Does Post-task Declarative Learning Have an Influence on Early Motor Memory Consolidation Over Day? An fMRI Study.

PubMed

Rothkirch, Inken; Wolff, Stephan; Margraf, Nils G; Pedersen, Anya; Witt, Karsten

2018-01-01

Previous studies demonstrated the influence of the post-learning period on procedural motor memory consolidation. In an early period after the acquisition, motor skills are vulnerable to modifications during wakefulness. Indeed, specific interventions such as world-list learning within this early phase of motor memory consolidation seem to enhance motor performance as an indicator for successful consolidation. This finding highlights the idea that manipulations of procedural and declarative memory systems during the early phase of memory consolidation over wakefulness may influence off-line consolidation. Using functional magnetic resonance imaging (fMRI) during initial motor sequence learning and motor sequence recall, we indirectly assess the influence of a secondary task taken place in the early phase of memory consolidation. All participants were scanned using fMRI during the learning phase of a serial reaction time task (SRTT) at 8 a.m. Afterwards, they were randomly assigned to one of five conditions. One group performed a declarative verbal, one a declarative nonverbal learning task. Two groups worked on attention tasks. A control group passed a resting condition. Participants stayed awake the whole day and performed the SRTT in the MRI scanner 12 h later at 8 p.m. At the behavioral level, the analysis of the reaction times failed to show a significant group difference. The primary analysis assessing fMRI data based on the contrast (sequence - random) between learning and retrieval also did not show any significant group differences. Therefore, our main analysis do not support the hypothesis that a secondary task influences the retrieval of the SRTT. In a more liberal fMRI analysis, we compared only the sequence blocks of the SRTT from learning to recall. BOLD signal decreased in the ipsilateral cerebellum and the supplementary motor area solely in the verbal learning group. Although our primary analysis failed to show significant changes between our groups, results of the secondary analysis could be an indication for a beneficial effect of the verbal declarative task in the early post-learning phase. A nonverbal learning task did not affect the activation within the motor network. Further studies are needed to replicate this finding and to assess the usefulness of this manipulation.

Resting State fMRI in the moving fetus: a robust framework for motion, bias field and spin history correction.

PubMed

Ferrazzi, Giulio; Kuklisova Murgasova, Maria; Arichi, Tomoki; Malamateniou, Christina; Fox, Matthew J; Makropoulos, Antonios; Allsop, Joanna; Rutherford, Mary; Malik, Shaihan; Aljabar, Paul; Hajnal, Joseph V

2014-11-01

There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies. Copyright © 2014 Elsevier Inc. All rights reserved.

Topologic analysis and comparison of brain activation in children with epilepsy versus controls: an fMRI study

NASA Astrophysics Data System (ADS)

Oweis, Khalid J.; Berl, Madison M.; Gaillard, William D.; Duke, Elizabeth S.; Blackstone, Kaitlin; Loew, Murray H.; Zara, Jason M.

2010-03-01

This paper describes the development of novel computer-aided analysis algorithms to identify the language activation patterns at a certain Region of Interest (ROI) in Functional Magnetic Resonance Imaging (fMRI). Previous analysis techniques have been used to compare typical and pathologic activation patterns in fMRI images resulting from identical tasks but none of them analyzed activation topographically in a quantitative manner. This paper presents new analysis techniques and algorithms capable of identifying a pattern of language activation associated with localization related epilepsy. fMRI images of 64 healthy individuals and 31 patients with localization related epilepsy have been studied and analyzed on an ROI basis. All subjects are right handed with normal MRI scans and have been classified into three age groups (4-6, 7-9, 10-12 years). Our initial efforts have focused on investigating activation in the Left Inferior Frontal Gyrus (LIFG). A number of volumetric features have been extracted from the data. The LIFG has been cut into slices and the activation has been investigated topographically on a slice by slice basis. Overall, a total of 809 features have been extracted, and correlation analysis was applied to eliminate highly correlated features. Principal Component analysis was then applied to account only for major components in the data and One-Way Analysis of Variance (ANOVA) has been applied to test for significantly different features between normal and patient groups. Twenty Nine features have were found to be significantly different (p<0.05) between patient and control groups

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.

Abnormal resting-state brain activities in patients with first-episode obsessive-compulsive disorder

PubMed Central

Niu, Qihui; Yang, Lei; Song, Xueqin; Chu, Congying; Liu, Hao; Zhang, Lifang; Li, Yan; Zhang, Xiang; Cheng, Jingliang; Li, Youhui

2017-01-01

Objective This paper attempts to explore the brain activity of patients with obsessive-compulsive disorder (OCD) and its correlation with the disease at resting duration in patients with first-episode OCD, providing a forceful imaging basis for clinic diagnosis and pathogenesis of OCD. Methods Twenty-six patients with first-episode OCD and 25 healthy controls (HC group; matched for age, sex, and education level) underwent functional magnetic resonance imaging (fMRI) scanning at resting state. Statistical parametric mapping 8, data processing assistant for resting-state fMRI analysis toolkit, and resting state fMRI data analysis toolkit packages were used to process the fMRI data on Matlab 2012a platform, and the difference of regional homogeneity (ReHo) values between the OCD group and HC group was detected with independent two-sample t-test. With age as a concomitant variable, the Pearson correlation analysis was adopted to study the correlation between the disease duration and ReHo value of whole brain. Results Compared with HC group, the ReHo values in OCD group were decreased in brain regions, including left thalamus, right thalamus, right paracentral lobule, right postcentral gyrus, and the ReHo value was increased in the left angular gyrus region. There was a negative correlation between disease duration and ReHo value in the bilateral orbitofrontal cortex (OFC). Conclusion OCD is a multifactorial disease generally caused by abnormal activities of many brain regions at resting state. Worse brain activity of the OFC is related to the OCD duration, which provides a new insight to the pathogenesis of OCD. PMID:28243104

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.

Effect of phase-encoding direction on group analysis of resting-state functional magnetic resonance imaging.

PubMed

Mori, Yasuo; Miyata, Jun; Isobe, Masanori; Son, Shuraku; Yoshihara, Yujiro; Aso, Toshihiko; Kouchiyama, Takanori; Murai, Toshiya; Takahashi, Hidehiko

2018-05-17

Echo-planar imaging is a common technique used in functional magnetic resonance imaging (fMRI), however it suffers from image distortion and signal loss because of large susceptibility effects that are related to the phase-encoding direction of the scan. Despite this relationship, the majority of neuroimaging studies have not considered the influence of phase-encoding direction. Here, we aimed to clarify how phase-encoding direction can affect the outcome of an fMRI connectivity study of schizophrenia. Resting-state fMRI using anterior to posterior (A-P) and posterior to anterior (P-A) directions was used to examine 25 patients with schizophrenia (SC) and 37 matched healthy controls (HC). We conducted a functional connectivity analysis using independent component analysis and performed three group comparisons: A-P vs. P-A (all participants), SC vs. HC for the A-P and P-A datasets, and the interaction between phase-encoding direction and participant group. The estimated functional connectivity differed between the two phase-encoding directions in areas that were more extensive than those where signal loss has been reported. Although functional connectivity in the SC group was lower than that in the HC group for both directions, the A-P and P-A conditions did not exhibit the same specific pattern of differences. Further, we observed an interaction between participant group and the phase-encoding direction in the left temporo-parietal junction and left fusiform gyrus. Phase-encoding direction can influence the results of functional connectivity studies. Thus, appropriate selection and documentation of phase-encoding direction will be important in future resting-state fMRI studies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

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

Within-subject joint independent component analysis of simultaneous fMRI/ERP in an auditory oddball paradigm

PubMed Central

MANGALATHU-ARUMANA, J.; BEARDSLEY, S. A.; LIEBENTHAL, E.

2012-01-01

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1–8 μV in the time window of the P300 (350–700ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post- central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI. PMID:22377443

Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses.

PubMed

Esposito, Fabrizio; Singer, Neomi; Podlipsky, Ilana; Fried, Itzhak; Hendler, Talma; Goebel, Rainer

2013-02-01

Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on the surface of the human cerebral cortex is of tremendous importance for a better understanding of detailed brain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography (iEEG) measure two technically unrelated but spatially and temporally complementary sets of functional descriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mapping at the population-level, an effective comparative framework for the cortex-based inter-subject analysis of iEEG and fMRI data sets is needed. We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of passive movie viewing and music listening to explore the degree of local spatial correspondence and temporal coupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulations across the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simple model of the iEEG activity spread around each electrode location and the cortex-based inter-subject alignment procedure to transform discrete iEEG measurements into cortically distributed group patterns by establishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects. Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis for combining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigm but with different iEEG electrode coverage. The proposed iEEG-fMRI framework allows for improved group statistics in a common anatomical space and preserves the dynamic link between the temporal features of the two modalities. Copyright © 2012 Elsevier Inc. All rights reserved.

Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI.

PubMed

Spisák, Tamás; Jakab, András; Kis, Sándor A; Opposits, Gábor; Aranyi, Csaba; Berényi, Ervin; Emri, Miklós

2014-01-01

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

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

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

Directional connectivity of resting state human fMRI data using cascaded ICA-PDC analysis.

PubMed

Silfverhuth, Minna J; Remes, Jukka; Starck, Tuomo; Nikkinen, Juha; Veijola, Juha; Tervonen, Osmo; Kiviniemi, Vesa

2011-11-01

Directional connectivity measures, such as partial directed coherence (PDC), give us means to explore effective connectivity in the human brain. By utilizing independent component analysis (ICA), the original data-set reduction was performed for further PDC analysis. To test this cascaded ICA-PDC approach in causality studies of human functional magnetic resonance imaging (fMRI) data. Resting state group data was imaged from 55 subjects using a 1.5 T scanner (TR 1800 ms, 250 volumes). Temporal concatenation group ICA in a probabilistic ICA and further repeatability runs (n = 200) were overtaken. The reduced data-set included the time series presentation of the following nine ICA components: secondary somatosensory cortex, inferior temporal gyrus, intracalcarine cortex, primary auditory cortex, amygdala, putamen and the frontal medial cortex, posterior cingulate cortex and precuneus, comprising the default mode network components. Re-normalized PDC (rPDC) values were computed to determine directional connectivity at the group level at each frequency. The integrative role was suggested for precuneus while the role of major divergence region may be proposed to primary auditory cortex and amygdala. This study demonstrates the potential of the cascaded ICA-PDC approach in directional connectivity studies of human fMRI.

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

Further fMRI Validation of the Visual Half Field Technique as an Indicator of Language Laterality: A Large-Group Analysis

ERIC Educational Resources Information Center

Van der Haegen, Lise; Cai, Qing; Seurinck, Ruth; Brysbaert, Marc

2011-01-01

The best established lateralized cerebral function is speech production, with the majority of the population having left hemisphere dominance. An important question is how to best assess the laterality of this function. Neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI) are increasingly used in clinical settings to…

Multiple imputation of missing fMRI data in whole brain analysis

PubMed Central

Vaden, Kenneth I.; Gebregziabher, Mulugeta; Kuchinsky, Stefanie E.; Eckert, Mark A.

2012-01-01

Whole brain fMRI analyses rarely include the entire brain because of missing data that result from data acquisition limits and susceptibility artifact, in particular. This missing data problem is typically addressed by omitting voxels from analysis, which may exclude brain regions that are of theoretical interest and increase the potential for Type II error at cortical boundaries or Type I error when spatial thresholds are used to establish significance. Imputation could significantly expand statistical map coverage, increase power, and enhance interpretations of fMRI results. We examined multiple imputation for group level analyses of missing fMRI data using methods that leverage the spatial information in fMRI datasets for both real and simulated data. Available case analysis, neighbor replacement, and regression based imputation approaches were compared in a general linear model framework to determine the extent to which these methods quantitatively (effect size) and qualitatively (spatial coverage) increased the sensitivity of group analyses. In both real and simulated data analysis, multiple imputation provided 1) variance that was most similar to estimates for voxels with no missing data, 2) fewer false positive errors in comparison to mean replacement, and 3) fewer false negative errors in comparison to available case analysis. Compared to the standard analysis approach of omitting voxels with missing data, imputation methods increased brain coverage in this study by 35% (from 33,323 to 45,071 voxels). In addition, multiple imputation increased the size of significant clusters by 58% and number of significant clusters across statistical thresholds, compared to the standard voxel omission approach. While neighbor replacement produced similar results, we recommend multiple imputation because it uses an informed sampling distribution to deal with missing data across subjects that can include neighbor values and other predictors. Multiple imputation is anticipated to be particularly useful for 1) large fMRI data sets with inconsistent missing voxels across subjects and 2) addressing the problem of increased artifact at ultra-high field, which significantly limit the extent of whole brain coverage and interpretations of results. PMID:22500925

One-year test-retest reliability of intrinsic connectivity network fMRI in older adults

PubMed Central

Guo, Cong C.; Kurth, Florian; Zhou, Juan; Mayer, Emeran A.; Eickhoff, Simon B; Kramer, Joel H.; Seeley, William W.

2014-01-01

“Resting-state” or task-free fMRI can assess intrinsic connectivity network (ICN) integrity in health and disease, suggesting a potential for use of these methods as disease-monitoring biomarkers. Numerous analytical options are available, including model-driven ROI-based correlation analysis and model-free, independent component analysis (ICA). High test-retest reliability will be a necessary feature of a successful ICN biomarker, yet available reliability data remains limited. Here, we examined ICN fMRI test-retest reliability in 24 healthy older subjects scanned roughly one year apart. We focused on the salience network, a disease-relevant ICN not previously subjected to reliability analysis. Most ICN analytical methods proved reliable (intraclass coefficients > 0.4) and could be further improved by wavelet analysis. Seed-based ROI correlation analysis showed high map-wise reliability, whereas graph theoretical measures and temporal concatenation group ICA produced the most reliable individual unit-wise outcomes. Including global signal regression in ROI-based correlation analyses reduced reliability. Our study provides a direct comparison between the most commonly used ICN fMRI methods and potential guidelines for measuring intrinsic connectivity in aging control and patient populations over time. PMID:22446491

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

Nonlinear Complexity Analysis of Brain fMRI Signals in Schizophrenia

PubMed Central

Sokunbi, Moses O.; Gradin, Victoria B.; Waiter, Gordon D.; Cameron, George G.; Ahearn, Trevor S.; Murray, Alison D.; Steele, Douglas J.; Staff, Roger T.

2014-01-01

We investigated the differences in brain fMRI signal complexity in patients with schizophrenia while performing the Cyberball social exclusion task, using measures of Sample entropy and Hurst exponent (H). 13 patients meeting diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM IV) criteria for schizophrenia and 16 healthy controls underwent fMRI scanning at 1.5 T. The fMRI data of both groups of participants were pre-processed, the entropy characterized and the Hurst exponent extracted. Whole brain entropy and H maps of the groups were generated and analysed. The results after adjusting for age and sex differences together show that patients with schizophrenia exhibited higher complexity than healthy controls, at mean whole brain and regional levels. Also, both Sample entropy and Hurst exponent agree that patients with schizophrenia have more complex fMRI signals than healthy controls. These results suggest that schizophrenia is associated with more complex signal patterns when compared to healthy controls, supporting the increase in complexity hypothesis, where system complexity increases with age or disease, and also consistent with the notion that schizophrenia is characterised by a dysregulation of the nonlinear dynamics of underlying neuronal systems. PMID:24824731

Registering and Analyzing Rat fMRI Data in the Stereotaxic Framework by Exploiting Intrinsic Anatomical Features

PubMed Central

Lu, Hanbing; Scholl, Clara A.; Zuo, Yantao; Demny, Steven; Rea, William; Stein, Elliot A.; Yang, Yihong

2009-01-01

The value of analyzing neuroimaging data on a group level has been well established in human studies. However, there is no standard procedure for registering and analyzing fMRI data into common space in rodent functional magnetic resonance imaging (fMRI) studies. An approach for performing rat imaging data analysis in the stereotaxic framework is presented. This method is rooted in the biological observation that the skull shape and size of rat brain are essentially the same as long as their weights are within certain range. Registration is performed using rigid-body transformations without scaling or shearing, preserving the unique properties of the stable shape and size inherent in rat brain structure. Also, it does not require brain tissue masking, and is not biased towards surface coil sensitivity profile. A standard rat brain atlas is used to facilitate the identification of activated areas in common space, allowing accurate region-of-interest (ROI) analysis. This technique is evaluated from a group of rats (n = 11) undergoing routine MRI scans; the registration accuracy is estimated to be within 400 μm. The analysis of fMRI data acquired with an electrical forepaw stimulation model demonstrates the utility of this technique. The method is implemented within the AFNI framework and can be readily extended to other studies. PMID:19608368

Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA

PubMed Central

Michael, Andrew M.; Anderson, Mathew; Miller, Robyn L.; Adalı, Tülay; Calhoun, Vince D.

2014-01-01

Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3) both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4) if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5) in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic) and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift). PMID:25018704

Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study

PubMed Central

Hernández-Martin, Estefania; Marcano, Francisco; Casanova, Oscar; Modroño, Cristian; Plata-Bello, Julio; González-Mora, Jose Luis

2017-01-01

Abstract. Diffuse optical tomography (DOT) measures concentration changes in both oxy- and deoxyhemoglobin providing three-dimensional images of local brain activations. A pilot study, which compares both DOT and functional magnetic resonance imaging (fMRI) volumes through t-maps given by canonical statistical parametric mapping (SPM) processing for both data modalities, is presented. The DOT series were processed using a method that is based on a Bayesian filter application on raw DOT data to remove physiological changes and minimum description length application index to select a number of singular values, which reduce the data dimensionality during image reconstruction and adaptation of DOT volume series to normalized standard space. Therefore, statistical analysis is performed with canonical SPM software in the same way as fMRI analysis is done, accepting DOT volumes as if they were fMRI volumes. The results show the reproducibility and ruggedness of the method to process DOT series on group analysis using cognitive paradigms on the prefrontal cortex. Difficulties such as the fact that scalp–brain distances vary between subjects or cerebral activations are difficult to reproduce due to strategies used by the subjects to solve arithmetic problems are considered. T-images given by fMRI and DOT volume series analyzed in SPM show that at the functional level, both DOT and fMRI measures detect the same areas, although DOT provides complementary information to fMRI signals about cerebral activity. PMID:28386575

A novel approach to analyzing fMRI and SNP data via parallel independent component analysis

NASA Astrophysics Data System (ADS)

Liu, Jingyu; Pearlson, Godfrey; Calhoun, Vince; Windemuth, Andreas

2007-03-01

There is current interest in understanding genetic influences on brain function in both the healthy and the disordered brain. Parallel independent component analysis, a new method for analyzing multimodal data, is proposed in this paper and applied to functional magnetic resonance imaging (fMRI) and a single nucleotide polymorphism (SNP) array. The method aims to identify the independent components of each modality and the relationship between the two modalities. We analyzed 92 participants, including 29 schizophrenia (SZ) patients, 13 unaffected SZ relatives, and 50 healthy controls. We found a correlation of 0.79 between one fMRI component and one SNP component. The fMRI component consists of activations in cingulate gyrus, multiple frontal gyri, and superior temporal gyrus. The related SNP component is contributed to significantly by 9 SNPs located in sets of genes, including those coding for apolipoprotein A-I, and C-III, malate dehydrogenase 1 and the gamma-aminobutyric acid alpha-2 receptor. A significant difference in the presences of this SNP component is found between the SZ group (SZ patients and their relatives) and the control group. In summary, we constructed a framework to identify the interactions between brain functional and genetic information; our findings provide new insight into understanding genetic influences on brain function in a common mental disorder.

Lack of sex effect on brain activity during a visuomotor response task: functional MR imaging study.

PubMed

Mikhelashvili-Browner, Nina; Yousem, David M; Wu, Colin; Kraut, Michael A; Vaughan, Christina L; Oguz, Kader Karli; Calhoun, Vince D

2003-03-01

As more individuals are enrolled in clinical functional MR imaging (fMRI) studies, an understanding of how sex may influence fMRI-measured brain activation is critical. We used fixed- and random-effects models to study the influence of sex on fMRI patterns of brain activation during a simple visuomotor reaction time task in the group of 26 age-matched men and women. We evaluated the right visual, left visual, left primary motor, left supplementary motor, and left anterior cingulate areas. Volumes of activations did not significantly differ between the groups in any defined regions. Analysis of variance failed to show any significant correlations between sex and volumes of brain activation in any location studied. Mean percentage signal-intensity changes for all locations were similar between men and women. A two-way t test of brain activation in men and women, performed as a part of random-effects modeling, showed no significant difference at any site. Our results suggest that sex seems to have little influence on fMRI brain activation when we compared performance on the simple reaction-time task. The need to control for sex effects is not critical in the analysis of this task with fMRI.

Functional Magnetic Resonance Imaging of Working Memory and Executive Dysfunction in Systemic Lupus Erythematosus and Antiphospholipid Antibody-Positive Patients.

PubMed

Kozora, E; Uluğ, A M; Erkan, D; Vo, A; Filley, C M; Ramon, G; Burleson, A; Zimmerman, R; Lockshin, M D

2016-11-01

Standardized cognitive tests and functional magnetic resonance imaging (fMRI) studies of systemic lupus erythematosus (SLE) patients demonstrate deficits in working memory and executive function. These neurobehavioral abnormalities are not well studied in antiphospholipid syndrome, which may occur independently of or together with SLE. This study compares an fMRI paradigm involving motor skills, working memory, and executive function in SLE patients without antiphospholipid antibody (aPL) (the SLE group), aPL-positive non-SLE patients (the aPL-positive group), and controls. Brain MRI, fMRI, and standardized cognitive assessment results were obtained from 20 SLE, 20 aPL-positive, and 10 healthy female subjects with no history of neuropsychiatric abnormality. Analysis of fMRI data showed no differences in performance across groups on bilateral motor tasks. When analysis of variance was used, significant group differences were found in 2 executive function tasks (word generation and word rhyming) and in a working memory task (N-Back). Patients positive for aPL demonstrated higher activation in bilateral frontal, temporal, and parietal cortices compared to controls during working memory and executive function tasks. SLE patients also demonstrated bilateral frontal and temporal activation during working memory and executive function tasks. Compared to controls, both aPL-positive and SLE patients had elevated cortical activation, primarily in the frontal lobes, during tasks involving working memory and executive function. These findings are consistent with cortical overactivation as a compensatory mechanism for early white matter neuropathology in these disorders. © 2016, American College of Rheumatology.

Inter-subject phase synchronization for exploratory analysis of task-fMRI.

PubMed

Bolt, Taylor; Nomi, Jason S; Vij, Shruti G; Chang, Catie; Uddin, Lucina Q

2018-08-01

Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity. Copyright © 2018 Elsevier Inc. All rights reserved.

Diagnosing Developmental Dyscalculia on the Basis of Reliable Single Case FMRI Methods: Promises and Limitations

PubMed Central

Dinkel, Philipp Johannes; Willmes, Klaus; Krinzinger, Helga; Konrad, Kerstin; Koten Jr, Jan Willem

2013-01-01

FMRI-studies are mostly based on a group study approach, either analyzing one group or comparing multiple groups, or on approaches that correlate brain activation with clinically relevant criteria or behavioral measures. In this study we investigate the potential of fMRI-techniques focusing on individual differences in brain activation within a test-retest reliability context. We employ a single-case analysis approach, which contrasts dyscalculic children with a control group of typically developing children. In a second step, a support-vector machine analysis and cluster analysis techniques served to investigate similarities in multivariate brain activation patterns. Children were confronted with a non-symbolic number comparison and a non-symbolic exact calculation task during fMRI acquisition. Conventional second level group comparison analysis only showed small differences around the angular gyrus bilaterally and the left parieto-occipital sulcus. Analyses based on single-case statistical procedures revealed that developmental dyscalculia is characterized by individual differences predominantly in visual processing areas. Dyscalculic children seemed to compensate for relative under-activation in the primary visual cortex through an upregulation in higher visual areas. However, overlap in deviant activation was low for the dyscalculic children, indicating that developmental dyscalculia is a disorder characterized by heterogeneous brain activation differences. Using support vector machine analysis and cluster analysis, we tried to group dyscalculic and typically developing children according to brain activation. Fronto-parietal systems seem to qualify for a distinction between the two groups. However, this was only effective when reliable brain activations of both tasks were employed simultaneously. Results suggest that deficits in number representation in the visual-parietal cortex get compensated for through finger related aspects of number representation in fronto-parietal cortex. We conclude that dyscalculic children show large individual differences in brain activation patterns. Nonetheless, the majority of dyscalculic children can be differentiated from controls employing brain activation patterns when appropriate methods are used. PMID:24349547

Diagnosing developmental dyscalculia on the basis of reliable single case FMRI methods: promises and limitations.

PubMed

Dinkel, Philipp Johannes; Willmes, Klaus; Krinzinger, Helga; Konrad, Kerstin; Koten, Jan Willem

2013-01-01

FMRI-studies are mostly based on a group study approach, either analyzing one group or comparing multiple groups, or on approaches that correlate brain activation with clinically relevant criteria or behavioral measures. In this study we investigate the potential of fMRI-techniques focusing on individual differences in brain activation within a test-retest reliability context. We employ a single-case analysis approach, which contrasts dyscalculic children with a control group of typically developing children. In a second step, a support-vector machine analysis and cluster analysis techniques served to investigate similarities in multivariate brain activation patterns. Children were confronted with a non-symbolic number comparison and a non-symbolic exact calculation task during fMRI acquisition. Conventional second level group comparison analysis only showed small differences around the angular gyrus bilaterally and the left parieto-occipital sulcus. Analyses based on single-case statistical procedures revealed that developmental dyscalculia is characterized by individual differences predominantly in visual processing areas. Dyscalculic children seemed to compensate for relative under-activation in the primary visual cortex through an upregulation in higher visual areas. However, overlap in deviant activation was low for the dyscalculic children, indicating that developmental dyscalculia is a disorder characterized by heterogeneous brain activation differences. Using support vector machine analysis and cluster analysis, we tried to group dyscalculic and typically developing children according to brain activation. Fronto-parietal systems seem to qualify for a distinction between the two groups. However, this was only effective when reliable brain activations of both tasks were employed simultaneously. Results suggest that deficits in number representation in the visual-parietal cortex get compensated for through finger related aspects of number representation in fronto-parietal cortex. We conclude that dyscalculic children show large individual differences in brain activation patterns. Nonetheless, the majority of dyscalculic children can be differentiated from controls employing brain activation patterns when appropriate methods are used.

Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data.

PubMed

Calhoun, V D; Adali, T; Giuliani, N R; Pekar, J J; Kiehl, K A; Pearlson, G D

2006-01-01

The acquisition of both structural MRI (sMRI) and functional MRI (fMRI) data for a given study is a very common practice. However, these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform independent component analysis across image modalities, specifically, gray matter images and fMRI activation images as well as a joint histogram visualization technique. Joint independent component analysis (jICA) is used to decompose a matrix with a given row consisting of an fMRI activation image resulting from auditory oddball target stimuli and an sMRI gray matter segmentation image, collected from the same individual. We analyzed data collected on a group of schizophrenia patients and healthy controls using the jICA approach. Spatially independent joint-components are estimated and resulting components were further analyzed only if they showed a significant difference between patients and controls. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in gray matter were associated with bilateral temporal regions activated by the auditory oddball target stimuli. A finding of less patient gray matter and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, gray matter concentrations were larger in patients vs. controls, suggesting that more gray matter may be related to less functional connectivity in the auditory oddball fMRI task. Hum Brain Mapp, 2005. (c) 2005 Wiley-Liss, Inc.

Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: a meta-analysis of 75 fMRI studies.

