is confined to the left hemisphere, whereas concrete words are processed also by right-hemispheric brain areas. These are thought to provide additional information from an imaginal representational system, as postulated in the dual-coding theory of memory and cognition. Here we report new event-related fMRI data on the processing of concrete and abstract words in a lexical decision task. While abstract words activated a subregion of the left inferior frontal gyrus (BA 45) more strongly than concrete words, specific activity for concrete words was observed in the left basal temporal cortex. These data as well as data from other neuroimaging studies reviewed here are not compatible with the assumption of a specific right-hemispheric involvement for concrete words. The combined findings rather suggest a revised view of the neuroanatomical bases of the imaginal representational system assumed in the dual-coding theory, at least with respect to word recognition.
Item-Specific and Generalization Effects on Brain Activation when Learning Chinese Characters

ERIC Educational Resources Information Center

Deng, Yuan; Booth, James R.; Chou, Tai-Li; Ding, Guo-Sheng; Peng, Dan-Ling

2008-01-01

Neural changes related to learning of the meaning of Chinese characters in English speakers were examined using functional magnetic resonance imaging (fMRI). We examined item specific learning effects for trained characters, but also the generalization of semantic knowledge to novel transfer characters that shared a semantic radical (part of a…
Rapid and minimum invasive functional brain mapping by real-time visualization of high gamma activity during awake craniotomy.

PubMed

Ogawa, Hiroshi; Kamada, Kyousuke; Kapeller, Christoph; Hiroshima, Satoru; Prueckl, Robert; Guger, Christoph

2014-11-01

Electrocortical stimulation (ECS) is the gold standard for functional brain mapping during an awake craniotomy. The critical issue is to set aside enough time to identify eloquent cortices by ECS. High gamma activity (HGA) ranging between 80 and 120 Hz on electrocorticogram is assumed to reflect localized cortical processing. In this report, we used real-time HGA mapping and functional neuronavigation integrated with functional magnetic resonance imaging (fMRI) for rapid and reliable identification of motor and language functions. Four patients with intra-axial tumors in their dominant hemisphere underwent preoperative fMRI and lesion resection with an awake craniotomy. All patients showed significant fMRI activation evoked by motor and language tasks. During the craniotomy, we recorded electrocorticogram activity by placing subdural grids directly on the exposed brain surface. Each patient performed motor and language tasks and demonstrated real-time HGA dynamics in hand motor areas and parts of the inferior frontal gyrus. Sensitivity and specificity of HGA mapping were 100% compared with ECS mapping in the frontal lobe, which suggested HGA mapping precisely indicated eloquent cortices. We found different HGA dynamics of language tasks in frontal and temporal regions. Specificities of the motor and language-fMRI did not reach 85%. The results of HGA mapping was mostly consistent with those of ECS mapping, although fMRI tended to overestimate functional areas. This novel technique enables rapid and accurate identification of motor and frontal language areas. Furthermore, real-time HGA mapping sheds light on underlying physiological mechanisms related to human brain functions. Copyright © 2014 Elsevier Inc. All rights reserved.
Parametric fMRI of paced motor responses uncovers novel whole-brain imaging biomarkers in spinocerebellar ataxia type 3.

PubMed

Duarte, João Valente; Faustino, Ricardo; Lobo, Mercês; Cunha, Gil; Nunes, César; Ferreira, Carlos; Januário, Cristina; Castelo-Branco, Miguel

2016-10-01

Machado-Joseph Disease, inherited type 3 spinocerebellar ataxia (SCA3), is the most common form worldwide. Neuroimaging and neuropathology have consistently demonstrated cerebellar alterations. Here we aimed to discover whole-brain functional biomarkers, based on parametric performance-level-dependent signals. We assessed 13 patients with early SCA3 and 14 healthy participants. We used a combined parametric behavioral/functional neuroimaging design to investigate disease fingerprints, as a function of performance levels, coupled with structural MRI and voxel-based morphometry. Functional magnetic resonance imaging (fMRI) was designed to parametrically analyze behavior and neural responses to audio-paced bilateral thumb movements at temporal frequencies of 1, 3, and 5 Hz. Our performance-level-based design probing neuronal correlates of motor coordination enabled the discovery that neural activation and behavior show critical loss of parametric modulation specifically in SCA3, associated with frequency-dependent cortico/subcortical activation/deactivation patterns. Cerebellar/cortical rate-dependent dissociation patterns could clearly differentiate between groups irrespective of grey matter loss. Our findings suggest functional reorganization of the motor network and indicate a possible role of fMRI as a tool to monitor disease progression in SCA3. Accordingly, fMRI patterns proved to be potential biomarkers in early SCA3, as tested by receiver operating characteristic analysis of both behavior and neural activation at different frequencies. Discrimination analysis based on BOLD signal in response to the applied parametric finger-tapping task significantly often reached >80% sensitivity and specificity in single regions-of-interest.Functional fingerprints based on cerebellar and cortical BOLD performance dependent signal modulation can thus be combined as diagnostic and/or therapeutic targets in hereditary ataxia. Hum Brain Mapp 37:3656-3668, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds.

PubMed

De Angelis, Vittoria; De Martino, Federico; Moerel, Michelle; Santoro, Roberta; Hausfeld, Lars; Formisano, Elia

2017-11-13

Pitch is a perceptual attribute related to the fundamental frequency (or periodicity) of a sound. So far, the cortical processing of pitch has been investigated mostly using synthetic sounds. However, the complex harmonic structure of natural sounds may require different mechanisms for the extraction and analysis of pitch. This study investigated the neural representation of pitch in human auditory cortex using model-based encoding and decoding analyses of high field (7 T) functional magnetic resonance imaging (fMRI) data collected while participants listened to a wide range of real-life sounds. Specifically, we modeled the fMRI responses as a function of the sounds' perceived pitch height and salience (related to the fundamental frequency and the harmonic structure respectively), which we estimated with a computational algorithm of pitch extraction (de Cheveigné and Kawahara, 2002). First, using single-voxel fMRI encoding, we identified a pitch-coding region in the antero-lateral Heschl's gyrus (HG) and adjacent superior temporal gyrus (STG). In these regions, the pitch representation model combining height and salience predicted the fMRI responses comparatively better than other models of acoustic processing and, in the right hemisphere, better than pitch representations based on height/salience alone. Second, we assessed with model-based decoding that multi-voxel response patterns of the identified regions are more informative of perceived pitch than the remainder of the auditory cortex. Further multivariate analyses showed that complementing a multi-resolution spectro-temporal sound representation with pitch produces a small but significant improvement to the decoding of complex sounds from fMRI response patterns. In sum, this work extends model-based fMRI encoding and decoding methods - previously employed to examine the representation and processing of acoustic sound features in the human auditory system - to the representation and processing of a relevant perceptual attribute such as pitch. Taken together, the results of our model-based encoding and decoding analyses indicated that the pitch of complex real life sounds is extracted and processed in lateral HG/STG regions, at locations consistent with those indicated in several previous fMRI studies using synthetic sounds. Within these regions, pitch-related sound representations reflect the modulatory combination of height and the salience of the pitch percept. Copyright © 2017 Elsevier Inc. All rights reserved.

The temporal derivative of expected utility: a neural mechanism for dynamic decision-making.

PubMed

Zhang, Xian; Hirsch, Joy

2013-01-15

Real world tasks involving moving targets, such as driving a vehicle, are performed based on continuous decisions thought to depend upon the temporal derivative of the expected utility (∂V/∂t), where the expected utility (V) is the effective value of a future reward. However, the neural mechanisms that underlie dynamic decision-making are not well understood. This study investigates human neural correlates of both V and ∂V/∂t using fMRI and a novel experimental paradigm based on a pursuit-evasion game optimized to isolate components of dynamic decision processes. Our behavioral data show that players of the pursuit-evasion game adopt an exponential discounting function, supporting the expected utility theory. The continuous functions of V and ∂V/∂t were derived from the behavioral data and applied as regressors in fMRI analysis, enabling temporal resolution that exceeded the sampling rate of image acquisition, hyper-temporal resolution, by taking advantage of numerous trials that provide rich and independent manipulation of those variables. V and ∂V/∂t were each associated with distinct neural activity. Specifically, ∂V/∂t was associated with anterior and posterior cingulate cortices, superior parietal lobule, and ventral pallidum, whereas V was primarily associated with supplementary motor, pre and post central gyri, cerebellum, and thalamus. The association between the ∂V/∂t and brain regions previously related to decision-making is consistent with the primary role of the temporal derivative of expected utility in dynamic decision-making. Copyright © 2012 Elsevier Inc. All rights reserved.
The Temporal Derivative of Expected Utility: A Neural Mechanism for Dynamic Decision-making

PubMed Central

Zhang, Xian; Hirsch, Joy

2012-01-01

Real world tasks involving moving targets, such as driving a vehicle, are performed based on continuous decisions thought to depend upon the temporal derivative of the expected utility (∂V/∂t), where the expected utility (V) is the effective value of a future reward. However, those neural mechanisms that underlie dynamic decision-making are not well understood. This study investigates human neural correlates of both V and ∂V/∂t using fMRI and a novel experimental paradigm based on a pursuit-evasion game optimized to isolate components of dynamic decision processes. Our behavioral data show that players of the pursuit-evasion game adopt an exponential discounting function, supporting the expected utility theory. The continuous functions of V and ∂V/∂t were derived from the behavioral data and applied as regressors in fMRI analysis, enabling temporal resolution that exceeded the sampling rate of image acquisition, hyper-temporal resolution, by taking advantage of numerous trials that provide rich and independent manipulation of those variables. V and ∂V/∂t were each associated with distinct neural activity. Specifically, ∂V/∂t was associated with anterior and posterior cingulate cortices, superior parietal lobule, and ventral pallidum, whereas V was primarily associated with supplementary motor, pre and post central gyri, cerebellum, and thalamus. The association between the ∂V/∂t and brain regions previously related to decision-making is consistent with the primary role of the temporal derivative of expected utility in dynamic decision-making. PMID:22963852
Innovative approaches to the rehabilitation of upper extremity hemiparesis using virtual environments

PubMed Central

MERIANS, A. S.; TUNIK, E.; FLUET, G. G.; QIU, Q.; ADAMOVICH, S. V.

2017-01-01

Aim Upper-extremity interventions for hemiparesis are a challenging aspect of stroke rehabilitation. Purpose of this paper is to report the feasibility of using virtual environments (VEs) in combination with robotics to assist recovery of hand-arm function and to present preliminary data demonstrating the potential of using sensory manipulations in VE to drive activation in targeted neural regions. Methods We trained 8 subjects for 8 three hour sessions using a library of complex VE’s integrated with robots, comparing training arm and hand separately to training arm and hand together. Instrumented gloves and hand exoskeleton were used for hand tracking and haptic effects. Haptic Master robotic arm was used for arm tracking and generating three-dimensional haptic VEs. To investigate the use of manipulations in VE to drive neural activations, we created a “virtual mirror” that subjects used while performing a unimanual task. Cortical activation was measured with functional MRI (fMRI) and transcranial magnetic stimulation. Results Both groups showed improvement in kinematics and measures of real-world function. The group trained using their arm and hand together showed greater improvement. In a stroke subject, fMRI data suggested virtual mirror feedback could activate the sensorimotor cortex contralateral to the reflected hand (ipsilateral to the moving hand) thus recruiting the lesioned hemisphere. Conclusion Gaming simulations interfaced with robotic devices provide a training medium that can modify movement patterns. In addition to showing that our VE therapies can optimize behavioral performance, we show preliminary evidence to support the potential of using specific sensory manipulations to selectively recruit targeted neural circuits. PMID:19158659
A universal role of the ventral striatum in reward-based learning: Evidence from human studies

PubMed Central

Daniel, Reka; Pollmann, Stefan

2014-01-01

Reinforcement learning enables organisms to adjust their behavior in order to maximize rewards. Electrophysiological recordings of dopaminergic midbrain neurons have shown that they code the difference between actual and predicted rewards, i.e., the reward prediction error, in many species. This error signal is conveyed to both the striatum and cortical areas and is thought to play a central role in learning to optimize behavior. However, in human daily life rewards are diverse and often only indirect feedback is available. Here we explore the range of rewards that are processed by the dopaminergic system in human participants, and examine whether it is also involved in learning in the absence of explicit rewards. While results from electrophysiological recordings in humans are sparse, evidence linking dopaminergic activity to the metabolic signal recorded from the midbrain and striatum with functional magnetic resonance imaging (fMRI) is available. Results from fMRI studies suggest that the human ventral striatum (VS) receives valuation information for a diverse set of rewarding stimuli. These range from simple primary reinforcers such as juice rewards over abstract social rewards to internally generated signals on perceived correctness, suggesting that the VS is involved in learning from trial-and-error irrespective of the specific nature of provided rewards. In addition, we summarize evidence that the VS can also be implicated when learning from observing others, and in tasks that go beyond simple stimulus-action-outcome learning, indicating that the reward system is also recruited in more complex learning tasks. PMID:24825620
Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies.

PubMed

Liu, Jia; Duffy, Ben A; Bernal-Casas, David; Fang, Zhongnan; Lee, Jin Hyung

2017-02-15

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods' performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.
Is hunger important to model in fMRI visual food-cue reactivity paradigms in adults with obesity and how should this be done?

PubMed

Chin, Shao-Hua; Kahathuduwa, Chanaka N; Stearns, Macy B; Davis, Tyler; Binks, Martin

2018-01-01

We considered 1) influence of self-reported hunger in behavioral and fMRI food-cue reactivity (fMRI-FCR) 2) optimal methods to model this. Adults (N = 32; 19-60 years; F = 21; BMI 30-39.9 kg/m 2 ) participated in an fMRI-FCR task that required rating 240 images of food and matched objects for 'appeal'. Hunger, satiety, thirst, fullness and emptiness were measured pre- and post-scan (visual analogue scales). Hunger, satiety, fullness and emptiness were combined to form a latent factor (appetite). Post-vs. pre-scores were compared using paired t-tests. In mixed-effects models, appeal/fMRI-FCR responses were regressed on image (i.e. food/objects), with random intercepts and slopes of image for functional runs nested within subjects. Each of hunger, satiety, thirst, fullness, emptiness and appetite were added as covariates in 4 forms (separate models): 1) change; 2) post- and pre-mean; 3) pre-; 4) change and pre-. Satiety decreased (Δ = -13.39, p = 0.001) and thirst increased (Δ = 11.78, p = 0.006) during the scan. Changes in other constructs were not significant (p's > 0.05). Including covariates did not influence food vs. object contrast of appeal ratings/fMRI-FCR. Significant image X covariate interactions were observed in some fMRI models. However, including these constructs did not improve the overall model fit. While some subjective, self-reported hunger, satiety and related constructs may be moderating fMRI-FCR, these constructs do not appear to be salient influences on appeal/fMRI-FCR in people with obesity undergoing fMRI. Copyright © 2017 Elsevier Ltd. All rights reserved.
A Comparative Study of Average, Linked Mastoid, and REST References for ERP Components Acquired during fMRI

PubMed Central

Yang, Ping; Fan, Chenggui; Wang, Min; Li, Ling

2017-01-01

In simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies, average reference (AR), and digitally linked mastoid (LM) are popular re-referencing techniques in event-related potential (ERP) analyses. However, they may introduce their own physiological signals and alter the EEG/ERP outcome. A reference electrode standardization technique (REST) that calculated a reference point at infinity was proposed to solve this problem. To confirm the advantage of REST in ERP analyses of synchronous EEG-fMRI studies, we compared the reference effect of AR, LM, and REST on task-related ERP results of a working memory task during an fMRI scan. As we hypothesized, we found that the adopted reference did not change the topography map of ERP components (N1 and P300 in the present study), but it did alter the task-related effect on ERP components. LM decreased or eliminated the visual working memory (VWM) load effect on P300, and the AR distorted the distribution of VWM location-related effect at left posterior electrodes as shown in the statistical parametric scalp mapping (SPSM) of N1. ERP cortical source estimates, which are independent of the EEG reference choice, were used as the golden standard to infer the relative utility of different references on the ERP task-related effect. By comparison, REST reference provided a more integrated and reasonable result. These results were further confirmed by the results of fMRI activations and a corresponding EEG-only study. Thus, we recommend the REST, especially with a realistic head model, as the optimal reference method for ERP data analysis in simultaneous EEG-fMRI studies. PMID:28529472
Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging

PubMed Central

Tsvetanov, Kamen A.; Cam‐CAN; Henson, Richard N.

2017-01-01

Abstract Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc. PMID:28544076
A Comparative Study of Average, Linked Mastoid, and REST References for ERP Components Acquired during fMRI.

PubMed

Yang, Ping; Fan, Chenggui; Wang, Min; Li, Ling

2017-01-01

In simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies, average reference (AR), and digitally linked mastoid (LM) are popular re-referencing techniques in event-related potential (ERP) analyses. However, they may introduce their own physiological signals and alter the EEG/ERP outcome. A reference electrode standardization technique (REST) that calculated a reference point at infinity was proposed to solve this problem. To confirm the advantage of REST in ERP analyses of synchronous EEG-fMRI studies, we compared the reference effect of AR, LM, and REST on task-related ERP results of a working memory task during an fMRI scan. As we hypothesized, we found that the adopted reference did not change the topography map of ERP components (N1 and P300 in the present study), but it did alter the task-related effect on ERP components. LM decreased or eliminated the visual working memory (VWM) load effect on P300, and the AR distorted the distribution of VWM location-related effect at left posterior electrodes as shown in the statistical parametric scalp mapping (SPSM) of N1. ERP cortical source estimates, which are independent of the EEG reference choice, were used as the golden standard to infer the relative utility of different references on the ERP task-related effect. By comparison, REST reference provided a more integrated and reasonable result. These results were further confirmed by the results of fMRI activations and a corresponding EEG-only study. Thus, we recommend the REST, especially with a realistic head model, as the optimal reference method for ERP data analysis in simultaneous EEG-fMRI studies.
Alzheimer's and Dementia Testing for Earlier Diagnosis

MedlinePlus

... focused on early detection of Alzheimer's disease. Imaging technologies used in Alzheimer's research Structural imaging provides information ... chemical changes linked to specific diseases. Molecular imaging technologies include PET, fMRI and single photon emission computed ...
Neuropsychology and cognitive neuroscience in the fMRI era: A recapitulation of localizationist and connectionist views.

PubMed

Sutterer, Matthew J; Tranel, Daniel

2017-11-01

We highlight the past 25 years of cognitive neuroscience and neuropsychology, focusing on the impact to the field of the introduction in 1992 of functional MRI (fMRI). We reviewed the past 25 years of literature in cognitive neuroscience and neuropsychology, focusing on the relation and interplay of fMRI studies and studies utilizing the "lesion method" in human participants with focal brain damage. Our review highlights the state of localist/connectionist research debates in cognitive neuroscience and neuropsychology circa 1992, and details how the introduction of fMRI into the field at that time catalyzed a new wave of efforts to map complex human behavior to specific brain regions. This, in turn, eventually evolved into many studies that focused on networks and connections between brain areas, culminating in recent years with large-scale investigations such as the Human Connectome Project. We argue that throughout the past 25 years, neuropsychology-and more precisely, the "lesion method" in humans-has continued to play a critical role in arbitrating conclusions and theories derived from inferred patterns of local brain activity or wide-spread connectivity from functional imaging approaches. We conclude by highlighting the future for neuropsychology in the context of an increasingly complex methodological armamentarium. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
The posterior parietal paradox: Why do functional magnetic resonance imaging and lesion studies on episodic memory produce conflicting results?

PubMed

Schoo, L A; van Zandvoort, M J E; Biessels, G J; Kappelle, L J; Postma, A; de Haan, E H F

2011-03-01

Recent functional magnetic resonance imaging (fMRI) studies addressing healthy subjects point towards posterior parietal cortex (PPC) involvement in episodic memory tasks. This is noteworthy, since neuropsychological studies usually do not connect parietal lesions to episodic memory impairments. Therefore an inventory of the possible factors behind this apparent paradox is warranted. This review compared fMRI studies which demonstrated PPC activity in episodic memory tasks, with findings with studies of patients with PPC lesions. A systematic evaluation of possible explanations for the posterior parietal paradox indicates that PPC activation in fMRI studies does not appear to be attributable to confounding cognitive/psychomotor processes, such as button pressing or stimulus processing. What may be of more importance is the extent to which an episodic memory task loads on three closely related cognitive processes: effort and attention, self-related activity, and scene and image construction. We discuss to what extent these cognitive processes can account for the paradox between lesion and fMRI results. They are strongly intertwined with the episodic memory and may critically determine in how far the PPC plays a role in a given memory task. Future patient studies might profit from specifically taking these cognitive factors into consideration in the task design. ©2010 The British Psychological Society.
Is Rest Really Rest? Resting State Functional Connectivity during Rest and Motor Task Paradigms.

PubMed

Jurkiewicz, Michael T; Crawley, Adrian P; Mikulis, David J

2018-04-18

Numerous studies have identified the default mode network (DMN) within the brain of healthy individuals, which has been attributed to the ongoing mental activity of the brain during the wakeful resting-state. While engaged during specific resting-state fMRI paradigms, it remains unclear as to whether traditional block-design simple movement fMRI experiments significantly influence the default mode network or other areas. Using blood-oxygen level dependent (BOLD) fMRI we characterized the pattern of functional connectivity in healthy subjects during a resting-state paradigm and compared this to the same resting-state analysis performed on motor task data residual time courses after regressing out the task paradigm. Using seed-voxel analysis to define the DMN, the executive control network (ECN), and sensorimotor, auditory and visual networks, the resting-state analysis of the residual time courses demonstrated reduced functional connectivity in the motor network and reduced connectivity between the insula and the ECN compared to the standard resting-state datasets. Overall, performance of simple self-directed motor tasks does little to change the resting-state functional connectivity across the brain, especially in non-motor areas. This would suggest that previously acquired fMRI studies incorporating simple block-design motor tasks could be mined retrospectively for assessment of the resting-state connectivity.
Cognitive dissonance induction in everyday life: An fMRI study.

PubMed

de Vries, Jan; Byrne, Mark; Kehoe, Elizabeth

2015-01-01

This functional magnetic resonance imaging (fMRI) study explored the neural substrates of cognitive dissonance during dissonance "induction." A novel task was developed based on the results of a separate item selection study (n = 125). Items were designed to generate dissonance by prompting participants to reflect on everyday personal experiences that were inconsistent with values they had expressed support for. One experimental condition (dissonance) and three control conditions (justification, consonance, and non-self-related inconsistency) were used for comparison. Items of all four types were presented to each participant (n = 14) in a randomized design. The fMRI analysis used a whole-brain approach focusing on the moments dissonance was induced. Results showed that in comparison with the control conditions the dissonance experience led to higher levels of activation in several brain regions. Specifically dissonance was associated with increased neural activation in key brain regions including the anterior cingulate cortex (ACC), anterior insula, inferior frontal gyrus, and precuneus. This supports current perspectives that emphasize the role of anterior cingulate and insula in dissonance processing. Less extensive activation in the prefrontal cortex than in some previous studies is consistent with this study's emphasis on dissonance induction, rather than reduction. This article also contains a short review and comparison with other fMRI studies of cognitive dissonance.
A Comparison of Two FMRI Methods for Predicting Verbal Memory Decline After Left Temporal Lobectomy: Language Lateralization vs. Hippocampal Activation Asymmetry

PubMed Central

Binder, Jeffrey R.; Swanson, Sara J.; Sabsevitz, David S.; Hammeke, Thomas A.; Raghavan, Manoj; Mueller, Wade M.

2010-01-01

Purpose Language lateralization measured by preoperative fMRI was shown recently to be predictive of verbal memory outcome in patients undergoing left anterior temporal lobe (L-ATL) resection. The aim of this study was to determine whether language lateralization or hippocampal activation asymmetry is a better predictor of memory outcome in this setting. Methods Thirty L-ATL patients underwent preoperative language fMRI, preoperative hippocampal fMRI using a scene encoding task, and pre- and postoperative neuropsychological testing. A group of 37 right ATL surgery patients who underwent the same testing procedures was included for comparison. Results Verbal memory decline occurred in roughly half of the L-ATL patients. Preoperative language lateralization was correlated with postoperative verbal memory change. Hippocampal activation asymmetry was strongly related to side of seizure focus and to Wada memory asymmetry but was unrelated to verbal memory outcome. Discussion Preoperative hippocampal activation asymmetry elicited by a scene encoding task is not predictive of verbal memory outcome. Risk of verbal memory decline is likely to be related to lateralization of material-specific verbal memory networks, which are more closely correlated with language lateralization than with overall asymmetry of episodic memory processes. PMID:19817807
Brain networks for confidence weighting and hierarchical inference during probabilistic learning.

PubMed

Meyniel, Florent; Dehaene, Stanislas

2017-05-09

Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.
Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis

PubMed Central

Abdulrahman, Hunar; Henson, Richard N.

2016-01-01

Functional magnetic resonance imaging (fMRI) studies typically employ rapid, event-related designs for behavioral reasons and for reasons associated with statistical efficiency. Efficiency is calculated from the precision of the parameters (Betas) estimated from a General Linear Model (GLM) in which trial onsets are convolved with a Hemodynamic Response Function (HRF). However, previous calculations of efficiency have ignored likely variability in the neural response from trial to trial, for example due to attentional fluctuations, or different stimuli across trials. Here we compare three GLMs in their efficiency for estimating average and individual Betas across trials as a function of trial variability, scan noise and Stimulus Onset Asynchrony (SOA): “Least Squares All” (LSA), “Least Squares Separate” (LSS) and “Least Squares Unitary” (LSU). Estimation of responses to individual trials in particular is important for both functional connectivity using “Beta-series correlation” and “multi-voxel pattern analysis” (MVPA). Our simulations show that the ratio of trial-to-trial variability to scan noise impacts both the optimal SOA and optimal GLM, especially for short SOAs < 5 s: LSA is better when this ratio is high, whereas LSS and LSU are better when the ratio is low. For MVPA, the consistency across voxels of trial variability and of scan noise is also critical. These findings not only have important implications for design of experiments using Beta-series regression and MVPA, but also statistical parametric mapping studies that seek only efficient estimation of the mean response across trials. PMID:26549299
Brain networks for confidence weighting and hierarchical inference during probabilistic learning

PubMed Central

Meyniel, Florent; Dehaene, Stanislas

2017-01-01

Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This “confidence weighting” implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain’s learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences. PMID:28439014
Task-specific Aspects of Goal-directed Word Generation Identified via Simultaneous EEG-fMRI.

PubMed

Shapira-Lichter, Irit; Klovatch, Ilana; Nathan, Dana; Oren, Noga; Hendler, Talma

2016-09-01

Generating words according to a given rule relies on retrieval-related search and postretrieval control processes. Using fMRI, we recently characterized neural patterns of word generation in response to episodic, semantic, and phonemic cues by comparing free recall of wordlists, category fluency, and letter fluency [Shapira-Lichter, I., Oren, N., Jacob, Y., Gruberger, M., & Hendler, T. Portraying the unique contribution of the default mode network to internally driven mnemonic processes. Proceedings of the National Academy of Sciences, U.S.A., 110, 4950-4955, 2013]. Distinct selectivity for each condition was evident, representing discrete aspects of word generation-related memory retrieval. For example, the precuneus, implicated in processing spatiotemporal information, emerged as a key contributor to the episodic condition, which uniquely requires this information. Gamma band is known to play a central role in memory, and increased gamma power has been observed before word generation. Yet, gamma modulation in response to task demands has not been investigated. To capture the task-specific modulation of gamma power, we analyzed the EEG data recorded simultaneously with the aforementioned fMRI, focusing on the activity locked to and immediately preceding word articulation. Transient increases in gamma power were identified in a parietal electrode immediately before episodic and semantic word generation, however, within a different time frame relative to articulation. Gamma increases were followed by an alpha-theta decrease in the episodic condition, a gamma decrease in the semantic condition. This pattern indicates a task-specific modulation of the gamma signal corresponding to the specific demands of each word generation task. The gamma power and fMRI signal from the precuneus were correlated during the episodic condition, implying the existence of a common cognitive construct uniquely required for this task, possibly the reactivation or processing of spatiotemporal information.
The cortical basis of true memory and false memory for motion.

PubMed

Karanian, Jessica M; Slotnick, Scott D

2014-02-01

Behavioral evidence indicates that false memory, like true memory, can be rich in sensory detail. By contrast, there is fMRI evidence that true memory for visual information produces greater activity in earlier visual regions than false memory, which suggests true memory is associated with greater sensory detail. However, false memory in previous fMRI paradigms may have lacked sufficient sensory detail to recruit earlier visual processing regions. To investigate this possibility in the present fMRI study, we employed a paradigm that produced feature-specific false memory with a high degree of visual detail. During the encoding phase, moving or stationary abstract shapes were presented to the left or right of fixation. During the retrieval phase, shapes from encoding were presented at fixation and participants classified each item as previously "moving" or "stationary" within each visual field. Consistent with previous fMRI findings, true memory but not false memory for motion activated motion processing region MT+, while both true memory and false memory activated later cortical processing regions. In addition, false memory but not true memory for motion activated language processing regions. The present findings indicate that true memory activates earlier visual regions to a greater degree than false memory, even under conditions of detailed retrieval. Thus, the dissociation between previous behavioral findings and fMRI findings do not appear to be task dependent. Future work will be needed to assess whether the same pattern of true memory and false memory activity is observed for different sensory modalities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Motor imagery training: Kinesthetic imagery strategy and inferior parietal fMRI activation.

PubMed

Lebon, Florent; Horn, Ulrike; Domin, Martin; Lotze, Martin

2018-04-01

Motor imagery (MI) is the mental simulation of action frequently used by professionals in different fields. However, with respect to performance, well-controlled functional imaging studies on MI training are sparse. We investigated changes in fMRI representation going along with performance changes of a finger sequence (error and velocity) after MI training in 48 healthy young volunteers. Before training, we tested the vividness of kinesthetic and visual imagery. During tests, participants were instructed to move or to imagine moving the fingers of the right hand in a specific order. During MI training, participants repeatedly imagined the sequence for 15 min. Imaging analysis was performed using a full-factorial design to assess brain changes due to imagery training. We also used regression analyses to identify those who profited from training (performance outcome and gain) with initial imagery scores (vividness) and fMRI activation magnitude during MI at pre-test (MI pre ). After training, error rate decreased and velocity increased. We combined both parameters into a common performance index. FMRI activation in the left inferior parietal lobe (IPL) was associated with MI and increased over time. In addition, fMRI activation in the right IPL during MI pre was associated with high initial kinesthetic vividness. High kinesthetic imagery vividness predicted a high performance after training. In contrast, occipital activation, associated with visual imagery strategies, showed a negative predictive value for performance. Our data echo the importance of high kinesthetic vividness for MI training outcome and consider IPL as a key area during MI and through MI training. © 2018 Wiley Periodicals, Inc.
Increased hippocampal activation in ApoE-4 carriers and non-carriers with amnestic mild cognitive impairment.

PubMed

Tran, Tammy T; Speck, Caroline L; Pisupati, Aparna; Gallagher, Michela; Bakker, Arnold

2017-01-01

Increased fMRI activation in the hippocampus is recognized as a signature characteristic of the amnestic mild cognitive impairment (aMCI) stage of Alzheimer's disease (AD). Previous work has localized this increased activation to the dentate gyrus/CA3 subregion of the hippocampus and showed a correlation with memory impairments in those patients. Increased hippocampal activation has also been reported in carriers of the ApoE-4 allelic variation independently of mild cognitive impairment although these findings were not localized to a hippocampal subregion. To assess the ApoE-4 contribution to increased hippocampal fMRI activation, patients with aMCI genotyped for ApoE-4 status and healthy age-matched control participants completed a high-resolution fMRI scan while performing a memory task designed to tax hippocampal subregion specific functions. Consistent with previous reports, patients with aMCI showed increased hippocampal activation in the left dentate gyrus/CA3 region of the hippocampus as well as memory task errors attributable to this subregion. However, this increased fMRI activation in the hippocampus did not differ between ApoE-4 carriers and ApoE-4 non-carriers and the proportion of memory errors attributable to dentate gyrus/CA3 function did not differ between ApoE-4 carriers and ApoE-4 non-carriers. These results indicate that increased fMRI activation of the hippocampus observed in patients with aMCI is independent of ApoE-4 status and that ApoE-4 does not contribute to the dysfunctional hippocampal activation or the memory errors attributable to this subregion in these patients.
The Cognitive Neuroscience of Sign Language: Engaging Undergraduate Students' Critical Thinking Skills Using the Primary Literature.

PubMed

Stevens, Courtney

2015-01-01

This article presents a modular activity on the neurobiology of sign language that engages undergraduate students in reading and analyzing the primary functional magnetic resonance imaging (fMRI) literature. Drawing on a seed empirical article and subsequently published critique and rebuttal, students are introduced to a scientific debate concerning the functional significance of right-hemisphere recruitment observed in some fMRI studies of sign language processing. The activity requires minimal background knowledge and is not designed to provide students with a specific conclusion regarding the debate. Instead, the activity and set of articles allow students to consider key issues in experimental design and analysis of the primary literature, including critical thinking regarding the cognitive subtractions used in blocked-design fMRI studies, as well as possible confounds in comparing results across different experimental tasks. By presenting articles representing different perspectives, each cogently argued by leading scientists, the readings and activity also model the type of debate and dialogue critical to science, but often invisible to undergraduate science students. Student self-report data indicate that undergraduates find the readings interesting and that the activity enhances their ability to read and interpret primary fMRI articles, including evaluating research design and considering alternate explanations of study results. As a stand-alone activity completed primarily in one 60-minute class block, the activity can be easily incorporated into existing courses, providing students with an introduction both to the analysis of empirical fMRI articles and to the role of debate and critique in the field of neuroscience.
Layer-Specific fMRI Reflects Different Neuronal Computations at Different Depths in Human V1

PubMed Central

Olman, Cheryl A.; Harel, Noam; Feinberg, David A.; He, Sheng; Zhang, Peng; Ugurbil, Kamil; Yacoub, Essa

2012-01-01

Recent work has established that cerebral blood flow is regulated at a spatial scale that can be resolved by high field fMRI to show cortical columns in humans. While cortical columns represent a cluster of neurons with similar response properties (spanning from the pial surface to the white matter), important information regarding neuronal interactions and computational processes is also contained within a single column, distributed across the six cortical lamina. A basic understanding of underlying neuronal circuitry or computations may be revealed through investigations of the distribution of neural responses at different cortical depths. In this study, we used T2-weighted imaging with 0.7 mm (isotropic) resolution to measure fMRI responses at different depths in the gray matter while human subjects observed images with either recognizable or scrambled (physically impossible) objects. Intact and scrambled images were partially occluded, resulting in clusters of activity distributed across primary visual cortex. A subset of the identified clusters of voxels showed a preference for scrambled objects over intact; in these clusters, the fMRI response in middle layers was stronger during the presentation of scrambled objects than during the presentation of intact objects. A second experiment, using stimuli targeted at either the magnocellular or the parvocellular visual pathway, shows that laminar profiles in response to parvocellular-targeted stimuli peak in more superficial layers. These findings provide new evidence for the differential sensitivity of high-field fMRI to modulations of the neural responses at different cortical depths. PMID:22448223
Automated Real-Time Behavioral and Physiological Data Acquisition and Display Integrated with Stimulus Presentation for fMRI

PubMed Central

Voyvodic, James T.; Glover, Gary H.; Greve, Douglas; Gadde, Syam

2011-01-01

Functional magnetic resonance imaging (fMRI) is based on correlating blood oxygen-level dependent (BOLD) signal fluctuations in the brain with other time-varying signals. Although the most common reference for correlation is the timing of a behavioral task performed during the scan, many other behavioral and physiological variables can also influence fMRI signals. Variations in cardiac and respiratory functions in particular are known to contribute significant BOLD signal fluctuations. Variables such as skin conduction, eye movements, and other measures that may be relevant to task performance can also be correlated with BOLD signals and can therefore be used in image analysis to differentiate multiple components in complex brain activity signals. Combining real-time recording and data management of multiple behavioral and physiological signals in a way that can be routinely used with any task stimulus paradigm is a non-trivial software design problem. Here we discuss software methods that allow users control of paradigm-specific audio–visual or other task stimuli combined with automated simultaneous recording of multi-channel behavioral and physiological response variables, all synchronized with sub-millisecond temporal accuracy. We also discuss the implementation and importance of real-time display feedback to ensure data quality of all recorded variables. Finally, we discuss standards and formats for storage of temporal covariate data and its integration into fMRI image analysis. These neuroinformatics methods have been adopted for behavioral task control at all sites in the Functional Biomedical Informatics Research Network (FBIRN) multi-center fMRI study. PMID:22232596
Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627
The Cognitive Neuroscience of Sign Language: Engaging Undergraduate Students’ Critical Thinking Skills Using the Primary Literature

PubMed Central

Stevens, Courtney

2015-01-01

This article presents a modular activity on the neurobiology of sign language that engages undergraduate students in reading and analyzing the primary functional magnetic resonance imaging (fMRI) literature. Drawing on a seed empirical article and subsequently published critique and rebuttal, students are introduced to a scientific debate concerning the functional significance of right-hemisphere recruitment observed in some fMRI studies of sign language processing. The activity requires minimal background knowledge and is not designed to provide students with a specific conclusion regarding the debate. Instead, the activity and set of articles allow students to consider key issues in experimental design and analysis of the primary literature, including critical thinking regarding the cognitive subtractions used in blocked-design fMRI studies, as well as possible confounds in comparing results across different experimental tasks. By presenting articles representing different perspectives, each cogently argued by leading scientists, the readings and activity also model the type of debate and dialogue critical to science, but often invisible to undergraduate science students. Student self-report data indicate that undergraduates find the readings interesting and that the activity enhances their ability to read and interpret primary fMRI articles, including evaluating research design and considering alternate explanations of study results. As a stand-alone activity completed primarily in one 60-minute class block, the activity can be easily incorporated into existing courses, providing students with an introduction both to the analysis of empirical fMRI articles and to the role of debate and critique in the field of neuroscience. PMID:26557797
Test-retest reliability of evoked heat stimulation BOLD fMRI.

PubMed

Upadhyay, Jaymin; Lemme, Jordan; Anderson, Julie; Bleakman, David; Large, Thomas; Evelhoch, Jeffrey L; Hargreaves, Richard; Borsook, David; Becerra, Lino

2015-09-30

To date, the blood oxygenated-level dependent (BOLD) functional magnetic resonance imaging (fMRI) technique has enabled an objective and deeper understanding of pain processing mechanisms embedded within the human central nervous system (CNS). In order to further comprehend the benefits and limitations of BOLD fMRI in the context of pain as well as the corresponding subjective pain ratings, we evaluated the univariate response, test-retest reliability and confidence intervals (CIs) at the 95% level of both data types collected during evoked stimulation of 40°C (non-noxious), 44°C (mildly noxious) and a subject-specific temperature eliciting a 7/10 pain rating. The test-retest reliability between two scanning sessions was determined by calculating group-level interclass correlation coefficients (ICCs) and at the single-subject level. Across the three stimuli, we initially observed a graded response of increasing magnitude for both VAS (visual analog score) pain ratings and fMRI data. Test-retest reliability was observed to be highest for VAS pain ratings obtained during the 7/10 pain stimulation (ICC=0.938), while ICC values of pain fMRI data for a distribution of CNS structures ranged from 0.5 to 0.859 (p<0.05). Importantly, the upper and lower confidence interval CI bounds reported herein could be utilized in subsequent trials involving healthy volunteers to hypothesize the magnitude of effect required to overcome inherent variability of either VAS pain ratings or BOLD responses evoked during innocuous or noxious thermal stimulation. Copyright © 2015 Elsevier B.V. All rights reserved.
Gaussian process based independent analysis for temporal source separation in fMRI.

PubMed

Hald, Ditte Høvenhoff; Henao, Ricardo; Winther, Ole

2017-05-15

Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its temporal nature. fMRI source signals, biological stimuli and non-stimuli-related artifacts are all smooth over a time-scale compatible with the sampling time (TR). We therefore propose Gaussian process ICA (GPICA), which facilitates temporal dependency by the use of Gaussian process source priors. On two fMRI data sets with different sampling frequency, we show that the GPICA-inferred temporal components and associated spatial maps allow for a more definite interpretation than standard temporal ICA methods. The temporal structures of the sources are controlled by the covariance of the Gaussian process, specified by a kernel function with an interpretable and controllable temporal length scale parameter. We propose a hierarchical model specification, considering both instantaneous and convolutive mixing, and we infer source spatial maps, temporal patterns and temporal length scale parameters by Markov Chain Monte Carlo. A companion implementation made as a plug-in for SPM can be downloaded from https://github.com/dittehald/GPICA. Copyright © 2017 Elsevier Inc. All rights reserved.
The Iowa Gambling Task in fMRI Images

PubMed Central

Li, Xiangrui; Lu, Zhong-Lin; D'Argembeau, Arnaud; Ng, Marie; Bechara, Antoine

2009-01-01

The Iowa Gambling Task (IGT) is a sensitive test for the detection of decision-making impairments in several neurologic and psychiatric populations. Very few studies have employed the IGT in fMRI investigations, in part, because the task is cognitively complex. Here we report a method for exploring brain activity using fMRI during performance of the IGT. Decision-making during the IGT was associated with activity in several brain regions in a group of healthy individuals. The activated regions were consistent with the neural circuitry hypothesized to underlie somatic marker activation and decision-making. Specifically, a neural circuitry involving the dorsolateral prefrontal cortex (for working memory), the insula and posterior cingulate cortex (for representations of emotional states), the mesial orbitofrontal and ventromedial prefrontal cortex (for coupling the two previous processes), the ventral striatum and anterior cingulate/SMA (supplementary motor area) for implementing behavioral decisions was engaged. These results have implications for using the IGT to study abnormal mechanisms of decision making in a variety of clinical populations. PMID:19777556
Amplitude of low-frequency oscillations associated with emotional conflict control.

PubMed

Xue, Song; Wang, Xu; Chang, Jingjing; Liu, Jia; Qiu, Jiang

2016-09-01

Previous fMRI studies related to emotional conflict focused on task activation during the specific experimental paradigm. Yet, the underlying spontaneous neural activity was largely unknown. Here, this was the first study using resting-state fMRI to explore the spontaneous neural activity related to emotional conflict. We used the whole-brain analysis to investigate the association between emotional conflict and amplitude of low-frequency fluctuations (ALFF) in a large sample. We found that the emotional conflict effect was negatively correlated with ALFF in the right AMY. These findings implied that AMY was the key region which plays a crucial role in emotional conflict.
An adaptive, individualized fMRI delay discounting procedure to increase flexibility and optimize scanner time.

PubMed

Koffarnus, Mikhail N; Deshpande, Harshawardhan U; Lisinski, Jonathan M; Eklund, Anders; Bickel, Warren K; LaConte, Stephen M

2017-11-01

Research on the rate at which people discount the value of future rewards has become increasingly prevalent as discount rate has been shown to be associated with many unhealthy patterns of behavior such as drug abuse, gambling, and overeating. fMRI research points to a fronto-parietal-limbic pathway that is active during decisions between smaller amounts of money now and larger amounts available after a delay. Researchers in this area have used different variants of delay discounting tasks and reported various contrasts between choice trials of different types from these tasks. For instance, researchers have compared 1) choices of delayed monetary amounts to choices of the immediate monetary amounts, 2) 'hard' choices made near one's point of indifference to 'easy' choices that require little thought, and 3) trials where an immediate choice is available versus trials where one is unavailable, regardless of actual eventual choice. These differences in procedure and analysis make comparison of results across studies difficult. In the present experiment, we designed a delay discounting task with the intended capability of being able to construct contrasts of all three comparisons listed above while optimizing scanning time to reduce costs and avoid participant fatigue. This was accomplished with an algorithm that customized the choice trials presented to each participant with the goal of equalizing choice trials of each type. We compared this task, which we refer to here as the individualized discounting task (IDT), to two other delay discounting tasks previously reported in the literature (McClure et al., 2004; Amlung et al., 2014) in 18 participants. Results show that the IDT can examine each of the three contrasts mentioned above, while yielding a similar degree of activation as the reference tasks. This suggests that this new task could be used in delay discounting fMRI studies to allow researchers to more easily compare their results to a majority of previous research while minimizing scanning duration. Copyright © 2017 Elsevier Inc. All rights reserved.
A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders

PubMed Central

Du, Yuhui; Pearlson, Godfrey D; Liu, Jingyu; Sui, Jing; Yu, Qingbao; He, Hao; Castro, Eduardo; Calhoun, Vince D.

2015-01-01

Schizophrenia (SZ), bipolar disorder (BP) and schizoaffective disorder (SAD) share some common symptoms, and there is a debate about whether SAD is an independent category. To the best of our knowledge, no study has been done to differentiate these three disorders or to investigate the distinction of SAD as an independent category using fMRI data. The present study is aimed to explore biomarkers from resting-state fMRI networks for differentiating these disorders and investigate the relationship among these disorders based on fMRI networks with an emphasis on SAD. Firstly, a novel group ICA method, group information guided independent component analysis (GIG-ICA), was applied to extract subject-specific brain networks from fMRI data of 20 healthy controls (HC), 20 SZ patients, 20 BP patients, 20 patients suffering SAD with manic episodes (SADM), and 13 patients suffering SAD with depressive episodes exclusively (SADD). Then, five-level one-way analysis of covariance and multiclass support vector machine recursive feature elimination were employed to identify discriminative regions from the networks. Subsequently, the t-distributed stochastic neighbor embedding (t-SNE) projection and the hierarchical clustering methods were implemented to investigate the relationship among those groups. Finally, to evaluate the generalization ability, 16 new subjects were classified based on the found regions and the trained model using original 93 subjects. Results show that the discriminative regions mainly include frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insula and supramarginal cortices, which performed well in distinguishing different groups. SADM and SADD were the most similar to each other, although SADD had greater similarity to SZ compared to other groups, which indicates SAD may be an independent category. BP was closer to HC compared with other psychotic disorders. In summary, resting-state fMRI brain networks extracted via GIG-ICA provide a promising potential to differentiate SZ, BP, and SAD. PMID:26216278
Abnormalities of Dorsolateral Prefrontal Function in Women With Premenstrual Dysphoric Disorder: A Multimodal Neuroimaging Study

PubMed Central

Baller, Erica B.; Wei, Shau-Ming; Kohn, Philip D.; Rubinow, David R.; Alarcón, Gabriela; Schmidt, Peter J.; Berman, Karen F.

2014-01-01

Objective To investigate the neural substrate of premenstrual dysphoric disorder (PMDD), the authors used [15O]H2O positron emission tomography (PET) regional cerebral blood flow (rCBF) and blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal measurements during working memory in conjunction with a 6-month hormone manipulation protocol. Method PET and fMRI scans were obtained from women with prospectively confirmed PMDD and asymptomatic comparison subjects while they completed the n-back task during three hormone conditions: ovarian suppression induced by the gonadotropin-releasing hormone agonist leuprolide acetate, leuprolide plus estradiol, and leuprolide plus progesterone. Fifteen patients and 15 matched comparison subjects underwent PET imaging. Fourteen patients and 14 comparison subjects underwent fMRI. For each hormone condition, rCBF was measured with [15O]H2O PET, and BOLD signal was measured with fMRI, both during an n-back working memory paradigm. Global Assessment of Functioning Scale (GAF) scores and clinical characteristics were obtained for each patient before hormone manipulation, and symptoms were measured before and during the protocol. Results In both the PET and fMRI studies, a main effect of diagnosis was observed, with PMDD patients showing greater prefrontal activation than comparison subjects. In the patient group, the degree to which dorsolateral prefrontal cortex activation was abnormally increased correlated with several dimensions of disease: disability as indicated by GAF scores, age at symptom onset, duration of PMDD, and differences in pre- and postmenses PMDD symptoms. Conclusions Abnormal working memory activation in PMDD, specifically in the dorsolateral prefrontal cortex, is related to PMDD severity, symptoms, age at onset, and disease burden. These results support the clinical relevance of the findings and the proposal that dorsolateral prefrontal cortex dysfunction represents a substrate of risk for PMDD. The concordance of the fMRI and PET data attests to the neurobiological validity of the results. PMID:23361612
Abnormalities of dorsolateral prefrontal function in women with premenstrual dysphoric disorder: a multimodal neuroimaging study.

PubMed

Baller, Erica B; Wei, Shau-Ming; Kohn, Philip D; Rubinow, David R; Alarcón, Gabriela; Schmidt, Peter J; Berman, Karen F

2013-03-01

To investigate the neural substrate of premenstrual dysphoric disorder (PMDD), the authors used [15O]H2O positron emission tomography (PET) regional cerebral blood flow (rCBF) and blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal measurements during working memory in conjunction with a 6-month hormone manipulation protocol. PET and fMRI scans were obtained from women with prospectively confirmed PMDD and asymptomatic comparison subjects while they completed the n-back task during three hormone conditions: ovarian suppression induced by the gonadotropin-releasing hormone agonist leuprolide acetate, leuprolide plus estradiol, and leuprolide plus progesterone. Fifteen patients and 15 matched comparison subjects underwent PET imaging. Fourteen patients and 14 comparison subjects underwent fMRI. For each hormone condition, rCBF was measured with [15O]H2O PET, and BOLD signal was measured with fMRI, both during an n-back working memory paradigm. Global Assessment of Functioning Scale (GAF) scores and clinical characteristics were obtained for each patient before hormone manipulation, and symptoms were measured before and during the protocol. In both the PET and fMRI studies, a main effect of diagnosis was observed, with PMDD patients showing greater prefrontal activation than comparison subjects. In the patient group, the degree to which dorsolateral prefrontal cortex activation was abnormally increased correlated with several dimensions of disease: disability as indicated by GAF scores, age at symptom onset, duration of PMDD, and differences in pre- and postmenses PMDD symptoms. Abnormal working memory activation in PMDD, specifically in the dorsolateral prefrontal cortex, is related to PMDD severity, symptoms, age at onset, and disease burden. These results support the clinical relevance of the findings and the proposal that dorsolateral prefrontal cortex dysfunction represents a substrate of risk for PMDD. The concordance of the fMRI and PET data attests to the neurobiological validity of the results.
Specific and Nonspecific Neural Activity during Selective Processing of Visual Representations in Working Memory

ERIC Educational Resources Information Center

Oh, Hwamee; Leung, Hoi-Chung

2010-01-01

In this fMRI study, we investigated prefrontal cortex (PFC) and visual association regions during selective information processing. We recorded behavioral responses and neural activity during a delayed recognition task with a cue presented during the delay period. A specific cue ("Face" or "Scene") was used to indicate which one of the two…
Haptic fMRI: Reliability and performance of electromagnetic haptic interfaces for motion and force neuroimaging experiments.

PubMed

Menon, Samir; Zhu, Jack; Goyal, Deeksha; Khatib, Oussama

2017-07-01

Haptic interfaces compatible with functional magnetic resonance imaging (Haptic fMRI) promise to enable rich motor neuroscience experiments that study how humans perform complex manipulation tasks. Here, we present a large-scale study (176 scans runs, 33 scan sessions) that characterizes the reliability and performance of one such electromagnetically actuated device, Haptic fMRI Interface 3 (HFI-3). We outline engineering advances that ensured HFI-3 did not interfere with fMRI measurements. Observed fMRI temporal noise levels with HFI-3 operating were at the fMRI baseline (0.8% noise to signal). We also present results from HFI-3 experiments demonstrating that high resolution fMRI can be used to study spatio-temporal patterns of fMRI blood oxygenation dependent (BOLD) activation. These experiments include motor planning, goal-directed reaching, and visually-guided force control. Observed fMRI responses are consistent with existing literature, which supports Haptic fMRI's effectiveness at studying the brain's motor regions.
Functional Specificity of the Visual Word Form Area: General Activation for Words and Symbols but Specific Network Activation for Words

ERIC Educational Resources Information Center

Reinke, Karen; Fernandes, Myra; Schwindt, Graeme; O'Craven, Kathleen; Grady, Cheryl L.

2008-01-01

The functional specificity of the brain region known as the Visual Word Form Area (VWFA) was examined using fMRI. We explored whether this area serves a general role in processing symbolic stimuli, rather than being selective for the processing of words. Brain activity was measured during a visual 1-back task to English words, meaningful symbols…
Population receptive field (pRF) measurements of chromatic responses in human visual cortex using fMRI.

PubMed

Welbourne, Lauren E; Morland, Antony B; Wade, Alex R

2018-02-15

The spatial sensitivity of the human visual system depends on stimulus color: achromatic gratings can be resolved at relatively high spatial frequencies while sensitivity to isoluminant color contrast tends to be more low-pass. Models of early spatial vision often assume that the receptive field size of pattern-sensitive neurons is correlated with their spatial frequency sensitivity - larger receptive fields are typically associated with lower optimal spatial frequency. A strong prediction of this model is that neurons coding isoluminant chromatic patterns should have, on average, a larger receptive field size than neurons sensitive to achromatic patterns. Here, we test this assumption using functional magnetic resonance imaging (fMRI). We show that while spatial frequency sensitivity depends on chromaticity in the manner predicted by behavioral measurements, population receptive field (pRF) size measurements show no such dependency. At any given eccentricity, the mean pRF size for neuronal populations driven by luminance, opponent red/green and S-cone isolating contrast, are identical. Changes in pRF size (for example, an increase with eccentricity and visual area hierarchy) are also identical across the three chromatic conditions. These results suggest that fMRI measurements of receptive field size and spatial resolution can be decoupled under some circumstances - potentially reflecting a fundamental dissociation between these parameters at the level of neuronal populations. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline.

PubMed

Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh; Glahn, David C; Blangero, John; Reynolds, Richard C; Cox, Robert W; Fieremans, Els; Veraart, Jelle; Novikov, Dmitry S; Nichols, Thomas E; Hong, L Elliot; Thompson, Paul M; Kochunov, Peter

2018-01-01

Big data initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analysis consortium (ENIGMA), combine data collected by independent studies worldwide to achieve more generalizable estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized image analyses protocols to extract phenotypes consistently. This harmonization is particularly challenging for resting state fMRI due to the wide variability of acquisition protocols and scanner platforms; this leads to site-to-site variance in quality, resolution and temporal signal-to-noise ratio (tSNR). An effective harmonization should provide optimal measures for data of different qualities. We developed a multi-site rsfMRI analysis pipeline to allow research groups around the world to process rsfMRI scans in a harmonized way, to extract consistent and quantitative measurements of connectivity and to perform coordinated statistical tests. We used the single-modality ENIGMA rsfMRI preprocessing pipeline based on modelfree Marchenko-Pastur PCA based denoising to verify and replicate resting state network heritability estimates. We analyzed two independent cohorts, GOBS (Genetics of Brain Structure) and HCP (the Human Connectome Project), which collected data using conventional and connectomics oriented fMRI protocols, respectively. We used seed-based connectivity and dual-regression approaches to show that the rsfMRI signal is consistently heritable across twenty major functional network measures. Heritability values of 20-40% were observed across both cohorts.

Separation of parallel encoded complex-valued slices (SPECS) from a single complex-valued aliased coil image.