PubMed

Li, Hui-Jie; Hou, Xiao-Hui; Liu, Han-Hui; Yue, Chun-Lin; He, Yong; Zuo, Xi-Nian

2015-03-01

Most of the previous task functional magnetic resonance imaging (fMRI) studies found abnormalities in distributed brain regions in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and few studies investigated the brain network dysfunction from the system level. In this meta-analysis, we aimed to examine brain network dysfunction in MCI and AD. We systematically searched task-based fMRI studies in MCI and AD published between January 1990 and January 2014. Activation likelihood estimation meta-analyses were conducted to compare the significant group differences in brain activation, the significant voxels were overlaid onto seven referenced neuronal cortical networks derived from the resting-state fMRI data of 1,000 healthy participants. Thirty-nine task-based fMRI studies (697 MCI patients and 628 healthy controls) were included in MCI-related meta-analysis while 36 task-based fMRI studies (421 AD patients and 512 healthy controls) were included in AD-related meta-analysis. The meta-analytic results revealed that MCI and AD showed abnormal regional brain activation as well as large-scale brain networks. MCI patients showed hypoactivation in default, frontoparietal, and visual networks relative to healthy controls, whereas AD-related hypoactivation mainly located in visual, default, and ventral attention networks relative to healthy controls. Both MCI-related and AD-related hyperactivation fell in frontoparietal, ventral attention, default, and somatomotor networks relative to healthy controls. MCI and AD presented different pathological while shared similar compensatory large-scale networks in fulfilling the cognitive tasks. These system-level findings are helpful to link the fundamental declines of cognitive tasks to brain networks in MCI and AD. © 2014 Wiley Periodicals, Inc.

Voluntary Enhancement of Neural Signatures of Affiliative Emotion Using fMRI Neurofeedback

PubMed Central

Moll, Jorge; Weingartner, Julie H.; Bado, Patricia; Basilio, Rodrigo; Sato, João R.; Melo, Bruno R.; Bramati, Ivanei E.; de Oliveira-Souza, Ricardo; Zahn, Roland

2014-01-01

In Ridley Scott’s film “Blade Runner”, empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior. PMID:24847819

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.

PubMed

Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Sipola, Tuomo; Burunat, Iballa; Toiviainen, Petri; Nandi, Asoke K; Brattico, Elvira; Ristaniemi, Tapani

2014-02-15

Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps. Copyright © 2013 Elsevier B.V. All rights reserved.

Quantitative representations of an exaggerated anxiety response in the brain of female spider phobics-a parametric fMRI study.

PubMed

Zilverstand, Anna; Sorger, Bettina; Kaemingk, Anita; Goebel, Rainer

2017-06-01

We employed a novel parametric spider picture set in the context of a parametric fMRI anxiety provocation study, designed to tease apart brain regions involved in threat monitoring from regions representing an exaggerated anxiety response in spider phobics. For the stimulus set, we systematically manipulated perceived proximity of threat by varying a depicted spider's context, size, and posture. All stimuli were validated in a behavioral rating study (phobics n = 20; controls n = 20; all female). An independent group participated in a subsequent fMRI anxiety provocation study (phobics n = 7; controls n = 7; all female), in which we compared a whole-brain categorical to a whole-brain parametric analysis. Results demonstrated that the parametric analysis provided a richer characterization of the functional role of the involved brain networks. In three brain regions-the mid insula, the dorsal anterior cingulate, and the ventrolateral prefrontal cortex-activation was linearly modulated by perceived proximity specifically in the spider phobia group, indicating a quantitative representation of an exaggerated anxiety response. In other regions (e.g., the amygdala), activation was linearly modulated in both groups, suggesting a functional role in threat monitoring. Prefrontal regions, such as dorsolateral prefrontal cortex, were activated during anxiety provocation but did not show a stimulus-dependent linear modulation in either group. The results confirm that brain regions involved in anxiety processing hold a quantitative representation of a pathological anxiety response and more generally suggest that parametric fMRI designs may be a very powerful tool for clinical research in the future, particularly when developing novel brain-based interventions (e.g., neurofeedback training). Hum Brain Mapp 38:3025-3038, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Double temporal sparsity based accelerated reconstruction of compressively sensed resting-state fMRI.

PubMed

Aggarwal, Priya; Gupta, Anubha

2017-12-01

A number of reconstruction methods have been proposed recently for accelerated functional Magnetic Resonance Imaging (fMRI) data collection. However, existing methods suffer with the challenge of greater artifacts at high acceleration factors. This paper addresses the issue of accelerating fMRI collection via undersampled k-space measurements combined with the proposed method based on l 1 -l 1 norm constraints, wherein we impose first l 1 -norm sparsity on the voxel time series (temporal data) in the transformed domain and the second l 1 -norm sparsity on the successive difference of the same temporal data. Hence, we name the proposed method as Double Temporal Sparsity based Reconstruction (DTSR) method. The robustness of the proposed DTSR method has been thoroughly evaluated both at the subject level and at the group level on real fMRI data. Results are presented at various acceleration factors. Quantitative analysis in terms of Peak Signal-to-Noise Ratio (PSNR) and other metrics, and qualitative analysis in terms of reproducibility of brain Resting State Networks (RSNs) demonstrate that the proposed method is accurate and robust. In addition, the proposed DTSR method preserves brain networks that are important for studying fMRI data. Compared to the existing methods, the DTSR method shows promising potential with an improvement of 10-12 dB in PSNR with acceleration factors upto 3.5 on resting state fMRI data. Simulation results on real data demonstrate that DTSR method can be used to acquire accelerated fMRI with accurate detection of RSNs. Copyright © 2017 Elsevier Ltd. 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

Training Efficiency and Transfer Success in an Extended Real-Time Functional MRI Neurofeedback Training of the Somatomotor Cortex of Healthy Subjects

PubMed Central

Auer, Tibor; Schweizer, Renate; Frahm, Jens

2015-01-01

This study investigated the level of self-regulation of the somatomotor cortices (SMCs) attained by an extended functional magnetic resonance imaging (fMRI) neurofeedback training. Sixteen healthy subjects performed 12 real-time functional magnetic resonance imaging neurofeedback training sessions within 4 weeks, involving motor imagery of the dominant right as well as the non-dominant left hand. Target regions of interests in the SMC were individually localized prior to the training by overt finger movements. The feedback signal (FS) was defined as the difference between fMRI activation in the contra- and ipsilateral SMC and visually presented to the subjects. Training efficiency was determined by an off-line general linear model analysis determining the fMRI percent signal changes in the SMC target areas accomplished during the neurofeedback training. Transfer success was assessed by comparing the pre- and post-training transfer task, i.e., the neurofeedback paradigm without the presentation of the FS. Group results show a distinct increase in feedback performance (FP) in the transfer task for the trained group compared to a matched untrained control group, as well as an increase in the time course of the training, indicating an efficient training and a successful transfer. Individual analysis revealed that the training efficiency was not only highly correlated to the transfer success but also predictive. Trainings with at least 12 efficient training runs were associated with a successful transfer outcome. A group analysis of the hemispheric contributions to the FP showed that it is mainly driven by increased fMRI activation in the contralateral SMC, although some individuals relied on ipsilateral deactivation. Training and transfer results showed no difference between left- and right-hand imagery, with a slight indication of more ipsilateral deactivation in the early right-hand trainings. PMID:26500521

Functional response in ventral temporal cortex differentiates mild cognitive impairment from normal aging

PubMed Central

Gold, Brian T.; Jiang, Yang; Jicha, Greg A.; Smith, Charles D.

2010-01-01

The present study sought to identify altered brain activation patterns in amnestic mild cognitive impairment (MCI) that could precede frank task impairment and neocortical atrophy. A high accuracy lexical decision (LD) task was therefore employed. Both MCI and normal senior (NS) groups completed the LD task while functional magnetic resonance imaging (fMRI) was performed. Accuracy on the LD task was high (≥ 89% correct for both groups), and both groups activated a network of occipitotemporal regions and inferior frontal cortex. However, compared to the NS group, the MCI group showed reduced fMRI activation in these regions and increased activation in bilateral portions of anterior cingluate cortex. Results from a voxel-based morphometry analysis indicate that altered activations in the MCI group were not within regions of atrophy. Receiver operating characteristic curves demonstrate that reduced fMRI response in the left and right mid-fusiform gyri accurately discriminate MCI from NS. When activation magnitude in both fusiform gyri were included in a single logistic regression model, group classification accuracy was very high (area under the curve = 0.93). These results show that a disrupted functional response in the ventral temporal lobe accurately distinguishes individuals with MCI from normal seniors, a finding which may have implications for identifying seniors at risk for cognitive decline. PMID:20063353

FMRI group analysis combining effect estimates and their variances

PubMed Central

Chen, Gang; Saad, Ziad S.; Nath, Audrey R.; Beauchamp, Michael S.; Cox, Robert W.

2012-01-01

Conventional functional magnetic resonance imaging (FMRI) group analysis makes two key assumptions that are not always justified. First, the data from each subject is condensed into a single number per voxel, under the assumption that within-subject variance for the effect of interest is the same across all subjects or is negligible relative to the cross-subject variance. Second, it is assumed that all data values are drawn from the same Gaussian distribution with no outliers. We propose an approach that does not make such strong assumptions, and present a computationally efficient frequentist approach to FMRI group analysis, which we term mixed-effects multilevel analysis (MEMA), that incorporates both the variability across subjects and the precision estimate of each effect of interest from individual subject analyses. On average, the more accurate tests result in higher statistical power, especially when conventional variance assumptions do not hold, or in the presence of outliers. In addition, various heterogeneity measures are available with MEMA that may assist the investigator in further improving the modeling. Our method allows group effect t-tests and comparisons among conditions and among groups. In addition, it has the capability to incorporate subject-specific covariates such as age, IQ, or behavioral data. Simulations were performed to illustrate power comparisons and the capability of controlling type I errors among various significance testing methods, and the results indicated that the testing statistic we adopted struck a good balance between power gain and type I error control. Our approach is instantiated in an open-source, freely distributed program that may be used on any dataset stored in the universal neuroimaging file transfer (NIfTI) format. To date, the main impediment for more accurate testing that incorporates both within- and cross-subject variability has been the high computational cost. Our efficient implementation makes this approach practical. We recommend its use in lieu of the less accurate approach in the conventional group analysis. PMID:22245637

Parallel group independent component analysis for massive fMRI data sets.

PubMed

Chen, Shaojie; Huang, Lei; Qiu, Huitong; Nebel, Mary Beth; Mostofsky, Stewart H; Pekar, James J; Lindquist, Martin A; Eloyan, Ani; Caffo, Brian S

2017-01-01

Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.

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.

The therapeutic effect of Xueshuan Xinmai tablets on memory injury and brain activity in post-stroke patients: a pilot placebo controlled fMRI study.

PubMed

Wei, Dongfeng; Lv, Chenlong; Zhang, Junying; Peng, Dantao; Hu, Liangping; Zhang, Zhanjun; Wang, Yongyan

2015-01-01

The purpose of this study was to explore the effects of Xueshuan Xinmai tablets (XXMT) for the treatment of cognition, brain activation in the rehabilitation period of ischemic stroke patients. 28 adults patients, aged 50-80 years, in the rehabilitation period of ischemic stroke were divided into XXMT treatment group and placebo control group. Patients received 3 months treatment (oral 0.8 g, 3 times per day). Before and after treatment, all patients were evaluated by a series of neuropsychological tests followed by resting-state functional magnetic resonance imaging (fMRI). In the XXMT treatment group, the patients' episodic memory showed significant improvement. The resting-state fMRI analysis indicated that a significant decline in the fractional amplitude of low-frequency fluctuation value was observed in the bilateral middle cingulate gyrus. Yiqi Huoxue effect under XXMT administration has a favorable mediation on episodic memory, consequently suppresses the activation of the cingulate gyrus in the rehabilitation period of ischemic stroke patients.

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.

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.

Structure-seeking multilinear methods for the analysis of fMRI data.

PubMed

Andersen, Anders H; Rayens, William S

2004-06-01

In comprehensive fMRI studies of brain function, the data structures often contain higher-order ways such as trial, task condition, subject, and group in addition to the intrinsic dimensions of time and space. While multivariate bilinear methods such as principal component analysis (PCA) have been used successfully for extracting information about spatial and temporal features in data from a single fMRI run, the need to unfold higher-order data sets into bilinear arrays has led to decompositions that are nonunique and to the loss of multiway linkages and interactions present in the data. These additional dimensions or ways can be retained in multilinear models to produce structures that are unique and which admit interpretations that are neurophysiologically meaningful. Multiway analysis of fMRI data from multiple runs of a bilateral finger-tapping paradigm was performed using the parallel factor (PARAFAC) model. A trilinear model was fitted to a data cube of dimensions voxels by time by run. Similarly, a quadrilinear model was fitted to a higher-way structure of dimensions voxels by time by trial by run. The spatial and temporal response components were extracted and validated by comparison to results from traditional SVD/PCA analyses based on scenarios of unfolding into lower-order bilinear structures.

Non-Inferential Multi-Subject Study of Functional Connectivity during Visual Stimulation.

PubMed

Esposito, F; Cirillo, M; Aragri, A; Caranci, F; Cirillo, L; Di Salle, F; Cirillo, S

2007-01-31

Independent component analysis (ICA) is a powerful technique for the multivariate, non-inferential, data-driven analysis of functional magnetic resonance imaging (fMRI) data-sets. The non-inferential nature of ICA makes this a suitable technique for the study of complex mental states whose temporal evolution would be difficult to describe analytically in terms of classical statistical regressors. Taking advantage of this feature, ICA can extract a number of functional connectivity patterns regardless of the task executed by the subject. The technique is so powerful that functional connectivity patterns can be derived even when the subject is just resting in the scanner, opening the opportunity for functional investigation of the human mind at its basal "default" state, which has been proposed to be altered in several brain disorders. However, one major drawback of ICA consists in the difficulty of managing its results, which are not represented by a single functional image as in inferential studies. This produces the need for a classification of ICA results and exacerbates the difficulty of obtaining group "averaged" functional connectivity patterns, while preserving the interpretation of individual differences. Addressing the subject-level variability in the very same framework of "grouping" appears to be a favourable approach towards the clinical evaluation and application of ICA-based methodologies. Here we present a novel strategy for group-level ICA analyses, namely the self-organizing group-level ICA (sog-ICA), which is used on visual activation fMRI data from a block-design experiment repeated on six subjects. We propose the sog-ICA as a multi-subject analysis tool for grouping ICA data while assessing the similarity and variability of the fMRI results of individual subject decompositions.

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.

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

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

PubMed Central

Gopal, Shruti; Miller, Robyn L.; Michael, Andrew; Adali, Tulay; Cetin, Mustafa; Rachakonda, Srinivas; Bustillo, Juan R.; Cahill, Nathan; Baum, Stefi A.; Calhoun, Vince D.

2016-01-01

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects. PMID:26106217

Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder.

PubMed

Xing, Mengqi; Tadayonnejad, Reza; MacNamara, Annmarie; Ajilore, Olusola; DiGangi, Julia; Phan, K Luan; Leow, Alex; Klumpp, Heide

2017-01-01

Functional magnetic resonance imaging (fMRI) resting-state studies show generalized social anxiety disorder (gSAD) is associated with disturbances in networks involved in emotion regulation, emotion processing, and perceptual functions, suggesting a network framework is integral to elucidating the pathophysiology of gSAD. However, fMRI does not measure the fast dynamic interconnections of functional networks. Therefore, we examined whole-brain functional connectomics with electroencephalogram (EEG) during resting-state. Resting-state EEG data was recorded for 32 patients with gSAD and 32 demographically-matched healthy controls (HC). Sensor-level connectivity analysis was applied on EEG data by using Weighted Phase Lag Index (WPLI) and graph analysis based on WPLI was used to determine clustering coefficient and characteristic path length to estimate local integration and global segregation of networks. WPLI results showed increased oscillatory midline coherence in the theta frequency band indicating higher connectivity in the gSAD relative to HC group during rest. Additionally, WPLI values positively correlated with state anxiety levels within the gSAD group but not the HC group. Our graph theory based connectomics analysis demonstrated increased clustering coefficient and decreased characteristic path length in theta-based whole brain functional organization in subjects with gSAD compared to HC. Theta-dependent interconnectivity was associated with state anxiety in gSAD and an increase in information processing efficiency in gSAD (compared to controls). Results may represent enhanced baseline self-focused attention, which is consistent with cognitive models of gSAD and fMRI studies implicating emotion dysregulation and disturbances in task negative networks (e.g., default mode network) in gSAD.

fMRI neurofeedback of amygdala response to aversive stimuli enhances prefrontal-limbic brain connectivity.

PubMed

Paret, Christian; Ruf, Matthias; Gerchen, Martin Fungisai; Kluetsch, Rosemarie; Demirakca, Traute; Jungkunz, Martin; Bertsch, Katja; Schmahl, Christian; Ende, Gabriele

2016-01-15

Down-regulation of the amygdala with real-time fMRI neurofeedback (rtfMRI NF) potentially allows targeting brain circuits of emotion processing and may involve prefrontal-limbic networks underlying effective emotion regulation. Little research has been dedicated to the effect of rtfMRI NF on the functional connectivity of the amygdala and connectivity patterns in amygdala down-regulation with neurofeedback have not been addressed yet. Using psychophysiological interaction analysis of fMRI data, we present evidence that voluntary amygdala down-regulation by rtfMRI NF while viewing aversive pictures was associated with increased connectivity of the right amygdala with the ventromedial prefrontal cortex (vmPFC) in healthy subjects (N=16). In contrast, a control group (N=16) receiving sham feedback did not alter amygdala connectivity (Group×Condition t-contrast: p<.05 at cluster-level). Task-dependent increases in amygdala-vmPFC connectivity were predicted by picture arousal (β=.59, p<.05). A dynamic causal modeling analysis with Bayesian model selection aimed at further characterizing the underlying causal structure and favored a bottom-up model assuming predominant information flow from the amygdala to the vmPFC (xp=.90). The results were complemented by the observation of task-dependent alterations in functional connectivity of the vmPFC with the visual cortex and the ventrolateral PFC in the experimental group (Condition t-contrast: p<.05 at cluster-level). Taken together, the results underscore the potential of amygdala fMRI neurofeedback to influence functional connectivity in key networks of emotion processing and regulation. This may be beneficial for patients suffering from severe emotion dysregulation by improving neural self-regulation. Copyright © 2015 Elsevier Inc. All rights reserved.

Complexity and Synchronicity of Resting State BOLD FMRI in Normal Aging and Cognitive Decline

PubMed Central

Liu, Collin Y; Krishnan, Anitha P; Yan, Lirong; Smith, Robert X; Kilroy, Emily; Alger, Jeffery R; Ringman, John M; Wang, Danny JJ

2012-01-01

Purpose To explore the use of approximate entropy (ApEn) as an index of the complexity and the synchronicity of resting state BOLD fMRI in normal aging and cognitive decline associated with familial Alzheimer’s disease (fAD). Materials and Methods Resting state BOLD fMRI data were acquired at 3T from 2 independent cohorts of subjects consisting of healthy young (age 23±2 years, n=8) and aged volunteers (age 66±3 years, n=8), as well as 22 fAD associated subjects (14 mutation carriers, age 41.2±15.8 years; and 8 non-mutation carrying family members, age 28.8±5.9 years). Mean ApEn values were compared between the two age groups, and correlated with cognitive performance in the fAD group. Cross-ApEn (C-ApEn) was further calculated to assess the asynchrony between precuneus and the rest of the brain. Results Complexity of brain activity measured by mean ApEn in gray and white matter decreased with normal aging. In the fAD group, cognitive impairment was associated with decreased mean ApEn in gray matter as well as decreased regional ApEn in right precuneus, right lateral parietal regions, left precentral gyrus, and right paracentral gyrus. A pattern of asynchrony between BOLD fMRI series emerged from C-ApEn analysis, with significant regional anti-correlation with cross-correlation coefficient of functional connectivity analysis. Conclusion ApEn and C-ApEn may be useful for assessing the complexity and synchronicity of brain activity in normal aging and cognitive decline associated with neurodegenerative diseases PMID:23225622

Identifying autism from neural representations of social interactions: neurocognitive markers of autism.

PubMed

Just, Marcel Adam; Cherkassky, Vladimir L; Buchweitz, Augusto; Keller, Timothy A; Mitchell, Tom M

2014-01-01

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought's underlying brain activation patterns.

Identifying Autism from Neural Representations of Social Interactions: Neurocognitive Markers of Autism

PubMed Central

Just, Marcel Adam; Cherkassky, Vladimir L.; Buchweitz, Augusto; Keller, Timothy A.; Mitchell, Tom M.

2014-01-01

Autism is a psychiatric/neurological condition in which alterations in social interaction (among other symptoms) are diagnosed by behavioral psychiatric methods. The main goal of this study was to determine how the neural representations and meanings of social concepts (such as to insult) are altered in autism. A second goal was to determine whether these alterations can serve as neurocognitive markers of autism. The approach is based on previous advances in fMRI analysis methods that permit (a) the identification of a concept, such as the thought of a physical object, from its fMRI pattern, and (b) the ability to assess the semantic content of a concept from its fMRI pattern. These factor analysis and machine learning methods were applied to the fMRI activation patterns of 17 adults with high-functioning autism and matched controls, scanned while thinking about 16 social interactions. One prominent neural representation factor that emerged (manifested mainly in posterior midline regions) was related to self-representation, but this factor was present only for the control participants, and was near-absent in the autism group. Moreover, machine learning algorithms classified individuals as autistic or control with 97% accuracy from their fMRI neurocognitive markers. The findings suggest that psychiatric alterations of thought can begin to be biologically understood by assessing the form and content of the altered thought’s underlying brain activation patterns. PMID:25461818

Ageing differentially affects neural processing of different conflict types-an fMRI study.

PubMed

Korsch, Margarethe; Frühholz, Sascha; Herrmann, Manfred

2014-01-01

Interference control and conflict resolution is affected by ageing. There is increasing evidence that ageing does not compromise interference control in general but rather shows distinctive effects on different components of interference control. Different conflict types, [e.g., stimulus-stimulus (S-S) or stimulus-response (S-R) conflicts] trigger different cognitive processes and thus activate different neural networks. In the present functional magnetic resonance imaging (fMRI) study, we used a combined Flanker and Stimulus Response Conflict (SRC) task to investigate the effect of ageing on S-S and S-R conflicts. Behavioral data analysis revealed larger SRC effects in elderly. fMRI Results show that both age groups recruited similar regions [caudate nucleus, cingulate gyrus and middle occipital gyrus (MOG)] during Flanker conflict processing. Furthermore, elderly show an additional activation pattern in parietal and frontal areas. In contrast, no common activation of both age groups was found in response to the SRC. These data suggest that ageing has distinctive effects on S-S and S-R conflicts.

Using SPM 12’s Second-Level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users

PubMed Central

Han, Hyemin; Park, Joonsuk

2018-01-01

Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely P < 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parameters for Bayesian inference and how to interpret the results, such as Bayes factors, from the inference. We also discuss the practical and philosophical benefits of Bayesian inference and directions for future research. PMID:29456498

Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations.

PubMed

Demertzi, Athena; Gómez, Francisco; Crone, Julia Sophia; Vanhaudenhuyse, Audrey; Tshibanda, Luaba; Noirhomme, Quentin; Thonnard, Marie; Charland-Verville, Vanessa; Kirsch, Murielle; Laureys, Steven; Soddu, Andrea

2014-03-01

In healthy conditions, group-level fMRI resting state analyses identify ten resting state networks (RSNs) of cognitive relevance. Here, we aim to assess the ten-network model in severely brain-injured patients suffering from disorders of consciousness and to identify those networks which will be most relevant to discriminate between patients and healthy subjects. 300 fMRI volumes were obtained in 27 healthy controls and 53 patients in minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS) and coma. Independent component analysis (ICA) reduced data dimensionality. The ten networks were identified by means of a multiple template-matching procedure and were tested on neuronality properties (neuronal vs non-neuronal) in a data-driven way. Univariate analyses detected between-group differences in networks' neuronal properties and estimated voxel-wise functional connectivity in the networks, which were significantly less identifiable in patients. A nearest-neighbor "clinical" classifier was used to determine the networks with high between-group discriminative accuracy. Healthy controls were characterized by more neuronal components compared to patients in VS/UWS and in coma. Compared to healthy controls, fewer patients in MCS and VS/UWS showed components of neuronal origin for the left executive control network, default mode network (DMN), auditory, and right executive control network. The "clinical" classifier indicated the DMN and auditory network with the highest accuracy (85.3%) in discriminating patients from healthy subjects. FMRI multiple-network resting state connectivity is disrupted in severely brain-injured patients suffering from disorders of consciousness. When performing ICA, multiple-network testing and control for neuronal properties of the identified RSNs can advance fMRI system-level characterization. Automatic data-driven patient classification is the first step towards future single-subject objective diagnostics based on fMRI resting state acquisitions. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

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

Characterizing Response to Elemental Unit of Acoustic Imaging Noise: An fMRI Study

PubMed Central

Luh, Wen-Ming; Talavage, Thomas M.

2010-01-01

Acoustic imaging noise produced during functional magnetic resonance imaging (fMRI) studies can hinder auditory fMRI research analysis by altering the properties of the acquired time-series data. Acoustic imaging noise can be especially confounding when estimating the time course of the hemodynamic response (HDR) in auditory event-related fMRI (fMRI) experiments. This study is motivated by the desire to establish a baseline function that can serve not only as a comparison to other quantities of acoustic imaging noise for determining how detrimental is one's experimental noise, but also as a foundation for a model that compensates for the response to acoustic imaging noise. Therefore, the amplitude and spatial extent of the HDR to the elemental unit of acoustic imaging noise (i.e., a single ping) associated with echoplanar acquisition were characterized and modeled. Results from this fMRI study at 1.5 T indicate that the group-averaged HDR in left and right auditory cortex to acoustic imaging noise (duration of 46 ms) has an estimated peak magnitude of 0.29% (right) to 0.48% (left) signal change from baseline, peaks between 3 and 5 s after stimulus presentation, and returns to baseline and remains within the noise range approximately 8 s after stimulus presentation. PMID:19304477

Resting-state FMRI confounds and cleanup

PubMed Central

Murphy, Kevin; Birn, Rasmus M.; Bandettini, Peter A.

2013-01-01

The goal of resting-state functional magnetic resonance imaging (FMRI) is to investigate the brain’s functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain “at rest” as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of FMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state FMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO2 concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state FMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state FMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline. PMID:23571418

Complex versus simple ankle movement training in stroke using telerehabilitation: a randomized controlled trial.

PubMed

Deng, Huiqiong; Durfee, William K; Nuckley, David J; Rheude, Brandon S; Severson, Amy E; Skluzacek, Katie M; Spindler, Kristen K; Davey, Cynthia S; Carey, James R

2012-02-01

Telerehabilitation allows rehabilitative training to continue remotely after discharge from acute care and can include complex tasks known to create rich conditions for neural change. The purposes of this study were: (1) to explore the feasibility of using telerehabilitation to improve ankle dorsiflexion during the swing phase of gait in people with stroke and (2) to compare complex versus simple movements of the ankle in promoting behavioral change and brain reorganization. This study was a pilot randomized controlled trial. Training was done in the participant's home. Testing was done in separate research labs involving functional magnetic resonance imaging (fMRI) and multi-camera gait analysis. Sixteen participants with chronic stroke and impaired ankle dorsiflexion were assigned randomly to receive 4 weeks of telerehabilitation of the paretic ankle. Participants received either computerized complex movement training (track group) or simple movement training (move group). Behavioral changes were measured with the 10-m walk test and gait analysis using a motion capture system. Brain reorganization was measured with ankle tracking during fMRI. Dorsiflexion during gait was significantly larger in the track group compared with the move group. For fMRI, although the volume, percent volume, and intensity of cortical activation failed to show significant changes, the frequency count of the number of participants showing an increase versus a decrease in these values from pretest to posttest measurements was significantly different between the 2 groups, with the track group decreasing and the move group increasing. Limitations of this study were that no follow-up test was conducted and that a small sample size was used. The results suggest that telerehabilitation, emphasizing complex task training with the paretic limb, is feasible and can be effective in promoting further dorsiflexion in people with chronic stroke.

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.

Different brain activations between own- and other-race face categorization: an fMRI study using group independent component analysis

NASA Astrophysics Data System (ADS)

Wei, Wenjuan; Liu, Jiangang; Dai, Ruwei; Feng, Lu; Li, Ling; Tian, Jie

2014-03-01

Previous behavioral research has proved that individuals process own- and other-race faces differently. One well-known effect is the other-race effect (ORE), which indicates that individuals categorize other-race faces more accurately and faster than own-race faces. The existed functional magnetic resonance imaging (fMRI) studies of the other-race effect mainly focused on the racial prejudice and the socio-affective differences towards own- and other-race face. In the present fMRI study, we adopted a race-categorization task to determine the activation level differences between categorizing own- and other-race faces. Thirty one Chinese participants who live in China with Chinese as the majority and who had no direct contact with Caucasian individual were recruited in the present study. We used the group independent component analysis (ICA), which is a method of blind source signal separation that has proven to be promising for analysis of fMRI data. We separated the entail data into 56 components which is estimated based on one subject using the Minimal Description Length (MDL) criteria. The components sorted based on the multiple linear regression temporal sorting criteria, and the fit regression parameters were used in performing statistical test to evaluate the task-relatedness of the components. The one way anova was performed to test the significance of the component time course in different conditions. Our result showed that the areas, which coordinates is similar to the right FFA coordinates that previous studies reported, were greater activated for own-race faces than other-race faces, while the precuneus showed greater activation for other-race faces than own-race faces.

Resting-state functional MRI as a tool for evaluating brain hemodynamic responsiveness to external stimuli in rats.

PubMed

Paasonen, Jaakko; Salo, Raimo A; Huttunen, Joanna K; Gröhn, Olli

2017-09-01

Anesthesia is a major confounding factor in functional MRI (fMRI) experiments attributed to its effects on brain function. Recent evidence suggests that parameters obtained with resting-state fMRI (rs-fMRI) are coupled with anesthetic depth. Therefore, we investigated whether parameters obtained with rs-fMRI, such as functional connectivity (FC), are also directly related to blood-oxygen-level-dependent (BOLD) responses. A simple rs-fMRI protocol was implemented in a pharmacological fMRI study to evaluate the coupling between hemodynamic responses and FC under five anesthetics (α-chloralose, isoflurane, medetomidine, thiobutabarbital, and urethane). Temporal change in the FC was evaluated at 1-hour interval. Supplementary forepaw stimulation experiments were also conducted. Under thiobutabarbital anesthesia, FC was clearly coupled with nicotine-induced BOLD responses. Good correlation values were also obtained under isoflurane and medetomidine anesthesia. The observations in the thiobutabarbital group were supported by forepaw stimulation experiments. Additionally, the rs-fMRI protocol revealed significant temporal changes in the FC in the α-chloralose, thiobutabarbital, and urethane groups. Our results suggest that FC can be used to estimate brain hemodynamic responsiveness to stimuli and evaluate the level and temporal changes of anesthesia. Therefore, analysis of the fMRI baseline signal may be highly valuable tool for controlling the outcome of preclinical fMRI experiments. Magn Reson Med 78:1136-1146, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Attenuation of the neural response to sad faces in major depression by antidepressant treatment: a prospective, event-related functional magnetic resonance imaging study.

PubMed

Fu, Cynthia H Y; Williams, Steven C R; Cleare, Anthony J; Brammer, Michael J; Walsh, Nicholas D; Kim, Jieun; Andrew, Chris M; Pich, Emilio Merlo; Williams, Pauline M; Reed, Laurence J; Mitterschiffthaler, Martina T; Suckling, John; Bullmore, Edward T

2004-09-01

Depression is associated with interpersonal difficulties related to abnormalities in affective facial processing. To map brain systems activated by sad facial affect processing in patients with depression and to identify brain functional correlates of antidepressant treatment and symptomatic response. Two groups underwent scanning twice using functional magnetic resonance imaging (fMRI) during an 8-week period. The event-related fMRI paradigm entailed incidental affect recognition of facial stimuli morphed to express discriminable intensities of sadness. Participants were recruited by advertisement from the local population; depressed subjects were treated as outpatients. We matched 19 medication-free, acutely symptomatic patients satisfying DSM-IV criteria for unipolar major depressive disorder by age, sex, and IQ with 19 healthy volunteers. Intervention After the baseline assessment, patients received fluoxetine hydrochloride, 20 mg/d, for 8 weeks. Average activation (capacity) and differential response to variable affective intensity (dynamic range) were estimated in each fMRI time series. We used analysis of variance to identify brain regions that demonstrated a main effect of group (depressed vs healthy subjects) and a group x time interaction (attributable to antidepressant treatment). Change in brain activation associated with reduction of depressive symptoms in the patient group was identified by means of regression analysis. Permutation tests were used for inference. Over time, depressed subjects showed reduced capacity for activation in the left amygdala, ventral striatum, and frontoparietal cortex and a negatively correlated increase of dynamic range in the prefrontal cortex. Symptomatic improvement was associated with reduction of dynamic range in the pregenual cingulate cortex, ventral striatum, and cerebellum. Antidepressant treatment reduces left limbic, subcortical, and neocortical capacity for activation in depressed subjects and increases the dynamic range of the left prefrontal cortex. Changes in anterior cingulate function associated with symptomatic improvement indicate that fMRI may be a useful surrogate marker of antidepressant treatment response.

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.

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.

[Effects of acupuncture at left and right Hegu (LI 4) for cerebral function laterality].

PubMed

Wang, Linying; Xu, Chunsheng; Zhu, Yifang; Li, Chuanfu; Yang, Jun

2015-08-01

To explore the cerebral function laterality of acupuncture at left and right Hegu (LI 4) by using functional magnetic resonance imaging (fMRI) and provide objective evidences for side selection of Hegu (LI 4) in the clinical application. Eighty healthy volunteers were randomly divided into a left-acupoint group and a right-acupoint group, and they were treated with acupuncture at left Hegu (LI 4) and right Hegu (LI 4) respectively. After the arrival of qi, the task-state fMRI data in both groups was collected, and analysis of functional neuroimages (AFNI) software was used to perform intra-group and between-group comparisons. After acupuncture, acupuncture feelings were recorded and MGH acupuncture sensation scale (MASS) was recorded. The difference of MASS between the two groups was not significant (P>0. 05). The result of left-acupoint group showed an increased signal on right cerebral hemisphere, while the right-acupoint group showed extensive signal changes in both cerebral hemispheres. The analysis between left-acupoint group and retroflex right-acupoint group showed differences in brain areas. The central effect of acupuncture at left and right Hegu (LI 4) is dissymmetry, indicating right hemisphere laterality. The right lobus insularis and cingulate gyrus may be the key regions in the acupuncture at Hegu (LI 4).

Abnormal functional specialization within medial prefrontal cortex in high-functioning autism: a multi-voxel similarity analysis

PubMed Central

Meuwese, Julia D.I.; Towgood, Karren J.; Frith, Christopher D.; Burgess, Paul W.

2009-01-01

Multi-voxel pattern analyses have proved successful in ‘decoding’ mental states from fMRI data, but have not been used to examine brain differences associated with atypical populations. We investigated a group of 16 (14 males) high-functioning participants with autism spectrum disorder (ASD) and 16 non-autistic control participants (12 males) performing two tasks (spatial/verbal) previously shown to activate medial rostral prefrontal cortex (mrPFC). Each task manipulated: (i) attention towards perceptual versus self-generated information and (ii) reflection on another person's mental state (‘mentalizing'versus ‘non-mentalizing’) in a 2 × 2 design. Behavioral performance and group-level fMRI results were similar between groups. However, multi-voxel similarity analyses revealed strong differences. In control participants, the spatial distribution of activity generalized significantly between task contexts (spatial/verbal) when examining the same function (attention/mentalizing) but not when comparing different functions. This pattern was disrupted in the ASD group, indicating abnormal functional specialization within mrPFC, and demonstrating the applicability of multi-voxel pattern analysis to investigations of atypical populations. PMID:19174370

Self-regulation of primary motor cortex activity with motor imagery induces functional connectivity modulation: A real-time fMRI neurofeedback study.

PubMed

Makary, Meena M; Seulgi, Eun; Kyungmo Park

2017-07-01

Recent developments in data acquisition of functional magnetic resonance imaging (fMRI) have led to rapid preprocessing and analysis of brain activity in a quasireal-time basis, what so called real-time fMRI neurofeedback (rtfMRI-NFB). This information is fed back to subjects allowing them to gain a voluntary control over their own region-specific brain activity. Forty-one healthy participants were randomized into an experimental (NFB) group, who received a feedback directly proportional to their brain activity from the primary motor cortex (M1), and a control (CTRL) group who received a sham feedback. The M1 ROI was functionally localized during motor execution and imagery tasks. A resting-state functional run was performed before and after the neurofeedback training to investigate the default mode network (DMN) modulation after training. The NFB group revealed increased DMN functional connectivity after training to the cortical and subcortical sensory/motor areas (M1/S1 and caudate nucleus, respectively), which may be associated with sensorimotor processing of learning in the resting state. These results show that motor imagery training through rtfMRI-NFB could modulate the DMN functional connectivity to motor-related areas, suggesting that this modulation potentially subserved the establishment of motor learning in the NFB group.

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.

Intrinsic Brain Connectivity in Chronic Pain: A Resting-State fMRI Study in Patients with Rheumatoid Arthritis

PubMed Central

Flodin, Pär; Martinsen, Sofia; Altawil, Reem; Waldheim, Eva; Lampa, Jon; Kosek, Eva; Fransson, Peter

2016-01-01

Background: Rheumatoid arthritis (RA) is commonly accompanied by pain that is discordant with the degree of peripheral pathology. Very little is known about the cerebral processes involved in pain processing in RA. Here we investigated resting-state brain connectivity associated with prolonged pain in RA. Methods: 24 RA subjects and 19 matched controls were compared with regard to both behavioral measures of pain perception and resting-resting state fMRI data acquired subsequently to fMRI sessions involving pain stimuli. The resting-state fMRI brain connectivity was investigated using 159 seed regions located in cardinal pain processing brain regions. Additional principal component based multivariate pattern analysis of the whole brain connectivity pattern was carried out in a data driven analysis to localize group differences in functional connectivity. Results: When RA patients were compared to controls, we observed significantly lower pain resilience for pressure on the affected finger joints (i.e., P50-joint) and an overall heightened level of perceived global pain in RA patients. Relative to controls, RA patients displayed increased brain connectivity predominately for the supplementary motor areas, mid-cingulate cortex, and the primary sensorimotor cortex. Additionally, we observed an increase in brain connectivity between the insula and prefrontal cortex as well as between anterior cingulate cortex and occipital areas for RA patients. None of the group differences in brain connectivity were significantly correlated with behavioral parameters. Conclusion: Our study provides experimental evidence of increased connectivity between frontal midline regions that are implicated in affective pain processing and bilateral sensorimotor regions in RA patients. PMID:27014038

Disrupted functional brain connectivity in partial epilepsy: a resting-state fMRI study.

PubMed

Luo, Cheng; Qiu, Chuan; Guo, Zhiwei; Fang, Jiajia; Li, Qifu; Lei, Xu; Xia, Yang; Lai, Yongxiu; Gong, Qiyong; Zhou, Dong; Yao, Dezhong

2011-01-01

Examining the spontaneous activity to understand the neural mechanism of brain disorder is a focus in recent resting-state fMRI. In the current study, to investigate the alteration of brain functional connectivity in partial epilepsy in a systematical way, two levels of analyses (functional connectivity analysis within resting state networks (RSNs) and functional network connectivity (FNC) analysis) were carried out on resting-state fMRI data acquired from the 30 participants including 14 healthy controls(HC) and 16 partial epilepsy patients. According to the etiology, all patients are subdivided into temporal lobe epilepsy group (TLE, included 7 patients) and mixed partial epilepsy group (MPE, 9 patients). Using group independent component analysis, eight RSNs were identified, and selected to evaluate functional connectivity and FNC between groups. Compared with the controls, decreased functional connectivity within all RSNs was found in both TLE and MPE. However, dissociating patterns were observed within the 8 RSNs between two patient groups, i.e, compared with TLE, we found decreased functional connectivity in 5 RSNs increased functional connectivity in 1 RSN, and no difference in the other 2 RSNs in MPE. Furthermore, the hierarchical disconnections of FNC was found in two patient groups, in which the intra-system connections were preserved for all three subsystems while the lost connections were confined to intersystem connections in patients with partial epilepsy. These findings may suggest that decreased resting state functional connectivity and disconnection of FNC are two remarkable characteristics of partial epilepsy. The selective impairment of FNC implicated that it is unsuitable to understand the partial epilepsy only from global or local perspective. We presumed that studying epilepsy in the multi-perspective based on RSNs may be a valuable means to assess the functional changes corresponding to specific RSN and may contribute to the understanding of the neuro-pathophysiological mechanism of epilepsy.

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.

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

A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli.

PubMed

Raut, Savita V; Yadav, Dinkar M

2018-03-28

This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.

The Perception of Dynamic and Static Facial Expressions of Happiness and Disgust Investigated by ERPs and fMRI Constrained Source Analysis

PubMed Central

Trautmann-Lengsfeld, Sina Alexa; Domínguez-Borràs, Judith; Escera, Carles; Herrmann, Manfred; Fehr, Thorsten

2013-01-01

A recent functional magnetic resonance imaging (fMRI) study by our group demonstrated that dynamic emotional faces are more accurately recognized and evoked more widespread patterns of hemodynamic brain responses than static emotional faces. Based on this experimental design, the present study aimed at investigating the spatio-temporal processing of static and dynamic emotional facial expressions in 19 healthy women by means of multi-channel electroencephalography (EEG), event-related potentials (ERP) and fMRI-constrained regional source analyses. ERP analysis showed an increased amplitude of the LPP (late posterior positivity) over centro-parietal regions for static facial expressions of disgust compared to neutral faces. In addition, the LPP was more widespread and temporally prolonged for dynamic compared to static faces of disgust and happiness. fMRI constrained source analysis on static emotional face stimuli indicated the spatio-temporal modulation of predominantly posterior regional brain activation related to the visual processing stream for both emotional valences when compared to the neutral condition in the fusiform gyrus. The spatio-temporal processing of dynamic stimuli yielded enhanced source activity for emotional compared to neutral conditions in temporal (e.g., fusiform gyrus), and frontal regions (e.g., ventromedial prefrontal cortex, medial and inferior frontal cortex) in early and again in later time windows. The present data support the view that dynamic facial displays trigger more information reflected in complex neural networks, in particular because of their changing features potentially triggering sustained activation related to a continuing evaluation of those faces. A combined fMRI and EEG approach thus provides an advanced insight to the spatio-temporal characteristics of emotional face processing, by also revealing additional neural generators, not identifiable by the only use of an fMRI approach. PMID:23818974

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.

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

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

PubMed

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

Analytic programming with FMRI data: a quick-start guide for statisticians using R.

PubMed

Eloyan, Ani; Li, Shanshan; Muschelli, John; Pekar, Jim J; Mostofsky, Stewart H; Caffo, Brian S

2014-01-01

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Functional feature embedded space mapping of fMRI data.

PubMed

Hu, Jin; Tian, Jie; Yang, Lei

2006-01-01

We have proposed a new method for fMRI data analysis which is called Functional Feature Embedded Space Mapping (FFESM). Our work mainly focuses on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the frequency domain. A nonlinear dimension reduction technique Isomap is applied to the high dimensional features obtained from frequency domain of the fMRI data for the first time. Finally, the presence of activated time series is identified by the clustering method in which the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. The feasibility of our algorithm is demonstrated by real human experiments. Although we focus on analyzing periodic fMRI data, the approach can be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the Fourier analysis with a wavelet analysis.

Medial temporal lobe function during emotional memory in early Alzheimer's disease, mild cognitive impairment and healthy ageing: an fMRI study.

PubMed

Parra, Mario A; Pattan, Vivek; Wong, Dichelle; Beaglehole, Anna; Lonie, Jane; Wan, Hong I; Honey, Garry; Hall, Jeremy; Whalley, Heather C; Lawrie, Stephen M

2013-03-06

Relative to intentional memory encoding, which quickly declines in Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD), incidental memory for emotional stimuli appears to deteriorate more slowly. We hypothesised that tests of incidental emotional memory may inform on different aspects of cognitive decline in MCI and AD. Patients with MCI, AD and Healthy Controls (HC) were asked to attend to emotional pictures (i.e., positive and neutral) sequentially presented during an fMRI session. Attention was monitored behaviourally. A surprise post-scan recognition test was then administered. The groups remained attentive within the scanner. The post-scan recognition pattern was in the form of (HC = MCI) > AD, with only the former group showing a clear benefit from emotional pictures. fMRI analysis of incidental encoding demonstrated clusters of activation in para-hippocampal regions and in the hippocampus in HC and MCI patients but not in AD patients. The pattern of activation observed in MCI patients tended to be greater than that found in HC. The results suggest that incidental emotional memory might offer a suitable platform to investigate, using behavioural and fMRI measures, subtle changes in the process of developing AD. These changes seem to differ from those found using standard episodic memory tests. The underpinnings of such differences and the potential clinical use of this methodology are discussed in depth.

Mild Jugular Compression Collar Ameliorated Changes in Brain Activation of Working Memory after One Soccer Season in Female High School Athletes.

PubMed

Yuan, Weihong; Dudley, Jonathan; Barber Foss, Kim D; Ellis, Jonathan D; Thomas, Staci; Galloway, Ryan T; DiCesare, Christopher A; Leach, James L; Adams, Janet; Maloney, Thomas; Gadd, Brooke; Smith, David; Epstein, Jeff N; Grooms, Dustin R; Logan, Kelsey; Howell, David R; Altaye, Mekibib; Myer, Gregory D

2018-06-01

Recent neuroimaging studies have suggested that repetitive subconcussive head impacts, even after only one sport season, may lead to pre- to post-season structural and functional alterations in male high school football athletes. However, data on female athletes are limited. In the current investigation, we aimed to (1) assess the longitudinal pre- to post-season changes in functional MRI (fMRI) of working memory and working memory performance, (2) quantify the association between the pre- to post-season change in fMRI of working memory and the exposure to head impact and working memory performance, and (3) assess whether wearing a neck collar designed to reduce intracranial slosh via mild compression of the jugular veins can ameliorate the changes in fMRI brain activation observed in the female high school athletes who did not wear collars after a full soccer season. A total of 48 female high school soccer athletes (age range: 14.00-17.97 years) were included in the study. These athletes were assigned to the non-collar group (n = 21) or to the collar group (n = 27). All athletes undewent MRI at both pre-season and post-season. In each session, a fMRI verbal N-Back task was used to engage working memory. A significant pre- to post-season increase in fMRI blood oxygen level dependent (BOLD) signal was demonstrated when performing the N-back working memory task in the non-collar group but not in the collar group, despite the comparable exposure to head impacts during the season between the two groups. The collar group demonstrated significantly smaller pre- to post-season change in fMRI BOLD signal than the non-collar group, suggesting a potential protective effect from the collar device. Significant correlations were also found between the pre- to post-season increase in fMRI brain activation and the decrease in task accuracy in the non-collar group, indicating an association between the compensatory mechanism in underlying neurophysiology and the alteration in the behavioral outcomes.

Analysis of Pseudohomophone Orthographic Errors through Functional Magnetic Resonance Imaging (fMRI).

PubMed

Guardia-Olmos, Joan; Zarabozo-Hurtado, Daniel; Peró-Cebollero, Maribe; Gudayol-Farré, Esteban; Gómez-Velázquez, Fabiola R; González-Garrido, Andrés

2017-12-04

The study of orthographic errors in a transparent language such as Spanish is an important topic in relation to writing acquisition because in Spanish it is common to write pseudohomophones as valid words. The main objective of the present study was to explore the possible differences in activation patterns in brain areas while processing pseudohomophone orthographic errors between participants with high (High Spelling Skills (HSS)) and low (Low Spelling Skills (LSS)) spelling orthographic abilities. We hypothesize that (a) the detection of orthographic errors will activate bilateral inferior frontal gyri, and that (b) this effect will be greater in the HSS group. Two groups of 12 Mexican participants, each matched by age, were formed based on their results in a group of spelling-related ad hoc tests: HSS and LSS groups. During the fMRI session, two experimental tasks were applied involving correct and pseudohomophone substitution of Spanish words. First, a spelling recognition task and second a letter searching task. The LSS group showed, as expected, a lower number of correct responses (F(1, 21) = 52.72, p <.001, η2 = .715) and higher reaction times compared to the HSS group for the spelling task (F(1, 21) = 60.03, p <.001, η2 = .741). However, this pattern was reversed when the participants were asked to decide on the presence of a vowel in the words, regardless of spelling. The fMRI data showed an engagement of the right inferior frontal gyrus in HSS group during the spelling task. However, temporal, frontal, and subcortical brain regions of the LSS group were activated during the same task.

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.

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.

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.

Feasibility of using fMRI to study mothers responding to infant cries.

PubMed

Lorberbaum, J P; Newman, J D; Dubno, J R; Horwitz, A R; Nahas, Z; Teneback, C C; Bloomer, C W; Bohning, D E; Vincent, D; Johnson, M R; Emmanuel, N; Brawman-Mintzer, O; Book, S W; Lydiard, R B; Ballenger, J C; George, M S

1999-01-01

While parenting is a universal human behavior, its neuroanatomic basis is currently unknown. Animal data suggest that the cingulate may play an important function in mammalian parenting behavior. For example, in rodents cingulate lesions impair maternal behavior. Here, in an attempt to understand the brain basis of human maternal behavior, we had mothers listen to recorded infant cries and white noise control sounds while they underwent functional MRI (fMRI) of the brain. We hypothesized that mothers would show significantly greater cingulate activity during the cries compared to the control sounds. Of 7 subjects scanned, 4 had fMRI data suitable for analysis. When fMRI data were averaged for these 4 subjects, the anterior cingulate and right medial prefrontal cortex were the only brain regions showing statistically increased activity with the cries compared to white noise control sounds (cluster analysis with one-tailed z-map threshold of P < 0.001 and spatial extent threshold of P < 0.05). These results demonstrate the feasibility of using fMRI to study brain activity in mothers listening to infant cries and that the anterior cingulate may be involved in mothers listening to crying babies. We are currently replicating this study in a larger group of mothers. Future work in this area may help (1) unravel the functional neuroanatomy of the parent-infant bond and (2) examine whether markers of this bond, such as maternal brain response to infant crying, can predict maternal style (i.e., child neglect), offspring temperament, or offspring depression or anxiety.

Evidence of stimulus correlated empathy modes--Group ICA of fMRI data.

PubMed

Vemuri, Kavita; Surampudi, Bapi Raju

2015-03-01

The analysis of the cognitive processes in response to a narrative as presented in a movie provides an insight into momentary reaction to a single depicted action like a facial expression and an aggregate processing of the entire sequence of events. In this study we report results from fMRI data analyzed by group independent component analysis (ICA) method from a free viewing experiment using a diverse set of movie clips--an animation, a Hollywood and an Indian Hindi movie. The fMRI data were collected from 15 college students as they viewed 5-8 min clips from three movies. The movie clips were rated for depiction of emotional expressions, the emotion as per the narrative and the viewer's own empathy response. The neural correlates attributed to cognitive, motor and emotional empathy were the focus of the study. The methodology of using long duration stimuli in free viewing mode combined with ICA analysis has the potential to tease out spatially distributed but temporally coherent brain activity as demonstrated in this study. The independent components obtained from group ICA method isolated spatial maps with activations which can be safely ascribed to stimulus processing. We found that the activity in the areas attributable to cognitive and motor empathy was comparable for all the three stimuli while certain critical areas for emotional empathy were not noticed for the animation movie. These findings lead to interesting questions on possible differential emotion response in viewer(s) for computer generated actors compared to actors in live-action movies and the role of narrative and exposure to different genre of movies on racial, ethnic and cultural differences in empathy response. Copyright © 2015 Elsevier Inc. All rights reserved.

Neural substrates of framing effects in social contexts: A meta-analytical approach.

PubMed

Wang, X T; Rao, Li-Lin; Zheng, Hongming

2017-06-01

We hypothesize that framing effects (risk-averse in the positive frame and risk-seeking in the negative frame) are likely to occur when ambiguous social contexts result in ambiguous or ambivalent risk preferences, leading the decision-maker to search for more subtle cues, such as verbal framing. In a functional magnetic resonance imaging (fMRI) study, we examined framing effects in both unambiguous homogeneous group and more ambiguous heterogeneous group contexts. We began by conducting a meta-analysis and identified three regions of interest: the right inferior frontal gyrus, the left anterior cingulate (ACC)/ventromedial prefrontal cortex (vmPFC), and the left amygdala. Our own fMRI data were collected while the participants made choices between a sure option and a gamble framed in terms of the number of lives to either save or die. The framing effect was evident in a heterogeneous context with a mixture of kin and strangers, but disappeared in a homogeneous group of either all kin-members or all strangers. The fMRI results revealed a greater activation in the right middle/inferior frontal gyrus under the negative than the positive framing, and less ACC/vmPFC deactivation under positive framing in the heterogamous/ambiguous context. The activation of the amygdala was correlated with greater risk-seeking preference in homogeneous kinship contexts.

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

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

Functional MRI compliance in children with attention deficit hyperactivity disorder

PubMed Central

Karakaş, Sirel; Dinçer, Elvin Doğutepe; Ceylan, Arzu Özkan; Tileylioğlu, Emre; Karakaş, Hakkı Muammer; Talı, E. Turgut

2015-01-01

PURPOSE We aimed to test the effect of prescan training and orientation in functional magnetic resonance imaging (fMRI) in children with attention deficit hyperactivity disorder (ADHD) and to investigate whether fMRI compliance was modified by state anxiety. METHODS Subjects included 77 males aged 6–12 years; there were 53 patients in the ADHD group and 24 participants in the healthy control group. Exclusion criteria included neurological and/or psychiatric comorbidities (other than ADHD), the use of psychoactive drugs, and an intelligence quotient outside the normal range. Children were individually subjected to prescan orientation and training. Data were acquired using a 1.5 Tesla scanner and an 8-channel head coil. Functional scans were performed using a standard neurocognitive task. RESULTS The neurocognitive task led to reliable fMRI maps. Compliance was not significantly different between ADHD and control groups based on success, failure, and repetition rates of fMRI. Compliance of ADHD patients with extreme levels of anxiety was also not significantly different. CONCLUSION The fMRI compliance of ADHD children is typically lower than that of healthy children. However, compliance can be increased to the level of age-matched healthy control children by addressing concerns about the technical and procedural aspects of fMRI, providing orientation programs, and performing on-task training. In patients thus trained, compliance does not change with the level of state anxiety suggesting that the anxiety hypothesis of fMRI compliance is not supported. PMID:25519454

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.

Complex Versus Simple Ankle Movement Training in Stroke Using Telerehabilitation: A Randomized Controlled Trial

PubMed Central

Deng, Huiqiong; Durfee, William K.; Nuckley, David J.; Rheude, Brandon S.; Severson, Amy E.; Skluzacek, Katie M.; Spindler, Kristen K.; Davey, Cynthia S.