PubMed

Rowe, Daniel B; Bruce, Iain P; Nencka, Andrew S; Hyde, James S; Kociuba, Mary C

2016-04-01

Achieving a reduction in scan time with minimal inter-slice signal leakage is one of the significant obstacles in parallel MR imaging. In fMRI, multiband-imaging techniques accelerate data acquisition by simultaneously magnetizing the spatial frequency spectrum of multiple slices. The SPECS model eliminates the consequential inter-slice signal leakage from the slice unaliasing, while maintaining an optimal reduction in scan time and activation statistics in fMRI studies. When the combined k-space array is inverse Fourier reconstructed, the resulting aliased image is separated into the un-aliased slices through a least squares estimator. Without the additional spatial information from a phased array of receiver coils, slice separation in SPECS is accomplished with acquired aliased images in shifted FOV aliasing pattern, and a bootstrapping approach of incorporating reference calibration images in an orthogonal Hadamard pattern. The aliased slices are effectively separated with minimal expense to the spatial and temporal resolution. Functional activation is observed in the motor cortex, as the number of aliased slices is increased, in a bilateral finger tapping fMRI experiment. The SPECS model incorporates calibration reference images together with coefficients of orthogonal polynomials into an un-aliasing estimator to achieve separated images, with virtually no residual artifacts and functional activation detection in separated images. Copyright © 2015 Elsevier Inc. All rights reserved.
Neural adaptation to thin and fat bodies in the fusiform body area and middle occipital gyrus: an fMRI adaptation study.

PubMed

Hummel, Dennis; Rudolf, Anne K; Brandi, Marie-Luise; Untch, Karl-Heinz; Grabhorn, Ralph; Hampel, Harald; Mohr, Harald M

2013-12-01

Visual perception can be strongly biased due to exposure to specific stimuli in the environment, often causing neural adaptation and visual aftereffects. In this study, we investigated whether adaptation to certain body shapes biases the perception of the own body shape. Furthermore, we aimed to evoke neural adaptation to certain body shapes. Participants completed a behavioral experiment (n = 14) to rate manipulated pictures of their own bodies after adaptation to demonstratively thin or fat pictures of their own bodies. The same stimuli were used in a second experiment (n = 16) using functional magnetic resonance imaging (fMRI) adaptation. In the behavioral experiment, after adapting to a thin picture of the own body participants also judged a thinner than actual body picture to be the most realistic and vice versa, resembling a typical aftereffect. The fusiform body area (FBA) and the right middle occipital gyrus (rMOG) show neural adaptation to specific body shapes while the extrastriate body area (EBA) bilaterally does not. The rMOG cluster is highly selective for bodies and perhaps body parts. The findings of the behavioral experiment support the existence of a perceptual body shape aftereffect, resulting from a specific adaptation to thin and fat pictures of one's own body. The fMRI results imply that body shape adaptation occurs in the FBA and the rMOG. The role of the EBA in body shape processing remains unclear. The results are also discussed in the light of clinical body image disturbances. Copyright © 2012 Wiley Periodicals, Inc.
Individual differences in solving arithmetic word problems

PubMed Central

2013-01-01

Background With the present functional magnetic resonance imaging (fMRI) study at 3 T, we investigated the neural correlates of visualization and verbalization during arithmetic word problem solving. In the domain of arithmetic, visualization might mean to visualize numbers and (intermediate) results while calculating, and verbalization might mean that numbers and (intermediate) results are verbally repeated during calculation. If the brain areas involved in number processing are domain-specific as assumed, that is, that the left angular gyrus (AG) shows an affinity to the verbal domain, and that the left and right intraparietal sulcus (IPS) shows an affinity to the visual domain, the activation of these areas should show a dependency on an individual’s cognitive style. Methods 36 healthy young adults participated in the fMRI study. The participants habitual use of visualization and verbalization during solving arithmetic word problems was assessed with a short self-report assessment. During the fMRI measurement, arithmetic word problems that had to be solved by the participants were presented in an event-related design. Results We found that visualizers showed greater brain activation in brain areas involved in visual processing, and that verbalizers showed greater brain activation within the left angular gyrus. Conclusions Our results indicate that cognitive styles or preferences play an important role in understanding brain activation. Our results confirm, that strong visualizers use mental imagery more strongly than weak visualizers during calculation. Moreover, our results suggest that the left AG shows a specific affinity to the verbal domain and subserves number processing in a modality-specific way. PMID:23883107
Brain Activation During Autobiographical Memory Retrieval with Special Reference to Default Mode Network

PubMed Central

Ino, Tadashi; Nakai, Ryusuke; Azuma, Takashi; Kimura, Toru; Fukuyama, Hidenao

2011-01-01

Recent neuroimaging studies have suggested that brain regions activated during retrieval of autobiographical memory (ABM) overlap with the default mode network (DMN), which shows greater activation during rest than cognitively demanding tasks and is considered to be involved in self-referential processing. However, detailed overlap and segregation between ABM and DMN remain unclear. This fMRI study focuses first on revealing components of the DMN which are related to ABM and those which are unrelated to ABM, and second on extracting the neural bases which are specifically devoted to ABM. Brain activities relative to rest during three tasks matched in task difficulty assessed by reaction time were investigated by fMRI; category cued recall from ABM, category cued recall from semantic memory, and number counting task. We delineated the overlap between the regions that showed less activation during semantic memory and number counting relative to rest, which correspond to the DMN, and the areas that showed greater or less activation during ABM relative to rest. ABM-specific activation was defined as the overlap between the contrast of ABM versus rest and the contrast of ABM versus semantic memory. The fMRI results showed that greater activation as well as less activation during ABM relative to rest overlapped considerably with the DMN, indicating that the DMN is segregated to the regions which are functionally related to ABM and the regions which are unrelated to ABM. ABM-specific activation was observed in the left-lateralized brain regions and most of them fell within the DMN. PMID:21643504
Abstract semantics in the motor system? - An event-related fMRI study on passive reading of semantic word categories carrying abstract emotional and mental meaning.

PubMed

Dreyer, Felix R; Pulvermüller, Friedemann

2018-03-01

Previous research showed that modality-preferential sensorimotor areas are relevant for processing concrete words used to speak about actions. However, whether modality-preferential areas also play a role for abstract words is still under debate. Whereas recent functional magnetic resonance imaging (fMRI) studies suggest an involvement of motor cortex in processing the meaning of abstract emotion words as, for example, 'love', other non-emotional abstract words, in particular 'mental words', such as 'thought' or 'logic', are believed to engage 'amodal' semantic systems only. In the present event-related fMRI experiment, subjects passively read abstract emotional and mental nouns along with concrete action related words. Contrary to expectation, the results indicate a specific involvement of face motor areas in the processing of mental nouns, resembling that seen for face related action words. This result was confirmed when subject-specific regions of interest (ROIs) defined by motor localizers were used. We conclude that a role of motor systems in semantic processing is not restricted to concrete words but extends to at least some abstract mental symbols previously thought to be entirely 'disembodied' and divorced from semantically related sensorimotor processing. Implications for neurocognitive theories of semantics and clinical applications will be highlighted, paying specific attention to the role of brain activations as indexes of cognitive processes and their relationships to 'causal' studies addressing lesion and transcranial magnetic stimulation (TMS) effects. Possible implications for clinical practice, in particular speech language therapy, are discussed in closing. Copyright © 2017. Published by Elsevier Ltd.
Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions

PubMed Central

Gawryluk, Jodie R.; Mazerolle, Erin L.; D'Arcy, Ryan C. N.

2014-01-01

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that allows for visualization of activated brain regions. Until recently, fMRI studies have focused on gray matter. There are two main reasons white matter fMRI remains controversial: (1) the blood oxygen level dependent (BOLD) fMRI signal depends on cerebral blood flow and volume, which are lower in white matter than gray matter and (2) fMRI signal has been associated with post-synaptic potentials (mainly localized in gray matter) as opposed to action potentials (the primary type of neural activity in white matter). Despite these observations, there is no direct evidence against measuring fMRI activation in white matter and reports of fMRI activation in white matter continue to increase. The questions underlying white matter fMRI activation are important. White matter fMRI activation has the potential to greatly expand the breadth of brain connectivity research, as well as improve the assessment and diagnosis of white matter and connectivity disorders. The current review provides an overview of the motivation to investigate white matter fMRI activation, as well as the published evidence of this phenomenon. We speculate on possible neurophysiologic bases of white matter fMRI signals, and discuss potential explanations for why reports of white matter fMRI activation are relatively scarce. We end with a discussion of future basic and clinical research directions in the study of white matter fMRI. PMID:25152709
Effects of resting state condition on reliability, trait specificity, and network connectivity of brain function measured with arterial spin labeled perfusion MRI.

PubMed

Li, Zhengjun; Vidorreta, Marta; Katchmar, Natalie; Alsop, David C; Wolf, Daniel H; Detre, John A

2018-06-01

Resting state fMRI (rs-fMRI) provides imaging biomarkers of task-independent brain function that can be associated with clinical variables or modulated by interventions such as behavioral training or pharmacological manipulations. These biomarkers include time-averaged regional brain function as manifested by regional cerebral blood flow (CBF) measured using arterial spin labeled (ASL) perfusion MRI and correlated temporal fluctuations of function across brain networks with either ASL or blood oxygenation level dependent (BOLD) fMRI. Resting-state studies are typically carried out using just one of several prescribed state conditions such as eyes closed (EC), eyes open (EO), or visual fixation on a cross-hair (FIX), which may affect the reliability and specificity of rs-fMRI. In this study, we collected test-retest ASL MRI data during 4 resting-state task conditions: EC, EO, FIX and PVT (low-frequency psychomotor vigilance task), and examined the effects of these task conditions on reliability and reproducibility as well as trait specificity of regional brain function. We also acquired resting-state BOLD fMRI under FIX and compared the network connectivity reliabilities between the four ASL conditions and the BOLD FIX condition. For resting-state ASL data, EC provided the highest CBF reliability, reproducibility, trait specificity, and network connectivity reliability, followed by EO, while FIX was lowest on all of these measures. PVT demonstrated lower CBF reliability, reproducibility and trait specificity than EO and EC. Overall network connectivity reliability was comparable between ASL and BOLD. Our findings confirm ASL CBF as a reliable, stable, and consistent measure of resting-state regional brain function and support the use of EC or EO over FIX and PVT as the resting-state condition. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Brain mechanisms of successful recognition through retrieval of semantic context

PubMed Central

Flegal, Kristin E.; Marín-Gutiérrez, Alejandro; Ragland, J. Daniel; Ranganath, Charan

2017-01-01

Episodic memory is associated with the encoding and retrieval of context information, and with a subjective sense of re-experiencing past events. The neural correlates of episodic retrieval have been extensively studied using fMRI, leading to the identification of a “general recollection network” including medial temporal, parietal, and prefrontal regions. However, in these studies, it is difficult to disentangle the effects of context retrieval from recollection. In the present study, we used functional magnetic resonance imaging (fMRI) to determine the extent to which the recruitment of regions in the recollection network is contingent on context reinstatement. Participants were scanned during a cued recognition test for target words from encoded sentences. Studied target words were preceded by either a cue word studied in the same sentence (thus congruent with encoding context), or a cue word studied in a different sentence (thus incongruent with encoding context). Converging fMRI results from independently-defined regions of interest and whole-brain analysis showed regional specificity in the recollection network. Activity in hippocampus and parahippocampal cortex was specifically increased during successful retrieval following congruent context cues, whereas parietal and prefrontal components of the general recollection network were associated with confident retrieval irrespective of contextual congruency. Our findings implicate medial temporal regions in the retrieval of semantic context, contributing to, but dissociable from, recollective experience. PMID:24564467
What has fMRI told us about the Development of Cognitive Control through Adolescence?

PubMed Central

Luna, Beatriz; Padmanabhan, Aarthi; O’Hearn, Kirsten

2009-01-01

Cognitive control, the ability to voluntarily guide our behavior, continues to improve throughout adolescence. Below we review the literature on age-related changes in brain function related to response inhibition and working memory, which support cognitive control. Findings from studies using functional magnetic imaging (fMRI) indicate that processing errors, sustaining a cognitive control state, and reaching adult levels of precision, persist through adolescence. Developmental changes in patterns of brain function suggest that core regions of the circuitry underlying cognitive control are on-line early in development. However, age-related changes in localized processes across the brain and in establishing long range connections that support top-down modulation of behavior may support more effective neural processing for optimal mature executive function. While great progress has been made in understanding the age-related changes in brain processes underlying cognitive development, there are still important challenges in developmental neuroimaging methods and the interpretation of data that need to be addressed. PMID:19765880
Informed consent for MRI and fMRI research: Analysis of a sample of Canadian consent documents

PubMed Central

2011-01-01

Background Research ethics and the measures deployed to ensure ethical oversight of research (e.g., informed consent forms, ethics review) are vested with extremely important ethical and practical goals. Accordingly, these measures need to function effectively in real-world research and to follow high level standards. Methods We examined approved consent forms for Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) studies approved by Canadian research ethics boards (REBs). Results We found evidence of variability in consent forms in matters of physical and psychological risk reporting. Approaches used to tackle the emerging issue of incidental findings exposed extensive variability between and within research sites. Conclusion The causes of variability in approved consent forms and studies need to be better understood. However, mounting evidence of administrative and practical hurdles within current ethics governance systems combined with potential sub-optimal provision of information to and protection of research subjects support other calls for more scrutiny of research ethics practices and applicable revisions. PMID:21235768
Distinct neural substrates for semantic knowledge and naming in the temporoparietal network.

PubMed

Gesierich, Benno; Jovicich, Jorge; Riello, Marianna; Adriani, Michela; Monti, Alessia; Brentari, Valentina; Robinson, Simon D; Wilson, Stephen M; Fairhall, Scott L; Gorno-Tempini, Maria Luisa

2012-10-01

Patients with anterior temporal lobe (ATL) lesions show semantic and lexical retrieval deficits, and the differential role of this area in the 2 processes is debated. Functional neuroimaging in healthy individuals has not clarified the matter because semantic and lexical processes usually occur simultaneously and automatically. Furthermore, the ATL is a region challenging for functional magnetic resonance imaging (fMRI) due to susceptibility artifacts, especially at high fields. In this study, we established an optimized ATL-sensitive fMRI acquisition protocol at 4 T and applied an event-related paradigm to study the identification (i.e., association of semantic biographical information) of celebrities, with and without the ability to retrieve their proper names. While semantic processing reliably activated the ATL, only more posterior areas in the left temporal and temporal-parietal junction were significantly modulated by covert lexical retrieval. These results suggest that within a temporoparietal network, the ATL is relatively more important for semantic processing, and posterior language regions are relatively more important for lexical retrieval.
Visual analytics of brain networks.

PubMed

Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

2012-05-15

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.
A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic testing of functional connectivity and brain graphs.

PubMed

Patel, Ameera X; Bullmore, Edward T

2016-11-15

Connectome mapping using techniques such as functional magnetic resonance imaging (fMRI) has become a focus of systems neuroscience. There remain many statistical challenges in analysis of functional connectivity and network architecture from BOLD fMRI multivariate time series. One key statistic for any time series is its (effective) degrees of freedom, df, which will generally be less than the number of time points (or nominal degrees of freedom, N). If we know the df, then probabilistic inference on other fMRI statistics, such as the correlation between two voxel or regional time series, is feasible. However, we currently lack good estimators of df in fMRI time series, especially after the degrees of freedom of the "raw" data have been modified substantially by denoising algorithms for head movement. Here, we used a wavelet-based method both to denoise fMRI data and to estimate the (effective) df of the denoised process. We show that seed voxel correlations corrected for locally variable df could be tested for false positive connectivity with better control over Type I error and greater specificity of anatomical mapping than probabilistic connectivity maps using the nominal degrees of freedom. We also show that wavelet despiked statistics can be used to estimate all pairwise correlations between a set of regional nodes, assign a P value to each edge, and then iteratively add edges to the graph in order of increasing P. These probabilistically thresholded graphs are likely more robust to regional variation in head movement effects than comparable graphs constructed by thresholding correlations. Finally, we show that time-windowed estimates of df can be used for probabilistic connectivity testing or dynamic network analysis so that apparent changes in the functional connectome are appropriately corrected for the effects of transient noise bursts. Wavelet despiking is both an algorithm for fMRI time series denoising and an estimator of the (effective) df of denoised fMRI time series. Accurate estimation of df offers many potential advantages for probabilistically thresholding functional connectivity and network statistics tested in the context of spatially variant and non-stationary noise. Code for wavelet despiking, seed correlational testing and probabilistic graph construction is freely available to download as part of the BrainWavelet Toolbox at www.brainwavelet.org. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems

PubMed Central

Smith, Jason F.; Pillai, Ajay; Chen, Kewei; Horwitz, Barry

2012-01-01

Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a “node” in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an “instantaneous” connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis. PMID:22279430
Functional-anatomic study of episodic retrieval using fMRI. I. Retrieval effort versus retrieval success.

PubMed

Buckner, R L; Koutstaal, W; Schacter, D L; Wagner, A D; Rosen, B R

1998-04-01

A number of recent functional imaging studies have identified brain areas activated during tasks involving episodic memory retrieval. The identification of such areas provides a foundation for targeted hypotheses regarding the more specific contributions that these areas make to episodic retrieval. As a beginning effort toward such an endeavor, whole-brain functional magnetic resonance imaging (fMRI) was used to examine 14 subjects during episodic word recognition in a block-designed fMRI experiment. Study conditions were manipulated by presenting either shallow or deep encoding tasks. This manipulation yielded two recognition conditions that differed with regard to retrieval effort and retrieval success: shallow encoding yielded low levels of recognition success with high levels of retrieval effort, and deep encoding yielded high levels of recognition success with low levels of effort. Many brain areas were activated in common by these two recognition conditions compared to a low-level fixation condition, including left and right prefrontal regions often detected during PET episodic retrieval paradigms (e.g., R. L. Buckner et al., 1996, J. Neurosci. 16, 6219-6235) thereby generalizing these findings to fMRI. Characterization of the activated regions in relation to the separate recognition conditions showed (1) bilateral anterior insular regions and a left dorsal prefrontal region were more active after shallow encoding, when retrieval demanded greatest effort, and (2) right anterior prefrontal cortex, which has been implicated in episodic retrieval, was most active during successful retrieval after deep encoding. We discuss these findings in relation to component processes involved in episodic retrieval and in the context of a companion study using event-related fMRI.
Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

PubMed Central

Sadeh, Boaz; Yovel, Galit

2014-01-01

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator's coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes. PMID:24893706
Individual Movement Variability Magnitudes Are Explained by Cortical Neural Variability.

PubMed

Haar, Shlomi; Donchin, Opher; Dinstein, Ilan

2017-09-13

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior-parietal cortex of individual subjects explained their movement-extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities. SIGNIFICANCE STATEMENT Neural activity and movement kinematics are remarkably variable. Although intertrial variability is rarely studied, here, we demonstrate that individual human subjects exhibit distinct magnitudes of neural and kinematic variability that are reproducible across movements to different targets and when performing these movements with either arm. Furthermore, when examining the relationship between cortical variability and movement variability, we find that cortical fMRI variability in parietal cortex of individual subjects explained their movement extent variability. This enabled us to explain why some subjects performed more variable movements than others based on their cortical variability magnitudes. Copyright © 2017 the authors 0270-6474/17/379076-10$15.00/0.
Application of calibrated fMRI in Alzheimer's disease.

PubMed

Lajoie, Isabelle; Nugent, Scott; Debacker, Clément; Dyson, Kenneth; Tancredi, Felipe B; Badhwar, AmanPreet; Belleville, Sylvie; Deschaintre, Yan; Bellec, Pierre; Doyon, Julien; Bocti, Christian; Gauthier, Serge; Arnold, Douglas; Kergoat, Marie-Jeanne; Chertkow, Howard; Monchi, Oury; Hoge, Richard D

2017-01-01

Calibrated fMRI based on arterial spin-labeling (ASL) and blood oxygen-dependent contrast (BOLD), combined with periods of hypercapnia and hyperoxia, can provide information on cerebrovascular reactivity (CVR), resting blood flow (CBF), oxygen extraction fraction (OEF), and resting oxidative metabolism (CMRO 2 ). Vascular and metabolic integrity are believed to be affected in Alzheimer's disease (AD), thus, the use of calibrated fMRI in AD may help understand the disease and monitor therapeutic responses in future clinical trials. In the present work, we applied a calibrated fMRI approach referred to as Quantitative O2 (QUO2) in a cohort of probable AD dementia and age-matched control participants. The resulting CBF, OEF and CMRO 2 values fell within the range from previous studies using positron emission tomography (PET) with 15 O labeling. Moreover, the typical parietotemporal pattern of hypoperfusion and hypometabolism in AD was observed, especially in the precuneus, a particularly vulnerable region. We detected no deficit in frontal CBF, nor in whole grey matter CVR, which supports the hypothesis that the effects observed were associated specifically with AD rather than generalized vascular disease. Some key pitfalls affecting both ASL and BOLD methods were encountered, such as prolonged arterial transit times (particularly in the occipital lobe), the presence of susceptibility artifacts obscuring medial temporal regions, and the challenges associated with the hypercapnic manipulation in AD patients and elderly participants. The present results are encouraging and demonstrate the promise of calibrated fMRI measurements as potential biomarkers in AD. Although CMRO 2 can be imaged with 15 O PET, the QUO2 method uses more widely available imaging infrastructure, avoids exposure to ionizing radiation, and integrates with other MRI-based measures of brain structure and function.
Towards neural correlates of auditory stimulus processing: A simultaneous auditory evoked potentials and functional magnetic resonance study using an odd-ball paradigm

PubMed Central

Milner, Rafał; Rusiniak, Mateusz; Lewandowska, Monika; Wolak, Tomasz; Ganc, Małgorzata; Piątkowska-Janko, Ewa; Bogorodzki, Piotr; Skarżyński, Henryk

2014-01-01

Background The neural underpinnings of auditory information processing have often been investigated using the odd-ball paradigm, in which infrequent sounds (deviants) are presented within a regular train of frequent stimuli (standards). Traditionally, this paradigm has been applied using either high temporal resolution (EEG) or high spatial resolution (fMRI, PET). However, used separately, these techniques cannot provide information on both the location and time course of particular neural processes. The goal of this study was to investigate the neural correlates of auditory processes with a fine spatio-temporal resolution. A simultaneous auditory evoked potentials (AEP) and functional magnetic resonance imaging (fMRI) technique (AEP-fMRI), together with an odd-ball paradigm, were used. Material/Methods Six healthy volunteers, aged 20–35 years, participated in an odd-ball simultaneous AEP-fMRI experiment. AEP in response to acoustic stimuli were used to model bioelectric intracerebral generators, and electrophysiological results were integrated with fMRI data. Results fMRI activation evoked by standard stimuli was found to occur mainly in the primary auditory cortex. Activity in these regions overlapped with intracerebral bioelectric sources (dipoles) of the N1 component. Dipoles of the N1/P2 complex in response to standard stimuli were also found in the auditory pathway between the thalamus and the auditory cortex. Deviant stimuli induced fMRI activity in the anterior cingulate gyrus, insula, and parietal lobes. Conclusions The present study showed that neural processes evoked by standard stimuli occur predominantly in subcortical and cortical structures of the auditory pathway. Deviants activate areas non-specific for auditory information processing. PMID:24413019
Generalised filtering and stochastic DCM for fMRI.

PubMed

Li, Baojuan; Daunizeau, Jean; Stephan, Klaas E; Penny, Will; Hu, Dewen; Friston, Karl

2011-09-15

This paper is about the fitting or inversion of dynamic causal models (DCMs) of fMRI time series. It tries to establish the validity of stochastic DCMs that accommodate random fluctuations in hidden neuronal and physiological states. We compare and contrast deterministic and stochastic DCMs, which do and do not ignore random fluctuations or noise on hidden states. We then compare stochastic DCMs, which do and do not ignore conditional dependence between hidden states and model parameters (generalised filtering and dynamic expectation maximisation, respectively). We first characterise state-noise by comparing the log evidence of models with different a priori assumptions about its amplitude, form and smoothness. Face validity of the inversion scheme is then established using data simulated with and without state-noise to ensure that DCM can identify the parameters and model that generated the data. Finally, we address construct validity using real data from an fMRI study of internet addiction. Our analyses suggest the following. (i) The inversion of stochastic causal models is feasible, given typical fMRI data. (ii) State-noise has nontrivial amplitude and smoothness. (iii) Stochastic DCM has face validity, in the sense that Bayesian model comparison can distinguish between data that have been generated with high and low levels of physiological noise and model inversion provides veridical estimates of effective connectivity. (iv) Relaxing conditional independence assumptions can have greater construct validity, in terms of revealing group differences not disclosed by variational schemes. Finally, we note that the ability to model endogenous or random fluctuations on hidden neuronal (and physiological) states provides a new and possibly more plausible perspective on how regionally specific signals in fMRI are generated. Copyright © 2011. Published by Elsevier Inc.

Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PubMed Central

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204
Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

PubMed

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.
Decoding Humor Experiences from Brain Activity of People Viewing Comedy Movies

PubMed Central

Sawahata, Yasuhito; Komine, Kazuteru; Morita, Toshiya; Hiruma, Nobuyuki

2013-01-01

Humans naturally have a sense of humor. Experiencing humor not only encourages social interactions, but also produces positive physiological effects on the human body, such as lowering blood pressure. Recent neuro-imaging studies have shown evidence for distinct mental state changes at work in people experiencing humor. However, the temporal characteristics of these changes remain elusive. In this paper, we objectively measured humor-related mental states from single-trial functional magnetic resonance imaging (fMRI) data obtained while subjects viewed comedy TV programs. Measured fMRI data were labeled on the basis of the lag before or after the viewer’s perception of humor (humor onset) determined by the viewer-reported humor experiences during the fMRI scans. We trained multiple binary classifiers, or decoders, to distinguish between fMRI data obtained at each lag from ones obtained during a neutral state in which subjects were not experiencing humor. As a result, in the right dorsolateral prefrontal cortex and the right temporal area, the decoders showed significant classification accuracies even at two seconds ahead of the humor onsets. Furthermore, given a time series of fMRI data obtained during movie viewing, we found that the decoders with significant performance were also able to predict the upcoming humor events on a volume-by-volume basis. Taking into account the hemodynamic delay, our results suggest that the upcoming humor events are encoded in specific brain areas up to about five seconds before the awareness of experiencing humor. Our results provide evidence that there exists a mental state lasting for a few seconds before actual humor perception, as if a viewer is expecting the future humorous events. PMID:24324656
Stochastic resonance therapy induces increased movement related caudate nucleus activity.

PubMed

Kaut, Oliver; Becker, Benjamin; Schneider, Christine; Zhou, Feng; Fliessbach, Klaus; Hurlemann, René; Wüllner, Ullrich

2016-10-12

Whole-body vibration can be used to supplement canonical physical treatment. It is performed while probands stand on a vibrating platform. Therapeutic vibration can be generated as a stochastic vibratory pattern, referred to as stochastic resonance whole-body vibration (SR-WBV). Despite the widespread use of SR-WBV its neurophysiological mechanism is unclear. A randomized sham-controlled double-blinded trial was performed as a pilot study. The experimental group received 6 cycles of SR-WBV at a frequency of 7 Hz with the SR-Zeptor device, and the sham group received the same treatment at a frequency of 1 Hz. At baseline 1.5 T functional magnetic resonance imaging (fMRI) was performed in the resting state, together with a finger/foot tapping test. A second fMRI was carried out after SR-WBV as sham treatment in both groups. Subsequently, a second cycle of SR-WBV was performed as sham or verum with consecutive fMRI, followed by a final fMRI on day 2. Nineteen healthy volunteers were allocated to the experimental or sham group, respectively. Analyses of specific effects revealed a significant treatment × time interaction effect (p < 0.05, small-volume corrected (SVC FWE-corrected)) in the left caudate nucleus during intermediate difficulty when comparing pre- vs post-SR-WBV treatment in the verum group. This proof-of-concept study suggests the existence of cerebral effects of SR-WBV.
Encoding and immediate retrieval tasks in patients with epilepsy: A functional MRI study of verbal and visual memory.