2012-01-01

Background Telerehabilitation allows rehabilitative training to continue remotely after discharge from acute care and can include complex tasks known to create rich conditions for neural change. Objectives The purposes of this study were: (1) to explore the feasibility of using telerehabilitation to improve ankle dorsiflexion during the swing phase of gait in people with stroke and (2) to compare complex versus simple movements of the ankle in promoting behavioral change and brain reorganization. Design This study was a pilot randomized controlled trial. Setting Training was done in the participant's home. Testing was done in separate research labs involving functional magnetic resonance imaging (fMRI) and multi-camera gait analysis. Patients Sixteen participants with chronic stroke and impaired ankle dorsiflexion were assigned randomly to receive 4 weeks of telerehabilitation of the paretic ankle. Intervention Participants received either computerized complex movement training (track group) or simple movement training (move group). Measurements Behavioral changes were measured with the 10-m walk test and gait analysis using a motion capture system. Brain reorganization was measured with ankle tracking during fMRI. Results Dorsiflexion during gait was significantly larger in the track group compared with the move group. For fMRI, although the volume, percent volume, and intensity of cortical activation failed to show significant changes, the frequency count of the number of participants showing an increase versus a decrease in these values from pretest to posttest measurements was significantly different between the 2 groups, with the track group decreasing and the move group increasing. Limitations Limitations of this study were that no follow-up test was conducted and that a small sample size was used. Conclusions The results suggest that telerehabilitation, emphasizing complex task training with the paretic limb, is feasible and can be effective in promoting further dorsiflexion in people with chronic stroke. PMID:22095209

Neurodevelopmental delay with critical congenital heart disease is mainly from prenatal injury not infant cardiac surgery: current evidence based on a meta-analysis of functional magnetic resonance imaging.

PubMed

Li, Y; Yin, S; Fang, J; Hua, Y; Wang, C; Mu, D; Zhou, K

2015-06-01

No consensus has been reached regarding whether brain injury related to congenital heart disease (CHD) is caused by infant cardiac surgery and/or prenatal injury resulting from the CHD. We performed this meta-analysis to identify the likely cause of neurodevelopmental delay in CHD patients. We carried out a literature search without language restriction in December 2013, retrieving records from PubMed, EMBASE, the Cochrane Library and the World Health Organization trials center, to identify studies applying functional magnetic resonance imaging (fMRI) evaluation of brain function before surgery and, in some cases, after surgery (both immediate term and short term postoperatively). The preoperative and postoperative fMRI results were extracted, and meta-analysis was performed using Revman 5.1.1 and STATA 11.0, according to the guidelines from the Cochrane review and MOOSE groups. The electronic search yielded 937 citations. Full text was retrieved for 15 articles and eight articles (nine studies) were eligible for inclusion: six studies (n = 312 cases) with fMRI analysis before surgery and three (n = 36 cases) with complete perioperative fMRI analysis. The overall average diffusivity of CHD cases was significantly higher than that of controls, with a summarized standard (std) mean difference of 1.39 (95% CI, 0.70-2.08), and the fractional anisotropy was lower in CHD cases, with a summarized mean difference of -1.43 (95% CI, -1.95 to -0.91). N-acetylaspartate (NAA)/choline (Cho) for the whole brain was significantly lower in CHD cases compared with healthy ones, while lactate/Cho was significantly higher in CHD cases. Immediate term postoperatively, significant changes in NAA/creatine and NAA/Cho, relative to preoperative values, were found. However, the difference did not persist at the short-term follow-up. This meta-analysis suggests that the delay in neurological development in newborns with CHD is due mainly to prenatal injury, and cardiac surgery might lead to mild brain injuries postoperatively, but fMRI shows recovery within a short period. Copyright © 2014 ISUOG. Published by John Wiley & Sons Ltd.

DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI.

PubMed

Chao-Gan, Yan; Yu-Feng, Zang

2010-01-01

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.

Reduced cortical activation in inferior frontal junction in Unverricht-Lundborg disease (EPM1) - A motor fMRI study.

PubMed

Könönen, Mervi; Danner, Nils; Koskenkorva, Päivi; Kälviäinen, Reetta; Hyppönen, Jelena; Mervaala, Esa; Karjalainen, Pasi; Vanninen, Ritva; Niskanen, Eini

2015-03-01

Unverricht-Lundborg disease (EPM1) is characterized by stimulus-sensitive and action-activated myoclonus, tonic-clonic seizures and ataxia. Several disease-related alterations in cortical structure and excitability have been associated with the motor symptoms of EPM1. This study aimed to elucidate possible alterations in cortical activation related to motor performance in EPM1. Fifteen EPM1-patients and 15 healthy volunteers matched for age and sex underwent motor functional MRI. Group differences in activations were evaluated in the primary and supplementary motor cortices and sensory cortical areas. Furthermore, in EPM1 patients, the quantitative fMRI parameters were correlated with the severity of the motor symptoms. The EPM1-patients exhibited decreased activation in the left inferior frontal junction (IFJ) during right hand voluntary motor task when compared with controls. In the quantitative analysis, EPM1-patients had significantly weaker activation than controls in the hand knob and supplementary motor areas (SMA). The volume of activation in M1 decreased with age and duration of disease in the patient group, whereas the volume increased with age in controls. Negative correlations were observed between fMRI parameters of SMA and disease duration or age in patients but not in controls. The weaker motor fMRI activation observed in EPM1 patients parallels previous neurophysiological findings and correlates with the motor symptoms of the disease. Thus, the observed decrease in IFJ activation in EPM1 patients may be associated with the difficulties in initiation or termination of motor execution, a typical clinical symptom in EPM1. The fMRI findings reflect the progressive nature of this disease. Copyright © 2015 Elsevier B.V. All rights reserved.

Hippocampal activations in mesial temporal lobe epilepsy due to hippocampal sclerosis- an observational study on intramural encoding-delayed recall paradigms using task-based memory fMRI.

PubMed

Rajesh, P G; Thomas, Bejoy; Pammi, V S Chandrasekhar; Kesavadas, C; Alexander, Aley; Radhakrishnan, Ashalatha; Thomas, S V; Menon, R N

2018-05-26

To validate concurrent utility of within-scanner encoding and delayed recognition-memory paradigms to ascertain hippocampal activations during task-based memory fMRI. Memory paradigms were designed for faces, word-pairs and abstract designs. A deep-encoding task was designed comprising of a total of 9 cycles run within a 1.5T MRI scanner. A recall session was performed after 1 h within the scanner using an event-related design. Group analysis was done with 'correct-incorrect' responses applied as parametric modulators in Statistical Parametric Mapping version 8 using boot-strap method to enable estimation of laterality indices (LI) using custom anatomical masks involving the medio-basal temporal structures. Twenty seven subjects with drug-resistant mesial temporal lobe epilepsy due to hippocampal sclerosis (MTLE-HS) [17 patients of left-MTLE and 10 patients of right-MTLE] and 21 right handed age-matched healthy controls (HC) were recruited. For the encoding paradigm blood oxygen level dependent (BOLD) responses in HC demonstrated right laterality for faces, left laterality for word pairs, and bilaterality for design encoding over the regions of interest. Both right and left MTLE-HS groups revealed left lateralisation for word-pair encoding, bilateral activation for face encoding, with design encoding in right MTLE-HS demonstrating a left shift. As opposed to lateralization shown in controls, group analysis of cued-recall BOLD signals acquired within scanner in left MTLE-HS demonstrated right lateralization for word-pairs with bilaterality for faces and designs. The right MTLE-HS group demonstrated bilateral activations for faces, word-pairs and designs. Recall-based fMRI paradigms indicate hippocampal plasticity in MTLE-HS, maximal for word-pair associate recall tasks. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Time-Resolved and Spatio-Temporal Analysis of Complex Cognitive Processes and their Role in Disorders like Developmental Dyscalculia

PubMed Central

Mórocz, István Akos; Janoos, Firdaus; van Gelderen, Peter; Manor, David; Karni, Avi; Breznitz, Zvia; von Aster, Michael; Kushnir, Tammar; Shalev, Ruth

2012-01-01

The aim of this article is to report on the importance and challenges of a time-resolved and spatio-temporal analysis of fMRI data from complex cognitive processes and associated disorders using a study on developmental dyscalculia (DD). Participants underwent fMRI while judging the incorrectness of multiplication results, and the data were analyzed using a sequence of methods, each of which progressively provided more a detailed picture of the spatio-temporal aspect of this disease. Healthy subjects and subjects with DD performed alike behaviorally though they exhibited parietal disparities using traditional voxel-based group analyses. Further and more detailed differences, however, surfaced with a time-resolved examination of the neural responses during the experiment. While performing inter-group comparisons, a third group of subjects with dyslexia (DL) but with no arithmetic difficulties was included to test the specificity of the analysis and strengthen the statistical base with overall fifty-eight subjects. Surprisingly, the analysis showed a functional dissimilarity during an initial reading phase for the group of dyslexic but otherwise normal subjects, with respect to controls, even though only numerical digits and no alphabetic characters were presented. Thus our results suggest that time-resolved multi-variate analysis of complex experimental paradigms has the ability to yield powerful new clinical insights about abnormal brain function. Similarly, a detailed compilation of aberrations in the functional cascade may have much greater potential to delineate the core processing problems in mental disorders. PMID:22368322

A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia.

PubMed

Demirci, Oguz; Clark, Vincent P; Calhoun, Vince D

2008-02-15

Schizophrenia is diagnosed based largely upon behavioral symptoms. Currently, no quantitative, biologically based diagnostic technique has yet been developed to identify patients with schizophrenia. Classification of individuals into patient with schizophrenia and healthy control groups based on quantitative biologically based data is of great interest to support and refine psychiatric diagnoses. We applied a novel projection pursuit technique on various components obtained with independent component analysis (ICA) of 70 subjects' fMRI activation maps obtained during an auditory oddball task. The validity of the technique was tested with a leave-one-out method and the detection performance varied between 80% and 90%. The findings suggest that the proposed data reduction algorithm is effective in classifying individuals into schizophrenia and healthy control groups and may eventually prove useful as a diagnostic tool.

Individualized real-time fMRI neurofeedback to attenuate craving in nicotine-dependent smokers.

PubMed

Hartwell, Karen J; Hanlon, Colleen A; Li, Xingbao; Borckardt, Jeffrey J; Canterberry, Melanie; Prisciandaro, James J; Moran-Santa Maria, Megan M Moran; LeMatty, Todd; George, Mark S; Brady, Kathleen T

2016-01-01

Cue-induced craving plays an important role in relapse, and the neural correlates of cue-induced craving have been elucidated using fMRI. This study examined the utility of real-time fMRI (rtfMRI) neurofeedback to strengthen self-regulation of craving-related neural activation and cue-reactivity in cigarette smokers. Nicotine-dependent smokers were randomized to rtfMRI neurofeedback or to a no-feedback control group. Participants completed 3 neuroimaging visits. Within each visit, an initial run during which smoking-related cues were used to provoke craving, an individualized craving-related region of interest (ROI) in the prefrontal cortex or anterior cingulate cortex was identified. In the rtfMRI group, activity from the ROI was fed back via a visual display during 3 subsequent runs while participants were instructed to reduce craving during cue exposure. The control group had an identical experience with no feedback provided. Forty-four nicotine-dependent smokers were recruited to participate in our study; data from the 33 participants who completed a 1-week follow-up visit were included in the analysis. Subjective craving ratings and cue-induced brain activation were lower in the rtfMRI group than in the control group. As participants were not seeking treatment, clinical outcomes are lacking. Nicotine-dependent smokers receiving rtfMRI feedback from an individualized ROI attenuated smoking cue-elicited neural activation and craving, relative to a control group. Further studies are needed in treatment-seeking smokers to determine if this intervention can translate into a clinically meaningful treatment modality.

Variability in memory performance in aged healthy individuals: an fMRI study.

PubMed

Grön, Georg; Bittner, Daniel; Schmitz, Bernd; Wunderlich, Arthur P; Tomczak, Reinhard; Riepe, Matthias W

2003-01-01

Episodic memory performance varies in older subjects but underlying biological correlates remain as yet ambiguous. We investigated episodic memory in healthy older individuals (n=24; mean age: 64.4+/-6.7 years) without subjective memory complaints or objective cognitive impairment. Episodic memory was assessed with repetitive learning and recall of abstract geometric patterns during fMRI. Group analysis of brain activity during initial learning and maximum recall revealed hippocampal activation. Correlation analysis of brain activation and task performance demonstrated significant hippocampal activity during initial learning and maximum recall in a success-dependent manner. Neither age nor gray matter densities correlated with hippocampal activation. Functional imaging of episodic memory thus permits to detect objectively variability in hippocampal recruitment in healthy aged individuals without subjective memory complaints. Correlation analysis of brain activation and performance during an episodic memory task may be used to determine and follow-up hippocampal malfunction in a very sensitive manner.

Research on spontaneous activity in adult anisometropic amblyopia with regional homogeneity

NASA Astrophysics Data System (ADS)

Huang, Yufeng; Zhou, Yifeng

2017-06-01

Amblyopia usually occurs in early childhood and results in monocular visual impairment. The functional magnetic resonance imaging (fMRI) studies have reflected functional anomaly in amblyopia. In resting-state fMRI study, spontaneous activity changes abnormally in anisometropic amblyopia could be revealed by the regional homogeneity (ReHo). Twenty two adult anisometropic amblyopes and Twenty one normal controls participated in this fMRI study. Two sample T test was carried out to analysis ReHo within the whole brain for the inter groups. Compare with normal group, our study found that the amblyopia’s ReHo mainly increased in the left frontal lobe, while decreased in the left cerebellum, the temporal lobe (left and right), and the left parietal lobe. And the ReHo values in middle and inferior temporal lobe, the prefrontal lobe, frontal lobe (positive) and parietal lobe and medial frontal gyrus (negative) could be correlated with the acuity deficit of amblyopia. The results increased in ReHo may indicate compensatory plasticity in higher vision information process, while the decreased in ReHo may reflect decreased ability in eye movement, spatial sense and visuo-motor coordination. The correlation revealed that the vision deficit may correspond to the spontaneous in certain brain area.

Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

PubMed Central

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film (“At land” by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative. PMID:22860044

A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series.

PubMed

Patel, Ameera X; Kundu, Prantik; Rubinov, Mikail; Jones, P Simon; Vértes, Petra E; Ersche, Karen D; Suckling, John; Bullmore, Edward T

2014-07-15

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N=22) and a new dataset on adults with stimulant drug dependence (N=40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www.brainwavelet.org. Copyright © 2014. Published by Elsevier Inc.

A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

PubMed Central

Patel, Ameera X.; Kundu, Prantik; Rubinov, Mikail; Jones, P. Simon; Vértes, Petra E.; Ersche, Karen D.; Suckling, John; Bullmore, Edward T.

2014-01-01

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N = 22) and a new dataset on adults with stimulant drug dependence (N = 40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www.brainwavelet.org. PMID:24657353

Medial temporal lobe function during emotional memory in early Alzheimer’s disease, mild cognitive impairment and healthy ageing: an fMRI study

PubMed Central

2013-01-01

Background Relative to intentional memory encoding, which quickly declines in Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), incidental memory for emotional stimuli appears to deteriorate more slowly. We hypothesised that tests of incidental emotional memory may inform on different aspects of cognitive decline in MCI and AD. Methods Patients with MCI, AD and Healthy Controls (HC) were asked to attend to emotional pictures (i.e., positive and neutral) sequentially presented during an fMRI session. Attention was monitored behaviourally. A surprise post-scan recognition test was then administered. Results The groups remained attentive within the scanner. The post-scan recognition pattern was in the form of (HC = MCI) > AD, with only the former group showing a clear benefit from emotional pictures. fMRI analysis of incidental encoding demonstrated clusters of activation in para-hippocampal regions and in the hippocampus in HC and MCI patients but not in AD patients. The pattern of activation observed in MCI patients tended to be greater than that found in HC. Conclusions The results suggest that incidental emotional memory might offer a suitable platform to investigate, using behavioural and fMRI measures, subtle changes in the process of developing AD. These changes seem to differ from those found using standard episodic memory tests. The underpinnings of such differences and the potential clinical use of this methodology are discussed in depth. PMID:23497150

Arcuate fasciculus laterality by diffusion tensor imaging correlates with language laterality by functional MRI in preadolescent children.

PubMed

Sreedharan, Ruma Madhu; Menon, Amitha C; James, Jija S; Kesavadas, Chandrasekharan; Thomas, Sanjeev V

2015-03-01

Language lateralization is unique to humans. Functional MRI (fMRI) and diffusion tensor imaging (DTI) enable the study of language areas and white matter fibers involved in language, respectively. The objective of this study was to correlate arcuate fasciculus (AF) laterality by diffusion tensor imaging with that by fMRI in preadolescent children which has not yet been reported. Ten children between 8 and 12 years were subjected to fMRI and DTI imaging using Siemens 1.5 T MRI. Two language fMRI paradigms--visual verb generation and word pair task--were used. Analysis was done using SPM8 software. In DTI, the fiber volume of the arcuate fasciculus (AFV) and fractional anisotropy (FA) was measured. The fMRI Laterality Index (fMRI-LI) and DTI Laterality Index (DTI-LI) were calculated and their correlation assessed using the Pearson Correlation Index. Of ten children, mean age 10.6 years, eight showed left lateralization while bilateral language lateralization was seen in two. AFV by DTI was more on the left side in seven of the eight children who had left lateralization by fMRI. DTI could not trace the AF in one child. Of the two with bilateral language lateralization on fMRI, one showed larger AFV on the right side while the other did not show any asymmetry. There was a significant correlation (p < 0.02) between fMRI-LI and DTI-LI. Group mean of AFV by DTI was higher on the left side (2659.89 ± 654.75 mm(3)) as compared to the right (1824.11 ± 582.81 mm(3)) (p < 0.01). Like fMRI, DTI also reveals language laterality in children with a high degree of correlation between the two imaging modalities.

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.

A Review of Challenges in the Use of fMRI for Disease Classification / Characterization and A Projection Pursuit Application from Multi-site fMRI Schizophrenia Study.

PubMed

Demirci, Oguz; Clark, Vincent P; Magnotta, Vincent A; Andreasen, Nancy C; Lauriello, John; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

2008-09-01

Functional magnetic resonance imaging (fMRI) is a fairly new technique that has the potential to characterize and classify brain disorders such as schizophrenia. It has the possibility of playing a crucial role in designing objective prognostic/diagnostic tools, but also presents numerous challenges to analysis and interpretation. Classification provides results for individual subjects, rather than results related to group differences. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions out of high dimensional data with a limited number of subjects, especially for heterogeneous disorders whose pathophysiology is unknown. Numerous research efforts have been reported in the field using fMRI activation of schizophrenia patients and healthy controls. However, the results are usually not generalizable to larger data sets and require careful definition of the techniques used both in designing algorithms and reporting prediction accuracies. In this review paper, we survey a number of previous reports and also identify possible biases (cross-validation, class size, e.g.) in class comparison/prediction problems. Some suggestions to improve the effectiveness of the presentation of the prediction accuracy results are provided. We also present our own results using a projection pursuit algorithm followed by an application of independent component analysis proposed in an earlier study. We classify schizophrenia versus healthy controls using fMRI data of 155 subjects from two sites obtained during three different tasks. The results are compared in order to investigate the effectiveness of each task and differences between patients with schizophrenia and healthy controls were investigated.

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.

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

Functional resonance magnetic imaging (fMRI) in adolescents with idiopathic musculoskeletal pain: a paradigm of experimental pain.

PubMed

Molina, Juliana; Amaro, Edson; da Rocha, Liana Guerra Sanches; Jorge, Liliana; Santos, Flavia Heloisa; Len, Claudio A

2017-11-14

Studies on functional magnetic resonance imaging (fMRI) have shown that adults with musculoskeletal pain syndromes tolerate smaller amount of pressure (pain) as well as differences in brain activation patterns in areas related to pain.The objective of this study was to evaluate, through fMRI, the brain activation in adolescents with idiopathic musculoskeletal pain (IMP) while performing an experimental paradigm of pain. The study included 10 consecutive adolescents with idiopathic musculoskeletal pain (average age 16.3±1.0) and 10 healthy adolescents age-matched. fMRI exams were performed in a 3 T scanner (Magnetom Trio, Siemens) using an event-related design paradigm. Pressure stimuli were performed in the nondominant hand thumb, divided into two stages, fixed pain and variable pain. The two local Research Ethics Committees (Ethics Committee from Universidade Federal de São Paulo- Brazil, process number 0688/11, on July 1st, 2011 and Ethics Committee from Hospital Israelita Albert Einsten - Brazil, process number 1673, on October 19th, 2011) approved the study. The idiopathic musculoskeletal pain (IMP) group showed a reduced threshold for pain (3.7 kg/cm 2 versus 4.45 kg/cm 2 , p = 0.005). Control group presented increased bain activation when compared to IMP group in the following areas: thalamus (p = 0.00001), precentral gyrus (p = 0.0004) and middle frontal gyrus (p = 0.03). In intragroup analysis, IMP group showed greater brain activation during the unpredictable stimuli of the variable pain stage, especially in the lingual gyrus (p = 0.0001), frontal lobe (p = 0.0001), temporal gyrus (p = 0.0001) and precentral gyrus (p = 0.03), when compared to predictable stimulus of fixed pain. The same intragroup analysis with the control group showed greater activation during the unpredictable stimuli in regions of the precentral gyrus (p = 0.0001), subcallosal area (p = 0.0001), right and left occipital fusiform gyrus (p = 0.0001; (p = 0.0007), middle gyrus (p = 0.01) and precuneus p = (0.02). Adolescents with idiopathic musculoskeletal pain (IMP) tend to request higher brain function in cognitive-emotional areas when interpreting unpredictable sensory-perceptual situations. Therefore, it is assumed that this difference in pain processing in adolescents with IMP make the subjective experience of pain something more intense and unpleasant.

Age-Dependent Mesial Temporal Lobe Lateralization in Language FMRI

PubMed Central

Sepeta, Leigh N.; Berl, Madison M.; Wilke, Marko; You, Xiaozhen; Mehta, Meera; Xu, Benjamin; Inati, Sara; Dustin, Irene; Khan, Omar; Austermuehle, Alison; Theodore, William H.; Gaillard, William D.

2015-01-01

Objective FMRI activation of the mesial temporal lobe (MTL) may be important for epilepsy surgical planning. We examined MTL activation and lateralization during language fMRI in children and adults with focal epilepsy. Methods 142 controls and patients with left hemisphere focal epilepsy (Pediatric: epilepsy, n = 17, mean age = 9.9 ± 2.0; controls, n = 48; mean age = 9.1 ± 2.6; Adult: epilepsy, n = 20, mean age = 26.7 ± 5.8; controls, n = 57, mean age = 26.2 ± 7.5) underwent 3T fMRI using a language task (auditory description decision task). Image processing and analyses were conducted in SPM8; ROIs included MTL, Broca’s area, and Wernicke’s area. We assessed group and individual MTL activation, and examined degree of lateralization. Results Patients and controls (pediatric and adult) demonstrated group and individual MTL activation during language fMRI. MTL activation was left lateralized for adults but less so in children (p’s < 0.005). Patients did not differ from controls in either age group. Stronger left-lateralized MTL activation was related to older age (p = 0.02). Language lateralization (Broca’s and Wernicke’s) predicted 19% of the variance in MTL lateralization for adults (p = 0.001), but not children. Significance Language fMRI may be used to elicit group and individual MTL activation. The developmental difference in MTL lateralization and its association with language lateralization suggests a developmental shift in lateralization of MTL function, with increased left lateralization across the age span. This shift may help explain why children have better memory outcomes following resection compared to adults. PMID:26696589

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-09-01

Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

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.

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

Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis

DTIC Science & Technology

2016-10-01

AWARD NUMBER: W81XWH-15-2-0032 TITLE: Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis PRINCIPAL...4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis 5b...Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The subject of the project is FY14 PRMRP Topic Area – Tinnitus . The broad

Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project

PubMed Central

Boubela, Roland N.; Kalcher, Klaudius; Huf, Wolfgang; Našel, Christian; Moser, Ewald

2016-01-01

Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets. PMID:26778951

Adult Attachment Affects Neural Response to Preference-Inferring in Ambiguous Scenarios: Evidence From an fMRI Study

PubMed Central

Zhang, Xing; Ran, Guangming; Xu, Wenjian; Ma, Yuanxiao; Chen, Xu

2018-01-01

Humans are highly social animals, and the ability to cater to the preferences of other individuals is encouraged by society. Preference-inferring is an important aspect of the theory of mind (TOM). Many previous studies have shown that attachment style is closely related to TOM ability. However, little is known about the effects of adult attachment style on preferences inferring under different levels of certainty. Here, we investigated how adult attachment style affects neural activity underlying preferences inferred under different levels of certainty by using functional magnetic resonance imaging (fMRI). The fMRI results demonstrated that adult attachment influenced the activation of anterior insula (AI) and inferior parietal lobule (IPL) in response to ambiguous preference-inferring. More specifically, in the ambiguous preference condition, the avoidant attached groups exhibited a significantly enhanced activation than secure and anxious attached groups in left IPL; the anxious attached groups exhibited a significantly reduced activation secure attached group in left IPL. In addition, the anxious attached groups exhibited a significantly reduced activation than secure and avoidant attached groups in left AI. These results were also further confirmed by the subsequent PPI analysis. The results from current study suggest that, under ambiguous situations, the avoidant attached individuals show lower sensitivity to the preference of other individuals and need to invest more cognitive resources for preference-reasoning; while compared with avoidant attached group, the anxious attached individuals express high tolerance for uncertainty and a higher ToM proficiency. Results from the current study imply that differences in preference-inferring under ambiguous conditions associated with different levels of individual attachment may explain the differences in interpersonal interaction. PMID:29559932

Music Therapy Using Singing Training Improves Psychomotor Speed in Patients with Alzheimer's Disease: A Neuropsychological and fMRI Study.

PubMed

Satoh, Masayuki; Yuba, Toru; Tabei, Ken-Ichi; Okubo, Yukari; Kida, Hirotaka; Sakuma, Hajime; Tomimoto, Hidekazu

2015-01-01

To investigate the effect of singing training on the cognitive function in Alzheimer's disease (AD) patients. Ten AD patients (mean age 78.1 years) participated in music therapy using singing training once a week for 6 months (music therapy group). Each session was performed with professional musicians using karaoke and a unique voice training method (the YUBA Method). Before and after the intervention period, each patient was assessed by neuropsychological batteries, and functional magnetic resonance imaging (fMRI) was performed while the patients sang familiar songs with a karaoke device. As the control group, another 10 AD patients were recruited (mean age 77.0 years), and neuropsychological assessments were performed twice with an interval of 6 months. In the music therapy group, the time for completion of the Japanese Raven's Colored Progressive Matrices was significantly reduced (p = 0.026), and the results obtained from interviewing the patients' caregivers revealed a significant decrease in the Neuropsychiatric Inventory score (p = 0.042) and a prolongation of the patients' sleep time (p = 0.039). The fMRI study revealed increased activity in the right angular gyrus and the left lingual gyrus in the before-minus-after subtraction analysis of the music therapy intervention. Music therapy intervention using singing training may be useful for dementia patients by improving the neural efficacy of cognitive processing.

fMRI brain activation in patients with insomnia disorder during a working memory task.