PubMed

Saddiki, Najat; Hennion, Sophie; Viard, Romain; Ramdane, Nassima; Lopes, Renaud; Baroncini, Marc; Szurhaj, William; Reyns, Nicolas; Pruvo, Jean Pierre; Delmaire, Christine

2018-05-01

Medial lobe temporal structures and more specifically the hippocampus play a decisive role in episodic memory. Most of the memory functional magnetic resonance imaging (fMRI) studies evaluate the encoding phase; the retrieval phase being performed outside the MRI. We aimed to determine the ability to reveal greater hippocampal fMRI activations during retrieval phase. Thirty-five epileptic patients underwent a two-step memory fMRI. During encoding phase, subjects were requested to identify the feminine or masculine gender of faces and words presented, in order to encourage stimulus encoding. One hour after, during retrieval phase, subjects had to recognize the word and face. We used an event-related design to identify hippocampal activations. There was no significant difference between patients with left temporal lobe epilepsy, patients with right temporal lobe epilepsy and patients with extratemporal lobe epilepsy on verbal and visual learning task. For words, patients demonstrated significantly more bilateral hippocampal activation for retrieval task than encoding task and when the tasks were associated than during encoding alone. Significant difference was seen between face-encoding alone and face retrieval alone. This study demonstrates the essential contribution of the retrieval task during a fMRI memory task but the number of patients with hippocampal activations was greater when the two tasks were taken into account. Copyright © 2018. Published by Elsevier Masson SAS.
Phenotypic regional fMRI activation patterns during memory encoding in MCI and AD

PubMed Central

Browndyke, Jeffrey N.; Giovanello, Kelly; Petrella, Jeffrey; Hayden, Kathleen; Chiba-Falek, Ornit; Tucker, Karen A.; Burke, James R.; Welsh-Bohmer, Kathleen A.

2014-01-01

Background Reliable blood-oxygen-level-dependent (BOLD) fMRI phenotypic biomarkers of Alzheimer's disease (AD) or mild cognitive impairment (MCI) are likely to emerge only from a systematic, quantitative, and aggregate examination of the functional neuroimaging research literature. Methods A series of random-effects, activation likelihood estimation (ALE) meta-analyses were conducted on studies of episodic memory encoding operations in AD and MCI samples relative to normal controls. ALE analyses were based upon a thorough literature search for all task-based functional neuroimaging studies in AD and MCI published up to January 2010. Analyses covered 16 fMRI studies, which yielded 144 distinct foci for ALE meta-analysis. Results ALE results indicated several regional task-based BOLD consistencies in MCI and AD patients relative to normal controls across the aggregate BOLD functional neuroimaging research literature. Patients with AD and those at significant risk (MCI) showed statistically significant consistent activation differences during episodic memory encoding in the medial temporal lobe (MTL), specifically parahippocampal gyrus, as well superior frontal gyrus, precuneus, and cuneus, relative to normal controls. Conclusions ALE consistencies broadly support the presence of frontal compensatory activity, MTL activity alteration, and posterior midline “default mode” hyperactivation during episodic memory encoding attempts in the diseased or prospective pre-disease condition. Taken together these robust commonalities may form the foundation for a task-based fMRI phenotype of memory encoding in AD. PMID:22841497
Object representations in ventral and dorsal visual streams: fMRI repetition effects depend on attention and part–whole configuration

PubMed Central

Thoma, Volker; Henson, Richard N.

2011-01-01

The effects of attention and object configuration on the neural responses to short-lag visual image repetition were investigated with fMRI. Attention to one of two object images in a prime display was cued spatially. The images were either intact or split vertically; a manipulation that negates the influence of view-based representations. A subsequent single intact probe image was named covertly. Behavioural priming observed as faster button presses was found for attended primes in both intact and split configurations, but only for uncued primes in the intact configuration. In a voxel-wise analysis, fMRI repetition suppression (RS) was observed in a left mid-fusiform region for attended primes, both intact and split, whilst a right intraparietal region showed repetition enhancement (RE) for intact primes, regardless of attention. In a factorial analysis across regions of interest (ROIs) defined from independent localiser contrasts, RS for attended objects in the ventral stream was significantly left-lateralised, whilst repetition effects in ventral and dorsal ROIs correlated with the amount of priming in specific conditions. These fMRI results extend hybrid theories of object recognition, implicating left ventral stream regions in analytic processing (requiring attention), consistent with prior hypotheses about hemispheric specialisation, and implicating dorsal stream regions in holistic processing (independent of attention). PMID:21554967
Investigating the neural basis for functional and effective connectivity. Application to fMRI

PubMed Central

Horwitz, Barry; Warner, Brent; Fitzer, Julie; Tagamets, M.-A; Husain, Fatima T; Long, Theresa W

2005-01-01

Viewing cognitive functions as mediated by networks has begun to play a central role in interpreting neuroscientific data, and studies evaluating interregional functional and effective connectivity have become staples of the neuroimaging literature. The neurobiological substrates of functional and effective connectivity are, however, uncertain. We have constructed neurobiologically realistic models for visual and auditory object processing with multiple interconnected brain regions that perform delayed match-to-sample (DMS) tasks. We used these models to investigate how neurobiological parameters affect the interregional functional connectivity between functional magnetic resonance imaging (fMRI) time-series. Variability is included in the models as subject-to-subject differences in the strengths of anatomical connections, scan-to-scan changes in the level of attention, and trial-to-trial interactions with non-specific neurons processing noise stimuli. We find that time-series correlations between integrated synaptic activities between the anterior temporal and the prefrontal cortex were larger during the DMS task than during a control task. These results were less clear when the integrated synaptic activity was haemodynamically convolved to generate simulated fMRI activity. As the strength of the model anatomical connectivity between temporal and frontal cortex was weakened, so too was the strength of the corresponding functional connectivity. These results provide a partial validation for using fMRI functional connectivity to assess brain interregional relations. PMID:16087450
Concept typicality responses in the semantic memory network.

PubMed

Santi, Andrea; Raposo, Ana; Frade, Sofia; Marques, J Frederico

2016-12-01

For decades concept typicality has been recognized as critical to structuring conceptual knowledge, but only recently has typicality been applied in better understanding the processes engaged by the neurological network underlying semantic memory. This previous work has focused on one region within the network - the Anterior Temporal Lobe (ATL). The ATL responds negatively to concept typicality (i.e., the more atypical the item, the greater the activation in the ATL). To better understand the role of typicality in the entire network, we ran an fMRI study using a category verification task in which concept typicality was manipulated parametrically. We argue that typicality is relevant to both amodal feature integration centers as well as category-specific regions. Both the Inferior Frontal Gyrus (IFG) and ATL demonstrated a negative correlation with typicality, whereas inferior parietal regions showed positive effects. We interpret this in light of functional theories of these regions. Interactions between category and typicality were not observed in regions classically recognized as category-specific, thus, providing an argument against category specific regions, at least with fMRI. Copyright © 2016 Elsevier Ltd. All rights reserved.
Multimodal and Multi-tissue Measures of Connectivity Revealed by Joint Independent Component Analysis.

PubMed

Franco, Alexandre R; Ling, Josef; Caprihan, Arvind; Calhoun, Vince D; Jung, Rex E; Heileman, Gregory L; Mayer, Andrew R

2008-12-01

The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.
Age and amyloid-related alterations in default network habituation to stimulus repetition

PubMed Central

Vannini, Patrizia; Hedden, Trey; Becker, John A.; Sullivan, Caroline; Putcha, Deepti; Rentz, Dorene; Johnson, Keith A.; Sperling, Reisa. A.

2011-01-01

The neural networks supporting encoding of new information are thought to decline with age, although mnemonic techniques such as repetition may enhance performance in older individuals. Accumulation of amyloid-β, one hallmark pathology of Alzheimer’s disease (AD), may contribute to functional alterations in memory networks measured with functional magnetic resonance imaging (fMRI) prior to onset of cognitive impairment. We investigated the effects of age and amyloid burden on fMRI activity in the default network and hippocampus during repetitive encoding. Older individuals, particularly those with high amyloid burden, demonstrated decreased task-induced deactivation in the posteromedial cortices during initial stimulus presentation and failed to modulate fMRI activity in response to repeated trials, whereas young subjects demonstrated a stepwise decrease in deactivation with repetition. The hippocampus demonstrated similar patterns across the groups, showing task-induced activity that decreased in response to repetition. These findings demonstrate that age and amyloid have dissociable functional effects on specific nodes within a distributed memory network, and suggest that functional brain changes may begin far in advance of symptomatic AD. PMID:21334099
Decoding attended information in short-term memory: an EEG study.

PubMed

LaRocque, Joshua J; Lewis-Peacock, Jarrod A; Drysdale, Andrew T; Oberauer, Klaus; Postle, Bradley R

2013-01-01

For decades it has been assumed that sustained, elevated neural activity--the so-called active trace--is the neural correlate of the short-term retention of information. However, a recent fMRI study has suggested that this activity may be more related to attention than to retention. Specifically, a multivariate pattern analysis failed to find evidence that information that was outside the focus of attention, but nonetheless in STM, was retained in an active state. Here, we replicate and extend this finding by querying the neural signatures of attended versus unattended information within STM with electroencephalograpy (EEG), a method sensitive to oscillatory neural activity to which the previous fMRI study was insensitive. We demonstrate that in the delay-period EEG activity, there is information only about memory items that are also in the focus of attention. Information about items outside the focus of attention is not detectable. This result converges with the fMRI findings to suggest that, contrary to conventional wisdom, an active memory trace may be unnecessary for the short-term retention of information.
When two are better than one: Bilateral mesial temporal lobe contributions associated with better vocabulary skills in children and adolescents.

PubMed

Bartha-Doering, Lisa; Novak, Astrid; Kollndorfer, Kathrin; Kasprian, Gregor; Schuler, Anna-Lisa; Berl, Madison M; Fischmeister, Florian Ph S; Gaillard, William D; Alexopoulos, Johanna; Prayer, Daniela; Seidl, Rainer

2018-06-15

This study considered the involvement of the mesial temporal lobe (MTL) in language and verbal memory functions in healthy children and adolescents. We investigated 30 healthy, right-handed children and adolescents, aged 7-16, with a fMRI language paradigm and a comprehensive cognitive test battery. We found significant MTL activations during language fMRI in all participants; 63% of them had left lateralized MTL activations, 20% exhibited right MTL lateralization, and 17% showed bilateral MTL involvement during the fMRI language paradigm. Group analyses demonstrated a strong negative correlation between the lateralization of MTL activations and language functions. Specifically, children with less lateralized MTL activation showed significantly better vocabulary skills. These findings suggest that the mesial temporal lobes of both hemispheres play an important role in language functioning, even in right-handers. Our results furthermore show that bilateral mesial temporal lobe involvement is advantageous for vocabulary skills in healthy, right-handed children and adolescents. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Emotional sensitivity, emotion regulation and impulsivity in borderline personality disorder: a critical review of fMRI studies.

PubMed

van Zutphen, Linda; Siep, Nicolette; Jacob, Gitta A; Goebel, Rainer; Arntz, Arnoud

2015-04-01

Emotional sensitivity, emotion regulation and impulsivity are fundamental topics in research of borderline personality disorder (BPD). Studies using fMRI examining the neural correlates concerning these topics is growing and has just begun understanding the underlying neural correlates in BPD. However, there are strong similarities but also important differences in results of different studies. It is therefore important to know in more detail what these differences are and how we should interpret these. In present review a critical light is shed on the fMRI studies examining emotional sensitivity, emotion regulation and impulsivity in BPD patients. First an outline of the methodology and the results of the studies will be given. Thereafter important issues that remained unanswered and topics to improve future research are discussed. Future research should take into account the limited power of previous studies and focus more on BPD specificity with regard to time course responses, different regulation strategies, manipulation of self-regulation, medication use, a wider range of stimuli, gender effects and the inclusion of a clinical control group. Copyright © 2015 Elsevier Ltd. All rights reserved.
Brain Entropy Mapping Using fMRI

PubMed Central

Wang, Ze; Li, Yin; Childress, Anna Rose; Detre, John A.

2014-01-01

Entropy is an important trait for life as well as the human brain. Characterizing brain entropy (BEN) may provide an informative tool to assess brain states and brain functions. Yet little is known about the distribution and regional organization of BEN in normal brain. The purpose of this study was to examine the whole brain entropy patterns using a large cohort of normal subjects. A series of experiments were first performed to validate an approximate entropy measure regarding its sensitivity, specificity, and reliability using synthetic data and fMRI data. Resting state fMRI data from a large cohort of normal subjects (n = 1049) from multi-sites were then used to derive a 3-dimensional BEN map, showing a sharp low-high entropy contrast between the neocortex and the rest of brain. The spatial heterogeneity of resting BEN was further studied using a data-driven clustering method, and the entire brain was found to be organized into 7 hierarchical regional BEN networks that are consistent with known structural and functional brain parcellations. These findings suggest BEN mapping as a physiologically and functionally meaningful measure for studying brain functions. PMID:24657999
Reward magnitude tracking by neural populations in ventral striatum

PubMed Central

Fiallos, Ana M.; Bricault, Sarah J.; Cai, Lili X.; Worku, Hermoon A.; Colonnese, Matthew T.; Westmeyer, Gil; Jasanoff, Alan

2017-01-01

Evaluation of the magnitudes of intrinsically rewarding stimuli is essential for assigning value and guiding behavior. By combining parametric manipulation of a primary reward, medial forebrain bundle (MFB) microstimulation, with functional magnetic imaging (fMRI) in rodents, we delineated a broad network of structures activated by behaviorally characterized levels of rewarding stimulation. Correlation of psychometric behavioral measurements with fMRI response magnitudes revealed regions whose activity corresponded closely to the subjective magnitude of rewards. The largest and most reliable focus of reward magnitude tracking was observed in the shell region of the nucleus accumbens (NAc). Although the nonlinear nature of neurovascular coupling complicates interpretation of fMRI findings in precise neurophysiological terms, reward magnitude tracking was not observed in vascular compartments and could not be explained by saturation of region-specific hemodynamic responses. In addition, local pharmacological inactivation of NAc changed the profile of animals’ responses to rewards of different magnitudes without altering mean reward response rates, further supporting a hypothesis that neural population activity in this region contributes to assessment of reward magnitudes. PMID:27789262
Resting-State Seed-Based Analysis: An Alternative to Task-Based Language fMRI and Its Laterality Index.

PubMed

Smitha, K A; Arun, K M; Rajesh, P G; Thomas, B; Kesavadas, C

2017-06-01

Language is a cardinal function that makes human unique. Preservation of language function poses a great challenge for surgeons during resection. The aim of the study was to assess the efficacy of resting-state fMRI in the lateralization of language function in healthy subjects to permit its further testing in patients who are unable to perform task-based fMRI. Eighteen healthy right-handed volunteers were prospectively evaluated with resting-state fMRI and task-based fMRI to assess language networks. The laterality indices of Broca and Wernicke areas were calculated by using task-based fMRI via a voxel-value approach. We adopted seed-based resting-state fMRI connectivity analysis together with parameters such as amplitude of low-frequency fluctuation and fractional amplitude of low-frequency fluctuation (fALFF). Resting-state fMRI connectivity maps for language networks were obtained from Broca and Wernicke areas in both hemispheres. We performed correlation analysis between the laterality index and the z scores of functional connectivity, amplitude of low-frequency fluctuation, and fALFF. Pearson correlation analysis between signals obtained from the z score of fALFF and the laterality index yielded a correlation coefficient of 0.849 ( P < .05). Regression analysis of the fALFF with the laterality index yielded an R 2 value of 0.721, indicating that 72.1% of the variance in the laterality index of task-based fMRI could be predicted from the fALFF of resting-state fMRI. The present study demonstrates that fALFF can be used as an alternative to task-based fMRI for assessing language laterality. There was a strong positive correlation between the fALFF of the Broca area of resting-state fMRI with the laterality index of task-based fMRI. Furthermore, we demonstrated the efficacy of fALFF for predicting the laterality of task-based fMRI. © 2017 by American Journal of Neuroradiology.
Flexible modulation of risk attitude during decision-making under quota.

PubMed

Fujimoto, Atsushi; Takahashi, Hidehiko

2016-10-01

Risk attitude is often regarded as an intrinsic parameter in the individual personality. However, ethological studies reported state-dependent strategy optimization irrespective of individual preference. To synthesize the two contrasting literatures, we developed a novel gambling task that dynamically manipulated the quota severity (required outcome to clear the task) in a course of choice trials and conducted a task-fMRI study in human participants. The participants showed their individual risk preference when they had no quota constraint ('individual-preference mode'), while they adopted state-dependent optimal strategy when they needed to achieve a quota ('strategy-optimization mode'). fMRI analyses illustrated that the interplay among prefrontal areas and salience-network areas reflected the quota severity and the utilization of the optimal strategy, shedding light on the neural substrates of the quota-dependent risk attitude. Our results demonstrated the complex nature of risk-sensitive decision-making and may provide a new perspective for the understanding of problematic risky behaviors in human. Copyright © 2016 Elsevier Inc. All rights reserved.
A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

PubMed

Kell, Alexander J E; Yamins, Daniel L K; Shook, Erica N; Norman-Haignere, Sam V; McDermott, Josh H

2018-05-02

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems. Copyright © 2018 Elsevier Inc. All rights reserved.
A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

PubMed

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.

A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

PubMed Central

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups. PMID:24905072
Adult brains don't fully overcome biases that lead to incorrect performance during cognitive development: an fMRI study in young adults completing a Piaget-like task.

PubMed

Leroux, Gaëlle; Spiess, Jeanne; Zago, Laure; Rossi, Sandrine; Lubin, Amélie; Turbelin, Marie-Renée; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie; Houdé, Olivier; Joliot, Marc

2009-03-01

A current issue in developmental science is that greater continuity in cognition between children and adults may exist than is usually appreciated in Piaget-like (stages or 'staircase') models. This phenomenon has been demonstrated at the behavioural level, but never at the brain level. Here we show with functional magnetic resonance imaging (fMRI), for the first time, that adult brains do not fully overcome the biases of childhood. More specifically, the aim of this fMRI study was to evaluate whether the perceptual bias that leads to incorrect performance during cognitive development in a Piaget-like task is still a bias in the adult brain and hence requires an executive network to overcome it. Here, we compared two numerical-judgment tasks, one being a Piaget-like task with number-length interference (called 'INT') and the other being a control task with number-length covariation ('COV'). We also used a colour-detection task to control for stimuli numerosity, spatial distribution, and frequency. Our behavioural results confirmed that INT remains a difficult task for young adults. Indeed, response times were significantly higher in INT than in COV. Moreover, we observed that only in INT did response times increase linearly as a function of the number of items. The fMRI results indicate that the brain network common to INT and COV shows a large rightward functional asymmetry, emphasizing the visuospatial nature of these two tasks. When INT was compared with COV, activations were found within a right frontal network, including the pre-supplementary motor area, the anterior cingulate cortex, and the middle frontal gyrus, which probably reflect detection of the number/length conflict and inhibition of the 'length-equals-number' response strategy. Finally, activations related to visuospatial and quantitative processing, enhanced or specifically recruited in the Piaget-like task, were found in bilateral posterior areas.
Mask_explorer: A tool for exploring brain masks in fMRI group analysis.

PubMed

Gajdoš, Martin; Mikl, Michal; Mareček, Radek

2016-10-01

Functional magnetic resonance imaging (fMRI) studies of the human brain are appearing in increasing numbers, providing interesting information about this complex system. Unique information about healthy and diseased brains is inferred using many types of experiments and analyses. In order to obtain reliable information, it is necessary to conduct consistent experiments with large samples of subjects and to involve statistical methods to confirm or reject any tested hypotheses. Group analysis is performed for all voxels within a group mask, i.e. a common space where all of the involved subjects contribute information. To our knowledge, a user-friendly interface with the ability to visualize subject-specific details in a common analysis space did not yet exist. The purpose of our work is to develop and present such interface. Several pitfalls have to be avoided while preparing fMRI data for group analysis. One such pitfall is spurious non-detection, caused by inferring conclusions in the volume of a group mask that has been corrupted due to a preprocessing failure. We describe a MATLAB toolbox, called the mask_explorer, designed for prevention of this pitfall. The mask_explorer uses a graphical user interface, enables a user-friendly exploration of subject masks and is freely available. It is able to compute subject masks from raw data and create lists of subjects with potentially problematic data. It runs under MATLAB with the widely used SPM toolbox. Moreover, we present several practical examples where the mask_explorer is usefully applied. The mask_explorer is designed to quickly control the quality of the group fMRI analysis volume and to identify specific failures related to preprocessing steps and acquisition. It helps researchers detect subjects with potentially problematic data and consequently enables inspection of the data. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Combined DTI Tractography and Functional MRI Study of the Language Connectome in Healthy Volunteers: Extensive Mapping of White Matter Fascicles and Cortical Activations.

PubMed

Vassal, François; Schneider, Fabien; Boutet, Claire; Jean, Betty; Sontheimer, Anna; Lemaire, Jean-Jacques

2016-01-01

Despite a better understanding of brain language organization into large-scale cortical networks, the underlying white matter (WM) connectivity is still not mastered. Here we combined diffusion tensor imaging (DTI) fiber tracking (FT) and language functional magnetic resonance imaging (fMRI) in twenty healthy subjects to gain new insights into the macroscopic structural connectivity of language. Eight putative WM fascicles for language were probed using a deterministic DTI-FT technique: the arcuate fascicle (AF), superior longitudinal fascicle (SLF), uncinate fascicle (UF), temporo-occipital fascicle, inferior fronto-occipital fascicle (IFOF), middle longitudinal fascicle (MdLF), frontal aslant fascicle and operculopremotor fascicle. Specific measurements (i.e. volume, length, fractional anisotropy) and precise cortical terminations were derived for each WM fascicle within both hemispheres. Connections between these WM fascicles and fMRI activations were studied to determine which WM fascicles are related to language. WM fascicle volumes showed asymmetries: leftward for the AF, temporoparietal segment of SLF and UF, and rightward for the frontoparietal segment of the SLF. The lateralization of the AF, IFOF and MdLF extended to differences in patterns of anatomical connections, which may relate to specific hemispheric abilities. The leftward asymmetry of the AF was correlated to the leftward asymmetry of fMRI activations, suggesting that the lateralization of the AF is a structural substrate of hemispheric language dominance. We found consistent connections between fMRI activations and terminations of the eight WM fascicles, providing a detailed description of the language connectome. WM fascicle terminations were also observed beyond fMRI-confirmed language areas and reached numerous cortical areas involved in different functional brain networks. These findings suggest that the reported WM fascicles are not exclusively involved in language and might be related to other cognitive functions such as visual recognition, spatial attention, executive functions, memory, and processing of emotional and behavioral aspects.
Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state.

PubMed

Cao, Hengyi; Plichta, Michael M; Schäfer, Axel; Haddad, Leila; Grimm, Oliver; Schneider, Michael; Esslinger, Christine; Kirsch, Peter; Meyer-Lindenberg, Andreas; Tost, Heike

2014-01-01

The investigation of the brain connectome with functional magnetic resonance imaging (fMRI) and graph theory analyses has recently gained much popularity, but little is known about the robustness of these properties, in particular those derived from active fMRI tasks. Here, we studied the test-retest reliability of brain graphs calculated from 26 healthy participants with three established fMRI experiments (n-back working memory, emotional face-matching, resting state) and two parcellation schemes for node definition (AAL atlas, functional atlas proposed by Power et al.). We compared the intra-class correlation coefficients (ICCs) of five different data processing strategies and demonstrated a superior reliability of task-regression methods with condition-specific regressors. The between-task comparison revealed significantly higher ICCs for resting state relative to the active tasks, and a superiority of the n-back task relative to the face-matching task for global and local network properties. While the mean ICCs were typically lower for the active tasks, overall fair to good reliabilities were detected for global and local connectivity properties, and for the n-back task with both atlases, smallworldness. For all three tasks and atlases, low mean ICCs were seen for the local network properties. However, node-specific good reliabilities were detected for node degree in regions known to be critical for the challenged functions (resting-state: default-mode network nodes, n-back: fronto-parietal nodes, face-matching: limbic nodes). Between-atlas comparison demonstrated significantly higher reliabilities for the functional parcellations for global and local network properties. Our findings can inform the choice of processing strategies, brain atlases and outcome properties for fMRI studies using active tasks, graph theory methods, and within-subject designs, in particular future pharmaco-fMRI studies. © 2013 Elsevier Inc. All rights reserved.
CATEGORY-SPECIFIC SEMANTIC MEMORY: CONVERGING EVIDENCE FROM BOLD fMRI AND ALZHEIMER’S DISEASE

PubMed Central

Grossman, Murray; Peelle, Jonathan E.; Smith, Edward E.; McMillan, Corey T.; Cook, Philip; Powers, John; Dreyfuss, Michael; Bonner, Michael F.; Richmond, Lauren; Boller, Ashley; Camp, Emily; Burkholder, Lisa

2012-01-01

Patients with Alzheimer’s disease have category-specific semantic memory difficulty for natural relative to manufactured objects. We assessed the basis for this deficit by asking healthy adults and patients to judge whether pairs of words share a feature (e.g. “banana:lemon – COLOR”). In an fMRI study, healthy adults showed gray matter (GM) activation of temporal-occipital cortex (TOC) where visual-perceptual features may be represented, and prefrontal cortex (PFC) which may contribute to feature selection. Tractography revealed dorsal and ventral stream white matter (WM) projections between PFC and TOC. Patients had greater difficulty with natural than manufactured objects. This was associated with greater overlap between diseased GM areas correlated with natural kinds in patients and fMRI activation in healthy adults for natural than manufactured artifacts, and the dorsal WM projection between PFC and TOC in patients correlated only with judgments of natural kinds. Patients thus remained dependent on the same neural network as controls during judgments of natural kinds, despite disease in these areas. For manufactured objects, patients’ judgments showed limited correlations with PFC and TOC GM areas activated by controls, and did not correlate with the PFC-TOC dorsal WM tract. Regions outside of the PFC–TOC network thus may help support patients’ judgments of manufactured objects. We conclude that a large-scale neural network for semantic memory implicates both feature knowledge representations in modality-specific association cortex and heteromodal regions important for accessing this knowledge, and that patients’ relative deficit for natural kinds is due in part to their dependence on this network despite disease in these areas. PMID:23220494
Category-specific semantic memory: converging evidence from bold fMRI and Alzheimer's disease.