PubMed

Son, Young-Don; Kang, Jae Myeong; Cho, Seong-Jin; Lee, Jung-Sun; Hwang, Hee Young; Kang, Seung-Gul

2018-05-01

This study used functional magnetic resonance imaging (fMRI) to investigate differences in the functional brain activation of patients with insomnia disorder (n = 21, mean age = 36.6) and of good sleepers (n = 26, mean age = 33.2) without other comorbidities or structural brain abnormalities during a working memory task. All participants completed a clinical questionnaire, were subjected to portable polysomnography (PSG), and performed the working memory task during an fMRI scan. The subjects who were suspected of major sleep disorder and comorbid psychiatric disorders except insomnia disorder were excluded. To compare the brain activation on working memory from the insomnia group with those from the good-sleeper group, a two-sample t test was performed. Statistical significance was determined using 3DClustSim with the updated algorithm to obtain a reasonable cluster size and p value for each analysis. We observed higher levels of brain activation in the right lateral inferior frontal cortex and the right superior temporal pole in the insomnia group compared to good sleepers (cluster-based multiple comparison correction, p < 0.001, k = 34 @ α = 0.01). Thus, patients with insomnia disorder showed increased brain activation during working memory relative to good sleepers, and this may be indicative of compensatory brain activation to maintain cognitive performance in patients with insomnia disorder without other comorbidities.

Music Therapy Using Singing Training Improves Psychomotor Speed in Patients with Alzheimer's Disease: A Neuropsychological and fMRI Study

PubMed Central

Satoh, Masayuki; Yuba, Toru; Tabei, Ken-ichi; Okubo, Yukari; Kida, Hirotaka; Sakuma, Hajime; Tomimoto, Hidekazu

2015-01-01

Background/Aims To investigate the effect of singing training on the cognitive function in Alzheimer's disease (AD) patients. Methods Ten AD patients (mean age 78.1 years) participated in music therapy using singing training once a week for 6 months (music therapy group). Each session was performed with professional musicians using karaoke and a unique voice training method (the YUBA Method). Before and after the intervention period, each patient was assessed by neuropsychological batteries, and functional magnetic resonance imaging (fMRI) was performed while the patients sang familiar songs with a karaoke device. As the control group, another 10 AD patients were recruited (mean age 77.0 years), and neuropsychological assessments were performed twice with an interval of 6 months. Results In the music therapy group, the time for completion of the Japanese Raven's Colored Progressive Matrices was significantly reduced (p = 0.026), and the results obtained from interviewing the patients' caregivers revealed a significant decrease in the Neuropsychiatric Inventory score (p = 0.042) and a prolongation of the patients' sleep time (p = 0.039). The fMRI study revealed increased activity in the right angular gyrus and the left lingual gyrus in the before-minus-after subtraction analysis of the music therapy intervention. Conclusion Music therapy intervention using singing training may be useful for dementia patients by improving the neural efficacy of cognitive processing. PMID:26483829

On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis.

PubMed

Pedersen, Mangor; Omidvarnia, Amir; Zalesky, Andrew; Jackson, Graeme D

2018-06-08

Correlation-based sliding window analysis (CSWA) is the most commonly used method to estimate time-resolved functional MRI (fMRI) connectivity. However, instantaneous phase synchrony analysis (IPSA) is gaining popularity mainly because it offers single time-point resolution of time-resolved fMRI connectivity. We aim to provide a systematic comparison between these two approaches, on both temporal and topological levels. For this purpose, we used resting-state fMRI data from two separate cohorts with different temporal resolutions (45 healthy subjects from Human Connectome Project fMRI data with repetition time of 0.72 s and 25 healthy subjects from a separate validation fMRI dataset with a repetition time of 3 s). For time-resolved functional connectivity analysis, we calculated tapered CSWA over a wide range of different window lengths that were temporally and topologically compared to IPSA. We found a strong association in connectivity dynamics between IPSA and CSWA when considering the absolute values of CSWA. The association between CSWA and IPSA was stronger for a window length of ∼20 s (shorter than filtered fMRI wavelength) than ∼100 s (longer than filtered fMRI wavelength), irrespective of the sampling rate of the underlying fMRI data. Narrow-band filtering of fMRI data (0.03-0.07 Hz) yielded a stronger relationship between IPSA and CSWA than wider-band (0.01-0.1 Hz). On a topological level, time-averaged IPSA and CSWA nodes were non-linearly correlated for both short (∼20 s) and long (∼100 s) windows, mainly because nodes with strong negative correlations (CSWA) displayed high phase synchrony (IPSA). IPSA and CSWA were anatomically similar in the default mode network, sensory cortex, insula and cerebellum. Our results suggest that IPSA and CSWA provide comparable characterizations of time-resolved fMRI connectivity for appropriately chosen window lengths. Although IPSA requires narrow-band fMRI filtering, we recommend the use of IPSA given that it does not mandate a (semi-)arbitrary choice of window length and window overlap. A code for calculating IPSA is provided. Copyright © 2018. Published by Elsevier Inc.

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

PubMed Central

Farràs-Permanyer, Laia; Guàrdia-Olmos, Joan; Peró-Cebollero, Maribel

2015-01-01

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results. PMID:26300802

Conduction Aphasia, Sensory-Motor Integration, and Phonological Short-term Memory – an Aggregate analysis of Lesion and fMRI data

PubMed Central

Buchsbaum, Bradley R.; Baldo, Juliana; Okada, Kayoko; Berman, Karen F.; Dronkers, Nina; D’Esposito, Mark; Hickok, Gregory

2011-01-01

Conduction aphasia is a language disorder characterized by frequent speech errors, impaired verbatim repetition, a deficit in phonological short-term memory, and naming difficulties in the presence of otherwise fluent and grammatical speech output. While traditional models of conduction aphasia have typically implicated white matter pathways, recent advances in lesions reconstruction methodology applied to groups of patients have implicated left temporoparietal zones. Parallel work using functional magnetic resonance imaging (fMRI) has pinpointed a region in the posterior most portion of the left planum temporale, area Spt, which is critical for phonological working memory. Here we show that the region of maximal lesion overlap in a sample of 14 patients with conduction aphasia perfectly circumscribes area Spt, as defined in an aggregate fMRI analysis of 105 subjects performing a phonological working memory task. We provide a review of the evidence supporting the idea that Spt is an interface site for the integration of sensory and vocal tract-related motor representations of complex sound sequences, such as speech and music and show how the symptoms of conduction aphasia can be explained by damage to this system. PMID:21256582

Conduction aphasia, sensory-motor integration, and phonological short-term memory - an aggregate analysis of lesion and fMRI data.

PubMed

Buchsbaum, Bradley R; Baldo, Juliana; Okada, Kayoko; Berman, Karen F; Dronkers, Nina; D'Esposito, Mark; Hickok, Gregory

2011-12-01

Conduction aphasia is a language disorder characterized by frequent speech errors, impaired verbatim repetition, a deficit in phonological short-term memory, and naming difficulties in the presence of otherwise fluent and grammatical speech output. While traditional models of conduction aphasia have typically implicated white matter pathways, recent advances in lesions reconstruction methodology applied to groups of patients have implicated left temporoparietal zones. Parallel work using functional magnetic resonance imaging (fMRI) has pinpointed a region in the posterior most portion of the left planum temporale, area Spt, which is critical for phonological working memory. Here we show that the region of maximal lesion overlap in a sample of 14 patients with conduction aphasia perfectly circumscribes area Spt, as defined in an aggregate fMRI analysis of 105 subjects performing a phonological working memory task. We provide a review of the evidence supporting the idea that Spt is an interface site for the integration of sensory and vocal tract-related motor representations of complex sound sequences, such as speech and music and show how the symptoms of conduction aphasia can be explained by damage to this system. 2011 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.

Partial Least Squares for Discrimination in fMRI Data

PubMed Central

Andersen, Anders H.; Rayens, William S.; Liu, Yushu; Smith, Charles D.

2011-01-01

Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using either principal component analysis (PCA), partial least squares (PLS), or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contains more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using fMRI as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk. PMID:22227352

A multiscale method for a robust detection of the default mode network

NASA Astrophysics Data System (ADS)

Baquero, Katherine; Gómez, Francisco; Cifuentes, Christian; Guldenmund, Pieter; Demertzi, Athena; Vanhaudenhuyse, Audrey; Gosseries, Olivia; Tshibanda, Jean-Flory; Noirhomme, Quentin; Laureys, Steven; Soddu, Andrea; Romero, Eduardo

2013-11-01

The Default Mode Network (DMN) is a resting state network widely used for the analysis and diagnosis of mental disorders. It is normally detected in fMRI data, but for its detection in data corrupted by motion artefacts or low neuronal activity, the use of a robust analysis method is mandatory. In fMRI it has been shown that the signal-to-noise ratio (SNR) and the detection sensitivity of neuronal regions is increased with di erent smoothing kernels sizes. Here we propose to use a multiscale decomposition based of a linear scale-space representation for the detection of the DMN. Three main points are proposed in this methodology: rst, the use of fMRI data at di erent smoothing scale-spaces, second, detection of independent neuronal components of the DMN at each scale by using standard preprocessing methods and ICA decomposition at scale-level, and nally, a weighted contribution of each scale by the Goodness of Fit measurement. This method was applied to a group of control subjects and was compared with a standard preprocesing baseline. The detection of the DMN was improved at single subject level and at group level. Based on these results, we suggest to use this methodology to enhance the detection of the DMN in data perturbed with artefacts or applied to subjects with low neuronal activity. Furthermore, the multiscale method could be extended for the detection of other resting state neuronal networks.

Neural substrates of cue reactivity and craving in gambling disorder

PubMed Central

Limbrick-Oldfield, E H; Mick, I; Cocks, R E; McGonigle, J; Sharman, S P; Goldstone, A P; Stokes, P R A; Waldman, A; Erritzoe, D; Bowden-Jones, H; Nutt, D; Lingford-Hughes, A; Clark, L

2017-01-01

Cue reactivity is an established procedure in addictions research for examining the subjective experience and neural basis of craving. This experiment sought to quantify cue-related brain responses in gambling disorder using personally tailored cues in conjunction with subjective craving, as well as a comparison with appetitive non-gambling stimuli. Participants with gambling disorder (n=19) attending treatment and 19 controls viewed personally tailored blocks of gambling-related cues, as well as neutral cues and highly appetitive (food) images during a functional magnetic resonance imaging (fMRI) scan performed ~2–3 h after a usual meal. fMRI analysis examined cue-related brain activity, cue-related changes in connectivity and associations with block-by-block craving ratings. Craving ratings in the participants with gambling disorder increased following gambling cues compared with non-gambling cues. fMRI analysis revealed group differences in left insula and anterior cingulate cortex, with the gambling disorder group showing greater reactivity to the gambling cues, but no differences to the food cues. In participants with gambling disorder, craving to gamble correlated positively with gambling cue-related activity in the bilateral insula and ventral striatum, and negatively with functional connectivity between the ventral striatum and the medial prefrontal cortex. Gambling cues, but not food cues, elicit increased brain responses in reward-related circuitry in individuals with gambling disorder (compared with controls), providing support for the incentive sensitization theory of addiction. Activity in the insula co-varied with craving intensity, and may be a target for interventions. PMID:28045460

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.

The relationship of resting cerebral blood flow and brain activation during a social cognition task in adolescents with chronic moderate to severe traumatic brain injury: a preliminary investigation.

PubMed

Newsome, Mary R; Scheibel, Randall S; Chu, Zili; Hunter, Jill V; Li, Xiaoqi; Wilde, Elisabeth A; Lu, Hanzhang; Wang, Zhiyue J; Lin, Xiaodi; Steinberg, Joel L; Vasquez, Ana C; Cook, Lori; Levin, Harvey S

2012-05-01

Alterations in cerebrovascular function are evident acutely in moderate to severe traumatic brain injury (TBI), although less is known about their chronic effects. Adolescent and adult patients with moderate to severe TBI have been reported to demonstrate diffuse activation throughout the brain during functional magnetic resonance imaging (fMRI). Because fMRI is a measure related to blood flow, it is possible that any deficits in blood flow may alter activation. An arterial spin labeling (ASL) perfusion sequence was performed on seven adolescents with chronic moderate to severe TBI and seven typically developing (TD) adolescents during the same session in which they had performed a social cognition task during fMRI. In the TD group, prefrontal CBF was positively related to prefrontal activation and negatively related to non-prefrontal, posterior, brain activation. This relationship was not seen in the TBI group, who demonstrated a greater positive relationship between prefrontal CBF and non-prefrontal activation than the TD group. An analysis of CBF data independent of fMRI showed reduced CBF in the right non-prefrontal region (p<.055) in the TBI group. To understand any role reduced CBF may play in diffuse extra-activation, we then related the right non-prefrontal CBF to activation. CBF in the right non-prefrontal region in the TD group was positively associated with prefrontal activation, suggesting an interactive role of non-prefrontal and prefrontal blood flow throughout the right hemisphere in healthy brains. However, the TBI group demonstrated a positive association with activation constrained to the right non-prefrontal region. These data suggest a relationship between impaired non-prefrontal CBF and the presence of non-prefrontal extra-activation, where the region with more limited blood flow is associated with activation limited to that region. In a secondary analysis, pathology associated with hyperintensities on T2-weighted FLAIR imaging over the whole brain was related to whole brain activation, revealing a negative relationship between lesion volume and frontal activation, and a positive relationship between lesion volume and posterior activation. These preliminary data, albeit collected with small sample sizes, suggest that reduced non-prefrontal CBF, and possibly pathological tissue associated with T2-hyperintensities, may provide contributions to the diffuse, primarily posterior extra-activation observed in adolescents following moderate to severe TBI. Published by Elsevier Ltd.

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

A novel fMRI paradigm suggests that pedaling-related brain activation is altered after stroke

PubMed Central

Promjunyakul, Nutta-on; Schmit, Brian D.; Schindler-Ivens, Sheila M.

2015-01-01

The purpose of this study was to examine the feasibility of using functional magnetic resonance imaging (fMRI) to measure pedaling-related brain activation in individuals with stroke and age-matched controls. We also sought to identify stroke-related changes in brain activation associated with pedaling. Fourteen stroke and 12 control subjects were asked to pedal a custom, MRI-compatible device during fMRI. Subjects also performed lower limb tapping to localize brain regions involved in lower limb movement. All stroke and control subjects were able to pedal while positioned for fMRI. Two control subjects were withdrawn due to claustrophobia, and one control data set was excluded from analysis due to an incidental finding. In the stroke group, one subject was unable to enter the gantry due to excess adiposity, and one stroke data set was excluded from analysis due to excessive head motion. Consequently, 81% of subjects (12/14 stroke, 9/12 control) completed all procedures and provided valid pedaling-related fMRI data. In these subjects, head motion was ≤3 mm. In both groups, brain activation localized to the medial aspect of M1, S1, and Brodmann’s area 6 (BA6) and to the cerebellum (vermis, lobules IV, V, VIII). The location of brain activation was consistent with leg areas. Pedaling-related brain activation was apparent on both sides of the brain, with values for laterality index (LI) of –0.06 (0.20) in the stroke cortex, 0.05 (±0.06) in the control cortex, 0.29 (0.33) in the stroke cerebellum, and 0.04 (0.15) in the control cerebellum. In the stroke group, activation in the cerebellum – but not cortex – was significantly lateralized toward the damaged side of the brain (p = 0.01). The volume of pedaling-related brain activation was smaller in stroke as compared to control subjects. Differences reached statistical significance when all active regions were examined together [p = 0.03; 27,694 (9,608) μL stroke; 37,819 (9,169) μL control]. When individual regions were examined separately, reduced brain activation volume reached statistical significance in BA6 [p = 0.04; 4,350 (2,347) μL stroke; 6,938 (3,134) μL control] and cerebellum [p = 0.001; 4,591 (1,757) μL stroke; 8,381 (2,835) μL control]. Regardless of whether activated regions were examined together or separately, there were no significant between-group differences in brain activation intensity [p = 0.17; 1.30 (0.25)% stroke; 1.16 (0.20)% control]. Reduced volume in the stroke group was not observed during lower limb tapping and could not be fully attributed to differences in head motion or movement rate. There was a tendency for pedaling-related brain activation volume to increase with increasing work performed by the paretic limb during pedaling (p = 0.08, r = 0.525). Hence, the results of this study provide two original and important contributions. First, we demonstrated that pedaling can be used with fMRI to examine brain activation associated with lower limb movement in people with stroke. Unlike previous lower limb movements examined with fMRI, pedaling involves continuous, reciprocal, multijoint movement of both limbs. In this respect, pedaling has many characteristics of functional lower limb movements, such as walking. Thus, the importance of our contribution lies in the establishment of a novel paradigm that can be used to understand how the brain adapts to stroke to produce functional lower limb movements. Second, preliminary observations suggest that brain activation volume is reduced during pedaling post-stroke. Reduced brain activation volume may be due to anatomic, physiology, and/or behavioral differences between groups, but methodological issues cannot be excluded. Importantly, brain action volume post-stroke was both task-dependent and mutable, which suggests that it could be modified through rehabilitation. Future work will explore these possibilities. PMID:26089789

The Effect of Passive Movement for Paretic Ankle-Foot and Brain Activity in Post-Stroke Patients.

PubMed

Vér, Csilla; Emri, Miklós; Spisák, Tamás; Berényi, Ervin; Kovács, Kázmér; Katona, Péter; Balkay, László; Menyhárt, László; Kardos, László; Csiba, László

2016-01-01

This study aims at investigating the short-term efficacy of the continuous passive motion (CPM) device developed for the therapy of ankle-foot paresis and to investigate by fMRI the blood oxygen level-dependent responses (BOLD) during ankle passive movement (PM). Sixty-four stroke patients were investigated. Patients were assigned into 2 groups: 49 patients received both 15 min manual and 30 min device therapy (M + D), while the other group (n = 15) received only 15 min manual therapy (M). A third group of stroke patients (n = 12) was investigated by fMRI before and immediately after 30 min CPM device therapy. There was no direct relation between the fMRI group and the other 2 groups. All subjects were assessed using the Modified Ashworth Scale (MAS) and a goniometer. Mean MAS decreased, the ankle's mean plantar flexion and dorsiflexion passive range of motion (PROM) increased and the equinovalgus improved significantly in the M + D group. In the fMRI group, the PM of the paretic ankle increased BOLD responses; this was observed in the contralateral pre- and postcentral gyrus, superior temporal gyrus, central opercular cortex, and in the ipsilateral postcentral gyrus, frontal operculum cortex and cerebellum. Manual therapy with CPM device therapy improved the ankle PROM, equinovalgus and severity of spasticity. The ankle PM increased ipsi- and contralateral cortical activation. © 2016 S. Karger AG, Basel.

Unobtrusive integration of data management with fMRI analysis.

PubMed

Poliakov, Andrew V; Hertzenberg, Xenia; Moore, Eider B; Corina, David P; Ojemann, George A; Brinkley, James F

2007-01-01

This note describes a software utility, called X-batch which addresses two pressing issues typically faced by functional magnetic resonance imaging (fMRI) neuroimaging laboratories (1) analysis automation and (2) data management. The first issue is addressed by providing a simple batch mode processing tool for the popular SPM software package (http://www.fil.ion. ucl.ac.uk/spm/; Welcome Department of Imaging Neuroscience, London, UK). The second is addressed by transparently recording metadata describing all aspects of the batch job (e.g., subject demographics, analysis parameters, locations and names of created files, date and time of analysis, and so on). These metadata are recorded as instances of an extended version of the Protégé-based Experiment Lab Book ontology created by the Dartmouth fMRI Data Center. The resulting instantiated ontology provides a detailed record of all fMRI analyses performed, and as such can be part of larger systems for neuroimaging data management, sharing, and visualization. The X-batch system is in use in our own fMRI research, and is available for download at http://X-batch.sourceforge.net/.

Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data.

PubMed

Kim, Yong-Hwan; Kim, Junghoe; Lee, Jong-Hwan

2012-12-01

This study proposes an iterative dual-regression (DR) approach with sparse prior regularization to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of functional magnetic resonance imaging (fMRI) data (i.e., Tc-GICA method). An ordinary DR approach estimates the spatial patterns (SPs) of neuronal activation and corresponding time courses (TCs) specific to each individual's fMRI data with two steps involving least-squares (LS) solutions. Our proposed approach employs iterative LS solutions to refine both the individual SPs and TCs with an additional a priori assumption of sparseness in the SPs (i.e., minimally overlapping SPs) based on L(1)-norm minimization. To quantitatively evaluate the performance of this approach, semi-artificial fMRI data were created from resting-state fMRI data with the following considerations: (1) an artificially designed spatial layout of neuronal activation patterns with varying overlap sizes across subjects and (2) a BOLD time series (TS) with variable parameters such as onset time, duration, and maximum BOLD levels. To systematically control the spatial layout variability of neuronal activation patterns across the "subjects" (n=12), the degree of spatial overlap across all subjects was varied from a minimum of 1 voxel (i.e., 0.5-voxel cubic radius) to a maximum of 81 voxels (i.e., 2.5-voxel radius) across the task-related SPs with a size of 100 voxels for both the block-based and event-related task paradigms. In addition, several levels of maximum percentage BOLD intensity (i.e., 0.5, 1.0, 2.0, and 3.0%) were used for each degree of spatial overlap size. From the results, the estimated individual SPs of neuronal activation obtained from the proposed iterative DR approach with a sparse prior showed an enhanced true positive rate and reduced false positive rate compared to the ordinary DR approach. The estimated TCs of the task-related SPs from our proposed approach showed greater temporal correlation coefficients with a reference hemodynamic response function than those of the ordinary DR approach. Moreover, the efficacy of the proposed DR approach was also successfully demonstrated by the results of real fMRI data acquired from left-/right-hand clenching tasks in both block-based and event-related task paradigms. Copyright © 2012 Elsevier Inc. 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).

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

PubMed

Mejia Tobar, Alejandra; Hyoudou, Rikiya; Kita, Kahori; Nakamura, Tatsuhiro; Kambara, Hiroyuki; Ogata, Yousuke; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

2017-01-01

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Functional MR imaging of the spinal cord in cervical spinal cord injury patients by acupuncture at LI 4 (Hegu) and LI 11(Quchi).

PubMed

Chen, Y X; Kong, K M; Wang, W D; Xie, C H; Wu, R H

2007-01-01

To investigate the cervical spinal cord mapping on acupuncture at LI 4 (Hegu) and LI 11 (Quchi) by using 'Signal Enhancement by Extravascular water Protons' (SEEP)-fMRI, and to establish the response of using acupuncture in the cervical spinal cord. This research may provide some laboratory evidences from the acupuncture treatment on the cervical spinal cord of injuried patients. Seven healthy volunteers (healthy group) and three cervical spinal cord injury patients (injury group) were underwent low-frequency electrical stimulation at LI 4 and LI 11. Meanwhile, a single-shot fast spin-echo (SSFSE) sequence was used to perform functional MR imaging on a 1.5 T GE Signa MR system. The signals from the cervical spinal cord activated was measured both in sagittal and transverse imaging planes and then analyzed by AFNI (Analysis of Functional Neuroimages) system. It was found that in the sagittal view, two groups had an fMRI response in the cervical spinal cord after given acupuncture treatments at LI 4 and LI 11. The localizations of the segmental fMRI activation were focused at C6 and C2 cervical spinal cord level. In the transverse imaging plane, significant fMRI responses could be measured from the four of seven healthy volunteers and from two of three cervical spinal cord injury patients. They were located at C6/7 segments. The cross-sectional localization of the activity measured in the spinal cord was most in terms of the ipsilateral posterior direction. The signal amplitude varied mainly between 6.8%17.8%. However, the difference found between the two groups had no statistical meaning. The fMRI technique had detected an activation focused at C6 and C2 cervical spinal cord levels by use of acupuncture at LI 4 and LI 11 on a 1.5T GE clinical system. This proved that the meridians and points are found to be in existence. The fMRI can be used as a harmless research method to discuss the mechanisms of acupuncture as well as study the mechanisms of spinal cord diseases. It can be used to direct or monitor the related therapy on the spinal cord injury patients.

Bringing memory fMRI to the clinic: comparison of seven memory fMRI protocols in temporal lobe epilepsy.

PubMed

Towgood, Karren; Barker, Gareth J; Caceres, Alejandro; Crum, William R; Elwes, Robert D C; Costafreda, Sergi G; Mehta, Mitul A; Morris, Robin G; von Oertzen, Tim J; Richardson, Mark P

2015-04-01

fMRI is increasingly implemented in the clinic to assess memory function. There are multiple approaches to memory fMRI, but limited data on advantages and reliability of different methods. Here, we compared effect size, activation lateralisation, and between-sessions reliability of seven memory fMRI protocols: Hometown Walking (block design), Scene encoding (block design and event-related design), Picture encoding (block and event-related), and Word encoding (block and event-related). All protocols were performed on three occasions in 16 patients with temporal lobe epilepsy (TLE). Group T-maps showed activity bilaterally in medial temporal lobe for all protocols. Using ANOVA, there was an interaction between hemisphere and seizure-onset lateralisation (P = 0.009) and between hemisphere, protocol and seizure-onset lateralisation (P = 0.002), showing that the distribution of memory-related activity between left and right temporal lobes differed between protocols and between patients with left-onset and right-onset seizures. Using voxelwise intraclass Correlation Coefficient, between-sessions reliability was best for Hometown and Scenes (block and event). The between-sessions spatial overlap of activated voxels was also greatest for Hometown and Scenes. Lateralisation of activity between hemispheres was most reliable for Scenes (block and event) and Words (event). Using receiver operating characteristic analysis to explore the ability of each fMRI protocol to classify patients as left-onset or right-onset TLE, only the Words (event) protocol achieved a significantly above-chance classification of patients at all three sessions. We conclude that Words (event) protocol shows the best combination of between-sessions reliability of the distribution of activity between hemispheres and reliable ability to distinguish between left-onset and right-onset patients. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Functional MRI and intraoperative brain mapping to evaluate brain plasticity in patients with brain tumours and hemiparesis

PubMed Central

Roux, F; Boulanouar, K; Ibarrola, D; Tremoulet, M; Chollet, F; Berry, I

2000-01-01

OBJECTIVE—To support the hypothesis about the potential compensatory role of ipsilateral corticofugal pathways when the contralateral pathways are impaired by brain tumours.
METHODS—Retrospective analysis was carried out on the results of functional MRI (fMRI) of a selected group of five paretic patients with Rolandic brain tumours who exhibited an abnormally high ipsilateral/contralateral ratio of activation—that is, movements of the paretic hand activated predominately the ipsilateral cortex. Brain activation was achieved with a flexion extension of the fingers. Statistical parametric activation was obtained using a t test and a threshold of p<0.001. These patients, candidates for tumour resection, also underwent cortical intraoperative stimulation that was correlated to the fMRI spatial data using three dimensional reconstructions of the brain. Three patients also had postoperative control fMRI.
RESULTS—The absence of fMRI activation of the primary sensorimotor cortex normally innervating the paretic hand for the threshold chosen, was correlated with completely negative cortical responses of the cortical hand area during the operation. The preoperative fMRI activation of these patients predominantly found in the ipsilateral frontal and primary sensorimotor cortices could be related to the residual ipsilateral hand function. Postoperatively, the fMRI activation returned to more classic patterns of activation, reflecting the consequences of therapy.
CONCLUSION—In paretic patients with brain tumours, ipsilateral control could be implicated in the residual hand function, when the normal primary pathways are impaired. The possibility that functional tissue still remains in the peritumorous sensorimotor cortex even when the preoperative fMRI and the cortical intraoperative stimulations are negative, should be taken into account when planning the tumour resection and during the operation.

 PMID:10990503

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.

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.

Functional versus Nonfunctional Rehabilitation in Chronic Ischemic Stroke: Evidences from a Randomized Functional MRI Study

PubMed Central

Pelicioni, Maristela C. X.; Novaes, Morgana M.; Peres, Andre S. C.; Lino de Souza, Altay A.; Minelli, Cesar; Fabio, Soraia R. C.; Pontes-Neto, Octavio M.; Santos, Antonio C.; de Araujo, Draulio B.

2016-01-01

Motor rehabilitation of stroke survivors may include functional and/or nonfunctional strategy. The present study aimed to compare the effect of these two rehabilitation strategies by means of clinical scales and functional Magnetic Resonance Imaging (fMRI). Twelve hemiparetic chronic stroke patients were selected. Patients were randomly assigned a nonfunctional (NFS) or functional (FS) rehabilitation scheme. Clinical scales (Fugl-Meyer, ARA test, and modified Barthel) and fMRI were applied at four moments: before rehabilitation (P1) and immediately after (P2), 1 month after (P3), and three months after (P4) the end of rehabilitation. The NFS group improved significantly and exclusively their Fugl-Meyer scores at P2, P3, and P4, when compared to P1. On the other hand, the FS group increased significantly in Fugl-Meyer at P2, when compared to P1, and also in their ARA and Barthel scores. fMRI inspection at the individual level revealed that both rehabilitation schemes most often led to decreased activation sparseness, decreased activity of contralesional M1, increased asymmetry of M1 activity to the ipsilesional side, decreased perilesional activity, and decreased SMA activity. Increased M1 asymmetry with rehabilitation was also confirmed by Lateralization Indexes. Our clinical analysis revealed subtle differences between FS and NFS. PMID:26839716

Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression.

PubMed

Sundermann, Benedikt; Olde Lütke Beverborg, Mona; Pfleiderer, Bettina

2014-01-01

Information derived from functional magnetic resonance imaging (fMRI) during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD). Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD, primarily to serve as feature selection for multivariate pattern analysis techniques (MVPA). Thirty two studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components including the precuneus and neighboring posterior cingulate cortices associated with self-referential processing and the subgenual anterior cingulate and neighboring medial frontal cortices) with lateral prefrontal areas related to externally-directed cognition. Other areas of hyperactivity/hyperconnectivity include the left lateral parietal cortex, right hippocampus and right cerebellum whereas hypoactivity/hypoconnectivity was observed mainly in the left temporal cortex, the insula, precuneus, superior frontal gyrus, lentiform nucleus and thalamus. Results are made available in two different data formats to be used as spatial hypotheses in future studies, particularly for diagnostic classification by MVPA.

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.

Linear mixed-effects modeling approach to FMRI group analysis

PubMed Central

Chen, Gang; Saad, Ziad S.; Britton, Jennifer C.; Pine, Daniel S.; Cox, Robert W.

2013-01-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance–covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance–covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. PMID:23376789

Linear mixed-effects modeling approach to FMRI group analysis.

PubMed

Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W

2013-06-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. Published by Elsevier Inc.

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.

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 © 2012 Elsevier Inc. All rights reserved.

Self-regulation of brain activity in patients with postherpetic neuralgia: a double-blind randomized study using real-time FMRI neurofeedback.

PubMed

Guan, Min; Ma, Lijia; Li, Li; Yan, Bin; Zhao, Lu; Tong, Li; Dou, Shewei; Xia, Linjie; Wang, Meiyun; Shi, Dapeng

2015-01-01

A pilot study has shown that real-time fMRI (rtfMRI) neurofeedback could be an alternative approach for chronic pain treatment. Considering the relative small sample of patients recruited and not strictly controlled condition, it is desirable to perform a replication as well as a double-blinded randomized study with a different control condition in chronic pain patients. Here we conducted a rtfMRI neurofeedback study in a subgroup of pain patients - patients with postherpetic neuralgia (PHN) and used a different sham neurofeedback control. We explored the feasibility of self-regulation of the rostral anterior cingulate cortex (rACC) activation in patients with PHN through rtfMRI neurofeedback and regulation of pain perception. Sixteen patients (46-71 years) with PHN were randomly allocated to a experimental group (n = 8) or a control group (n = 8). 2 patients in the control group were excluded for large head motion. The experimental group was given true feedback information from their rACC whereas the control group was given sham feedback information from their posterior cingulate cortex (PCC). All subjects were instructed to perform an imagery task to increase and decrease activation within the target region using rtfMRI neurofeedback. Online analysis showed 6/8 patients in the experimental group were able to increase and decrease the blood oxygen level dependent (BOLD) fMRI signal magnitude during intermittent feedback training. However, this modulation effect was not observed in the control group. Offline analysis showed that the percentage of BOLD signal change of the target region between the last and first training in the experimental group was significantly different from the control group's and was also significantly different than 0. The changes of pain perception reflected by numerical rating scale (NRS) in the experimental group were significantly different from the control group. However, there existed no significant correlations between BOLD signal change and NRS change. Patients with PHN could learn to voluntarily control over activation in rACC through rtfMRI neurofeedback and alter their pain perception level. The present study may provide new evidence that rtfMRI neurofeedback training may be a supplemental approach for chronic clinical pain management.

The ICCAM platform study: An experimental medicine platform for evaluating new drugs for relapse prevention in addiction. Part B: fMRI description

PubMed Central

McGonigle, John; Murphy, Anna; Paterson, Louise M; Reed, Laurence J; Nestor, Liam; Nash, Jonathan; Elliott, Rebecca; Ersche, Karen D; Flechais, Remy SA; Newbould, Rexford; Orban, Csaba; Smith, Dana G; Taylor, Eleanor M; Waldman, Adam D; Robbins, Trevor W; Deakin, JF William; Nutt, David J; Lingford-Hughes, Anne R; Suckling, John

2016-01-01

Objectives: We aimed to set up a robust multi-centre clinical fMRI and neuropsychological platform to investigate the neuropharmacology of brain processes relevant to addiction – reward, impulsivity and emotional reactivity. Here we provide an overview of the fMRI battery, carried out across three centres, characterizing neuronal response to the tasks, along with exploring inter-centre differences in healthy participants. Experimental design: Three fMRI tasks were used: monetary incentive delay to probe reward sensitivity, go/no-go to probe impulsivity and an evocative images task to probe emotional reactivity. A coordinate-based activation likelihood estimation (ALE) meta-analysis was carried out for the reward and impulsivity tasks to help establish region of interest (ROI) placement. A group of healthy participants was recruited from across three centres (total n=43) to investigate inter-centre differences. Principle observations: The pattern of response observed for each of the three tasks was consistent with previous studies using similar paradigms. At the whole brain level, significant differences were not observed between centres for any task. Conclusions: In developing this platform we successfully integrated neuroimaging data from three centres, adapted validated tasks and applied whole brain and ROI approaches to explore and demonstrate their consistency across centres. PMID:27703042

The ICCAM platform study: An experimental medicine platform for evaluating new drugs for relapse prevention in addiction. Part B: fMRI description.

PubMed

McGonigle, John; Murphy, Anna; Paterson, Louise M; Reed, Laurence J; Nestor, Liam; Nash, Jonathan; Elliott, Rebecca; Ersche, Karen D; Flechais, Remy Sa; Newbould, Rexford; Orban, Csaba; Smith, Dana G; Taylor, Eleanor M; Waldman, Adam D; Robbins, Trevor W; Deakin, Jf William; Nutt, David J; Lingford-Hughes, Anne R; Suckling, John

2017-01-01

We aimed to set up a robust multi-centre clinical fMRI and neuropsychological platform to investigate the neuropharmacology of brain processes relevant to addiction - reward, impulsivity and emotional reactivity. Here we provide an overview of the fMRI battery, carried out across three centres, characterizing neuronal response to the tasks, along with exploring inter-centre differences in healthy participants. Three fMRI tasks were used: monetary incentive delay to probe reward sensitivity, go/no-go to probe impulsivity and an evocative images task to probe emotional reactivity. A coordinate-based activation likelihood estimation (ALE) meta-analysis was carried out for the reward and impulsivity tasks to help establish region of interest (ROI) placement. A group of healthy participants was recruited from across three centres (total n=43) to investigate inter-centre differences. Principle observations: The pattern of response observed for each of the three tasks was consistent with previous studies using similar paradigms. At the whole brain level, significant differences were not observed between centres for any task. In developing this platform we successfully integrated neuroimaging data from three centres, adapted validated tasks and applied whole brain and ROI approaches to explore and demonstrate their consistency across centres.

Disrupted Prefrontal Activity during Emotion Processing in Complicated Grief: an fMRI Investigation

PubMed Central

Arizmendi, Brian; Kaszniak, Alfred W.; O’Connor, Mary-Frances

2015-01-01

Complicated Grief, marked by a persistent and intrusive grief lasting beyond the expected period of adaptation, is associated with a relative inability to disengage from idiographic loss-relevant stimuli (O’Connor & Arizmendi, 2014). In other populations, functional magnetic resonance imaging (fMRI) studies investigating the neural networks associated with this bias consistently implicate the anterior cingulate cortex (ACC) during emotion regulation. In the present study, twenty-eight older adults were categorized into three groups based on grief severity: Complicated Grief (n=8), Non-Complicated Grief (n=9), and Nonbereaved, married controls (n=11). Using a block design, all participants completed 8 blocks (20 stimuli per block) of the ecStroop task during fMRI data acquisition. Differences in neural activity during grief-related (as opposed to neutral) stimuli across groups were examined. Those with Complicated Grief showed an absence of increased rostral ACC (rACC) and fronto-cortical recruitment relative to Nonbereaved controls. Activity in the orbitofrontal cortex (x=6, y=54, z=−10) was significantly elevated in the Non-Complicated Grief group when compared to Nonbereaved controls. Post hoc analysis evidenced activity in the dorsal ACC in the Complicated Grief and Nonbereaved groups late in the task. These findings, supported by behavioral data, suggest a relative inability to recruit the regions necessary for successful completion of this emotional task in those with Complicated Grief. This deficit was not observed in recruitment of the orbitofrontal cortex and the rACC during processing of idiographic semantic stimuli in Non-Complicated Grief. PMID:26434802

Disrupted prefrontal activity during emotion processing in complicated grief: An fMRI investigation.

PubMed

Arizmendi, Brian; Kaszniak, Alfred W; O'Connor, Mary-Frances

2016-01-01

Complicated Grief, marked by a persistent and intrusive grief lasting beyond the expected period of adaptation, is associated with a relative inability to disengage from idiographic loss-relevant stimuli (O'Connor and Arizmendi, 2014). In other populations, functional magnetic resonance imaging (fMRI) studies investigating the neural networks associated with this bias consistently implicate the anterior cingulate cortex (ACC) during emotion regulation. In the present study, twenty-eight older adults were categorized into three groups based on grief severity: Complicated Grief (n=8), Non-Complicated Grief (n=9), and Nonbereaved, married controls (n=11). Using a block design, all participants completed 8 blocks (20 stimuli per block) of the ecStroop task during fMRI data acquisition. Differences in neural activity during grief-related (as opposed to neutral) stimuli across groups were examined. Those with Complicated Grief showed an absence of increased rostral ACC (rACC) and fronto-cortical recruitment relative to Nonbereaved controls. Activity in the orbitofrontal cortex (x=6, y=54, z=-10) was significantly elevated in the Non-Complicated Grief group when compared to Nonbereaved controls. Post hoc analysis evidenced activity in the dorsal ACC in the Complicated Grief and Nonbereaved groups late in the task. These findings, supported by behavioral data, suggest a relative inability to recruit the regions necessary for successful completion of this emotional task in those with Complicated Grief. This deficit was not observed in recruitment of the orbitofrontal cortex and the rACC during processing of idiographic semantic stimuli in Non-Complicated Grief. Copyright © 2015 Elsevier Inc. All rights reserved.

Assessment of brain activity during voluntary anal sphincter contraction: Comparative study in women with and without fecal incontinence.

PubMed

Parés, D; Martínez-Vilalta, M; Ortiz, H; Soriano-Mas, C; Maestre-Gonzalez, Y; Pujol, J; Grande, L

2018-04-14

Voluntary anal sphincter function is driven by an extended network of brain structures, most of which are still unknown. Disturbances in this function may cause fecal incontinence. The aim of this study was to characterize the cerebral areas involved in voluntary contraction of the anorectal sphincter in healthy women and in a group of patients with fecal incontinence by using a standardized functional magnetic resonance imaging (fMRI) protocol. This comparative study included 12 healthy women (mean age 53.17 ± 4.93 years) and 12 women with fecal incontinence (56.25 ± 6.94 years). An MRI-compatible anal manometer was used to register voluntary external anal sphincter contraction. During brain fMRI imaging, participants were cued to perform 10-s series of self-paced anal sphincter contractions at an approximate rate of 1 Hz. Brain structures linked to anal sphincter contractions were mapped and the findings were compared between the 2 study groups. There were no differences in the evoked brain activity between the 2 groups. In healthy women, group fMRI analysis revealed significant activations in medial primary motor cortices, supplementary motor area, bilateral putamen, and cerebellum, as well as in the supramarginal gyrus and visual areas. In patients with fecal incontinence, the activation pattern involved similar regions without significant differences with healthy women. This brain fMRI-anorectal protocol was able to map the brain regions linked to voluntary anal sphincter function in healthy and women with fecal incontinence. © 2018 John Wiley & Sons Ltd.

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.

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.

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

PubMed

Alahmadi, Hanin H; Shen, Yuan; Fouad, Shereen; Luft, Caroline Di B; Bentham, Peter; Kourtzi, Zoe; Tino, Peter

2016-01-01

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.

Unsupervised spatiotemporal analysis of fMRI data using graph-based visualizations of self-organizing maps.

PubMed

Katwal, Santosh B; Gore, John C; Marois, Rene; Rogers, Baxter P

2013-09-01

We present novel graph-based visualizations of self-organizing maps for unsupervised functional magnetic resonance imaging (fMRI) analysis. A self-organizing map is an artificial neural network model that transforms high-dimensional data into a low-dimensional (often a 2-D) map using unsupervised learning. However, a postprocessing scheme is necessary to correctly interpret similarity between neighboring node prototypes (feature vectors) on the output map and delineate clusters and features of interest in the data. In this paper, we used graph-based visualizations to capture fMRI data features based upon 1) the distribution of data across the receptive fields of the prototypes (density-based connectivity); and 2) temporal similarities (correlations) between the prototypes (correlation-based connectivity). We applied this approach to identify task-related brain areas in an fMRI reaction time experiment involving a visuo-manual response task, and we correlated the time-to-peak of the fMRI responses in these areas with reaction time. Visualization of self-organizing maps outperformed independent component analysis and voxelwise univariate linear regression analysis in identifying and classifying relevant brain regions. We conclude that the graph-based visualizations of self-organizing maps help in advanced visualization of cluster boundaries in fMRI data enabling the separation of regions with small differences in the timings of their brain responses.

Comparison of spatial working memory in children with prenatal alcohol exposure and those diagnosed with ADHD; A functional magnetic resonance imaging study

PubMed Central

2012-01-01

Background Alcohol related neurodevelopmental disorder (ARND) falls under the umbrella of fetal alcohol spectrum disorder (FASD), but individuals do not demonstrate the facial characteristics associated with fetal alcohol syndrome (FAS), making diagnosis difficult. While attentional problems in ARND are similar to those found in attention-deficit/hyperactivity disorder (ADHD), the underlying impairment in attention pathways may be different. Methods Functional magnetic resonance imaging (fMRI) of a working memory (1-back) task of 63 children, 10 to 14 years old, diagnosed with ARND and ADHD, as well as typically developing (TD) controls, was conducted at 3 T. Diffusion tensor imaging (DTI) data were also acquired. Results Activations were observed in posterior parietal and occipital regions in the TD group and in dorsolateral prefrontal and posterior parietal regions in the ARND group, whereas the ADHD group activated only dorsolateral prefrontal regions, during the working memory component of the task (1-back minus 0-back contrast). The increases in frontal and parietal activity were significantly greater in the ARND group compared to the other groups. This increased activity was associated with reduced accuracy and increased response time variability, suggesting that ARND subjects exert greater effort to manage short-term memory load. Significantly greater intra-subject variability, demonstrated by fMRI region-of-interest analysis, in the ADHD and ARND groups compared to the TD group suggests that moment-to-moment lapses in attention contributed to their poorer task performance. Differences in functional activity in ARND subjects with and without a diagnosis of ADHD resulted primarily from reduced activation by the ARND/ADHD + group during the 0-back task. In contrast, children with ADHD alone clearly showed reduced activations during the 1-back task. DTI analysis revealed that the TD group had significantly higher total tract volume and number of fibers than the ARND group. These measures were negatively correlated with errors on the 1-back task, suggesting a link between white matter integrity and task performance. Conclusions fMRI activations suggest that the similar behavior of children with ARND and ADHD on a spatial working memory task is the result of different cognitive events. The nature of ADHD in children with ARND appears to differ from that of children with ADHD alone. PMID:22958510

Self-Regulation of Brain Activity in Patients with Postherpetic Neuralgia: A Double-Blind Randomized Study Using Real-Time fMRI Neurofeedback

PubMed Central

Li, Li; Yan, Bin; Zhao, Lu; Tong, Li; Dou, Shewei; Xia, Linjie; Wang, Meiyun; Shi, Dapeng

2015-01-01

Background A pilot study has shown that real-time fMRI (rtfMRI) neurofeedback could be an alternative approach for chronic pain treatment. Considering the relative small sample of patients recruited and not strictly controlled condition, it is desirable to perform a replication as well as a double-blinded randomized study with a different control condition in chronic pain patients. Here we conducted a rtfMRI neurofeedback study in a subgroup of pain patients – patients with postherpetic neuralgia (PHN) and used a different sham neurofeedback control. We explored the feasibility of self-regulation of the rostral anterior cingulate cortex (rACC) activation in patients with PHN through rtfMRI neurofeedback and regulation of pain perception. Methods Sixteen patients (46–71 years) with PHN were randomly allocated to a experimental group (n = 8) or a control group (n = 8). 2 patients in the control group were excluded for large head motion. The experimental group was given true feedback information from their rACC whereas the control group was given sham feedback information from their posterior cingulate cortex (PCC). All subjects were instructed to perform an imagery task to increase and decrease activation within the target region using rtfMRI neurofeedback. Results Online analysis showed 6/8 patients in the experimental group were able to increase and decrease the blood oxygen level dependent (BOLD) fMRI signal magnitude during intermittent feedback training. However, this modulation effect was not observed in the control group. Offline analysis showed that the percentage of BOLD signal change of the target region between the last and first training in the experimental group was significantly different from the control group’s and was also significantly different than 0. The changes of pain perception reflected by numerical rating scale (NRS) in the experimental group were significantly different from the control group. However, there existed no significant correlations between BOLD signal change and NRS change. Conclusion Patients with PHN could learn to voluntarily control over activation in rACC through rtfMRI neurofeedback and alter their pain perception level. The present study may provide new evidence that rtfMRI neurofeedback training may be a supplemental approach for chronic clinical pain management. PMID:25848773

Regional brain activity during early visual perception in unaffected siblings of schizophrenia patients.

PubMed

Lee, Junghee; Cohen, Mark S; Engel, Stephen A; Glahn, David; Nuechterlein, Keith H; Wynn, Jonathan K; Green, Michael F

2010-07-01

Visual masking paradigms assess the early part of visual information processing, which may reflect vulnerability measures for schizophrenia. We examined the neural substrates of visual backward performance in unaffected sibling of schizophrenia patients using functional magnetic resonance imaging (fMRI). Twenty-one unaffected siblings of schizophrenia patients and 19 healthy controls performed a backward masking task and three functional localizer tasks to identify three visual processing regions of interest (ROI): lateral occipital complex (LO), the motion-sensitive area, and retinotopic areas. In the masking task, we systematically manipulated stimulus onset asynchronies (SOAs). We analyzed fMRI data in two complementary ways: 1) an ROI approach for three visual areas, and 2) a whole-brain analysis. The groups did not differ in behavioral performance. For ROI analysis, both groups increased activation as SOAs increased in LO. Groups did not differ in activation levels of the three ROIs. For whole-brain analysis, controls increased activation as a function of SOAs, compared with siblings in several regions (i.e., anterior cingulate cortex, posterior cingulate cortex, inferior prefrontal cortex, inferior parietal lobule). The study found: 1) area LO showed sensitivity to the masking effect in both groups; 2) siblings did not differ from controls in activation of LO; and 3) groups differed significantly in several brain regions outside visual processing areas that have been related to attentional or re-entrant processes. These findings suggest that LO dysfunction may be a disease indicator rather than a risk indicator for schizophrenia. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Multivariate pattern analysis of fMRI: the early beginnings.

PubMed

Haxby, James V

2012-08-15

In 2001, we published a paper on the representation of faces and objects in ventral temporal cortex that introduced a new method for fMRI analysis, which subsequently came to be called multivariate pattern analysis (MVPA). MVPA now refers to a diverse set of methods that analyze neural responses as patterns of activity that reflect the varying brain states that a cortical field or system can produce. This paper recounts the circumstances and events that led to the original study and later developments and innovations that have greatly expanded this approach to fMRI data analysis, leading to its widespread application. Copyright © 2012 Elsevier Inc. All rights reserved.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919

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

Fluorine-19 magnetic resonance imaging probe for the detection of tau pathology in female rTg4510 mice.

PubMed

Yanagisawa, Daijiro; Ibrahim, Nor Faeizah; Taguchi, Hiroyasu; Morikawa, Shigehiro; Kato, Tomoko; Hirao, Koichi; Shirai, Nobuaki; Sogabe, Takayuki; Tooyama, Ikuo

2018-05-01

Aggregation of tau into neurofibrillary tangles (NFTs) is characteristic of tauopathies, including Alzheimer's disease. Recent advances in tau imaging have attracted much attention because of its potential contributions to early diagnosis and monitoring of disease progress. Fluorine-19 magnetic resonance imaging ( 19 F-MRI) may be extremely useful for tau imaging once a high-quality probe has been formulated. In this investigation, a novel fluorine-19-labeling compound has been developed as a probe for tau imaging using 19 F-MRI. This compound is a buta-1,3-diene derivative with a polyethylene glycol side chain bearing a CF 3 group and is known as Shiga-X35. Female rTg4510 mice (a mouse model of tauopathy) and wild-type mice were intravenously injected with Shiga-X35, and magnetic resonance imaging of each mouse's head was conducted in a 7.0-T horizontal-bore magnetic resonance scanner. The 19 F-MRI in rTg4510 mice showed an intense signal in the forebrain region. Analysis of the signal intensity in the forebrain region revealed a significant accumulation of fluorine-19 magnetic resonance signal in the rTg4510 mice compared with the wild-type mice. Histological analysis showed fluorescent signals of Shiga-X35 binding to the NFTs in the brain sections of rTg4510 mice. Data collected as part of this investigation indicate that 19 F-MRI using Shiga-X35 could be a promising tool to evaluate tau pathology in the brain. © 2017 Wiley Periodicals, Inc.

Resting-state fMRI data reflects default network activity rather than null data: A defense of commonly employed methods to correct for multiple comparisons.

PubMed

Slotnick, Scott D

2017-07-01

Analysis of functional magnetic resonance imaging (fMRI) data typically involves over one hundred thousand independent statistical tests; therefore, it is necessary to correct for multiple comparisons to control familywise error. In a recent paper, Eklund, Nichols, and Knutsson used resting-state fMRI data to evaluate commonly employed methods to correct for multiple comparisons and reported unacceptable rates of familywise error. Eklund et al.'s analysis was based on the assumption that resting-state fMRI data reflect null data; however, their 'null data' actually reflected default network activity that inflated familywise error. As such, Eklund et al.'s results provide no basis to question the validity of the thousands of published fMRI studies that have corrected for multiple comparisons or the commonly employed methods to correct for multiple comparisons.

EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.

PubMed

Venkataraman, Archana; Van Dijk, Koene R A; Buckner, Randy L; Golland, Polina

2009-04-01

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering of the entire brain volume, we use the Nyström Method to approximate the necessary spectral decompositions. We apply K-Means, Spectral Clustering and Seed-Based Analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via Seed-Based Analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.

Test-Retest Reliability of Brain Activation Using the Face-Name Paired-Associates fMRI Task in Patients with Schizophrenia and Healthy Controls

NASA Astrophysics Data System (ADS)

Louis, Chelsey N.

Schizophrenia is a neurological disorder associated with cognitive impairments, and clinical symptoms of hallucinations and delusions. Recent imaging and behavioral studies have repeatedly shown aberrant brain activity in the hippocampal regions in relation to episodic memory impairments associated with schizophrenia. These findings have warranted further research to elucidate the neural processes associated with episodic memory. Therefore, the current study examined activity in a priori brain regions associated with episodic memory using the face-name paired-associates fMRI task to determine whether there was reliable activation patterns observed in healthy subjects and patients with self-reported schizophrenia. This was evaluated by using ROI analysis and whole brain analysis to examine activity between subjects during a session, and by using Pearson's R correlation coefficients to examine test-retest reliability over time. 30 schizophrenic (SZ) patients and 31 healthy control (HC) volunteers underwent a series of assessments including the fMRI behavioral task, face-name paired-associates task. The tests were conducted twice with a 14-day interval for the subjects. The results indicated no reliable brain activation in the hippocampus between scanning sessions for either the SZ or HC groups. However, distinct activation patterns were observed within sessions for both groups. These patterns were observed in the hippocampus, and regions of the frontal lobe and occipital lobe. Future studies should further explore these brain activity patterns across sessions in SZ patients compared to HC subjects to determine whether these patterns are due to pathological mechanisms associated with schizophrenia.

Functional MRI in Medulloblastoma Survivors Supports Prophylactic Reading Intervention during Tumor Treatment

PubMed Central

Zou, Ping; Conklin, Heather M.; Scoggins, Matthew A.; Li, Yimei; Li, Xingyu; Jones, Melissa M.; Palmer, Shawna L.; Gajjar, Amar; Ogg, Robert J.

2015-01-01

Background Development of reading skills is vulnerable to disruption in children treated for brain tumors. Interventions, remedial and prophylactic, are needed to mitigate reading and other learning difficulties faced by survivors. A functional magnetic resonance imaging (fMRI) study was conducted to investigate long-term effects of a prophylactic reading intervention administered during radiation therapy in children treated for medulloblastoma. Methods The fMRI study included 19 reading-intervention (age 11.7±0.6 years) and 21 standard-of-care (age 12.1±0.6 years) medulloblastoma survivors, and 21 typically developing children (age 12.3±0.6 years). The survivors were 2.5 [1.2, 5.4] years after completion of tumor therapies and reading-intervention survivors were 2.9 [1.6, 5.9] years after intervention. Five fMRI tasks (Rapid Automatized Naming, Continuous Performance Test using faces and letters, orthographic and phonological processing of letter pairs, implicit word reading, and story reading) were used to probe reading-related neural activation. Woodcock-Johnson Reading Fluency, Word Attack, and Sound Awareness subtests were used to evaluate reading abilities. Results At the time of fMRI, Sound Awareness scores were significantly higher in the reading-intervention group than in the standard-of-care group (p = 0.046). Brain activation during the fMRI tasks was detected in left inferior frontal, temporal, ventral occipitotemporal, and subcortical regions, and differed among the groups (p<0.05, FWE). The pattern of group activation differences, across brain areas and tasks, was a normative trend in the reading-intervention group. Conclusions Standardized reading scores and patterns of brain activation provide evidence of long-term effects of prophylactic reading intervention in children treated for medulloblastoma. PMID:25967954

[Fractional amplitude of low-frequency fluctuations in childhood and adolescence-onset schizophrenia: a resting state fMRI study].