PubMed

Grossman, Murray; Peelle, Jonathan E; Smith, Edward E; McMillan, Corey T; Cook, Philip; Powers, John; Dreyfuss, Michael; Bonner, Michael F; Richmond, Lauren; Boller, Ashley; Camp, Emily; Burkholder, Lisa

2013-03-01

Patients with Alzheimer's disease have category-specific semantic memory difficulty for natural relative to manufactured objects. We assessed the basis for this deficit by asking healthy adults and patients to judge whether pairs of words share a feature (e.g. "banana:lemon-COLOR"). In an fMRI study, healthy adults showed gray matter (GM) activation of temporal-occipital cortex (TOC) where visual-perceptual features may be represented, and prefrontal cortex (PFC) which may contribute to feature selection. Tractography revealed dorsal and ventral stream white matter (WM) projections between PFC and TOC. Patients had greater difficulty with natural than manufactured objects. This was associated with greater overlap between diseased GM areas correlated with natural kinds in patients and fMRI activation in healthy adults for natural kinds. The dorsal WM projection between PFC and TOC in patients correlated only with judgments of natural kinds. Patients thus remained dependent on the same neural network as controls during judgments of natural kinds, despite disease in these areas. For manufactured objects, patients' judgments showed limited correlations with PFC and TOC GM areas activated by controls, and did not correlate with the PFC-TOC dorsal WM tract. Regions outside of the PFC-TOC network thus may help support patients' judgments of manufactured objects. We conclude that a large-scale neural network for semantic memory implicates both feature knowledge representations in modality-specific association cortex and heteromodal regions important for accessing this knowledge, and that patients' relative deficit for natural kinds is due in part to their dependence on this network despite disease in these areas. Copyright © 2012 Elsevier Inc. All rights reserved.
Heightened Salience of Anger and Aggression in Female Adolescents With Borderline Personality Disorder—A Script-Based fMRI Study

PubMed Central

Krauch, Marlene; Ueltzhöffer, Kai; Brunner, Romuald; Kaess, Michael; Hensel, Saskia; Herpertz, Sabine C.; Bertsch, Katja

2018-01-01

Background: Anger and aggression belong to the core symptoms of borderline personality disorder. Although an early and specific treatment of BPD is highly relevant to prevent chronification, still little is known about anger and aggression and their neural underpinnings in adolescents with BPD. Method: Twenty female adolescents with BPD (age 15–17 years) and 20 female healthy adolescents (age 15–17 years) took part in this functional magnetic resonance imaging (fMRI) study. A script-driven imagery paradigm was used to induce rejection-based feelings of anger, which was followed by descriptions of self-directed and other-directed aggressive reactions. To investigate the specificity of the neural activation patterns for adolescent patients, results were compared with data from 34 female adults with BPD (age 18–50 years) and 32 female healthy adults (age 18–50 years). Results: Adolescents with BPD showed increased activations in the left posterior insula and left dorsal striatum as well as in the left inferior frontal cortex and parts of the mentalizing network during the rejection-based anger induction and the imagination of aggressive reactions compared to healthy adolescents. For the other-directed aggression phase, a significant diagnosis by age interaction confirmed that these results were specific for adolescents. Discussion: The results of this very first fMRI study on anger and aggression in adolescents with BPD suggest an enhanced emotional reactivity to and higher effort in controlling anger and aggression evoked by social rejection at an early developmental stage of the disorder. Since emotion dysregulation is a known mediator for aggression in BPD, the results point to the need of appropriate early interventions for adolescents with BPD. PMID:29632476
Frequency-specific attentional modulation in human primary auditory cortex and midbrain.

PubMed

Riecke, Lars; Peters, Judith C; Valente, Giancarlo; Poser, Benedikt A; Kemper, Valentin G; Formisano, Elia; Sorger, Bettina

2018-07-01

Paying selective attention to an audio frequency selectively enhances activity within primary auditory cortex (PAC) at the tonotopic site (frequency channel) representing that frequency. Animal PAC neurons achieve this 'frequency-specific attentional spotlight' by adapting their frequency tuning, yet comparable evidence in humans is scarce. Moreover, whether the spotlight operates in human midbrain is unknown. To address these issues, we studied the spectral tuning of frequency channels in human PAC and inferior colliculus (IC), using 7-T functional magnetic resonance imaging (FMRI) and frequency mapping, while participants focused on different frequency-specific sounds. We found that shifts in frequency-specific attention alter the response gain, but not tuning profile, of PAC frequency channels. The gain modulation was strongest in low-frequency channels and varied near-monotonically across the tonotopic axis, giving rise to the attentional spotlight. We observed less prominent, non-tonotopic spatial patterns of attentional modulation in IC. These results indicate that the frequency-specific attentional spotlight in human PAC as measured with FMRI arises primarily from tonotopic gain modulation, rather than adapted frequency tuning. Moreover, frequency-specific attentional modulation of afferent sound processing in human IC seems to be considerably weaker, suggesting that the spotlight diminishes toward this lower-order processing stage. Our study sheds light on how the human auditory pathway adapts to the different demands of selective hearing. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Investigation of BOLD fMRI Resonance Frequency Shifts and Quantitative Susceptibility Changes at 7 T

PubMed Central

Bianciardi, Marta; van Gelderen, Peter; Duyn, Jeff H.

2013-01-01

Although blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) experiments of brain activity generally rely on the magnitude of the signal, they also provide frequency information that can be derived from the phase of the signal. However, because of confounding effects of instrumental and physiological origin, BOLD related frequency information is difficult to extract and therefore rarely used. Here, we explored the use of high field (7 T) and dedicated signal processing methods to extract frequency information and use it to quantify and interpret blood oxygenation and blood volume changes. We found that optimized preprocessing improves detection of task-evoked and spontaneous changes in phase signals and resonance frequency shifts over large areas of the cortex with sensitivity comparable to that of magnitude signals. Moreover, our results suggest the feasibility of mapping BOLD quantitative susceptibility changes in at least part of the activated area and its largest draining veins. Comparison with magnitude data suggests that the observed susceptibility changes originate from neuronal activity through induced blood volume and oxygenation changes in pial and intracortical veins. Further, from frequency shifts and susceptibility values, we estimated that, relative to baseline, the fractional oxygen saturation in large vessels increased by 0.02–0.05 during stimulation, which is consistent to previously published estimates. Together, these findings demonstrate that valuable information can be derived from fMRI imaging of BOLD frequency shifts and quantitative susceptibility changes. PMID:23897623
Protracted development of executive and mnemonic brain systems underlying working memory in adolescence: A longitudinal fMRI study.

PubMed

Simmonds, Daniel J; Hallquist, Michael N; Luna, Beatriz

2017-08-15

Working memory (WM), the ability to hold information on-line to guide planned behavior, improves through adolescence in parallel with continued maturation of critical brain systems supporting cognitive control. Initial developmental neuroimaging studies with one or two timepoints have provided important though varied results limiting our understanding of which and how neural systems change during this transition into mature WM. In this study, we leverage functional magnetic resonance imaging (fMRI) longitudinal data spanning up to 9 years in 129 normally developing individuals to identify which systems demonstrate growth changes that accompany improvements in WM performance. We used a memory guided saccade task that allowed us to probe encoding, pure maintenance, and retrieval neural processes of WM. Consistent with prior research, we found that WM performance continued to improve into the early 20's. fMRI region of interest (ROI) analyses revealed developmental (1) increases in sensorimotor-related (encoding/retrieval) activity in visual cortex from childhood through early adulthood that were associated with WM accuracy and (2) decreases in sustained (maintenance) activity in executive regions from childhood through mid-adolescence that were associated with response latency in childhood and early adolescence. Together these results provide compelling evidence that underlying the maturation of WM is a transition from reliance on executive systems to specialized regions related to the domain of mnemonic requirements of the task leading to optimal performance. Copyright © 2017. Published by Elsevier Inc.
A Method for Accurate Group Difference Detection by Constraining the Mixing Coefficients in an ICA Framework

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Clark, Vincent P.; Calhoun, Vince D.

2009-01-01

Independent component analysis (ICA) is a promising method that is increasingly used to analyze brain imaging data such as functional magnetic resonance imaging (fMRI), structural MRI, and electroencephalography and has also proved useful for group comparison, e.g., differentiating healthy controls from patients. An advantage of ICA is its ability to identify components that are mixed in an unknown manner. However, ICA is not necessarily robust and optimal in identifying between-group effects, especially in highly noisy situations. Here, we propose a modified ICA framework for multi-group data analysis that incorporates prior information regarding group membership as a constraint into the mixing coefficients. Our approach, called coefficient-constrained ICA (CC-ICA), prioritizes identification of components that show a significant group difference. The performance of CC-ICA via synthetic and hybrid data simulations is evaluated under different hypothesis testing assumptions and signal to noise ratios (SNRs). Group analysis is also conducted on real multitask fMRI data. Results show that CC-ICA improves the estimation accuracy of the independent components greatly, especially those that have different patterns for different groups (e.g., patients vs. controls); In addition, it enhances the data extraction sensitivity to group differences by ranking components with P value or J-divergence more consistently with the ground truth. The proposed algorithm performs quite well for both group-difference detection and multitask fMRI data fusion, which may prove especially important for the identification of relevant disease biomarkers. PMID:19172631
Neurocognitive Dimensions of Lexical Complexity in Polish

ERIC Educational Resources Information Center

Szlachta, Zanna; Bozic, Mirjana; Jelowicka, Aleksandra; Marslen-Wilson, William D.

2012-01-01

Neuroimaging studies of English suggest that speech comprehension engages two interdependent systems: a bilateral fronto-temporal network responsible for general perceptual and cognitive processing, and a specialised left-lateralised network supporting specifically linguistic processing. Using fMRI we test this hypothesis in Polish, a Slavic…
Fuzzy cluster analysis of high-field functional MRI data.

PubMed

Windischberger, Christian; Barth, Markus; Lamm, Claus; Schroeder, Lee; Bauer, Herbert; Gur, Ruben C; Moser, Ewald

2003-11-01

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.
Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves.

PubMed

Schwalm, Miriam; Schmid, Florian; Wachsmuth, Lydia; Backhaus, Hendrik; Kronfeld, Andrea; Aedo Jury, Felipe; Prouvot, Pierre-Hugues; Fois, Consuelo; Albers, Franziska; van Alst, Timo; Faber, Cornelius; Stroh, Albrecht

2017-09-15

Spontaneous slow oscillation-associated slow wave activity represents an internally generated state which is characterized by alternations of network quiescence and stereotypical episodes of neuronal activity - slow wave events. However, it remains unclear which macroscopic signal is related to these active periods of the slow wave rhythm. We used optic fiber-based calcium recordings of local neural populations in cortex and thalamus to detect neurophysiologically defined slow calcium waves in isoflurane anesthetized rats. The individual slow wave events were used for an event-related analysis of simultaneously acquired whole-brain BOLD fMRI. We identified BOLD responses directly related to onsets of slow calcium waves, revealing a cortex-wide BOLD correlate: the entire cortex was engaged in this specific type of slow wave activity. These findings demonstrate a direct relation of defined neurophysiological events to a specific BOLD activity pattern and were confirmed for ongoing slow wave activity by independent component and seed-based analyses.
Cortex-wide BOLD fMRI activity reflects locally-recorded slow oscillation-associated calcium waves

PubMed Central

Backhaus, Hendrik; Kronfeld, Andrea; Aedo Jury, Felipe; Prouvot, Pierre-Hugues; Fois, Consuelo; Albers, Franziska; van Alst, Timo

2017-01-01

Spontaneous slow oscillation-associated slow wave activity represents an internally generated state which is characterized by alternations of network quiescence and stereotypical episodes of neuronal activity - slow wave events. However, it remains unclear which macroscopic signal is related to these active periods of the slow wave rhythm. We used optic fiber-based calcium recordings of local neural populations in cortex and thalamus to detect neurophysiologically defined slow calcium waves in isoflurane anesthetized rats. The individual slow wave events were used for an event-related analysis of simultaneously acquired whole-brain BOLD fMRI. We identified BOLD responses directly related to onsets of slow calcium waves, revealing a cortex-wide BOLD correlate: the entire cortex was engaged in this specific type of slow wave activity. These findings demonstrate a direct relation of defined neurophysiological events to a specific BOLD activity pattern and were confirmed for ongoing slow wave activity by independent component and seed-based analyses. PMID:28914607
Rapid whole-brain resting-state fMRI at 3 T: Efficiency-optimized three-dimensional EPI versus repetition time-matched simultaneous-multi-slice EPI.

PubMed

Stirnberg, Rüdiger; Huijbers, Willem; Brenner, Daniel; Poser, Benedikt A; Breteler, Monique; Stöcker, Tony

2017-12-01

State-of-the-art simultaneous-multi-slice (SMS-)EPI and 3D-EPI share several properties that benefit functional MRI acquisition. Both sequences employ equivalent parallel imaging undersampling with controlled aliasing to achieve high temporal sampling rates. As a volumetric imaging sequence, 3D-EPI offers additional means of acceleration complementary to 2D-CAIPIRINHA sampling, such as fast water excitation and elliptical sampling. We performed an application-oriented comparison between a tailored, six-fold CAIPIRINHA-accelerated 3D-EPI protocol at 530 ms temporal and 2.4 mm isotropic spatial resolution and an SMS-EPI protocol with identical spatial and temporal resolution for whole-brain resting-state fMRI at 3 T. The latter required eight-fold slice acceleration to compensate for the lack of elliptical sampling and fast water excitation. Both sequences used vendor-supplied on-line image reconstruction. We acquired test/retest resting-state fMRI scans in ten volunteers, with simultaneous acquisition of cardiac and respiration data, subsequently used for optional physiological noise removal (nuisance regression). We found that the 3D-EPI protocol has significantly increased temporal signal-to-noise ratio throughout the brain as compared to the SMS-EPI protocol, especially when employing motion and nuisance regression. Both sequence types reliably identified known functional networks with stronger functional connectivity values for the 3D-EPI protocol. We conclude that the more time-efficient 3D-EPI primarily benefits from reduced parallel imaging noise due to a higher, actual k-space sampling density compared to SMS-EPI. The resultant BOLD sensitivity increase makes 3D-EPI a valuable alternative to SMS-EPI for whole-brain fMRI at 3 T, with voxel sizes well below 3 mm isotropic and sampling rates high enough to separate dominant cardiac signals from BOLD signals in the frequency domain. Copyright © 2017 Elsevier Inc. All rights reserved.
ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging

PubMed Central

Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F.; Auerbach, Edward J.; Douaud, Gwenaëlle; Sexton, Claire E.; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E.; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L.; Smith, Stephen M.

2014-01-01

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses. PMID:24657355
ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.

PubMed

Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian F; Auerbach, Edward J; Douaud, Gwenaëlle; Sexton, Claire E; Zsoldos, Enikő; Ebmeier, Klaus P; Filippini, Nicola; Mackay, Clare E; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L; Smith, Stephen M

2014-07-15

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses. Copyright © 2014 Elsevier Inc. All rights reserved.
A comparison between EEG source localization and fMRI during the processing of emotional visual stimuli

NASA Astrophysics Data System (ADS)

Hu, Jin; Tian, Jie; Pan, Xiaohong; Liu, Jiangang

2007-03-01

The purpose of this paper is to compare between EEG source localization and fMRI during emotional processing. 108 pictures for EEG (categorized as positive, negative and neutral) and 72 pictures for fMRI were presented to 24 healthy, right-handed subjects. The fMRI data were analyzed using statistical parametric mapping with SPM2. LORETA was applied to grand averaged ERP data to localize intracranial sources. Statistical analysis was implemented to compare spatiotemporal activation of fMRI and EEG. The fMRI results are in accordance with EEG source localization to some extent, while part of mismatch in localization between the two methods was also observed. In the future we should apply the method for simultaneous recording of EEG and fMRI to our study.

Neurobiology of Insight Deficits in Schizophrenia: An fMRI Study

PubMed Central

Shad, Mujeeb U.; Keshavan, Matcheri S.

2015-01-01

Prior research has shown insight deficits in schizophrenia to be associated with specific neuroimaging changes (primarily structural) especially in the prefrontal sub-regions. However, little is known about the functional correlates of impaired insight. Seventeen patients with schizophrenia (mean age 40.0±10.3; M/F= 14/3) underwent fMRI on a Philips 3.0 T Achieva system while performing on a self-awareness task containing self- vs. other-directed sentence stimuli. SPM5 was used to process the imaging data. Preprocessing consisted of realignment, coregistration, and normalization, and smoothing. A regression analysis was used to examine the relationship between brain activation in response to self-directed versus other-directed sentence stimuli and average scores on behavioral measures of awareness of symptoms and attribution of symptoms to the illness from Scale to Assess Unawareness of Mental Disorders. Family Wise Error correction was employed in the fMRI analysis. Average scores on awareness of symptoms (1 = aware; 5 = unaware) were associated with activation of multiple brain regions, including prefrontal, parietal and limbic areas as well as basal ganglia. However, average scores on correct attribution of symptoms (1 = attribute; 5 = misattribute) were associated with relatively more localized activation of prefrontal cortex and basal ganglia. These findings suggest that unawareness and misattribution of symptoms may have different neurobiological basis in schizophrenia. While symptom unawareness may be a function of a more complex brain network, symptom misattribution may be mediated by specific brain regions. PMID:25957484
Encoding of Natural Sounds at Multiple Spectral and Temporal Resolutions in the Human Auditory Cortex

PubMed Central

Santoro, Roberta; Moerel, Michelle; De Martino, Federico; Goebel, Rainer; Ugurbil, Kamil; Yacoub, Essa; Formisano, Elia

2014-01-01

Functional neuroimaging research provides detailed observations of the response patterns that natural sounds (e.g. human voices and speech, animal cries, environmental sounds) evoke in the human brain. The computational and representational mechanisms underlying these observations, however, remain largely unknown. Here we combine high spatial resolution (3 and 7 Tesla) functional magnetic resonance imaging (fMRI) with computational modeling to reveal how natural sounds are represented in the human brain. We compare competing models of sound representations and select the model that most accurately predicts fMRI response patterns to natural sounds. Our results show that the cortical encoding of natural sounds entails the formation of multiple representations of sound spectrograms with different degrees of spectral and temporal resolution. The cortex derives these multi-resolution representations through frequency-specific neural processing channels and through the combined analysis of the spectral and temporal modulations in the spectrogram. Furthermore, our findings suggest that a spectral-temporal resolution trade-off may govern the modulation tuning of neuronal populations throughout the auditory cortex. Specifically, our fMRI results suggest that neuronal populations in posterior/dorsal auditory regions preferably encode coarse spectral information with high temporal precision. Vice-versa, neuronal populations in anterior/ventral auditory regions preferably encode fine-grained spectral information with low temporal precision. We propose that such a multi-resolution analysis may be crucially relevant for flexible and behaviorally-relevant sound processing and may constitute one of the computational underpinnings of functional specialization in auditory cortex. PMID:24391486
An Investigation of the Relationship Between fMRI and ERP Source Localized Measurements of Brain Activity during Face Processing

PubMed Central

Richards, Todd; Webb, Sara Jane; Murias, Michael; Merkle, Kristen; Kleinhans, Natalia M.; Johnson, L. Clark; Poliakov, Andrew; Aylward, Elizabeth; Dawson, Geraldine

2013-01-01

Brain activity patterns during face processing have been extensively explored with functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs). ERP source localization adds a spatial dimension to the ERP time series recordings, which allows for a more direct comparison and integration with fMRI findings. The goals for this study were (1) to compare the spatial descriptions of neuronal activity during face processing obtained with fMRI and ERP source localization using low-resolution electro-magnetic tomography (LORETA), and (2) to use the combined information from source localization and fMRI to explore how the temporal sequence of brain activity during face processing is summarized in fMRI activation maps. fMRI and high-density ERP data were acquired in separate sessions for 17 healthy adult males for a face and object processing task. LORETA statistical maps for the comparison of viewing faces and viewing houses were coregistered and compared to fMRI statistical maps for the same conditions. The spatial locations of face processing-sensitive activity measured by fMRI and LORETA were found to overlap in a number of areas including the bilateral fusiform gyri, the right superior, middle and inferior temporal gyri, and the bilateral precuneus. Both the fMRI and LORETA solutions additionally demon-strated activity in regions that did not overlap. fMRI and LORETA statistical maps of face processing-sensitive brain activity were found to converge spatially primarily at LORETA solution latencies that were within 18 ms of the N170 latency. The combination of data from these techniques suggested that electrical brain activity at the latency of the N170 is highly represented in fMRI statistical maps. PMID:19322649
Tracking brain arousal fluctuations with fMRI

PubMed Central

Chang, Catie; Leopold, David A.; Schölvinck, Marieke Louise; Mandelkow, Hendrik; Picchioni, Dante; Liu, Xiao; Ye, Frank Q.; Turchi, Janita N.; Duyn, Jeff H.

2016-01-01

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the time-varying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker. PMID:27051064
Breaking down the barriers: fMRI applications in pain, analgesia and analgesics

PubMed Central

Borsook, David; Becerra, Lino R

2006-01-01

This review summarizes functional magnetic resonance imaging (fMRI) findings that have informed our current understanding of pain, analgesia and related phenomena, and discusses the potential role of fMRI in improved therapeutic approaches to pain. It is divided into 3 main sections: (1) fMRI studies of acute and chronic pain. Physiological studies of pain have found numerous regions of the brain to be involved in the interpretation of the 'pain experience'; studies in chronic pain conditions have identified a significant CNS component; and fMRI studies of surrogate models of chronic pain are also being used to further this understanding. (2) fMRI studies of endogenous pain processing including placebo, empathy, attention or cognitive modulation of pain. (3) The use of fMRI to evaluate the effects of analgesics on brain function in acute and chronic pain. fMRI has already provided novel insights into the neurobiology of pain. These insights should significantly advance therapeutic approaches to chronic pain. PMID:16982005
Time course based artifact identification for independent components of resting-state FMRI.

PubMed

Rummel, Christian; Verma, Rajeev Kumar; Schöpf, Veronika; Abela, Eugenio; Hauf, Martinus; Berruecos, José Fernando Zapata; Wiest, Roland

2013-01-01

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.
Investigating Inhibitory Control in Children with Epilepsy: An fMRI Study

PubMed Central

Triplett, Regina L.; Velanova, Katerina; Luna, Beatriz; Padmanabhan, Aarthi; Gaillard, William D.; Asato, Miya R.

2014-01-01

SUMMARY Objective Deficits in executive function are increasingly noted in children with epilepsy and have been associated with poor academic and psychosocial outcomes. Impaired inhibitory control contributes to executive dysfunction in children with epilepsy; however, its neuroanatomic basis has not yet been investigated. We used functional Magnetic Resonance Imaging (fMRI) to probe the integrity of activation in brain regions underlying inhibitory control in children with epilepsy. Methods This cross-sectional study consisted of 34 children aged 8 to 17 years: 17 with well-controlled epilepsy and 17 age-and sex-matched controls. Participants performed the antisaccade (AS) task, representative of inhibitory control, during fMRI scanning. We compared AS performance during neutral and reward task conditions and evaluated task-related blood-oxygen level dependent (BOLD) activation. Results Children with epilepsy demonstrated impaired AS performance compared to controls during both neutral (non-reward) and reward trials, but exhibited significant task improvement during reward trials. Post-hoc analysis revealed that younger patients made more errors than older patients and all controls. fMRI results showed preserved activation in task-relevant regions in patients and controls, with the exception of increased activation in the left posterior cingulate gyrus in patients specifically with generalized epilepsy across neutral and reward trials. Significance Despite impaired inhibitory control, children with epilepsy accessed typical neural pathways as did their peers without epilepsy. Children with epilepsy showed improved behavioral performance in response to the reward condition, suggesting potential benefits of the use of incentives in cognitive remediation. PMID:25223606
A stereotactic method for image-guided transcranial magnetic stimulation validated with fMRI and motor-evoked potentials.

PubMed

Neggers, S F W; Langerak, T R; Schutter, D J L G; Mandl, R C W; Ramsey, N F; Lemmens, P J J; Postma, A

2004-04-01

Transcranial Magnetic Stimulation (TMS) delivers short magnetic pulses that penetrate the skull unattenuated, disrupting neural processing in a noninvasive, reversible way. To disrupt specific neural processes, coil placement over the proper site is critical. Therefore, a neural navigator (NeNa) was developed. NeNa is a frameless stereotactic device using structural and functional magnetic resonance imaging (fMRI) data to guide TMS coil placement. To coregister the participant's head to his MRI, 3D cursors are moved to anatomical landmarks on a skin rendering of the participants MRI on a screen, and measured at the head with a position measurement device. A method is proposed to calculate a rigid body transformation that can coregister both sets of coordinates under realistic noise conditions. After coregistration, NeNa visualizes in real time where the device is located with respect to the head, brain structures, and activated areas, enabling precise placement of the TMS coil over a predefined target region. NeNa was validated by stimulating 5 x 5 positions around the 'motor hotspot' (thumb movement area), which was marked on the scalp guided by individual fMRI data, while recording motor-evoked potentials (MEPs) from the abductor pollicis brevis (APB). The distance between the center of gravity (CoG) of MEP responses and the location marked on the scalp overlying maximum fMRI activation was on average less then 5 mm. The present results demonstrate that NeNa is a reliable method for image-guided TMS coil placement.
Assessing the sensitivity of diffusion MRI to detect neuronal activity directly.

PubMed

Bai, Ruiliang; Stewart, Craig V; Plenz, Dietmar; Basser, Peter J

2016-03-22

Functional MRI (fMRI) is widely used to study brain function in the neurosciences. Unfortunately, conventional fMRI only indirectly assesses neuronal activity via hemodynamic coupling. Diffusion fMRI was proposed as a more direct and accurate fMRI method to detect neuronal activity, yet confirmative findings have proven difficult to obtain. Given that the underlying relation between tissue water diffusion changes and neuronal activity remains unclear, the rationale for using diffusion MRI to monitor neuronal activity has yet to be clearly established. Here, we studied the correlation between water diffusion and neuronal activity in vitro by simultaneous calcium fluorescence imaging and diffusion MR acquisition. We used organotypic cortical cultures from rat brains as a biological model system, in which spontaneous neuronal activity robustly emerges free of hemodynamic and other artifacts. Simultaneous fluorescent calcium images of neuronal activity are then directly correlated with diffusion MR signals now free of confounds typically encountered in vivo. Although a simultaneous increase of diffusion-weighted MR signals was observed together with the prolonged depolarization of neurons induced by pharmacological manipulations (in which cell swelling was demonstrated to play an important role), no evidence was found that diffusion MR signals directly correlate with normal spontaneous neuronal activity. These results suggest that, whereas current diffusion MR methods could monitor pathological conditions such as hyperexcitability, e.g., those seen in epilepsy, they do not appear to be sensitive or specific enough to detect or follow normal neuronal activity.
Assessing the sensitivity of diffusion MRI to detect neuronal activity directly

PubMed Central

Bai, Ruiliang; Stewart, Craig V.; Plenz, Dietmar; Basser, Peter J.

2016-01-01

Functional MRI (fMRI) is widely used to study brain function in the neurosciences. Unfortunately, conventional fMRI only indirectly assesses neuronal activity via hemodynamic coupling. Diffusion fMRI was proposed as a more direct and accurate fMRI method to detect neuronal activity, yet confirmative findings have proven difficult to obtain. Given that the underlying relation between tissue water diffusion changes and neuronal activity remains unclear, the rationale for using diffusion MRI to monitor neuronal activity has yet to be clearly established. Here, we studied the correlation between water diffusion and neuronal activity in vitro by simultaneous calcium fluorescence imaging and diffusion MR acquisition. We used organotypic cortical cultures from rat brains as a biological model system, in which spontaneous neuronal activity robustly emerges free of hemodynamic and other artifacts. Simultaneous fluorescent calcium images of neuronal activity are then directly correlated with diffusion MR signals now free of confounds typically encountered in vivo. Although a simultaneous increase of diffusion-weighted MR signals was observed together with the prolonged depolarization of neurons induced by pharmacological manipulations (in which cell swelling was demonstrated to play an important role), no evidence was found that diffusion MR signals directly correlate with normal spontaneous neuronal activity. These results suggest that, whereas current diffusion MR methods could monitor pathological conditions such as hyperexcitability, e.g., those seen in epilepsy, they do not appear to be sensitive or specific enough to detect or follow normal neuronal activity. PMID:26941239
Neural substrates of Hanja (Logogram) and Hangul (Phonogram) character readings by functional magnetic resonance imaging.

PubMed

Cho, Zang-Hee; Kim, Nambeom; Bae, Sungbong; Chi, Je-Geun; Park, Chan-Woong; Ogawa, Seiji; Kim, Young-Bo

2014-10-01

The two basic scripts of the Korean writing system, Hanja (the logography of the traditional Korean character) and Hangul (the more newer Korean alphabet), have been used together since the 14th century. While Hanja character has its own morphemic base, Hangul being purely phonemic without morphemic base. These two, therefore, have substantially different outcomes as a language as well as different neural responses. Based on these linguistic differences between Hanja and Hangul, we have launched two studies; first was to find differences in cortical activation when it is stimulated by Hanja and Hangul reading to support the much discussed dual-route hypothesis of logographic and phonological routes in the brain by fMRI (Experiment 1). The second objective was to evaluate how Hanja and Hangul affect comprehension, therefore, recognition memory, specifically the effects of semantic transparency and morphemic clarity on memory consolidation and then related cortical activations, using functional magnetic resonance imaging (fMRI) (Experiment 2). The first fMRI experiment indicated relatively large areas of the brain are activated by Hanja reading compared to Hangul reading. The second experiment, the recognition memory study, revealed two findings, that is there is only a small difference in recognition memory for semantic transparency, while for the morphemic clarity was much larger between Hanja and Hangul. That is the morphemic clarity has significantly more effect than semantic transparency on recognition memory when studies by fMRI in correlation with behavioral study.
A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

PubMed Central

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks. PMID:29706880
A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.

PubMed

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.
Optimization of multifocal transcranial current stimulation for weighted cortical pattern targeting from realistic modeling of electric fields

PubMed Central

Ruffini, Giulio; Fox, Michael D.; Ripolles, Oscar; Miranda, Pedro Cavaleiro; Pascual-Leone, Alvaro

2014-01-01

Recently, multifocal transcranial current stimulation (tCS) devices using several relatively small electrodes have been used to achieve more focal stimulation of specific cortical targets. However, it is becoming increasingly recognized that many behavioral manifestations of neurological and psychiatric disease are not solely the result of abnormality in one isolated brain region but represent alterations in brain networks. In this paper we describe a method for optimizing the configuration of multifocal tCS for stimulation of brain networks, represented by spatially extended cortical targets. We show how, based on fMRI, PET, EEG or other data specifying a target map on the cortical surface for excitatory, inhibitory or neutral stimulation and a constraint of the maximal number of electrodes, a solution can be produced with the optimal currents and locations of the electrodes. The method described here relies on a fast calculation of multifocal tCS electric fields (including components normal and tangential to the cortical boundaries) using a five layer finite element model of a realistic head. Based on the hypothesis that the effects of current stimulation are to first order due to the interaction of electric fields with populations of elongated cortical neurons, it is argued that the optimization problem for tCS stimulation can be defined in terms of the component of the electric field normal to the cortical surface. Solutions are found using constrained least squares to optimize current intensities, while electrode number and their locations are selected using a genetic algorithm. For direct current tCS (tDCS) applications, we provide some examples of this technique using an available tCS system providing 8 small Ag/AgCl stimulation electrodes. We demonstrate the approach both for localized and spatially extended targets defined using rs-fcMRI and PET data, with clinical applications in stroke and depression. Finally, we extend these ideas to more general stimulation protocols, such as alternating current tCS (tACS). PMID:24345389
Evaluating the Specificity of Cognitive Control Deficits in Schizophrenia Using Antisaccades, Functional Magnetic Resonance Imaging, and Healthy Individuals With Poor Cognitive Control.

PubMed

Rodrigue, Amanda L; Schaeffer, David J; Pierce, Jordan E; Clementz, Brett A; McDowell, Jennifer E

2018-01-01

Cognitive control impairments in schizophrenia (SZ) can be evaluated using antisaccade tasks and functional magnetic resonance imaging (fMRI). Studies, however, often compare people with SZ to high performing healthy people, making it unclear if antisaccade-related disruptions are specific to the disease or due to generalized deficits in cognitive control. We included two healthy comparison groups in addition to people with SZ: healthy people with high cognitive control (HCC), who represent a more typical comparison group, and healthy people with low cognitive control (LCC), who perform similarly on antisaccade measures as people with SZ. Using two healthy comparison groups may help determine which antisaccade-related deficits are specific to SZ (distinguish SZ from LCC and HCC groups) and which are due to poor cognitive control (distinguish the LCC and SZ groups from the HCC group). People with SZ and healthy people with HCC or LCC performed an antisaccade task during fMRI acquisition. LCC and SZ groups showed under-activation of saccade circuitry. SZ-specific disruptions were observed in the left superior temporal gyrus and insula during error trials (suppression of activation in the SZ group compared to the LCC and HCC group). Differences related to antisaccade errors may distinguish people with SZ from healthy people with LCC.
Cortical and Subcortical Coordination of Visual Spatial Attention Revealed by Simultaneous EEG-fMRI Recording.

PubMed

Green, Jessica J; Boehler, Carsten N; Roberts, Kenneth C; Chen, Ling-Chia; Krebs, Ruth M; Song, Allen W; Woldorff, Marty G

2017-08-16

Visual spatial attention has been studied in humans with both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) individually. However, due to the intrinsic limitations of each of these methods used alone, our understanding of the systems-level mechanisms underlying attentional control remains limited. Here, we examined trial-to-trial covariations of concurrently recorded EEG and fMRI in a cued visual spatial attention task in humans, which allowed delineation of both the generators and modulators of the cue-triggered event-related oscillatory brain activity underlying attentional control function. The fMRI activity in visual cortical regions contralateral to the cued direction of attention covaried positively with occipital gamma-band EEG, consistent with activation of cortical regions representing attended locations in space. In contrast, fMRI activity in ipsilateral visual cortical regions covaried inversely with occipital alpha-band oscillations, consistent with attention-related suppression of the irrelevant hemispace. Moreover, the pulvinar nucleus of the thalamus covaried with both of these spatially specific, attention-related, oscillatory EEG modulations. Because the pulvinar's neuroanatomical geometry makes it unlikely to be a direct generator of the scalp-recorded EEG, these covariational patterns appear to reflect the pulvinar's role as a regulatory control structure, sending spatially specific signals to modulate visual cortex excitability proactively. Together, these combined EEG/fMRI results illuminate the dynamically interacting cortical and subcortical processes underlying spatial attention, providing important insight not realizable using either method alone. SIGNIFICANCE STATEMENT Noninvasive recordings of changes in the brain's blood flow using functional magnetic resonance imaging and electrical activity using electroencephalography in humans have individually shown that shifting attention to a location in space produces spatially specific changes in visual cortex activity in anticipation of a stimulus. The mechanisms controlling these attention-related modulations of sensory cortex, however, are poorly understood. Here, we recorded these two complementary measures of brain activity simultaneously and examined their trial-to-trial covariations to gain insight into these attentional control mechanisms. This multi-methodological approach revealed the attention-related coordination of visual cortex modulation by the subcortical pulvinar nucleus of the thalamus while also disentangling the mechanisms underlying the attentional enhancement of relevant stimulus input and those underlying the concurrent suppression of irrelevant input. Copyright © 2017 the authors 0270-6474/17/377803-08$15.00/0.
Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes

PubMed Central

Jacob, Yael; Winetraub, Yonatan; Raz, Gal; Ben-Simon, Eti; Okon-Singer, Hadas; Rosenberg-Katz, Keren; Hendler, Talma; Ben-Jacob, Eshel

2016-01-01

Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (DEPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the DEPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the DEPNA correctly captures the network’s hierarchy of influence. Applying DEPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that DEPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions. PMID:27271458
The orthographic sensitivity to written Chinese in the occipital-temporal cortex.

PubMed

Liu, Haicheng; Jiang, Yi; Zhang, Bo; Ma, Lifei; He, Sheng; Weng, Xuchu

2013-06-01

Previous studies have identified an area in the left lateral fusiform cortex that is highly responsive to written words and has been named the visual word form area (VWFA). However, there is disagreement on the specific functional role of this area in word recognition. Chinese characters, which are dramatically different from Roman alphabets in the visual form and in the form to phonological mapping, provide a unique opportunity to investigate the properties of the VWFA. Specifically, to clarify the orthographic sensitivity in the mid-fusiform cortex, we compared fMRI response amplitudes (Exp. 1) as well as the spatial patterns of response across multiple voxels (Exp. 2) between Chinese characters and stimuli derived from Chinese characters with different orthographic properties. The fMRI response amplitude results suggest the existence of orthographic sensitivity in the VWFA. The results from multi-voxel pattern analysis indicate that spatial distribution of the responses across voxels in the occipitotemporal cortex contained discriminative information between the different types of character-related stimuli. These results together suggest that the orthographic rules are likely represented in a distributed neural network with the VWFA containing the most specific information regarding a stimulus' orthographic regularity.
Optogenetic Functional MRI

PubMed Central

Lin, Peter; Fang, Zhongnan; Liu, Jia; Lee, Jin Hyung

2016-01-01

The investigation of the functional connectivity of precise neural circuits across the entire intact brain can be achieved through optogenetic functional magnetic resonance imaging (ofMRI), which is a novel technique that combines the relatively high spatial resolution of high-field fMRI with the precision of optogenetic stimulation. Fiber optics that enable delivery of specific wavelengths of light deep into the brain in vivo are implanted into regions of interest in order to specifically stimulate targeted cell types that have been genetically induced to express light-sensitive trans-membrane conductance channels, called opsins. fMRI is used to provide a non-invasive method of determining the brain's global dynamic response to optogenetic stimulation of specific neural circuits through measurement of the blood-oxygen-level-dependent (BOLD) signal, which provides an indirect measurement of neuronal activity. This protocol describes the construction of fiber optic implants, the implantation surgeries, the imaging with photostimulation and the data analysis required to successfully perform ofMRI. In summary, the precise stimulation and whole-brain monitoring ability of ofMRI are crucial factors in making ofMRI a powerful tool for the study of the connectomics of the brain in both healthy and diseased states. PMID:27167840
A variational Bayes spatiotemporal model for electromagnetic brain mapping.

PubMed

Nathoo, F S; Babul, A; Moiseev, A; Virji-Babul, N; Beg, M F

2014-03-01

In this article, we present a new variational Bayes approach for solving the neuroelectromagnetic inverse problem arising in studies involving electroencephalography (EEG) and magnetoencephalography (MEG). This high-dimensional spatiotemporal estimation problem involves the recovery of time-varying neural activity at a large number of locations within the brain, from electromagnetic signals recorded at a relatively small number of external locations on or near the scalp. Framing this problem within the context of spatial variable selection for an underdetermined functional linear model, we propose a spatial mixture formulation where the profile of electrical activity within the brain is represented through location-specific spike-and-slab priors based on a spatial logistic specification. The prior specification accommodates spatial clustering in brain activation, while also allowing for the inclusion of auxiliary information derived from alternative imaging modalities, such as functional magnetic resonance imaging (fMRI). We develop a variational Bayes approach for computing estimates of neural source activity, and incorporate a nonparametric bootstrap for interval estimation. The proposed methodology is compared with several alternative approaches through simulation studies, and is applied to the analysis of a multimodal neuroimaging study examining the neural response to face perception using EEG, MEG, and fMRI. © 2013, The International Biometric Society.

Increased fMRI signal with age in familial Alzheimer’s disease mutation carriers

PubMed Central

Braskie, Meredith N.; Medina, Luis D.; Rodriguez-Agudelo, Yaneth; Geschwind, Daniel H.; Macias-Islas, Miguel Angel; Cummings, Jeffrey L.; Bookheimer, Susan Y.; Ringman, John M.

2010-01-01

Although many Alzheimer’s disease (AD) patients have a family history of the disease, it is rarely inherited in a predictable way. Functional magnetic resonance imaging (fMRI) studies of non-demented adults carrying familial AD mutations provide an opportunity to prospectively identify brain differences associated with early AD-related changes. We compared fMRI activity of 18 non-demented autosomal dominant AD mutation carriers with fMRI activity in 8 of their non-carrier relatives as they performed a novelty encoding task in which they viewed novel and repeated images. Because age of disease onset is relatively consistent within families, we also correlated fMRI activity with subjects’ distance from the median age of diagnosis for their family. Mutation carriers did not show significantly different voxelwise fMRI activity from non-carriers as a group. However, as they approached their family age of disease diagnosis, only mutation carriers showed increased fMRI activity in the fusiform and middle temporal gyri. This suggests that during novelty encoding, increased fMRI activity in the temporal lobe may relate to incipient AD processes. PMID:21129823
Sources and implications of whole-brain fMRI signals in humans

PubMed Central

Power, Jonathan D; Plitt, Mark; Laumann, Timothy O; Martin, Alex

2016-01-01

Whole-brain fMRI signals are a subject of intense interest: variance in the global fMRI signal (the spatial mean of all signals in the brain) indexes subject arousal, and psychiatric conditions such as schizophrenia and autism have been characterized by differences in the global fMRI signal. Further, vigorous debates exist on whether global signals ought to be removed from fMRI data. However, surprisingly little research has focused on the empirical properties of whole-brain fMRI signals. Here we map the spatial and temporal properties of the global signal, individually, in 1000+ fMRI scans. Variance in the global fMRI signal is strongly linked to head motion, to hardware artifacts, and to respiratory patterns and their attendant physiologic changes. Many techniques used to prepare fMRI data for analysis fail to remove these uninteresting kinds of global signal fluctuations. Thus, many studies include, at the time of analysis, prominent global effects of yawns, breathing changes, and head motion, among other signals. Such artifacts will mimic dynamic neural activity and will spuriously alter signal covariance throughout the brain. Methods capable of isolating and removing global artifactual variance while preserving putative “neural” variance are needed; this paper adopts no position on the topic of global signal regression. PMID:27751941
Lateralized Spatial and Object Memory Encoding in Entorhinal and Perirhinal Cortices

ERIC Educational Resources Information Center

Bellgowan, Patrick S. F.; Buffalo, Elizabeth A.; Bodurka, Jerzy; Martin, Alex

2009-01-01

The perirhinal and entorhinal cortices are critical components of the medial temporal lobe (MTL) declarative memory system. Study of their specific functions using blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI), however, has suffered from severe magnetic susceptibility signal dropout resulting in poor…
Information-Processing Modules and Their Relative Modality Specificity

ERIC Educational Resources Information Center

Anderson, John R.; Qin, Yulin; Jung, Kwan-Jin; Carter, Cameron S.

2007-01-01

This research uses fMRI to understand the role of eight cortical regions in a relatively complex information-processing task. Modality of input (visual versus auditory) and modality of output (manual versus vocal) are manipulated. Two perceptual regions (auditory cortex and fusiform gyrus) only reflected perceptual encoding. Two motor regions were…
Presurgical language fMRI: Clinical practices and patient outcomes in epilepsy surgical planning.

PubMed

Benjamin, Christopher F A; Li, Alexa X; Blumenfeld, Hal; Constable, R Todd; Alkawadri, Rafeed; Bickel, Stephan; Helmstaedter, Christoph; Meletti, Stefano; Bronen, Richard; Warfield, Simon K; Peters, Jurriaan M; Reutens, David; Połczyńska, Monika; Spencer, Dennis D; Hirsch, Lawrence J

2018-03-12

The goal of this study was to document current clinical practice and report patient outcomes in presurgical language functional MRI (fMRI) for epilepsy surgery. Epilepsy surgical programs worldwide were surveyed as to the utility, implementation, and efficacy of language fMRI in the clinic; 82 programs responded. Respondents were predominantly US (61%) academic programs (85%), and evaluated adults (44%), adults and children (40%), or children only (16%). Nearly all (96%) reported using language fMRI. Surprisingly, fMRI is used to guide surgical margins (44% of programs) as well as lateralize language (100%). Sites using fMRI for localization most often use a distance margin around activation of 10mm. While considered useful, 56% of programs reported at least one instance of disagreement with other measures. Direct brain stimulation typically confirmed fMRI findings (74%) when guiding margins, but instances of unpredicted decline were reported by 17% of programs and 54% reported unexpected preservation of function. Programs reporting unexpected decline did not clearly differ from those which did not. Clinicians using fMRI to guide surgical margins do not typically map known language-critical areas beyond Broca's and Wernicke's. This initial data shows many clinical teams are confident using fMRI not only for language lateralization but also to guide surgical margins. Reported cases of unexpected language preservation when fMRI activation is resected, and cases of language decline when it is not, emphasize a critical need for further validation. Comprehensive studies comparing commonly-used fMRI paradigms to predict stimulation mapping and post-surgical language decline remain of high importance. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
Exploring connectivity with large-scale Granger causality on resting-state functional MRI.

PubMed

DSouza, Adora M; Abidin, Anas Z; Leistritz, Lutz; Wismüller, Axel

2017-08-01

Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters. Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86). Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem. Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution. Copyright © 2017 Elsevier B.V. All rights reserved.
Localization of cortical primary motor area of the hand using navigated transcranial magnetic stimulation, BOLD and arterial spin labeling fMRI.

PubMed

Kallioniemi, Elisa; Pitkänen, Minna; Könönen, Mervi; Vanninen, Ritva; Julkunen, Petro

2016-11-01

Although the relationship between neuronavigated transcranial magnetic stimulation (nTMS) and functional magnetic resonance imaging (fMRI) has been widely studied in motor mapping, it is unknown how the motor response type or the choice of motor task affect this relationship. Centers of gravity (CoGs) and response maxima were measured with blood-oxygen-level dependent (BOLD) and arterial spin labeling (ASL) fMRI during motor tasks against nTMS CoGs and response maxima, which were mapped with motor evoked potentials (MEPs) and silent periods (SPs). No differences in motor representations (CoGs and response maxima) were observed in lateral-medial direction (p=0.265). fMRI methods localized the motor representation more posterior than nTMS (p<0.001). This was not affected by the BOLD fMRI motor task (p>0.999) nor nTMS response type (p>0.999). ASL fMRI maxima did not differ from the nTMS nor BOLD fMRI CoGs (p≥0.070), but the ASL CoG was deeper in comparison to other methods (p≤0.042). The BOLD fMRI motor task did not influence the depth of the motor representation (p≥0.745). The median Euclidean distances between the nTMS and fMRI motor representations varied between 7.7mm and 14.5mm and did not differ between the methods (F≤1.23, p≥0.318). The relationship between fMRI and nTMS mapped excitatory (MEP) and inhibitory (SP) responses, and whether the choice of motor task affects this relationship, have not been studied before. The congruence between fMRI and nTMS is good. The choice of nTMS motor response type nor BOLD fMRI motor task had no effect on this relationship. Copyright © 2016 Elsevier B.V. All rights reserved.
Self-government of complex reading and writing brains informed by cingulo-opercular network for adaptive control and working memory components for language learning.

PubMed

Richards, Todd L; Abbott, Robert D; Yagle, Kevin; Peterson, Dan; Raskind, Wendy; Berninger, Virginia W

2017-01-01

To understand mental self-government of the developing reading and writing brain, correlations of clustering coefficients on fMRI reading or writing tasks with BASC 2 Adaptivity ratings (time 1 only) or working memory components (time 1 before and time 2 after instruction previously shown to improve achievement and change magnitude of fMRI connectivity) were investigated in 39 students in grades 4 to 9 who varied along a continuum of reading and writing skills. A Philips 3T scanner measured connectivity during six leveled fMRI reading tasks (subword-letters and sounds, word-word-specific spellings or affixed words, syntax comprehension-with and without homonym foils or with and without affix foils, and text comprehension) and three fMRI writing tasks-writing next letter in alphabet, adding missing letter in word spelling, and planning for composing. The Brain Connectivity Toolbox generated clustering coefficients based on the cingulo-opercular (CO) network; after controlling for multiple comparisons and movement, significant fMRI connectivity clustering coefficients for CO were identified in 8 brain regions bilaterally (cingulate gyrus, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, superior temporal gyrus, insula, cingulum-cingulate gyrus, and cingulum-hippocampus). BASC2 Parent Ratings for Adaptivity were correlated with CO clustering coefficients on three reading tasks (letter-sound, word affix judgments and sentence comprehension) and one writing task (writing next letter in alphabet). Before instruction, each behavioral working memory measure (phonology, orthography, morphology, and syntax coding, phonological and orthographic loops for integrating internal language and output codes, and supervisory focused and switching attention) correlated significantly with at least one CO clustering coefficient. After instruction, the patterning of correlations changed with new correlations emerging. Results show that the reading and writing brain's mental government, supported by both CO Adaptive Control and multiple working memory components, had changed in response to instruction during middle childhood/early adolescence.
The effects of physical activity on functional MRI activation associated with cognitive control in children: a randomized controlled intervention

PubMed Central

Chaddock-Heyman, Laura; Erickson, Kirk I.; Voss, Michelle W.; Knecht, Anya M.; Pontifex, Matthew B.; Castelli, Darla M.; Hillman, Charles H.; Kramer, Arthur F.

2013-01-01

This study used functional magnetic resonance imaging (fMRI) to examine the influence of a 9-month physical activity program on task-evoked brain activation during childhood. The results demonstrated that 8- to 9-year-old children who participated in 60+ min of physical activity, 5 days per week, for 9 months, showed decreases in fMRI brain activation in the right anterior prefrontal cortex coupled with within-group improvements in performance on a task of attentional and interference control. Children assigned to a wait-list control group did not show changes in brain function. Furthermore, at post-test, children in the physical activity group showed similar anterior frontal brain patterns and incongruent accuracy rates to a group of college-aged young adults. Children in the wait-list control group still differed from the young adults in terms of anterior prefrontal activation and performance at post-test. There were no significant changes in fMRI activation in the anterior cingulate cortex (ACC) for either group. These results suggest that physical activity during childhood may enhance specific elements of prefrontal cortex function involved in cognitive control. PMID:23487583
Functional Imaging of Sleep Vertex Sharp Transients

PubMed Central

Stern, John M.; Caporro, Matteo; Haneef, Zulfi; Yeh, Hsiang J.; Buttinelli, Carla; Lenartowicz, Agatha; Mumford, Jeanette A.; Parvizi, Josef; Poldrack, Russell A.

2011-01-01

Objective The vertex sharp transient (VST) is an electroencephalographic (EEG) discharge that is an early marker of non-REM sleep. It has been recognized since the beginning of sleep physiology research, but its source and function remain mostly unexplained. We investigated VST generation using functional MRI (fMRI). Methods Simultaneous EEG and fMRI were recorded from 7 individuals in drowsiness and light sleep. VST occurrences on EEG were modeled with fMRI using an impulse function convolved with a hemodynamic response function to identify cerebral regions correlating to the VSTs. A resulting statistical image was thresholded at Z>2.3. Results Two hundred VSTs were identified. Significantly increased signal was present bilaterally in medial central, lateral precentral, posterior superior temporal, and medial occipital cortex. No regions of decreased signal were present. Conclusion The regions are consistent with electrophysiologic evidence from animal models and functional imaging of human sleep, but the results are specific to VSTs. The regions principally encompass the primary sensorimotor cortical regions for vision, hearing, and touch. Significance The results depict a network comprising the presumed VST generator and its associated regions. The associated regions functional similarity for primary sensation suggests a role for VSTs in sensory experience during sleep. PMID:21310653
Source monitoring 15 years later: what have we learned from fMRI about the neural mechanisms of source memory?

PubMed

Mitchell, Karen J; Johnson, Marcia K

2009-07-01

Focusing primarily on functional magnetic resonance imaging (fMRI), this article reviews evidence regarding the roles of subregions of the medial temporal lobes, prefrontal cortex, posterior representational areas, and parietal cortex in source memory. In addition to evidence from standard episodic memory tasks assessing accuracy for neutral information, the article considers studies assessing the qualitative characteristics of memories, the encoding and remembering of emotional information, and false memories, as well as evidence from populations that show disrupted source memory (older adults, individuals with depression, posttraumatic stress disorder, or schizophrenia). Although there is still substantial work to be done, fMRI is advancing understanding of source memory and highlighting unresolved issues. A continued 2-way interaction between cognitive theory, as illustrated by the source monitoring framework (M. K. Johnson, S. Hashtroudi, & D. S. Lindsay, 1993), and evidence from cognitive neuroimaging studies should clarify conceptualization of cognitive processes (e.g., feature binding, retrieval, monitoring), prior knowledge (e.g., semantics, schemas), and specific features (e.g., perceptual and emotional information) and of how they combine to create true and false memories. Copyright (c) 2009 APA, all rights reserved.
Left Posterior Orbitofrontal Cortex Is Associated With Odor-Induced Autobiographical Memory: An fMRI Study.

PubMed

Watanabe, Keiko; Masaoka, Yuri; Kawamura, Mitsuru; Yoshida, Masaki; Koiwa, Nobuyoshi; Yoshikawa, Akira; Kubota, Satomi; Ida, Masahiro; Ono, Kenjiro; Izumizaki, Masahiko

2018-01-01

Autobiographical odor memory (AM-odor) accompanied by a sense of realism of a specific memory elicits strong emotions. AM-odor differs from memory triggered by other sensory modalities, possibly because olfaction involves a unique sensory process. Here, we examined the orbitofrontal cortex (OFC), using functional magnetic resonance imaging (fMRI) to determine which OFC subregions are related to AM-odor. Both AM-odor and a control odor successively increased subjective ratings of comfortableness and pleasantness. Importantly, AM-odor also increased arousal levels and the vividness of memories, and was associated with a deep and slow breathing pattern. fMRI analysis indicated robust activation in the left posterior OFC (L-POFC). Connectivity between the POFC and whole brain regions was estimated using psychophysiological interaction analysis (PPI). We detected several trends in connectivity between L-POFC and bilateral precuneus, bilateral rostral dorsal anterior cingulate cortex (rdACC), and left parahippocampus, which will be useful for targeting our hypotheses for future investigations. The slow breathing observed in AM-odor was correlated with rdACC activation. Odor associated with emotionally significant autobiographical memories was accompanied by slow and deep breathing, possibly involving rdACC processing.
Neurological soft signs are not "soft" in brain structure and functional networks: evidence from ALE meta-analysis.

PubMed

Zhao, Qing; Li, Zhi; Huang, Jia; Yan, Chao; Dazzan, Paola; Pantelis, Christos; Cheung, Eric F C; Lui, Simon S Y; Chan, Raymond C K

2014-05-01

Neurological soft signs (NSS) are associated with schizophrenia and related psychotic disorders. NSS have been conventionally considered as clinical neurological signs without localized brain regions. However, recent brain imaging studies suggest that NSS are partly localizable and may be associated with deficits in specific brain areas. We conducted an activation likelihood estimation meta-analysis to quantitatively review structural and functional imaging studies that evaluated the brain correlates of NSS in patients with schizophrenia and other psychotic disorders. Six structural magnetic resonance imaging (sMRI) and 15 functional magnetic resonance imaging (fMRI) studies were included. The results from meta-analysis of the sMRI studies indicated that NSS were associated with atrophy of the precentral gyrus, the cerebellum, the inferior frontal gyrus, and the thalamus. The results from meta-analysis of the fMRI studies demonstrated that the NSS-related task was significantly associated with altered brain activation in the inferior frontal gyrus, bilateral putamen, the cerebellum, and the superior temporal gyrus. Our findings from both sMRI and fMRI meta-analyses further support the conceptualization of NSS as a manifestation of the "cerebello-thalamo-prefrontal" brain network model of schizophrenia and related psychotic disorders.
Large-Scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

PubMed Central

Xu, Jiansong; Potenza, Marc N.; Calhoun, Vince D.; Zhang, Rubin; Yip, Sarah W.; Wall, John T.; Pearlson, Godfrey D.; Worhunsky, Patrick D.; Garrison, Kathleen A.; Moran, Joseph M.

2016-01-01

Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain’s properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings. PMID:27592153
High-resolution maps of real and illusory tactile activation in primary somatosensory cortex in individual monkeys with functional magnetic resonance imaging and optical imaging.

PubMed

Chen, Li M; Turner, Gregory H; Friedman, Robert M; Zhang, Na; Gore, John C; Roe, Anna W; Avison, Malcolm J

2007-08-22

Although blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has been widely used to explore human brain function, questions remain regarding the ultimate spatial resolution of positive BOLD fMRI, and indeed the extent to which functional maps revealed by positive BOLD correlate spatially with maps obtained with other high-spatial-resolution mapping techniques commonly used in animals, such as optical imaging of intrinsic signal (OIS) and single-unit electrophysiology. Here, we demonstrate that the positive BOLD signal at 9.4T can reveal the fine topography of individual fingerpads in single-condition activation maps in nonhuman primates. These digit maps are similar to maps obtained from the same animal using intrinsic optical imaging. Furthermore, BOLD fMRI reliably resolved submillimeter spatial shifts in activation in area 3b previously identified with OIS (Chen et al., 2003) as neural correlates of the "funneling illusion." These data demonstrate that at high field, high-spatial-resolution topographic maps can be achieved using the positive BOLD signal, weakening previous notions regarding the spatial specificity of the positive BOLD signal.
Altered Dynamics of the fMRI Response to Faces in Individuals with Autism

ERIC Educational Resources Information Center

Kleinhans, Natalia M.; Richards, Todd; Greenson, Jessica; Dawson, Geraldine; Aylward, Elizabeth

2016-01-01

Abnormal fMRI habituation in autism spectrum disorders (ASDs) has been proposed as a critical component in social impairment. This study investigated habituation to fearful faces and houses in ASD and whether fMRI measures of brain activity discriminate between ASD and typically developing (TD) controls. Two identical fMRI runs presenting masked…
The neural correlates of volitional attention: A combined fMRI and ERP study.