PubMed

Lü, D; Shao, R R; Liang, Y H; Xia, Y H; Guo, S Q

2016-11-22

Objective: To explore the whole brain activity features of childhood and adolescence-onset schizophrenia using resting state fMRI. Methods: A total of 63 childhood and adolescence-onset schizophrenia patients (patients group), admitted to the second affiliated hospital of Xinxiang Medical University from October 2013 to October 2015 and fulfilled our inclusion criteria, and 39 healthy controls with age, sex and education matched (control group) were enrolled, then a resting-state fMRI scan was conducted for each participant. Fractional amplitude of low-frequency fluctuations (fALFF) approach was used to explore the differences of resting-state brain function between patients and controls. Results: Compared with the healthy control group, patients group showed significantly decreased fALFF in left superior temporal gyrus and parietal lobe (MNI coordinate: x =-42, -57; y =-3, -21; z =-12, 9; voxels: 22, 32; t =-4.792 3, -5.269 7; Alphasim corrected, corrected P <0.05); patients group showed significantly increased fALFF in left frontal lobe and medial frontal gyrus, right superior frontal gyrus, Postcentral Gyrus, caudate, (MNI coordinate: x =-42, -21, 12, 27, 15; y=54, 39, 48, -18, 15; z =0, 21, 33, 30, 9; voxels: 12, 21, 17, 28, 18; t =4.784 8, 4.90 7, 4.861 5, 5.444 1, 4.270 4; Alphasim corrected, corrected P <0.05). When included age as a covariant, the analysis found that the brain region with significant fALFF change was the left thalamus with decreased fALFF (MNI coordinate: x =-6, y =-12, z=24; voxels: 9; t =-4.268 4; Alphasim corrected, corrected P <0.05) in patients group, while for other brain regions, there was no obvious change in the fALFF, compared with healthy group. Conclusion: Compared with control group, the results indicate that there are intrinsic brain activity abnormalities of some brain regions in childhood and adolescence-onset schizophrenia.

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.

Replicability of time-varying connectivity patterns in large resting state fMRI samples.

PubMed

Abrol, Anees; Damaraju, Eswar; Miller, Robyn L; Stephen, Julia M; Claus, Eric D; Mayer, Andrew R; Calhoun, Vince D

2017-12-01

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Replicability of time-varying connectivity patterns in large resting state fMRI samples

PubMed Central

Abrol, Anees; Damaraju, Eswar; Miller, Robyn L.; Stephen, Julia M.; Claus, Eric D.; Mayer, Andrew R.; Calhoun, Vince D.

2018-01-01

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain’s inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. PMID:28916181

Disrupted Cerebro-cerebellar Intrinsic Functional Connectivity in Young Adults with High-functioning Autism Spectrum Disorder: A Data-driven, Whole-brain, High Temporal Resolution fMRI Study.

PubMed

Arnold Anteraper, Sheeba; Guell, Xavier; D'Mello, Anila; Joshi, Neha; Whitfield-Gabrieli, Susan; Joshi, Gagan

2018-06-13

To examine the resting-state functional-connectivity (RsFc) in young adults with high-functioning autism spectrum disorder (HF-ASD) using state-of-the-art fMRI data acquisition and analysis techniques. Simultaneous multi-slice, high temporal resolution fMRI acquisition; unbiased whole-brain connectome-wide multivariate pattern analysis (MVPA) techniques for assessing RsFc; and post-hoc whole-brain seed-to-voxel analyses using MVPA results as seeds. MVPA revealed two clusters of abnormal connectivity in the cerebellum. Whole-brain seed-based functional connectivity analyses informed by MVPA-derived clusters showed significant under connectivity between the cerebellum and social, emotional, and language brain regions in the HF-ASD group compared to healthy controls. The results we report are coherent with existing structural, functional, and RsFc literature in autism, extend previous literature reporting cerebellar abnormalities in the neuropathology of autism, and highlight the cerebellum as a potential target for therapeutic, diagnostic, predictive, and prognostic developments in ASD. The description of functional connectivity abnormalities using whole-brain, data-driven analyses as reported in the present study may crucially advance the development of ASD biomarkers, targets for therapeutic interventions, and neural predictors for measuring treatment response.

Value of Frequency Domain Resting-State Functional Magnetic Resonance Imaging Metrics Amplitude of Low-Frequency Fluctuation and Fractional Amplitude of Low-Frequency Fluctuation in the Assessment of Brain Tumor-Induced Neurovascular Uncoupling.

PubMed

Agarwal, Shruti; Lu, Hanzhang; Pillai, Jay J

2017-08-01

The aim of this study was to explore whether the phenomenon of brain tumor-related neurovascular uncoupling (NVU) in resting-state blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) (rsfMRI) may also affect the resting-state fMRI (rsfMRI) frequency domain metrics the amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). Twelve de novo brain tumor patients, who underwent clinical fMRI examinations, including task-based fMRI (tbfMRI) and rsfMRI, were included in this Institutional Review Board-approved study. Each patient displayed decreased/absent tbfMRI activation in the primary ipsilesional (IL) sensorimotor cortex in the absence of a corresponding motor deficit or suboptimal task performance, consistent with NVU. Z-score maps for the motor tasks were obtained from general linear model analysis (reflecting motor activation vs. rest). Seed-based correlation analysis (SCA) maps of sensorimotor network, ALFF, and fALFF were calculated from rsfMRI data. Precentral and postcentral gyri in contralesional (CL) and IL hemispheres were parcellated using an automated anatomical labeling template for each patient. Region of interest (ROI) analysis was performed on four maps: tbfMRI, SCA, ALFF, and fALFF. Voxel values in the CL and IL ROIs of each map were divided by the corresponding global mean of ALFF and fALFF in the cortical brain tissue. Group analysis revealed significantly decreased IL ALFF (p = 0.02) and fALFF (p = 0.03) metrics compared with CL ROIs, consistent with similar findings of significantly decreased IL BOLD signal for tbfMRI (p = 0.0005) and SCA maps (p = 0.0004). The frequency domain metrics ALFF and fALFF may be markers of lesion-induced NVU in rsfMRI similar to previously reported alterations in tbfMRI activation and SCA-derived resting-state functional connectivity maps.

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.

Dynamic timecourse of typical childhood absence seizures: EEG, behavior and fMRI

PubMed Central

Bai, X; Vestal, M; Berman, R; Negishi, M; Spann, M; Vega, C; Desalvo, M; Novotny, EJ; Constable, RT; Blumenfeld, H

2010-01-01

Absence seizures are 5–10 second episodes of impaired consciousness accompanied by 3–4Hz generalized spike-and-wave discharge on electroencephalography (EEG). The timecourse of functional magnetic resonance imaging (fMRI) changes in absence seizures in relation to EEG and behavior is not known. We acquired simultaneous EEG-fMRI in 88 typical childhood absence seizures from 9 pediatric patients. We investigated behavior concurrently using a continuous performance task (CPT) or simpler repetitive tapping task (RTT). EEG time-frequency analysis revealed abrupt onset and end of 3–4 Hz spike-wave discharges with a mean duration of 6.6 s. Behavioral analysis also showed rapid onset and end of deficits associated with electrographic seizure start and end. In contrast, we observed small early fMRI increases in the orbital/medial frontal and medial/lateral parietal cortex >5s before seizure onset, followed by profound fMRI decreases continuing >20s after seizure end. This timecourse differed markedly from the hemodynamic response function (HRF) model used in conventional fMRI analysis, consisting of large increases beginning after electrical event onset, followed by small fMRI decreases. Other regions, such as the lateral frontal cortex, showed more balanced fMRI increases followed by approximately equal decreases. The thalamus showed delayed increases after seizure onset followed by small decreases, most closely resembling the HRF model. These findings reveal a complex and long lasting sequence of fMRI changes in absence seizures, which are not detectible by conventional HRF modeling in many regions. These results may be important mechanistically for seizure initiation and termination and may also contribute to changes in EEG and behavior. PMID:20427649

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.

Intersession reliability of fMRI activation for heat pain and motor tasks

PubMed Central

Quiton, Raimi L.; Keaser, Michael L.; Zhuo, Jiachen; Gullapalli, Rao P.; Greenspan, Joel D.

2014-01-01

As the practice of conducting longitudinal fMRI studies to assess mechanisms of pain-reducing interventions becomes more common, there is a great need to assess the test–retest reliability of the pain-related BOLD fMRI signal across repeated sessions. This study quantitatively evaluated the reliability of heat pain-related BOLD fMRI brain responses in healthy volunteers across 3 sessions conducted on separate days using two measures: (1) intraclass correlation coefficients (ICC) calculated based on signal amplitude and (2) spatial overlap. The ICC analysis of pain-related BOLD fMRI responses showed fair-to-moderate intersession reliability in brain areas regarded as part of the cortical pain network. Areas with the highest intersession reliability based on the ICC analysis included the anterior midcingulate cortex, anterior insula, and second somatosensory cortex. Areas with the lowest intersession reliability based on the ICC analysis also showed low spatial reliability; these regions included pregenual anterior cingulate cortex, primary somatosensory cortex, and posterior insula. Thus, this study found regional differences in pain-related BOLD fMRI response reliability, which may provide useful information to guide longitudinal pain studies. A simple motor task (finger-thumb opposition) was performed by the same subjects in the same sessions as the painful heat stimuli were delivered. Intersession reliability of fMRI activation in cortical motor areas was comparable to previously published findings for both spatial overlap and ICC measures, providing support for the validity of the analytical approach used to assess intersession reliability of pain-related fMRI activation. A secondary finding of this study is that the use of standard ICC alone as a measure of reliability may not be sufficient, as the underlying variance structure of an fMRI dataset can result in inappropriately high ICC values; a method to eliminate these false positive results was used in this study and is recommended for future studies of test–retest reliability. PMID:25161897

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.

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

Different cerebral connectivity of obese and lean children studied with fMRI

NASA Astrophysics Data System (ADS)

Anaya Moreno, Maryan A.; Hernández López, Javier M.; Hidalgo Tobón, Silvia; Dies Suarez, Pilar; Barragán Pérez, Eduardo; De Celis Alonso, Benito

2014-11-01

In this work we studied the different fMRI brain activations and connections between normal weighted (NW) and obese (OB) infants for different types of food odours. A total of 30 right handed volunteers (infants 8.4±2 years) of both sexes were studied. Infants were divided in two group, one with BMI between 19 and 24 kg/m2 and the other with BMI over 30 kg/m2. The first part of this project consisted of a study in which fMRI BOLD activations to pleasant, neutral and healthy food was performed on both groups. Cerebellum regions were found to be more active in the NW group over the OB when presented with odour cues. OB volunteers in contrast showed larger activations in cingulate cortex structures than their NW counterparts when presented with food odours. The second part of this study performed connectivity studies (ROI to ROI) comparing both groups for each smell. The NW group presented for the onion smell a strong reward anticipation connection between the gustatory cortex and the cingulate cortex which the OB group did not have. In contrast the OB group presented strong orbitofrontal connections (decision making) with gustatory and somatosensory cortex when stimulated with the chocolate odour which the NW did not present. We can conclude that clear differences in fMRI BOLD activation as well as connectivity between the OB and NW groups were found. This points at a very different processing mechanisms of odour cues in infants. To our knowledge this study has never been performed before on infants.

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

Decrease in fMRI brain activation during working memory performed after sleeping under 10 lux light.

PubMed

Kang, Seung-Gul; Yoon, Ho-Kyoung; Cho, Chul-Hyun; Kwon, Soonwook; Kang, June; Park, Young-Min; Lee, Eunil; Kim, Leen; Lee, Heon-Jeong

2016-11-09

The aim of this study was to investigate the effect of exposure to dim light at night (dLAN) when sleeping on functional brain activation during a working-memory tasks. We conducted the brain functional magnetic resonance imaging (fMRI) analysis on 20 healthy male subjects. All participants slept in a polysomnography laboratory without light exposure on the first and second nights and under a dim-light condition of either 5 or 10 lux on the third night. The fMRI scanning was conducted during n-back tasks after second and third nights. Statistical parametric maps revealed less activation in the right inferior frontal gyrus (IFG) after exposure to 10-lux light. The brain activity in the right and left IFG areas decreased more during the 2-back task than during the 1- or 0-back task in the 10-lux group. The exposure to 5-lux light had no significant effect on brain activities. The exposure to dLAN might influence the brain function which is related to the cognition.

Audio-visual speech perception in adult readers with dyslexia: an fMRI study.

PubMed

Rüsseler, Jascha; Ye, Zheng; Gerth, Ivonne; Szycik, Gregor R; Münte, Thomas F

2018-04-01

Developmental dyslexia is a specific deficit in reading and spelling that often persists into adulthood. In the present study, we used slow event-related fMRI and independent component analysis to identify brain networks involved in perception of audio-visual speech in a group of adult readers with dyslexia (RD) and a group of fluent readers (FR). Participants saw a video of a female speaker saying a disyllabic word. In the congruent condition, audio and video input were identical whereas in the incongruent condition, the two inputs differed. Participants had to respond to occasionally occurring animal names. The independent components analysis (ICA) identified several components that were differently modulated in FR and RD. Two of these components including fusiform gyrus and occipital gyrus showed less activation in RD compared to FR possibly indicating a deficit to extract face information that is needed to integrate auditory and visual information in natural speech perception. A further component centered on the superior temporal sulcus (STS) also exhibited less activation in RD compared to FR. This finding is corroborated in the univariate analysis that shows less activation in STS for RD compared to FR. These findings suggest a general impairment in recruitment of audiovisual processing areas in dyslexia during the perception of natural speech.

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.

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.

Differential amygdala response during facial recognition in patients with schizophrenia: an fMRI study.

PubMed

Kosaka, H; Omori, M; Murata, T; Iidaka, T; Yamada, H; Okada, T; Takahashi, T; Sadato, N; Itoh, H; Yonekura, Y; Wada, Y

2002-09-01

Human lesion or neuroimaging studies suggest that amygdala is involved in facial emotion recognition. Although impairments in recognition of facial and/or emotional expression have been reported in schizophrenia, there are few neuroimaging studies that have examined differential brain activation during facial recognition between patients with schizophrenia and normal controls. To investigate amygdala responses during facial recognition in schizophrenia, we conducted a functional magnetic resonance imaging (fMRI) study with 12 right-handed medicated patients with schizophrenia and 12 age- and sex-matched healthy controls. The experiment task was a type of emotional intensity judgment task. During the task period, subjects were asked to view happy (or angry/disgusting/sad) and neutral faces simultaneously presented every 3 s and to judge which face was more emotional (positive or negative face discrimination). Imaging data were investigated in voxel-by-voxel basis for single-group analysis and for between-group analysis according to the random effect model using Statistical Parametric Mapping (SPM). No significant difference in task accuracy was found between the schizophrenic and control groups. Positive face discrimination activated the bilateral amygdalae of both controls and schizophrenics, with more prominent activation of the right amygdala shown in the schizophrenic group. Negative face discrimination activated the bilateral amygdalae in the schizophrenic group whereas the right amygdala alone in the control group, although no significant group difference was found. Exaggerated amygdala activation during emotional intensity judgment found in the schizophrenic patients may reflect impaired gating of sensory input containing emotion. Copyright 2002 Elsevier Science B.V.

Analysis of electrical and magnetic bio-signals associated with motor performance and fatigue

NASA Astrophysics Data System (ADS)

Yao, Bing

This dissertation reports findings centered principally on comprehensive research related to human bio-signals (EEG, MEG, EMG and fMRI) acquired during repetitive maximal voluntary contractions (MVC) that induced severe fatigue. Fatigue is a common experience that reduces productivity and quality of life and increases chances of injury. Although abundant information has been gained in the last several decades regarding muscular and spinal-level mechanisms of muscle fatigue, very little is known about how cortical centers control and respond to fatigue. The main purpose of this study was to examine the fatigue effects on the central nervous system by analyzing the bio-signals collected in the designed experiments. Healthy human subjects were asked to perform a series of repetitive handgrip MVCs with their dominant hand until exhaustion. Handgrip forces, electrical activity (EMG) from primary and non-primary muscles, and EEG, MEG, or fMRI signals from different locations of the brain were recorded simultaneously. The time series data were segmented into several physiologically meaningful epochs (time phases), from rest to preparation to movement execution/sustaining. A series of studies, including motor-related cortical potential (MRCP) analysis, power spectrum analysis, time-frequency (spectrogram) analysis of EEG, EEG source localization and nonlinear analysis (fractal dimension and largest Lyapunov exponent), and fMRI analysis, was applied to the data. We hypothesized that the fatigue effects would act differently on brain signals of different phases. The MRCP results showed that the negative potential (NP) related to motor task preparation only had minimal changes with fatigue. The power of all EEG frequencies did not alter significantly during the preparation phase but decreased significantly during the sustained phase of the contraction. The fractal dimension and the largest Lyapunov exponent decreased significantly during the sustained phase as fatigue progressed. On the other hand, the fMRI results only exhibited insignificant fatigue-related reductions of brain activation volume and no significant change of dipole strength derived from multi-channel EEG data. These results have been interpreted by a hypothetical neurophysiological model, in which two groups of cortical neurons (phasic and tonic) are preferentially activated in each physiological phase of the voluntary motor action.

[Application of spatial working memory task fMRI in evaluation of primary insomnia patient's cognitive dysfunction].

PubMed

Dou, Shewei; Wang, Enfeng; Zhang, Hongju; Tong, Li; Zhang, Xiaoqi; Shi, Dapeng; Cheng, Jingliang; Li, Yongli

2015-06-02

To explore abnormal brain activation of spatial working memory in primary insomnia and its potential neuromechanism. we recruited 30 cases primary insomnia (PI) patients and 30 cases age, gender matched healthy control (HC) subjects from July 2013 to December 2013, the diagnosis of primary insomnia matched the diagnosis criterion of DSM-IV and Classification and diagnostic criteria of mental disorders in China third edition (CCMD-3). All the subjects attended the tests of PSQI, HAMA, HAMD and index of spatial working memory. And then, we collected the data of routine MRI and spatial working memory task fMRI on 3.0 T MRI scanner. After that, we used SPM8 and REST1.8 to analyze the fMRI data, compared difference of PSQI, HAMA, HAMD, index of spatial working memory and brain activation of spatial working memory between PI group and HC group. There were significant difference between PI group and HC group in PSQI, HAMA, HAMD and index of spatial working memory (P < 0.05). In the spatial working memory related activate brain region, compared with HC group, left temporal lobe, occipital lobe and right frontal lobe activation increased and bilateral parahippocampalis, temporal cortex, frontal cortex and superior parietal lobule activation reduced in PI group. Spatial working memory task fMRI revealed the pathological mechanisms of cognitive dysfunction of clinical spatial working memory and emotional disorder in primary insomnia patients.

Visual analysis of variance: a tool for quantitative assessment of fMRI data processing and analysis.

PubMed

McNamee, R L; Eddy, W F

2001-12-01

Analysis of variance (ANOVA) is widely used for the study of experimental data. Here, the reach of this tool is extended to cover the preprocessing of functional magnetic resonance imaging (fMRI) data. This technique, termed visual ANOVA (VANOVA), provides both numerical and pictorial information to aid the user in understanding the effects of various parts of the data analysis. Unlike a formal ANOVA, this method does not depend on the mathematics of orthogonal projections or strictly additive decompositions. An illustrative example is presented and the application of the method to a large number of fMRI experiments is discussed. Copyright 2001 Wiley-Liss, Inc.

Testosterone Administration Related Differences in Brain Activation during the Ultimatum Game

PubMed Central

Kopsida, Eleni; Berrebi, Jonathan; Petrovic, Predrag; Ingvar, Martin

2016-01-01

A plethora of studies on the Ultimatum Game have shown that responders forfeit the rule of profit maximization and punish unfair proposers, by rejecting their offers. This behavior has been linked to increased amygdala, insula, and dorsolateral prefrontal cortex activation. Studies have suggested a potential role of testosterone in the Ultimatum Game albeit with inconsistent findings. In the present study, we sought to further investigate the role of amygdala and testosterone in the Ultimatum Game, by conducting a double-blinded, single-administration study. Sixty milligram of Tostrex was administered to male and female healthy volunteers, 3 h prior to undergoing an fMRI session, during which they played a standard version of the Ultimatum Game. The behavioral analysis revealed a statistical trend, as participants in the testosterone group tended to accept a greater number of unfair offers than participants in the placebo group, irrespectively of gender. In terms of fMRI results, for the main contrast unfair>fair offers, the testosterone group displayed a greater activation in the right dlPFC compared to the placebo group. Increased testosterone levels were related to greater caudate activity. Our findings suggest a complex role of testosterone in social behavior and decision-making. PMID:26973448

An fMRI study of neural pathways following acupuncture in mild cognitive impairment patients

NASA Astrophysics Data System (ADS)

Feng, Yuanyuan; Bai, Lijun; Wang, Hu; Zhong, Chongguang; You, Youbo; Zhang, Wensheng; Tian, Jie

2012-03-01

While the use of acupuncture as a complementary therapeutic method for treating MCI is popular in certain parts of the world, the underlying mechanism is still elusive. In the current study, we adopted multivariate Granger causality analysis (mGCA) to explore the causal interactions of brain networks involving acupuncture in mild cognitive impairment (MCI) patients compared to healthy controls (HC). The fMRI experiment was performed with two different paradigms: namely, deep acupuncture (DA) and superficial acupuncture (SA) at acupoint KI3. Results demonstrated that deep acupuncture could modulate the abnormal regions in MCI group. These regions are implicated in memory encoding and retrieving. This may relate to the purported therapeutically beneficial effects of acupuncture for the treatment of MCI. However, the most significant causal interactions were found in the sensorimotor regions in HC group. This may because acupuncture has a greater modulatory effect on patients with a pathological imbalance. This paper provides the preliminary neurophysiological evidence for the potential efficacy effect of acupuncture on MCI.

Can fMRI safely replace the Wada test for preoperative assessment of language lateralisation? A meta-analysis and systematic review.

PubMed

Bauer, Prisca R; Reitsma, Johannes B; Houweling, Bernard M; Ferrier, Cyrille H; Ramsey, Nick F

2014-05-01

Recent studies have shown that fMRI (functional magnetic resonance imaging) may be of value for pre-surgical assessment of language lateralisation. The aim of this study was to systematically review and analyse the available literature. A systematic electronic search for studies comparing fMRI with Wada testing was conducted in the PubMed database between March 2009 and November 2011. Studies involving unilateral Wada testing, study population consisting exclusively of children younger than 12 years of age or involving five patients or fewer were excluded. 22 studies (504 patients) were included. A random effects meta-analysis was conducted to obtain pooled estimates of the positive and negative predictive values of the fMRI using the Wada test as the reference standard. The impact of several study features on the performance of fMRI was assessed. The results showed that 81% of patients were correctly classified as having left or right language dominance or mixed language representation. Techniques were discordant in 19% of patients. fMRI and Wada test agreed in 94% for typical language lateralisation and in 51% for atypical language lateralisation. Language production or language comprehension tasks and different regions of interest did not yield statistically significant different results. It can be concluded that fMRI is reliable when there is strong left-lateralised language. The Wada test is warranted when fMRI fails to show clear left-lateralisation.

Gender differences in the cognitive control of emotion: An fMRI study.

PubMed

Koch, Kathrin; Pauly, Katharina; Kellermann, Thilo; Seiferth, Nina Y; Reske, Martina; Backes, Volker; Stöcker, Tony; Shah, N Jon; Amunts, Katrin; Kircher, Tilo; Schneider, Frank; Habel, Ute

2007-09-20

The interaction of emotion and cognition has become a topic of major interest. However, the influence of gender on the interplay between the two processes, along with its neural correlates have not been fully analysed so far. In this functional magnetic resonance imaging (fMRI) study we induced negative emotion using negative olfactory stimulation while male (n=21) and female (n=19) participants performed an n-back verbal working memory task. Based on findings indicating increased emotional reactivity in women, we expected the female participants to exhibit stronger activation in characteristically emotion-associated areas during the interaction of emotional and cognitive processing in comparison to the male participants. Both groups were found to be significantly impaired in their working memory performance by negative emotion induction. However, fMRI analysis revealed distinct differences in neuronal activation between groups. In men, cognitive performance under negative emotion induction was associated with extended activation patterns in mainly prefrontal and superior parietal regions. In women, the interaction between emotion and working memory yielded a significantly stronger response in the amygdala and the orbitofrontal cortex (OFC) compared to their male counterparts. Our data suggest that in women the interaction of verbal working memory and negative emotion is associated with relative hyperactivation in more emotion-associated areas whereas in men regions commonly regarded as important for cognition and cognitive control are activated. These results provide new insights in gender-specific cerebral mechanisms.

Increased brain connectivity and activation after cognitive rehabilitation in Parkinson's disease: a randomized controlled trial.

PubMed

Díez-Cirarda, María; Ojeda, Natalia; Peña, Javier; Cabrera-Zubizarreta, Alberto; Lucas-Jiménez, Olaia; Gómez-Esteban, Juan Carlos; Gómez-Beldarrain, Maria Ángeles; Ibarretxe-Bilbao, Naroa

2017-12-01

Cognitive rehabilitation programs have demonstrated efficacy in improving cognitive functions in Parkinson's disease (PD), but little is known about cerebral changes associated with an integrative cognitive rehabilitation in PD. To assess structural and functional cerebral changes in PD patients, after attending a three-month integrative cognitive rehabilitation program (REHACOP). Forty-four PD patients were randomly divided into REHACOP group (cognitive rehabilitation) and a control group (occupational therapy). T1-weighted, diffusion weighted and functional magnetic resonance images (fMRI) during resting-state and during a memory paradigm (with learning and recognition tasks) were acquired at pre-treatment and post-treatment. Cerebral changes were assessed with repeated measures ANOVA 2 × 2 for group x time interaction. During resting-state fMRI, the REHACOP group showed significantly increased brain connectivity between the left inferior temporal lobe and the bilateral dorsolateral prefrontal cortex compared to the control group. Moreover, during the recognition fMRI task, the REHACOP group showed significantly increased brain activation in the left middle temporal area compared to the control group. During the learning fMRI task, the REHACOP group showed increased brain activation in the left inferior frontal lobe at post-treatment compared to pre-treatment. No significant structural changes were found between pre- and post-treatment. Finally, the REHACOP group showed significant and positive correlations between the brain connectivity and activation and the cognitive performance at post-treatment. This randomized controlled trial suggests that an integrative cognitive rehabilitation program can produce significant functional cerebral changes in PD patients and adds evidence to the efficacy of cognitive rehabilitation programs in the therapeutic approach for PD.

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.

Frontoparietal network involved in successful retrieval from episodic memory. Spatial and temporal analyses using fMRI and ERP.

PubMed

Iidaka, Tetsuya; Matsumoto, Atsushi; Nogawa, Junpei; Yamamoto, Yukiko; Sadato, Norihiro

2006-09-01

The neural basis for successful recognition of previously studied items, referred to as "retrieval success," has been investigated using either neuroimaging or brain potentials; however, few studies have used both modalities. Our study combined event-related functional magnetic resonance imaging (fMRI) and event-related potential (ERP) in separate groups of subjects. The neural responses were measured while the subjects performed an old/new recognition task with pictures that had been previously studied in either a deep- or shallow-encoding condition. The fMRI experiment showed that among the frontoparietal regions involved in retrieval success, the inferior frontal gyrus and intraparietal sulcus were crucial to conscious recollection because the activity of these regions was influenced by the depth of memory at encoding. The activity of the right parietal region in response to a repeated item was modulated by the repetition lag, indicating that this area would be critical to familiarity-based judgment. The results of structural equation modeling revealed that the functional connectivity among the regions in the left hemisphere was more significant than that in the right hemisphere. The results of the ERP experiment and independent component analysis paralleled those of the fMRI experiment and demonstrated that the repeated item produced an earlier peak than the hit item by approximately 50 ms.