PubMed

Bengson, Jesse J; Kelley, Todd A; Mangun, George R

2015-07-01

Studies of visual-spatial attention typically use instructional cues to direct attention to a relevant location, but in everyday vision, attention is often focused volitionally, in the absence of external signals. Although investigations of cued attention comprise hundreds of behavioral and physiological studies, remarkably few studies of voluntary attention have addressed the challenging question of how spatial attention is initiated and controlled in the absence of external instructions, which we refer to as willed attention. To explore this question, we employed a trial-by-trial spatial attention task using electroencephalography and functional magnetic resonance imaging (fMRI). The fMRI results reveal a unique network of brain regions for willed attention that includes the anterior cingulate cortex, left middle frontal gyrus (MFG), and the left and right anterior insula (AI). We also observed two event-related potentials (ERPs) associated with willed attention; one with a frontal distribution occurring 250-350 ms postdecision cue onset (EWAC: Early Willed Attention Component), and another occurring between 400 and 800 ms postdecision-cue onset (WAC: Willed Attention Component). In addition, each ERP component uniquely correlated across subjects with different willed attention-specific sites of BOLD activation. The EWAC was correlated with the willed attention-specific left AI and left MFG activations and the later WAC was correlated only with left AI. These results offer a comprehensive and novel view of the electrophysiological and anatomical profile of willed attention and further illustrate the relationship between scalp-recorded ERPs and the BOLD response. © 2015 Wiley Periodicals, Inc.
Single trial classification for the categories of perceived emotional facial expressions: an event-related fMRI study

NASA Astrophysics Data System (ADS)

Song, Sutao; Huang, Yuxia; Long, Zhiying; Zhang, Jiacai; Chen, Gongxiang; Wang, Shuqing

2016-03-01

Recently, several studies have successfully applied multivariate pattern analysis methods to predict the categories of emotions. These studies are mainly focused on self-experienced emotions, such as the emotional states elicited by music or movie. In fact, most of our social interactions involve perception of emotional information from the expressions of other people, and it is an important basic skill for humans to recognize the emotional facial expressions of other people in a short time. In this study, we aimed to determine the discriminability of perceived emotional facial expressions. In a rapid event-related fMRI design, subjects were instructed to classify four categories of facial expressions (happy, disgust, angry and neutral) by pressing different buttons, and each facial expression stimulus lasted for 2s. All participants performed 5 fMRI runs. One multivariate pattern analysis method, support vector machine was trained to predict the categories of facial expressions. For feature selection, ninety masks defined from anatomical automatic labeling (AAL) atlas were firstly generated and each were treated as the input of the classifier; then, the most stable AAL areas were selected according to prediction accuracies, and comprised the final feature sets. Results showed that: for the 6 pair-wise classification conditions, the accuracy, sensitivity and specificity were all above chance prediction, among which, happy vs. neutral , angry vs. disgust achieved the lowest results. These results suggested that specific neural signatures of perceived emotional facial expressions may exist, and happy vs. neutral, angry vs. disgust might be more similar in information representation in the brain.
Enhanced emotional reactivity after selective REM sleep deprivation in humans: an fMRI study

PubMed Central

Rosales-Lagarde, Alejandra; Armony, Jorge L.; del Río-Portilla, Yolanda; Trejo-Martínez, David; Conde, Ruben; Corsi-Cabrera, Maria

2012-01-01

Converging evidence from animal and human studies suggest that rapid eye movement (REM) sleep modulates emotional processing. The aim of the present study was to explore the effects of selective REM sleep deprivation (REM-D) on emotional responses to threatening visual stimuli and their brain correlates using functional magnetic resonance imaging (fMRI). Twenty healthy subjects were randomly assigned to two groups: selective REM-D, by awakening them at each REM sleep onset, or non-rapid eye movement sleep interruptions (NREM-I) as control for potential non-specific effects of awakenings and lack of sleep. In a within-subject design, a visual emotional reactivity task was performed in the scanner before and 24 h after sleep manipulation. Behaviorally, emotional reactivity was enhanced relative to baseline (BL) in the REM deprived group only. In terms of fMRI signal, there was, as expected, an overall decrease in activity in the NREM-I group when subjects performed the task the second time, particularly in regions involved in emotional processing, such as occipital and temporal areas, as well as in the ventrolateral prefrontal cortex, involved in top-down emotion regulation. In contrast, activity in these areas remained the same level or even increased in the REM-D group, compared to their BL level. Taken together, these results suggest that lack of REM sleep in humans is associated with enhanced emotional reactivity, both at behavioral and neural levels, and thus highlight the specific role of REM sleep in regulating the neural substrates for emotional responsiveness. PMID:22719723
fMRI studies of successful emotional memory encoding: a quantitative meta-analysis

PubMed Central

Murty, Vishnu P.; Ritchey, Maureen; Adcock, R. Alison; LaBar, Kevin S.

2010-01-01

Over the past decade, fMRI techniques have been increasingly used to interrogate the neural correlates of successful emotional memory encoding. These investigations have typically aimed to either characterize the contributions of the amygdala and medial temporal lobe (MTL) memory system, replicating results in animals, or delineate the neural correlates of specific behavioral phenomena. It has remained difficult, however, to synthesize these findings into a systems neuroscience account of how networks across the whole brain support the enhancing effects of emotion on memory encoding. To this end, the present study employed a meta-analytic approach using activation likelihood estimates to assess the anatomical specificity and reliability of event-related fMRI activations related to successful memory encoding for emotional versus neutral information. The meta-analysis revealed consistent clusters within bilateral amygdala, anterior hippocampus, anterior and posterior parahippocampal gyrus, the ventral visual stream, left lateral prefrontal cortex and right ventral parietal cortex. The results within the amygdala and MTL support a wealth of findings from the animal literature linking these regions to arousal-mediated memory effects. The consistency of findings in cortical targets, including the visual, prefrontal, and parietal cortices, underscores the importance of generating hypotheses regarding their participation in emotional memory formation. In particular, we propose that the amygdala interacts with these structures to promote enhancements in perceptual processing, semantic elaboration, and attention, which serve to benefit subsequent memory for emotional material. These findings may motivate future research on emotional modulation of widespread neural systems and the implications of this modulation for cognition. PMID:20688087

Early life social stress and resting state functional connectivity in postpartum rat anterior cingulate circuits.

PubMed

Nephew, Benjamin C; Febo, Marcelo; Huang, Wei; Colon-Perez, Luis M; Payne, Laurellee; Poirier, Guillaume L; Greene, Owen; King, Jean A

2018-03-15

Continued development and refinement of resting state functional connectivity (RSFC) fMRI techniques in both animal and clinical studies has enhanced our comprehension of the adverse effects of stress on psychiatric health. The objective of the current study was to assess both maternal behavior and resting state functional connectivity (RSFC) changes in these animals when they were dams caring for their own young. It was hypothesized that ECSS exposed dams would express depressed maternal care and exhibit similar (same networks), yet different specific changes in RSFC (different individual nuclei) than reported when they were adult females. We have developed an ethologically relevant transgenerational model of the role of chronic social stress (CSS) in the etiology of postpartum depression and anxiety. Initial fMRI investigation of the CSS model indicates that early life exposure to CSS (ECSS) induces long term changes in functional connectivity in adult nulliparous female F1 offspring. ECSS in F1 dams resulted in depressed maternal care specifically during early lactation, consistent with previous CSS studies, and induced changes in functional connectivity in regions associated with sensory processing, maternal and emotional responsiveness, memory, and the reward pathway, with robust changes in anterior cingulate circuits. The sample sizes for the fMRI groups were low, limiting statistical power. This behavioral and functional neuroanatomical foundation can now be used to enhance our understanding of the neural etiology of early life stress associated disorders and test preventative measures and treatments for stress related disorders. Copyright © 2018 Elsevier B.V. All rights reserved.
Does the hippocampus mediate objective binding or subjective remembering?

PubMed

Slotnick, Scott D

2010-01-15

Human functional magnetic resonance imaging (fMRI) evidence suggests the hippocampus is associated with context memory to a greater degree than item memory (where only context memory requires item-in-context binding). A separate line of fMRI research suggests the hippocampus is associated with "remember" responses to a greater degree than "know" or familiarity based responses (where only remembering reflects the subjective experience of specific detail). Previous studies, however, have confounded context memory with remembering and item memory with knowing. The present fMRI study independently tested the binding hypothesis and remembering hypothesis of hippocampal function by evaluating activity within hippocampal regions-of-interest (ROIs). At encoding, participants were presented with colored and gray abstract shapes and instructed to remember each shape and whether it was colored or gray. At retrieval, old and new shapes were presented in gray and participants classified each shape as "old and previously colored", "old and previously gray", or "new", followed by a "remember" or "know" response. In 3 of 11 hippocampal ROIs, activity was significantly greater for context memory than item memory, the context memory-item memory by remember-know interaction was significant, and activity was significantly greater for context memory-knowing than item memory-remembering. This pattern of activity only supports the binding hypothesis. The analogous pattern of activity that would have supported the remembering hypothesis was never observed in the hippocampus. However, a targeted analysis revealed remembering specific activity in the left inferior parietal cortex. The present results suggest parietal cortex may be associated with subjective remembering while the hippocampus mediates binding.
A functional magnetic resonance imaging study of human brain in pain-related areas induced by electrical stimulation with different intensities.

PubMed

Yuan, Wang; Ming, Zhang; Rana, Netra; Hai, Liu; Chen-wang, Jin; Shao-hui, Ma

2010-01-01

Pain-related studies have mainly been performed through traditional methods, which lack the rigorous analysis of anatomical locations. Functional magnetic resonance imaging (fMRI) is a noninvasive method detecting neural activity, and has the ability to precisely locate related activations in vivo. Moreover, few studies have used painful stimulation of changed intensity to investigate relevant functioning nuclei in the human brain. This study mainly focused on the pain-related activations induced by electrical stimulation with different intensities using fMRI. Furthermore, the electrophysiological characteristics of different pain-susceptible-neurons were analyzed to construct the pain modulatory network, which was corresponding to painful stimulus of changed intensity. Twelve volunteers underwent functional scanning receiving different electrical stimulation. The data were collected and analyzed to generate the corresponding functional activation maps and response time curves related to pain. The common activations were mainly located in several specific regions, including the secondary somatosensory cortex (SII), insula, anterior cingulate cortex (ACC), thalamus, and other cerebral regions. Moreover, innocuous electrical stimulation primarily activated the lateral portions of SII and thalamus, as well as the posterior insula, anterior ACC, whereas noxious electrical stimulation primarily activated the medial portions of SII and thalamus, as well as the anterior insula, the posterior ACC, with larger extensions and greater intensities. Several specified cerebral regions displayed different response patterns during electrical stimulation by means of fMRI, which implied that the corresponding pain-susceptible-neurons might process specific aspects of pain. Elucidation of functions on pain-related regions will help to understand the delicate pain modulation of human brain.
Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407
Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-03-22

Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information. The proposed algorithms differ from the K-SVD in their sparse coding and dictionary update stages. The first two algorithms account for the known correlation structure in the fMRI data by using the squared Q, R-norm instead of the Frobenius norm for matrix approximation. The third and last algorithm account for both the known correlation structure in the fMRI data and the temporal smoothness. The temporal smoothness is incorporated in the dictionary update stage via regularization of the dictionary atoms obtained with penalization. The performance of the proposed dictionary learning algorithms are illustrated through simulations and applications on real fMRI data.
Generation of Individual Whole-Brain Atlases With Resting-State fMRI Data Using Simultaneous Graph Computation and Parcellation.

PubMed

Wang, J; Hao, Z; Wang, H

2018-01-01

The human brain can be characterized as functional networks. Therefore, it is important to subdivide the brain appropriately in order to construct reliable networks. Resting-state functional connectivity-based parcellation is a commonly used technique to fulfill this goal. Here we propose a novel individual subject-level parcellation approach based on whole-brain resting-state functional magnetic resonance imaging (fMRI) data. We first used a supervoxel method known as simple linear iterative clustering directly on resting-state fMRI time series to generate supervoxels, and then combined similar supervoxels to generate clusters using a clustering method known as graph-without-cut (GWC). The GWC approach incorporates spatial information and multiple features of the supervoxels by energy minimization, simultaneously yielding an optimal graph and brain parcellation. Meanwhile, it theoretically guarantees that the actual cluster number is exactly equal to the initialized cluster number. By comparing the results of the GWC approach and those of the random GWC approach, we demonstrated that GWC does not rely heavily on spatial structures, thus avoiding the challenges encountered in some previous whole-brain parcellation approaches. In addition, by comparing the GWC approach to two competing approaches, we showed that GWC achieved better parcellation performances in terms of different evaluation metrics. The proposed approach can be used to generate individualized brain atlases for applications related to cognition, development, aging, disease, personalized medicine, etc. The major source codes of this study have been made publicly available at https://github.com/yuzhounh/GWC.
Toward brain correlates of natural behavior: fMRI during violent video games.

PubMed

Mathiak, Klaus; Weber, René

2006-12-01

Modern video games represent highly advanced virtual reality simulations and often contain virtual violence. In a significant amount of young males, playing video games is a quotidian activity, making it an almost natural behavior. Recordings of brain activation with functional magnetic resonance imaging (fMRI) during gameplay may reflect neuronal correlates of real-life behavior. We recorded 13 experienced gamers (18-26 years; average 14 hrs/week playing) while playing a violent first-person shooter game (a violent computer game played in self-perspective) by means of distortion and dephasing reduced fMRI (3 T; single-shot triple-echo echo-planar imaging [EPI]). Content analysis of the video and sound with 100 ms time resolution achieved relevant behavioral variables. These variables explained significant signal variance across large distributed networks. Occurrence of violent scenes revealed significant neuronal correlates in an event-related design. Activation of dorsal and deactivation of rostral anterior cingulate and amygdala characterized the mid-frontal pattern related to virtual violence. Statistics and effect sizes can be considered large at these areas. Optimized imaging strategies allowed for single-subject and for single-trial analysis with good image quality at basal brain structures. We propose that virtual environments can be used to study neuronal processes involved in semi-naturalistic behavior as determined by content analysis. Importantly, the activation pattern reflects brain-environment interactions rather than stimulus responses as observed in classical experimental designs. We relate our findings to the general discussion on social effects of playing first-person shooter games. (c) 2006 Wiley-Liss, Inc.
Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI

PubMed Central

Mangalathu-Arumana, Jain; Liebenthal, Einat; Beardsley, Scott A.

2018-01-01

Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations. Neural sources were simulated with spatiotemporal and noise attributes derived from experimental data. The best performance, maximizing both cross-modal data fusion and the separation of brain sources, occurred with a moderate number of representations of the ERP/fMRI data (10–30), as in a mixed block/event related experimental design. Importantly, the type of relationship between instantiations of the ERP/fMRI activity, whether linear, non-linear or uncoupled, did not in itself impact jICA performance, and was accurately recovered in the common profiles (i.e., mixing coefficients). Thus, jICA provides an unbiased way to characterize the relationship between ERP and fMRI activity across brain regions, in individual data, rendering it potentially useful for characterizing pathological conditions in which neurovascular coupling is adversely affected. PMID:29410611
Sex-dependent dissociation between emotional appraisal and memory: a large-scale behavioral and fMRI study.

PubMed

Spalek, Klara; Fastenrath, Matthias; Ackermann, Sandra; Auschra, Bianca; Coynel, David; Frey, Julia; Gschwind, Leo; Hartmann, Francina; van der Maarel, Nadine; Papassotiropoulos, Andreas; de Quervain, Dominique; Milnik, Annette

2015-01-21

Extensive evidence indicates that women outperform men in episodic memory tasks. Furthermore, women are known to evaluate emotional stimuli as more arousing than men. Because emotional arousal typically increases episodic memory formation, the females' memory advantage might be more pronounced for emotionally arousing information than for neutral information. Here, we report behavioral data from 3398 subjects, who performed picture rating and memory tasks, and corresponding fMRI data from up to 696 subjects. We were interested in the interaction between sex and valence category on emotional appraisal, memory performances, and fMRI activity. The behavioral results showed that females evaluate in particular negative (p < 10(-16)) and positive (p = 2 × 10(-4)), but not neutral pictures, as emotionally more arousing (pinteraction < 10(-16)) than males. However, in the free recall females outperformed males not only in positive (p < 10(-16)) and negative (p < 5 × 10(-5)), but also in neutral picture recall (p < 3.4 × 10(-8)), with a particular advantage for positive pictures (pinteraction < 4.4 × 10(-10)). Importantly, females' memory advantage during free recall was absent in a recognition setting. We identified activation differences in fMRI, which corresponded to the females' stronger appraisal of especially negative pictures, but no activation differences that reflected the interaction effect in the free recall memory task. In conclusion, females' valence-category-specific memory advantage is only observed in a free recall, but not a recognition setting and does not depend on females' higher emotional appraisal. Copyright © 2015 the authors 0270-6474/15/350920-16$15.00/0.
fMRI brain activation changes following treatment of a first bipolar manic episode.

PubMed

Strakowski, Stephen M; Fleck, David E; Welge, Jeffrey; Eliassen, James C; Norris, Matthew; Durling, Michelle; Komoroski, Richard A; Chu, Wen-Jang; Weber, Wade; Dudley, Jonathan A; Blom, Thomas J; Stover, Amanda; Klein, Christina; Strawn, Jeffrey R; DelBello, Melissa P; Lee, Jing-Huei; Adler, Caleb M

2016-09-01

We tested the hypothesis that, with treatment, functional magnetic resonance imaging (fMRI) regional brain activation in first-episode mania would normalize - i.e., that differences from healthy subjects would diminish over time, and would be associated with clinical remission status, potentially identifying neuroanatomic treatment response markers. Forty-two participants with bipolar I disorder were recruited during their first manic episode, pseudo-randomized to open-label lithium or quetiapine, and followed for 8 weeks. fMRI scans were obtained at baseline and then after 1 and 8 weeks of treatment, while participants performed a continuous performance task with emotional distracters. Healthy participants received fMRI scans at these same intervals. Specific region-of-interest (ROI) activations within prefrontal emotional networks were assessed as potential measures of treatment response. ROI data were reduced using exploratory factor analysis, which identified five factors that were organizationally consistent with functional anatomic models of human emotion modulation. Half of the participants with bipolar disorder achieved remission by Week 8 and were contrasted with the other half that did not. Analyses demonstrated that, in the bipolar disorder group in general, treatment led to decreases in activation across brain regions toward healthy subject values. However, differences in activation changes were observed between subjects with bipolar disorder who did or did not achieve remission in subcortical and amygdala factors. These findings provide evidence for potential neuroanatomic treatment response markers in first-episode bipolar disorder. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Neuroimaging Correlates of Post-Stroke Aphasia Rehabilitation in a Pilot Randomized Trial of Constraint-Induced Aphasia Therapy.

PubMed

Nenert, Rodolphe; Allendorfer, Jane B; Martin, Amber M; Banks, Christi; Ball, Angel; Vannest, Jennifer; Dietz, Aimee R; Szaflarski, Jerzy P

2017-07-18

BACKGROUND Recovery from post-stroke aphasia is a long and complex process with an uncertain outcome. Various interventions have been proposed to augment the recovery, including constraint-induced aphasia therapy (CIAT). CIAT has been applied to patients suffering from post-stroke aphasia in several unblinded studies to show mild-to-moderate linguistic gains. The aim of the present study was to evaluate the neuroimaging correlates of CIAT in patients with chronic aphasia related to left middle cerebral artery stroke. MATERIAL AND METHODS Out of 24 patients recruited in a pilot randomized blinded trial of CIAT, 19 patients received fMRI of language. Eleven of them received CIAT (trained) and eight served as a control group (untrained). Each patient participated in three fMRI sessions (before training, after training, and 3 months later) that included semantic decision and verb generation fMRI tasks, and a battery of language tests. Matching healthy control participants were also included (N=38; matching based on age, handedness, and sex). RESULTS Language testing showed significantly improved performance on Boston Naming Test (BNT; p<0.001) in both stroke groups over time and fMRI showed differences in the distribution of the areas involved in language production between groups that were not present at baseline. Further, regression analysis with BNT indicated changes in brain regions correlated with behavioral performance (temporal gyrus, postcentral gyrus, precentral gyrus, thalamus, left middle and superior frontal gyri). CONCLUSIONS Overall, our results suggest the possibility of language-related cortical plasticity following stroke-induced aphasia with no specific effect from CIAT training.
An intra-neural microstimulation system for ultra-high field magnetic resonance imaging and magnetoencephalography.

PubMed

Glover, Paul M; Watkins, Roger H; O'Neill, George C; Ackerley, Rochelle; Sanchez-Panchuelo, Rosa; McGlone, Francis; Brookes, Matthew J; Wessberg, Johan; Francis, Susan T

2017-10-01

Intra-neural microstimulation (INMS) is a technique that allows the precise delivery of low-current electrical pulses into human peripheral nerves. Single unit INMS can be used to stimulate individual afferent nerve fibres during microneurography. Combining this with neuroimaging allows the unique monitoring of central nervous system activation in response to unitary, controlled tactile input, with functional magnetic resonance imaging (fMRI) providing exquisite spatial localisation of brain activity and magnetoencephalography (MEG) high temporal resolution. INMS systems suitable for use within electrophysiology laboratories have been available for many years. We describe an INMS system specifically designed to provide compatibility with both ultra-high field (7T) fMRI and MEG. Numerous technical and safety issues are addressed. The system is fully analogue, allowing for arbitrary frequency and amplitude INMS stimulation. Unitary recordings obtained within both the MRI and MEG screened-room environments are comparable with those obtained in 'clean' electrophysiology recording environments. Single unit INMS (current <7μA, 200μs pulses) of individual mechanoreceptive afferents produces appropriate and robust responses during fMRI and MEG. This custom-built MRI- and MEG-compatible stimulator overcomes issues with existing INMS approaches; it allows well-controlled switching between recording and stimulus mode, prevents electrical shocks because of long cable lengths, permits unlimited patterns of stimulation, and provides a system with improved work-flow and participant comfort. We demonstrate that the requirements for an INMS-integrated system, which can be used with both fMRI and MEG imaging systems, have been fully met. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.
Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study

PubMed Central

Yu, Qingbao; Wu, Lei; Bridwell, David A.; Erhardt, Erik B.; Du, Yuhui; He, Hao; Chen, Jiayu; Liu, Peng; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D.

2016-01-01

The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics. PMID:27733821
Frequency-dependent tACS modulation of BOLD signal during rhythmic visual stimulation.

PubMed

Chai, Yuhui; Sheng, Jingwei; Bandettini, Peter A; Gao, Jia-Hong

2018-05-01

Transcranial alternating current stimulation (tACS) has emerged as a promising tool for modulating cortical oscillations. In previous electroencephalogram (EEG) studies, tACS has been found to modulate brain oscillatory activity in a frequency-specific manner. However, the spatial distribution and hemodynamic response for this modulation remains poorly understood. Functional magnetic resonance imaging (fMRI) has the advantage of measuring neuronal activity in regions not only below the tACS electrodes but also across the whole brain with high spatial resolution. Here, we measured fMRI signal while applying tACS to modulate rhythmic visual activity. During fMRI acquisition, tACS at different frequencies (4, 8, 16, and 32 Hz) was applied along with visual flicker stimulation at 8 and 16 Hz. We analyzed the blood-oxygen-level-dependent (BOLD) signal difference between tACS-ON vs tACS-OFF, and different frequency combinations (e.g., 4 Hz tACS, 8 Hz flicker vs 8 Hz tACS, 8 Hz flicker). We observed significant tACS modulation effects on BOLD responses when the tACS frequency matched the visual flicker frequency or the second harmonic frequency. The main effects were predominantly seen in regions that were activated by the visual task and targeted by the tACS current distribution. These findings bridge different scientific domains of tACS research and demonstrate that fMRI could localize the tACS effect on stimulus-induced brain rhythms, which could lead to a new approach for understanding the high-level cognitive process shaped by the ongoing oscillatory signal. © 2018 Wiley Periodicals, Inc.
Adaptive cyclic physiologic noise modeling and correction in functional MRI.

PubMed

Beall, Erik B

2010-03-30

Physiologic noise in BOLD-weighted MRI data is known to be a significant source of the variance, reducing the statistical power and specificity in fMRI and functional connectivity analyses. We show a dramatic improvement on current noise correction methods in both fMRI and fcMRI data that avoids overfitting. The traditional noise model is a Fourier series expansion superimposed on the periodicity of parallel measured breathing and cardiac cycles. Correction using this model results in removal of variance matching the periodicity of the physiologic cycles. Using this framework allows easy modeling of noise. However, using a large number of regressors comes at the cost of removing variance unrelated to physiologic noise, such as variance due to the signal of functional interest (overfitting the data). It is our hypothesis that there are a small variety of fits that describe all of the significantly coupled physiologic noise. If this is true, we can replace a large number of regressors used in the model with a smaller number of the fitted regressors and thereby account for the noise sources with a smaller reduction in variance of interest. We describe these extensions and demonstrate that we can preserve variance in the data unrelated to physiologic noise while removing physiologic noise equivalently, resulting in data with a higher effective SNR than with current corrections techniques. Our results demonstrate a significant improvement in the sensitivity of fMRI (up to a 17% increase in activation volume for fMRI compared with higher order traditional noise correction) and functional connectivity analyses. Copyright (c) 2010 Elsevier B.V. All rights reserved.
Correlated Disruption of Resting-State fMRI, LFP, and Spike Connectivity between Area 3b and S2 following Spinal Cord Injury in Monkeys.

PubMed

Wu, Ruiqi; Yang, Pai-Feng; Chen, Li Min

2017-11-15

This study aims to understand how functional connectivity (FC) between areas 3b and S2 alters following input deprivation and the neuronal basis of disrupted FC of resting-state fMRI signals. We combined submillimeter fMRI with microelectrode recordings to localize the deafferented digit regions in areas 3b and S2 by mapping tactile stimulus-evoked fMRI activations before and after cervical dorsal column lesion in each male monkey. An average afferent disruption of 97% significantly reduced fMRI, local field potential (LFP), and spike responses to stimuli in both areas. Analysis of resting-state fMRI signal correlation, LFP coherence, and spike cross-correlation revealed significantly reduced functional connectivity between deafferented areas 3b and S2. The degrees of reductions in stimulus responsiveness and FC after deafferentation differed across fMRI, LFP, and spiking signals. The reduction of FC was much weaker than that of stimulus-evoked responses. Whereas the largest stimulus-evoked signal drop (∼80%) was observed in LFP signals, the greatest FC reduction was detected in the spiking activity (∼30%). fMRI signals showed mild reductions in stimulus responsiveness (∼25%) and FC (∼20%). The overall deafferentation-induced changes were quite similar in areas 3b and S2 across signals. Here we demonstrated that FC strength between areas 3b and S2 was much weakened by dorsal column lesion, and stimulus response reduction and FC disruption in fMRI covary with those of LFP and spiking signals in deafferented areas 3b and S2. These findings have important implications for fMRI studies aiming to probe FC alterations in pathological conditions involving deafferentation in humans. SIGNIFICANCE STATEMENT By directly comparing fMRI, local field potential, and spike signals in both tactile stimulation and resting states before and after severe disruption of dorsal column afferent, we demonstrated that reduction in fMRI responses to stimuli is accompanied by weakened resting-state fMRI functional connectivity (FC) in input-deprived and reorganized digit regions in area 3b of the S1 and S2. Concurrent reductions in local field potential and spike FC validated the use of resting-state fMRI signals for probing neural intrinsic FC alterations in pathological deafferented cortex, and indicated that disrupted FC between mesoscale functionally highly related regions may contribute to the behavioral impairments. Copyright © 2017 the authors 0270-6474/17/3711192-12$15.00/0.
Correlated Disruption of Resting-State fMRI, LFP, and Spike Connectivity between Area 3b and S2 following Spinal Cord Injury in Monkeys

PubMed Central

2017-01-01

This study aims to understand how functional connectivity (FC) between areas 3b and S2 alters following input deprivation and the neuronal basis of disrupted FC of resting-state fMRI signals. We combined submillimeter fMRI with microelectrode recordings to localize the deafferented digit regions in areas 3b and S2 by mapping tactile stimulus-evoked fMRI activations before and after cervical dorsal column lesion in each male monkey. An average afferent disruption of 97% significantly reduced fMRI, local field potential (LFP), and spike responses to stimuli in both areas. Analysis of resting-state fMRI signal correlation, LFP coherence, and spike cross-correlation revealed significantly reduced functional connectivity between deafferented areas 3b and S2. The degrees of reductions in stimulus responsiveness and FC after deafferentation differed across fMRI, LFP, and spiking signals. The reduction of FC was much weaker than that of stimulus-evoked responses. Whereas the largest stimulus-evoked signal drop (∼80%) was observed in LFP signals, the greatest FC reduction was detected in the spiking activity (∼30%). fMRI signals showed mild reductions in stimulus responsiveness (∼25%) and FC (∼20%). The overall deafferentation-induced changes were quite similar in areas 3b and S2 across signals. Here we demonstrated that FC strength between areas 3b and S2 was much weakened by dorsal column lesion, and stimulus response reduction and FC disruption in fMRI covary with those of LFP and spiking signals in deafferented areas 3b and S2. These findings have important implications for fMRI studies aiming to probe FC alterations in pathological conditions involving deafferentation in humans. SIGNIFICANCE STATEMENT By directly comparing fMRI, local field potential, and spike signals in both tactile stimulation and resting states before and after severe disruption of dorsal column afferent, we demonstrated that reduction in fMRI responses to stimuli is accompanied by weakened resting-state fMRI functional connectivity (FC) in input-deprived and reorganized digit regions in area 3b of the S1 and S2. Concurrent reductions in local field potential and spike FC validated the use of resting-state fMRI signals for probing neural intrinsic FC alterations in pathological deafferented cortex, and indicated that disrupted FC between mesoscale functionally highly related regions may contribute to the behavioral impairments. PMID:29038239
Optimizing Experimental Design for Comparing Models of Brain Function

PubMed Central

Daunizeau, Jean; Preuschoff, Kerstin; Friston, Karl; Stephan, Klaas

2011-01-01

This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work. PMID:22125485
Neural Mechanisms of Recognizing Camouflaged Objects: A Human fMRI Study

DTIC Science & Technology

2015-07-30

Unlimited Final Report: Neural Mechanisms of Recognizing Camouflaged Objects: A Human fMRI Study The views, opinions and/or findings contained in this...27709-2211 Visual search, Camouflage, Functional magnetic resonance imaging ( fMRI ), Perceptual learning REPORT DOCUMENTATION PAGE 11. SPONSOR...ABSTRACT Number of Papers published in peer-reviewed journals: Final Report: Neural Mechanisms of Recognizing Camouflaged Objects: A Human fMRI Study
Association of functional magnetic resonance imaging indices with postoperative language outcomes in patients with primary brain tumors.

PubMed

Kundu, Bornali; Penwarden, Amy; Wood, Joel M; Gallagher, Thomas A; Andreoli, Matthew J; Voss, Jed; Meier, Timothy; Nair, Veena A; Kuo, John S; Field, Aaron S; Moritz, Chad; Meyerand, M Elizabeth; Prabhakaran, Vivek

2013-04-01

Functional MRI (fMRI) has the potential to be a useful presurgical planning tool to treat patients with primary brain tumor. In this study the authors retrospectively explored relationships between language-related postoperative outcomes in such patients and multiple factors, including measures estimated from task fMRI maps (proximity of lesion to functional activation area, or lesion-to-activation distance [LAD], and activation-based language lateralization, or lateralization index [LI]) used in the clinical setting for presurgical planning, as well as other factors such as patient age, patient sex, tumor grade, and tumor volume. Patient information was drawn from a database of patients with brain tumors who had undergone preoperative fMRI-based language mapping of the Broca and Wernicke areas. Patients had performed a battery of tasks, including word-generation tasks and a text-versus-symbols reading task, as part of a clinical fMRI protocol. Individually thresholded task fMRI activation maps had been provided for use in the clinical setting. These clinical imaging maps were used to retrospectively estimate LAD and LI for the Broca and Wernicke areas. There was a relationship between postoperative language deficits and the proximity between tumor and Broca area activation (the LAD estimate), where shorter LADs were related to the presence of postoperative aphasia. Stratification by tumor location further showed that for posterior tumors within the temporal and parietal lobes, more bilaterally oriented Broca area activation (LI estimate close to 0) and a shorter Wernicke area LAD were associated with increased postoperative aphasia. Furthermore, decreasing LAD was related to decreasing LI for both Broca and Wernicke areas. Preoperative deficits were related to increasing patient age and a shorter Wernicke area LAD. Overall, LAD and LI, as determined using fMRI in the context of these paradigms, may be useful indicators of postsurgical outcomes. Whereas tumor location may influence postoperative deficits, the results indicated that tumor proximity to an activation area might also interact with how the language network is affected as a whole by the lesion. Although the derivation of LI must be further validated in individual patients by using spatially specific statistical methods, the current results indicated that fMRI is a useful tool for predicting postoperative outcomes in patients with a single brain tumor.

Interleaved EPI based fMRI improved by multiplexed sensitivity encoding (MUSE) and simultaneous multi-band imaging.

PubMed

Chang, Hing-Chiu; Gaur, Pooja; Chou, Ying-hui; Chu, Mei-Lan; Chen, Nan-kuei

2014-01-01

Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful imaging tool for detecting brain activities. The majority of fMRI studies are performed with single-shot echo-planar imaging (EPI) due to its high temporal resolution. Recent studies have demonstrated that, by increasing the spatial-resolution of fMRI, previously unidentified neuronal networks can be measured. However, it is challenging to improve the spatial resolution of conventional single-shot EPI based fMRI. Although multi-shot interleaved EPI is superior to single-shot EPI in terms of the improved spatial-resolution, reduced geometric distortions, and sharper point spread function (PSF), interleaved EPI based fMRI has two main limitations: 1) the imaging throughput is lower in interleaved EPI; 2) the magnitude and phase signal variations among EPI segments (due to physiological noise, subject motion, and B0 drift) are translated to significant in-plane aliasing artifact across the field of view (FOV). Here we report a method that integrates multiple approaches to address the technical limitations of interleaved EPI-based fMRI. Firstly, the multiplexed sensitivity-encoding (MUSE) post-processing algorithm is used to suppress in-plane aliasing artifacts resulting from time-domain signal instabilities during dynamic scans. Secondly, a simultaneous multi-band interleaved EPI pulse sequence, with a controlled aliasing scheme incorporated, is implemented to increase the imaging throughput. Thirdly, the MUSE algorithm is then generalized to accommodate fMRI data obtained with our multi-band interleaved EPI pulse sequence, suppressing both in-plane and through-plane aliasing artifacts. The blood-oxygenation-level-dependent (BOLD) signal detectability and the scan throughput can be significantly improved for interleaved EPI-based fMRI. Our human fMRI data obtained from 3 Tesla systems demonstrate the effectiveness of the developed methods. It is expected that future fMRI studies requiring high spatial-resolvability and fidelity will largely benefit from the reported techniques.
Reading for Meaning in Dyslexic and Young Children: Distinct Neural Pathways but Common Endpoints

ERIC Educational Resources Information Center

Schulz, Enrico; Maurer, Urs; van der Mark, Sanne; Bucher, Kerstin; Brem, Silvia; Martin, Ernst; Brandeis, Daniel

2009-01-01

Developmental dyslexia is a highly prevalent and specific disorder of reading acquisition characterised by impaired reading fluency and comprehension. We have previously identified fMRI- and ERP-based neural markers of impaired sentence reading in dyslexia that indicated both deviant basic word processing and deviant semantic incongruency…
Brain Mechanisms for Processing Direct and Averted Gaze in Individuals with Autism

ERIC Educational Resources Information Center

Pitskel, Naomi B.; Bolling, Danielle Z.; Hudac, Caitlin M.; Lantz, Stephen D.; Minshew, Nancy J.; Vander Wyk, Brent C.; Pelphrey, Kevin A.

2011-01-01

Prior studies have indicated brain abnormalities underlying social processing in autism, but no fMRI study has specifically addressed the differential processing of direct and averted gaze, a critical social cue. Fifteen adolescents and adults with autism and 14 typically developing comparison participants viewed dynamic virtual-reality videos…
Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS).

PubMed

Frederick, Blaise deB; Nickerson, Lisa D; Tong, Yunjie

2012-04-15

Confounding noise in BOLD fMRI data arises primarily from fluctuations in blood flow and oxygenation due to cardiac and respiratory effects, spontaneous low frequency oscillations (LFO) in arterial pressure, and non-task related neural activity. Cardiac noise is particularly problematic, as the low sampling frequency of BOLD fMRI ensures that these effects are aliased in recorded data. Various methods have been proposed to estimate the noise signal through measurement and transformation of the cardiac and respiratory waveforms (e.g. RETROICOR and respiration volume per time (RVT)) and model-free estimation of noise variance through examination of spatial and temporal patterns. We have previously demonstrated that by applying a voxel-specific time delay to concurrently acquired near infrared spectroscopy (NIRS) data, we can generate regressors that reflect systemic blood flow and oxygenation fluctuations effects. Here, we apply this method to the task of removing physiological noise from BOLD data. We compare the efficacy of noise removal using various sets of noise regressors generated from NIRS data, and also compare the noise removal to RETROICOR+RVT. We compare the results of resting state analyses using the original and noise filtered data, and we evaluate the bias for the different noise filtration methods by computing null distributions from the resting data and comparing them with the expected theoretical distributions. Using the best set of processing choices, six NIRS-generated regressors with voxel-specific time delays explain a median of 10.5% of the variance throughout the brain, with the highest reductions being seen in gray matter. By comparison, the nine RETROICOR+RVT regressors together explain a median of 6.8% of the variance in the BOLD data. Detection of resting state networks was enhanced with NIRS denoising, and there were no appreciable differences in the bias of the different techniques. Physiological noise regressors generated using Regressor Interpolation at Progressive Time Delays (RIPTiDe) offer an effective method for efficiently removing hemodynamic noise from BOLD data. Copyright Â© 2012 Elsevier Inc. All rights reserved.
Reconstructing Perceived and Retrieved Faces from Activity Patterns in Lateral Parietal Cortex.

PubMed

Lee, Hongmi; Kuhl, Brice A

2016-06-01

Recent findings suggest that the contents of memory encoding and retrieval can be decoded from the angular gyrus (ANG), a subregion of posterior lateral parietal cortex. However, typical decoding approaches provide little insight into the nature of ANG content representations. Here, we tested whether complex, multidimensional stimuli (faces) could be reconstructed from ANG by predicting underlying face components from fMRI activity patterns in humans. Using an approach inspired by computer vision methods for face recognition, we applied principal component analysis to a large set of face images to generate eigenfaces. We then modeled relationships between eigenface values and patterns of fMRI activity. Activity patterns evoked by individual faces were then used to generate predicted eigenface values, which could be transformed into reconstructions of individual faces. We show that visually perceived faces were reliably reconstructed from activity patterns in occipitotemporal cortex and several lateral parietal subregions, including ANG. Subjective assessment of reconstructed faces revealed specific sources of information (e.g., affect and skin color) that were successfully reconstructed in ANG. Strikingly, we also found that a model trained on ANG activity patterns during face perception was able to successfully reconstruct an independent set of face images that were held in memory. Together, these findings provide compelling evidence that ANG forms complex, stimulus-specific representations that are reflected in activity patterns evoked during perception and remembering. Neuroimaging studies have consistently implicated lateral parietal cortex in episodic remembering, but the functional contributions of lateral parietal cortex to memory remain a topic of debate. Here, we used an innovative form of fMRI pattern analysis to test whether lateral parietal cortex actively represents the contents of memory. Using a large set of human face images, we first extracted latent face components (eigenfaces). We then used machine learning algorithms to predict face components from fMRI activity patterns and, ultimately, to reconstruct images of individual faces. We show that activity patterns in a subregion of lateral parietal cortex, the angular gyrus, supported successful reconstruction of perceived and remembered faces, confirming a role for this region in actively representing remembered content. Copyright © 2016 the authors 0270-6474/16/366069-14$15.00/0.
Effects of cannabis on impulsivity: a systematic review of neuroimaging findings.

PubMed

Wrege, Johannes; Schmidt, Andre; Walter, Anna; Smieskova, Renata; Bendfeldt, Kerstin; Radue, Ernst-Wilhelm; Lang, Undine E; Borgwardt, Stefan

2014-01-01

We conducted a systematic review to assess the evidence for specific effects of cannabis on impulsivity, disinhibition and motor control. The review had a specific focus on neuroimaging findings associated with acute and chronic use of the drug and covers literature published up until May 2012. Seventeen studies were identified, of which 13 met the inclusion criteria; three studies investigated acute effects of cannabis (1 fMRI, 2 PET), while six studies investigated non-acute functional effects (4 fMRI, 2 PET), and four studies investigated structural alterations. Functional imaging studies of impulsivity studies suggest that prefrontal blood flow is lower in chronic cannabis users than in controls. Studies of acute administration of THC or marijuana report increased brain metabolism in several brain regions during impulsivity tasks. Structural imaging studies of cannabis users found differences in reduced prefrontal volumes and white matter integrity that might mediate the abnormal impulsivity and mood observed in marijuana users. To address the question whether impulsivity as a trait precedes cannabis consumption or whether cannabis aggravates impulsivity and discontinuation of usage more longitudinal study designs are warranted.
Sex differences in brain activation elicited by humor.

PubMed

Azim, Eiman; Mobbs, Dean; Jo, Booil; Menon, Vinod; Reiss, Allan L

2005-11-08

With recent investigation beginning to reveal the cortical and subcortical neuroanatomical correlates of humor appreciation, the present event-related functional MRI (fMRI) study was designed to elucidate sex-specific recruitment of these humor related networks. Twenty healthy subjects (10 females) underwent fMRI scanning while subjectively rating 70 verbal and nonverbal achromatic cartoons as funny or unfunny. Data were analyzed by comparing blood oxygenation-level-dependent signal activation during funny and unfunny stimuli. Males and females share an extensive humor-response strategy as indicated by recruitment of similar brain regions: both activate the temporal-occipital junction and temporal pole, structures implicated in semantic knowledge and juxtaposition, and the inferior frontal gyrus, likely to be involved in language processing. Females, however, activate the left prefrontal cortex more than males, suggesting a greater degree of executive processing and language-based decoding. Females also exhibit greater activation of mesolimbic regions, including the nucleus accumbens, implying greater reward network response and possibly less reward expectation. These results indicate sex-specific differences in neural response to humor with implications for sex-based disparities in the integration of cognition and emotion.
Effects of Cannabis on Impulsivity: A Systematic Review of Neuroimaging Findings

PubMed Central

Wrege, Johannes; Schmidt, André; Walter, Anna; Smieskova, Renata; Bendfeldt, Kerstin; Radue, Ernst-Wilhelm; Lang, Undine E.; Borgwardt, Stefan

2014-01-01

We conducted a systematic review to assess the evidence for specific effects of cannabis on impulsivity, disinhibition and motor control. The review had a specific focus on neuroimaging findings associated with acute and chronic use of the drug and covers literature published up until May 2012. Seventeen studies were identified, of which 13 met the inclusion criteria; three studies investigated acute effects of cannabis (1 fMRI, 2 PET), while six studies investigated non-acute functional effects (4 fMRI, 2 PET), and four studies investigated structural alterations. Functional imaging studies of impulsivity studies suggest that prefrontal blood flow is lower in chronic cannabis users than in controls. Studies of acute administration of THC or marijuana report increased brain metabolism in several brain regions during impulsivity tasks. Structural imaging studies of cannabis users found differences in reduced prefrontal volumes and white matter integrity that might mediate the abnormal impulsivity and mood observed in marijuana users. To address the question whether impulsivity as a trait precedes cannabis consumption or whether cannabis aggravates impulsivity and discontinuation of usage more longitudinal study designs are warranted. PMID:23829358
Controlling conflict from interfering long-term memory representations.

PubMed

Jost, Kerstin; Khader, Patrick H; Düsel, Peter; Richter, Franziska R; Rohde, Kristina B; Bien, Siegfried; Rösler, Frank

2012-05-01

Remembering is more than an activation of a memory trace. As retrieval cues are often not uniquely related to one specific memory, cognitive control should come into play to guide selective memory retrieval by focusing on relevant while ignoring irrelevant information. Here, we investigated, by means of EEG and fMRI, how the memory system deals with retrieval interference arising when retrieval cues are associated with two material types (faces and spatial positions), but only one is task-relevant. The topography of slow EEG potentials and the fMRI BOLD signal in posterior storage areas indicated that in such situations not only the relevant but also the irrelevant material becomes activated. This results in retrieval interference that triggers control processes mediated by the medial and lateral PFC, which are presumably involved in biasing target representations by boosting the task-relevant material. Moreover, memory-based conflict was found to be dissociable from response conflict that arises when the relevant and irrelevant materials imply different responses. The two types of conflict show different activations in the medial frontal cortex, supporting the claim of domain-specific prefrontal control systems.
Left prefrontal cortex control of novel occurrences during recollection: a psychopharmacological study using scopolamine and event-related fMRI.

PubMed

Bozzali, M; MacPherson, S E; Dolan, R J; Shallice, T

2006-10-15

Recollection and familiarity represent two processes involved in episodic memory retrieval. We investigated how scopolamine (an antagonist of acetylcholine muscarinic receptors) influenced brain activity during memory retrieval, using a paradigm that separated recollection and familiarity. Eighteen healthy volunteers were recruited in a randomized, placebo-controlled, double-blind design using event-related fMRI. Participants were required to perform a verbal recognition memory task within the scanner, either under placebo or scopolamine conditions. Depending on the subcondition, participants were required to make a simple recognition decision (old/new items) or base their decision on more specific information related to prior experience (target/non-target/new items). We show a drug modulation in left prefrontal and perirhinal cortex during recollection. Such an effect was specifically driven by novelty and showed an inverse correlation with accuracy performance. Additionally, we show a direct correlation between drug-related signal change in left prefrontal and perirhinal cortices. We discuss the findings in terms of acetylcholine mediation of the familiarity/novelty signal through perirhinal cortex and the control of the relative signal strength through prefrontal cortex.
Assessing the effects of cocaine dependence and pathological gambling using group-wise sparse representation of natural stimulus FMRI data.

PubMed

Ren, Yudan; Fang, Jun; Lv, Jinglei; Hu, Xintao; Guo, Cong Christine; Guo, Lei; Xu, Jiansong; Potenza, Marc N; Liu, Tianming

2017-08-01

Assessing functional brain activation patterns in neuropsychiatric disorders such as cocaine dependence (CD) or pathological gambling (PG) under naturalistic stimuli has received rising interest in recent years. In this paper, we propose and apply a novel group-wise sparse representation framework to assess differences in neural responses to naturalistic stimuli across multiple groups of participants (healthy control, cocaine dependence, pathological gambling). Specifically, natural stimulus fMRI (N-fMRI) signals from all three groups of subjects are aggregated into a big data matrix, which is then decomposed into a common signal basis dictionary and associated weight coefficient matrices via an effective online dictionary learning and sparse coding method. The coefficient matrices associated with each common dictionary atom are statistically assessed for each group separately. With the inter-group comparisons based on the group-wise correspondence established by the common dictionary, our experimental results demonstrated that the group-wise sparse coding and representation strategy can effectively and specifically detect brain networks/regions affected by different pathological conditions of the brain under naturalistic stimuli.
Transferring cognitive tasks between brain imaging modalities: implications for task design and results interpretation in FMRI studies.

PubMed

Warbrick, Tracy; Reske, Martina; Shah, N Jon

2014-09-22

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.
Enhanced Thalamic Functional Connectivity with No fMRI Responses to Affected Forelimb Stimulation in Stroke-Recovered Rats.

PubMed

Shim, Woo H; Suh, Ji-Yeon; Kim, Jeong K; Jeong, Jaeseung; Kim, Young R

2016-01-01

Neurological recovery after stroke has been extensively investigated to provide better understanding of neurobiological mechanism, therapy, and patient management. Recent advances in neuroimaging techniques, particularly functional MRI (fMRI), have widely contributed to unravel the relationship between the altered neural function and stroke-affected brain areas. As results of previous investigations, the plastic reorganization and/or gradual restoration of the hemodynamic fMRI responses to neural stimuli have been suggested as relevant mechanisms underlying the stroke recovery process. However, divergent study results and modality-dependent outcomes have clouded the proper interpretation of variable fMRI signals. Here, we performed both evoked and resting state fMRI (rs-fMRI) to clarify the link between the fMRI phenotypes and post-stroke functional recovery. The experiments were designed to examine the altered neural activity within the contra-lesional hemisphere and other undamaged brain regions using rat models with large unilateral stroke, which despite the severe injury, exhibited nearly full recovery at ∼6 months after stroke. Surprisingly, both blood oxygenation level-dependent and blood volume-weighted (CBVw) fMRI activities elicited by electrical stimulation of the stroke-affected forelimb were completely absent, failing to reveal the neural origin of the behavioral recovery. In contrast, the functional connectivity maps showed highly robust rs-fMRI activity concentrated in the contra-lesional ventromedial nucleus of thalamus (VM). The negative finding in the stimuli-induced fMRI study using the popular rat middle cerebral artery model denotes weak association between the fMRI hemodynamic responses and neurological improvement. The results strongly caution the indiscreet interpretation of stroke-affected fMRI signals and demonstrate rs-fMRI as a complementary tool for efficiently characterizing stroke recovery.
funcLAB/G-service-oriented architecture for standards-based analysis of functional magnetic resonance imaging in HealthGrids.

PubMed

Erberich, Stephan G; Bhandekar, Manasee; Chervenak, Ann; Kesselman, Carl; Nelson, Marvin D

2007-01-01

Functional MRI is successfully being used in clinical and research applications including preoperative planning, language mapping, and outcome monitoring. However, clinical use of fMRI is less widespread due to its complexity of imaging, image workflow, post-processing, and lack of algorithmic standards hindering result comparability. As a consequence, wide-spread adoption of fMRI as clinical tool is low contributing to the uncertainty of community physicians how to integrate fMRI into practice. In addition, training of physicians with fMRI is in its infancy and requires clinical and technical understanding. Therefore, many institutions which perform fMRI have a team of basic researchers and physicians to perform fMRI as a routine imaging tool. In order to provide fMRI as an advanced diagnostic tool to the benefit of a larger patient population, image acquisition and image post-processing must be streamlined, standardized, and available at any institution which does not have these resources available. Here we describe a software architecture, the functional imaging laboratory (funcLAB/G), which addresses (i) standardized image processing using Statistical Parametric Mapping and (ii) its extension to secure sharing and availability for the community using standards-based Grid technology (Globus Toolkit). funcLAB/G carries the potential to overcome the limitations of fMRI in clinical use and thus makes standardized fMRI available to the broader healthcare enterprise utilizing the Internet and HealthGrid Web Services technology.
The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

PubMed

Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

2018-01-25

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).
Validating a new methodology for optical probe design and image registration in fNIRS studies

PubMed Central

Wijeakumar, Sobanawartiny; Spencer, John P.; Bohache, Kevin; Boas, David A.; Magnotta, Vincent A.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an imaging technique that relies on the principle of shining near-infrared light through tissue to detect changes in hemodynamic activation. An important methodological issue encountered is the creation of optimized probe geometry for fNIRS recordings. Here, across three experiments, we describe and validate a processing pipeline designed to create an optimized, yet scalable probe geometry based on selected regions of interest (ROIs) from the functional magnetic resonance imaging (fMRI) literature. In experiment 1, we created a probe geometry optimized to record changes in activation from target ROIs important for visual working memory. Positions of the sources and detectors of the probe geometry on an adult head were digitized using a motion sensor and projected onto a generic adult atlas and a segmented head obtained from the subject's MRI scan. In experiment 2, the same probe geometry was scaled down to fit a child's head and later digitized and projected onto the generic adult atlas and a segmented volume obtained from the child's MRI scan. Using visualization tools and by quantifying the amount of intersection between target ROIs and channels, we show that out of 21 ROIs, 17 and 19 ROIs intersected with fNIRS channels from the adult and child probe geometries, respectively. Further, both the adult atlas and adult subject-specific MRI approaches yielded similar results and can be used interchangeably. However, results suggest that segmented heads obtained from MRI scans be used for registering children's data. Finally, in experiment 3, we further validated our processing pipeline by creating a different probe geometry designed to record from target ROIs involved in language and motor processing. PMID:25705757
Functional magnetic resonance imaging.

PubMed

Buchbinder, Bradley R

2016-01-01

Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks. © 2016 Elsevier B.V. All rights reserved.
Comparison of fMRI data analysis by SPM99 on different operating systems.

PubMed

Shinagawa, Hideo; Honda, Ei-ichi; Ono, Takashi; Kurabayashi, Tohru; Ohyama, Kimie

2004-09-01

The hardware chosen for fMRI data analysis may depend on the platform already present in the laboratory or the supporting software. In this study, we ran SPM99 software on multiple platforms to examine whether we could analyze fMRI data by SPM99, and to compare their differences and limitations in processing fMRI data, which can be attributed to hardware capabilities. Six normal right-handed volunteers participated in a study of hand-grasping to obtain fMRI data. Each subject performed a run that consisted of 98 images. The run was measured using a gradient echo-type echo planar imaging sequence on a 1.5T apparatus with a head coil. We used several personal computer (PC), Unix and Linux machines to analyze the fMRI data. There were no differences in the results obtained on several PC, Unix and Linux machines. The only limitations in processing large amounts of the fMRI data were found using PC machines. This suggests that the results obtained with different machines were not affected by differences in hardware components, such as the CPU, memory and hard drive. Rather, it is likely that the limitations in analyzing a huge amount of the fMRI data were due to differences in the operating system (OS).
7T Magnetization Transfer and Chemical Exchange Saturation Transfer MRI of Cortical Gray Matter: Can We Detect Neurochemical and Macromolecular Abnormalities

DTIC Science & Technology

2015-10-01

with fMRI , and CEST acquisitions. Analysis hurdles were noted in the qMT, which we discuss here. Recruitment continues in the MS cohort (all healthy...Saturation Transfer (CEST) • Magnetization Transfer (MT) • Brain • Cortical Gray Matter (cGM) • Multiple Sclerosis (MS) • Functional MRI ( fMRI ) • Pool Size...MPRAGE Anatomical – 2:12 • fMRI Resting State – 8:34 • fMRI N-Back task – 8:30 • fMRI Trailmaking task – 4:14 The current scan time for all scans is
Joint fMRI analysis and subject clustering using sparse dictionary learning

NASA Astrophysics Data System (ADS)

Kim, Seung-Jun; Dontaraju, Krishna K.

2017-08-01

Multi-subject fMRI data analysis methods based on sparse dictionary learning are proposed. In addition to identifying the component spatial maps by exploiting the sparsity of the maps, clusters of the subjects are learned by postulating that the fMRI volumes admit a subspace clustering structure. Furthermore, in order to tune the associated hyper-parameters systematically, a cross-validation strategy is developed based on entry-wise sampling of the fMRI dataset. Efficient algorithms for solving the proposed constrained dictionary learning formulations are developed. Numerical tests performed on synthetic fMRI data show promising results and provides insights into the proposed technique.

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