The BOLD signal and neurovascular coupling in autism.

PubMed

Reynell, Clare; Harris, Julia J

2013-10-01

BOLD (blood oxygen level dependent) fMRI (functional magnetic resonance imaging) is commonly used to study differences in neuronal activity between human populations. As the BOLD response is an indirect measure of neuronal activity, meaningful interpretation of differences in BOLD responses between groups relies upon a stable relationship existing between neuronal activity and the BOLD response across these groups. However, this relationship can be altered by changes in neurovascular coupling or energy consumption, which would lead to problems in identifying differences in neuronal activity. In this review, we focus on fMRI studies of people with autism, and comparisons that are made of their BOLD responses with those of control groups. We examine neurophysiological differences in autism that may alter neurovascular coupling or energy use, discuss recent studies that have used fMRI to identify differences between participants with autism and control participants, and explore experimental approaches that could help attribute between-group differences in BOLD signals to either neuronal or neurovascular factors. Copyright © 2013 Elsevier Ltd. All rights reserved.

The BOLD signal and neurovascular coupling in autism

PubMed Central

Reynell, Clare; Harris, Julia J.

2013-01-01

BOLD (blood oxygen level dependent) fMRI (functional magnetic resonance imaging) is commonly used to study differences in neuronal activity between human populations. As the BOLD response is an indirect measure of neuronal activity, meaningful interpretation of differences in BOLD responses between groups relies upon a stable relationship existing between neuronal activity and the BOLD response across these groups. However, this relationship can be altered by changes in neurovascular coupling or energy consumption, which would lead to problems in identifying differences in neuronal activity. In this review, we focus on fMRI studies of people with autism, and comparisons that are made of their BOLD responses with those of control groups. We examine neurophysiological differences in autism that may alter neurovascular coupling or energy use, discuss recent studies that have used fMRI to identify differences between participants with autism and control participants, and explore experimental approaches that could help attribute between-group differences in BOLD signals to either neuronal or neurovascular factors. PMID:23917518

New Insights into Signed Path Coefficient Granger Causality Analysis.

PubMed

Zhang, Jian; Li, Chong; Jiang, Tianzi

2016-01-01

Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation.

Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques.

PubMed

Long, Zhiying; Chen, Kewei; Wu, Xia; Reiman, Eric; Peng, Danling; Yao, Li

2009-02-01

Spatial Independent component analysis (sICA) has been widely used to analyze functional magnetic resonance imaging (fMRI) data. The well accepted implicit assumption is the spatially statistical independency of intrinsic sources identified by sICA, making the sICA applications difficult for data in which there exist interdependent sources and confounding factors. This interdependency can arise, for instance, from fMRI studies investigating two tasks in a single session. In this study, we introduced a linear projection approach and considered its utilization as a tool to separate task-related components from two-task fMRI data. The robustness and feasibility of the method are substantiated through simulation on computer data and fMRI real rest data. Both simulated and real two-task fMRI experiments demonstrated that sICA in combination with the projection method succeeded in separating spatially dependent components and had better detection power than pure model-based method when estimating activation induced by each task as well as both tasks.

Diagnostic Classification of Schizophrenia Patients on the Basis of Regional Reward-Related fMRI Signal Patterns

PubMed Central

Koch, Stefan P.; Hägele, Claudia; Haynes, John-Dylan; Heinz, Andreas; Schlagenhauf, Florian; Sterzer, Philipp

2015-01-01

Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way. PMID:25799236

INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.

PubMed

Shi, Ran; Guo, Ying

2016-12-01

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis

NASA Astrophysics Data System (ADS)

Kim, Dong-Youl; Lee, Jong-Hwan

2014-05-01

A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation- level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers (n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas, respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.

Functional neuroimaging for addiction medicine: From mechanisms to practical considerations.

PubMed

Ekhtiari, Hamed; Faghiri, Ashkan; Oghabian, Mohammad-Ali; Paulus, Martin P

2016-01-01

During last 20 years, neuroimaging with functional magnetic resonance imaging (fMRI) in people with drug addictions has introduced a wide range of quantitative biomarkers from brain's regional or network level activities during different cognitive functions. These quantitative biomarkers could be potentially used for assessment, planning, prediction, and monitoring for "addiction medicine" during screening, acute intoxication, admission to a program, completion of an acute program, admission to a long-term program, and postgraduation follow-up. In this chapter, we have briefly reviewed main neurocognitive targets for fMRI studies associated with addictive behaviors, main study types using fMRI among drug dependents, and potential applications for fMRI in addiction medicine. Main challenges and limitations for extending fMRI studies and evidences aiming at clinical applications in addiction medicine are also discussed. There is still a significant gap between available evidences from group-based fMRI studies and personalized decisions during daily practices in addiction medicine. It will be important to fill this gap with large-scale clinical trials and longitudinal studies using fMRI measures with a well-defined strategic plan for the future. © 2016 Elsevier B.V. All rights reserved.

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

PubMed Central

Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D.

2011-01-01

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA. PMID:21723948

A Set of Functional Brain Networks for the Comprehensive Evaluation of Human Characteristics.

PubMed

Sung, Yul-Wan; Kawachi, Yousuke; Choi, Uk-Su; Kang, Daehun; Abe, Chihiro; Otomo, Yuki; Ogawa, Seiji

2018-01-01

Many human characteristics must be evaluated to comprehensively understand an individual, and measurements of the corresponding cognition/behavior are required. Brain imaging by functional MRI (fMRI) has been widely used to examine brain function related to human cognition/behavior. However, few aspects of cognition/behavior of individuals or experimental groups can be examined through task-based fMRI. Recently, resting state fMRI (rs-fMRI) signals have been shown to represent functional infrastructure in the brain that is highly involved in processing information related to cognition/behavior. Using rs-fMRI may allow diverse information about the brain through a single MRI scan to be obtained, as rs-fMRI does not require stimulus tasks. In this study, we attempted to identify a set of functional networks representing cognition/behavior that are related to a wide variety of human characteristics and to evaluate these characteristics using rs-fMRI data. If possible, these findings would support the potential of rs-fMRI to provide diverse information about the brain. We used resting-state fMRI and a set of 130 psychometric parameters that cover most human characteristics, including those related to intelligence and emotional quotients and social ability/skill. We identified 163 brain regions by VBM analysis using regression analysis with 130 psychometric parameters. Next, using a 163 × 163 correlation matrix, we identified functional networks related to 111 of the 130 psychometric parameters. Finally, we made an 8-class support vector machine classifiers corresponding to these 111 functional networks. Our results demonstrate that rs-fMRI signals contain intrinsic information about brain function related to cognition/behaviors and that this set of 111 networks/classifiers can be used to comprehensively evaluate human characteristics.

A Stimulus-Locked Vector Autoregressive Model for Slow Event-Related fMRI Designs

PubMed Central

Siegle, Greg

2009-01-01

Summary Neuroscientists have become increasingly interested in exploring dynamic relationships among brain regions. Such a relationship, when directed from one region toward another, is denoted by “effective connectivity.” An fMRI experimental paradigm which is well-suited for examination of effective connectivity is the slow event-related design. This design presents stimuli at sufficient temporal spacing for determining within-trial trajectories of BOLD activation, allowing for the analysis of stimulus-locked temporal covariation of brain responses in multiple regions. This may be especially important for emotional stimuli processing, which can evolve over the course of several seconds, if not longer. However, while several methods have been devised for determining fMRI effective connectivity, few are adapted to event-related designs, which include non-stationary BOLD responses and multiple levels of nesting. We propose a model tailored for exploring effective connectivity of multiple brain regions in event-related fMRI designs - a semi-parametric adaptation of vector autoregressive (VAR) models, termed “stimulus-locked VAR” (SloVAR). Connectivity coefficients vary as a function of time relative to stimulus onset, are regularized via basis expansions, and vary randomly across subjects. SloVAR obtains flexible, data-driven estimates of effective connectivity and hence is useful for building connectivity models when prior information on dynamic regional relationships is sparse. Indices derived from the coefficient estimates can also be used to relate effective connectivity estimates to behavioral or clinical measures. We demonstrate the SloVAR model on a sample of clinically depressed and normal controls, showing that early but not late cortico-amygdala connectivity appears crucial to emotional control and early but not late cortico-cortico connectivity predicts depression severity in the depressed group, relationships that would have been missed in a more traditional VAR analysis. PMID:19236927

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.

Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

PubMed

Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

2017-07-01

Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

PubMed Central

Eklund, Anders; Dufort, Paul; Villani, Mattias; LaConte, Stephen

2014-01-01

Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/). PMID:24672471

Prolonged hemodynamic response during incidental facial emotion processing in inter-episode bipolar I disorder.

PubMed

Rosenfeld, Ethan S; Pearlson, Godfrey D; Sweeney, John A; Tamminga, Carol A; Keshavan, Matcheri S; Nonterah, Camilla; Stevens, Michael C

2014-03-01

This fMRI study examined whether hemodynamic responses to affectively-salient stimuli were abnormally prolonged in remitted bipolar disorder, possibly representing a novel illness biomarker. A group of 18 DSM-IV bipolar I-diagnosed adults in remission and a demographically-matched control group performed an event-related fMRI gender-discrimination task in which face stimuli had task-irrelevant neutral, happy or angry expressions designed to elicit incidental emotional processing. Participants' brain activation was modeled using a "fully informed" SPM5 basis set. Mixed-model ANOVA tested for diagnostic group differences in BOLD response amplitude and shape within brain regions-of-interest selected from ALE meta-analysis of previous comparable fMRI studies. Bipolar-diagnosed patients had a generally longer duration and/or later-peaking hemodynamic response in amygdala and numerous prefrontal cortex brain regions. Data are consistent with existing models of bipolar limbic hyperactivity, but the prolonged frontolimbic response more precisely details abnormalities recognized in previous studies. Prolonged hemodynamic responses were unrelated to stimulus type, task performance, or degree of residual mood symptoms, suggesting an important novel trait vulnerability brain dysfunction in bipolar disorder. Bipolar patients also failed to engage pregenual cingulate and left orbitofrontal cortex-regions important to models of automatic emotion regulation-while engaging a delayed dorsolateral prefrontal cortex response not seen in controls. These results raise questions about whether there are meaningful relationships between bipolar dysfunction of specific ventromedial prefrontal cortex regions believed to automatically regulate emotional reactions and the prolonged responses in more lateral aspects of prefrontal cortex.

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

PubMed

Ramasubbu, Rajamannar; Brown, Matthew R G; Cortese, Filmeno; Gaxiola, Ismael; Goodyear, Bradley; Greenshaw, Andrew J; Dursun, Serdar M; Greiner, Russell

2016-01-01

Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers. Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14-19), severe depression (HRSD 20-23), and very severe depression (HRSD ≥ 24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls. The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups. Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

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.

Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks

PubMed Central

Mantini, D.; Marzetti, L.; Corbetta, M.; Romani, G.L.; Del Gratta, C.

2017-01-01

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes. PMID:20052528

An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data.

PubMed

Della-Maggiore, Valeria; Chau, Wilkin; Peres-Neto, Pedro R; McIntosh, Anthony R

2002-09-01

We present the results from two sets of Monte Carlo simulations aimed at evaluating the robustness of some preprocessing parameters of SPM99 for the analysis of functional magnetic resonance imaging (fMRI). Statistical robustness was estimated by implementing parametric and nonparametric simulation approaches based on the images obtained from an event-related fMRI experiment. Simulated datasets were tested for combinations of the following parameters: basis function, global scaling, low-pass filter, high-pass filter and autoregressive modeling of serial autocorrelation. Based on single-subject SPM analysis, we derived the following conclusions that may serve as a guide for initial analysis of fMRI data using SPM99: (1) The canonical hemodynamic response function is a more reliable basis function to model the fMRI time series than HRF with time derivative. (2) Global scaling should be avoided since it may significantly decrease the power depending on the experimental design. (3) The use of a high-pass filter may be beneficial for event-related designs with fixed interstimulus intervals. (4) When dealing with fMRI time series with short interstimulus intervals (<8 s), the use of first-order autoregressive model is recommended over a low-pass filter (HRF) because it reduces the risk of inferential bias while providing a relatively good power. For datasets with interstimulus intervals longer than 8 seconds, temporal smoothing is not recommended since it decreases power. While the generalizability of our results may be limited, the methods we employed can be easily implemented by other scientists to determine the best parameter combination to analyze their data.

Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

PubMed Central

Mandelkow, Hendrik; de Zwart, Jacco A.; Duyn, Jeff H.

2016-01-01

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation. PMID:27065832

Spectral Dynamics of Resting State fMRI Within the Ventral Tegmental Area and Dorsal Raphe Nuclei in Medication-Free Major Depressive Disorder in Young Adults.

PubMed

Wohlschläger, Afra; Karne, Harish; Jordan, Denis; Lowe, Mark J; Jones, Stephen E; Anand, Amit

2018-01-01

Background: Dorsal raphe nucleus (DRN) and ventral tegmental area (VTA) are major brainstem monamine nuclei consisting of serotonin and dopamine neurons respectively. Animal studies show that firing patterns in both nuclei are altered when animals exhibit depression like behaviors. Functional MRI studies in humans have shown reduced VTA activation and DRN connectivity in depression. This study for the first time aims at investigating the functional integrity of local neuronal firing concurrently in both the VTA and DRN in vivo in humans using spectral analysis of resting state low frequency fluctuation fMRI. Method: A total of 97 medication-free subjects-67 medication-free young patients (ages 18-30) with major depressive disorder and 30 closely matched healthy controls were included in the study to detect aberrant dynamics in DRN and VTA. For the investigation of altered localized dynamics we conducted power spectral analysis and above this spectral cross correlation between the two groups. Complementary to this, spectral dependence of permutation entropy, an information theoretical measure, was compared between groups. Results: Patients displayed significant spectral slowing in VTA vs. controls ( p = 0.035, corrected). In DRN, spectral slowing was less pronounced, but the amount of slowing significantly correlated with 17-item Hamilton Depression Rating scores of depression severity ( p = 0.038). Signal complexity as assessed via permutation entropy showed spectral alterations inline with the results on spectral slowing. Conclusion: Our results indicate that altered functional dynamics of VTA and DRN in depression can be detected from regional fMRI signal. On this basis, impact of antidepressant treatment and treatment response can be assessed using these markers in future studies.

Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping

PubMed Central

Robinson, Jennifer; Calhoun, Vince

2018-01-01

Purpose To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. Methods A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Results Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. Conclusions The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization. PMID:29351339

Pomegranate Juice Augments Memory and fMRI Activity in Middle-Aged and Older Adults with Mild Memory Complaints

PubMed Central

Bookheimer, Susan Y.; Renner, Brian A.; Ekstrom, Arne; Henning, Susanne M.; Brown, Jesse A.; Jones, Mike; Moody, Teena; Small, Gary W.

2013-01-01

Despite increasing emphasis on the potential of dietary antioxidants in preventing memory loss and on diet as a precursor of neurological health, rigorous studies investigating the cognitive effects of foods and their components are rare. Recent animal studies have reported memory and other cognitive benefits of polyphenols, found abundantly in pomegranate juice. We performed a preliminary, placebo-controlled randomized trial of pomegranate juice in older subjects with age-associated memory complaints using memory testing and functional brain activation (fMRI) as outcome measures. Thirty-two subjects (28 completers) were randomly assigned to drink 8 ounces of either pomegranate juice or a flavor-matched placebo drink for 4 weeks. Subjects received memory testing, fMRI scans during cognitive tasks, and blood draws for peripheral biomarkers before and after the intervention. Investigators and subjects were all blind to group membership. After 4 weeks, only the pomegranate group showed a significant improvement in the Buschke selective reminding test of verbal memory and a significant increase in plasma trolox-equivalent antioxidant capacity (TEAC) and urolithin A-glucuronide. Furthermore, compared to the placebo group, the pomegranate group had increased fMRI activity during verbal and visual memory tasks. While preliminary, these results suggest a role for pomegranate juice in augmenting memory function through task-related increases in functional brain activity. PMID:23970941

Model-free fMRI group analysis using FENICA.

PubMed

Schöpf, V; Windischberger, C; Robinson, S; Kasess, C H; Fischmeister, F PhS; Lanzenberger, R; Albrecht, J; Kleemann, A M; Kopietz, R; Wiesmann, M; Moser, E

2011-03-01

Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. This approach, which we have called Fully Exploratory Network Independent Component Analysis (FENICA), identifies group activation in two stages. ICA is performed on the single-subject level, then consistent components are identified via spatial correlation. Group activation maps are generated in a second-level GLM analysis. FENICA is applied to data from three studies employing a wide range of stimulus and presentation designs. These are an event-related motor task, a block-design cognition task and an event-related chemosensory experiment. In all cases, the group maps identified by FENICA as being the most consistent over subjects correspond to task activation. There is good agreement between FENICA results and regions identified in prior GLM-based studies. In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects. Copyright © 2010 Elsevier Inc. All rights reserved.

The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery.

PubMed

Calhoun, Vince D; Miller, Robyn; Pearlson, Godfrey; Adalı, Tulay

2014-10-22

Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance imaging (fMRI) beyond simple scan-length averages and into approaches that capture time-varying properties of connectivity. In this Perspective we use the term "chronnectome" to describe metrics that allow a dynamic view of coupling. In the chronnectome, coupling refers to possibly time-varying levels of correlated or mutually informed activity between brain regions whose spatial properties may also be temporally evolving. We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis. We also discuss the potential these approaches offer to improve characterization and understanding of brain function. There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great promise for the study of both the healthy and the diseased brain.

Flexible Adaptive Paradigms for fMRI Using a Novel Software Package ‘Brain Analysis in Real-Time’ (BART)

PubMed Central

Hellrung, Lydia; Hollmann, Maurice; Zscheyge, Oliver; Schlumm, Torsten; Kalberlah, Christian; Roggenhofer, Elisabeth; Okon-Singer, Hadas; Villringer, Arno; Horstmann, Annette

2015-01-01

In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject’s compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject’s gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment’s runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI. PMID:25837719

Continuous vs. intermittent neurofeedback to regulate auditory cortex activity of tinnitus patients using real-time fMRI - A pilot study.

PubMed

Emmert, Kirsten; Kopel, Rotem; Koush, Yury; Maire, Raphael; Senn, Pascal; Van De Ville, Dimitri; Haller, Sven

2017-01-01

The emerging technique of real-time fMRI neurofeedback trains individuals to regulate their own brain activity via feedback from an fMRI measure of neural activity. Optimum feedback presentation has yet to be determined, particularly when working with clinical populations. To this end, we compared continuous against intermittent feedback in subjects with tinnitus. Fourteen participants with tinnitus completed the whole experiment consisting of nine runs (3 runs × 3 days). Prior to the neurofeedback, the target region was localized within the auditory cortex using auditory stimulation (1 kHz tone pulsating at 6 Hz) in an ON-OFF block design. During neurofeedback runs, participants received either continuous (n = 7, age 46.84 ± 12.01, Tinnitus Functional Index (TFI) 49.43 ± 15.70) or intermittent feedback (only after the regulation block) (n = 7, age 47.42 ± 12.39, TFI 49.82 ± 20.28). Participants were asked to decrease auditory cortex activity that was presented to them by a moving bar. In the first and the last session, participants also underwent arterial spin labeling (ASL) and resting-state fMRI imaging. We assessed tinnitus severity using the TFI questionnaire before all sessions, directly after all sessions and six weeks after all sessions. We then compared neuroimaging results from neurofeedback using a general linear model (GLM) and region-of-interest analysis as well as behavior measures employing a repeated-measures ANOVA. In addition, we looked at the seed-based connectivity of the auditory cortex using resting-state data and the cerebral blood flow using ASL data. GLM group analysis revealed that a considerable part of the target region within the auditory cortex was significantly deactivated during neurofeedback. When comparing continuous and intermittent feedback groups, the continuous group showed a stronger deactivation of parts of the target region, specifically the secondary auditory cortex. This result was confirmed in the region-of-interest analysis that showed a significant down-regulation effect for the continuous but not the intermittent group. Additionally, continuous feedback led to a slightly stronger effect over time while intermittent feedback showed best results in the first session. Behaviorally, there was no significant effect on the total TFI score, though on a descriptive level TFI scores tended to decrease after all sessions and in the six weeks follow up in the continuous group. Seed-based connectivity with a fixed-effects analysis revealed that functional connectivity increased over sessions in the posterior cingulate cortex, premotor area and part of the insula when looking at all patients while cerebral blood flow did not change significantly over time. Overall, these results show that continuous feedback is suitable for long-term neurofeedback experiments while intermittent feedback presentation promises good results for single session experiments when using the auditory cortex as a target region. In particular, the down-regulation effect is more pronounced in the secondary auditory cortex, which might be more susceptible to voluntary modulation in comparison to a primary sensory region.

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.

Menopause-related brain activation patterns during visual sexual arousal in menopausal women: An fMRI pilot study using time-course analysis.

PubMed

Kim, Gwang-Won; Jeong, Gwang-Woo

2017-02-20

The aging process and menopausal transition are important factors in sexual dysfunction of menopausal women. No neuroimaging study has assessed the age- and menopause-related changes on brain activation areas associated with sexual arousal in menopausal women. The purpose of this study was to evaluate the time course of regional brain activity associated with sexual arousal evoked by visual stimulation in premenopausal and menopausal women, and further to assess the effect of menopause on the brain areas associated with sexual arousal in menopausal women using functional magnetic resonance imaging (fMRI). Thirty volunteers consisting of 15 premenopausal and 15 menopausal women underwent the fMRI. For the activation condition, volunteers viewed sexually arousing visual stimulation. The brain areas with significantly higher activation in premenopausal women compared with menopausal women included the thalamus, amygdala, and anterior cingulate cortex (ACC) using analysis of covariance adjusting for age (p<0.005). Blood-oxygen-level-dependent signal changes in the amygdala while viewing erotic video were positively correlated with estrogen levels in the two groups. Our findings suggest that reduced brain activity of the thalamus, amygdala, and ACC in menopausal women may be associated with menopause-related decrease in sexual arousal. These findings might help elucidate the neural mechanisms associated with sexual dysfunction in menopausal women. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources.

PubMed

Ge, Ruiyang; Wang, Yubao; Zhang, Jipeng; Yao, Li; Zhang, Hang; Long, Zhiying

2016-04-01

As a blind source separation technique, independent component analysis (ICA) has many applications in functional magnetic resonance imaging (fMRI). Although either temporal or spatial prior information has been introduced into the constrained ICA and semi-blind ICA methods to improve the performance of ICA in fMRI data analysis, certain types of additional prior information, such as the sparsity, has seldom been added to the ICA algorithms as constraints. In this study, we proposed a SparseFastICA method by adding the source sparsity as a constraint to the FastICA algorithm to improve the performance of the widely used FastICA. The source sparsity is estimated through a smoothed ℓ0 norm method. We performed experimental tests on both simulated data and real fMRI data to investigate the feasibility and robustness of SparseFastICA and made a performance comparison between SparseFastICA, FastICA and Infomax ICA. Results of the simulated and real fMRI data demonstrated the feasibility and robustness of SparseFastICA for the source separation in fMRI data. Both the simulated and real fMRI experimental results showed that SparseFastICA has better robustness to noise and better spatial detection power than FastICA. Although the spatial detection power of SparseFastICA and Infomax did not show significant difference, SparseFastICA had faster computation speed than Infomax. SparseFastICA was comparable to the Infomax algorithm with a faster computation speed. More importantly, SparseFastICA outperformed FastICA in robustness and spatial detection power and can be used to identify more accurate brain networks than FastICA algorithm. Copyright © 2016 Elsevier B.V. All rights reserved.

High-Speed Real-Time Resting-State fMRI Using Multi-Slab Echo-Volumar Imaging

PubMed Central

Posse, Stefan; Ackley, Elena; Mutihac, Radu; Zhang, Tongsheng; Hummatov, Ruslan; Akhtari, Massoud; Chohan, Muhammad; Fisch, Bruce; Yonas, Howard

2013-01-01

We recently demonstrated that ultra-high-speed real-time fMRI using multi-slab echo-volumar imaging (MEVI) significantly increases sensitivity for mapping task-related activation and resting-state networks (RSNs) compared to echo-planar imaging (Posse et al., 2012). In the present study we characterize the sensitivity of MEVI for mapping RSN connectivity dynamics, comparing independent component analysis (ICA) and a novel seed-based connectivity analysis (SBCA) that combines sliding-window correlation analysis with meta-statistics. This SBCA approach is shown to minimize the effects of confounds, such as movement, and CSF and white matter signal changes, and enables real-time monitoring of RSN dynamics at time scales of tens of seconds. We demonstrate highly sensitive mapping of eloquent cortex in the vicinity of brain tumors and arterio-venous malformations, and detection of abnormal resting-state connectivity in epilepsy. In patients with motor impairment, resting-state fMRI provided focal localization of sensorimotor cortex compared with more diffuse activation in task-based fMRI. The fast acquisition speed of MEVI enabled segregation of cardiac-related signal pulsation using ICA, which revealed distinct regional differences in pulsation amplitude and waveform, elevated signal pulsation in patients with arterio-venous malformations and a trend toward reduced pulsatility in gray matter of patients compared with healthy controls. Mapping cardiac pulsation in cortical gray matter may carry important functional information that distinguishes healthy from diseased tissue vasculature. This novel fMRI methodology is particularly promising for mapping eloquent cortex in patients with neurological disease, having variable degree of cooperation in task-based fMRI. In conclusion, ultra-high-real-time speed fMRI enhances the sensitivity of mapping the dynamics of resting-state connectivity and cerebro-vascular pulsatility for clinical and neuroscience research applications. PMID:23986677

Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness.

PubMed

Soddu, Andrea; Gómez, Francisco; Heine, Lizette; Di Perri, Carol; Bahri, Mohamed Ali; Voss, Henning U; Bruno, Marie-Aurélie; Vanhaudenhuyse, Audrey; Phillips, Christophe; Demertzi, Athena; Chatelle, Camille; Schrouff, Jessica; Thibaut, Aurore; Charland-Verville, Vanessa; Noirhomme, Quentin; Salmon, Eric; Tshibanda, Jean-Flory Luaba; Schiff, Nicholas D; Laureys, Steven

2016-01-01

The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness. We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis. We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients. The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls. The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

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

The Global Signal in fMRI: Nuisance or Information?

PubMed Central

Nalci, Alican; Falahpour, Maryam

2017-01-01

The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches. PMID:28213118

Daily iTBS worsens hand motor training--a combined TMS, fMRI and mirror training study.

PubMed

Läppchen, C H; Ringer, T; Blessin, J; Schulz, K; Seidel, G; Lange, R; Hamzei, F

2015-02-15

Repetitive transcranial magnetic stimulation (rTMS) is used to increase regional excitability to improve motor function in combination with training after neurological diseases or events such as stroke. We investigated whether a daily application of intermittent theta burst stimulation (iTBS; a short-duration rTMS that increases regional excitability) improves the training effect compared with sham stimulation in association with a four-day hand training program using a mirror (mirror training, MT). The right dorsal premotor cortex (dPMC right) was chosen as the target region for iTBS because this region has recently been emphasized as a node within a network related to MT. Healthy subjects were randomized into the iTBS group or sham group (control group CG). In the iTBS group, iTBS was applied daily over dPMC right, which was functionally determined in an initial fMRI session prior to starting MT. MT involved 20 min of hand training daily in a mirror over four days. The hand tests, the intracortical excitability and fMRI were evaluated prior to and at the end of MT. The results of the hand training tests of the iTBS group were surprisingly significantly poorer compared with those from the CG group. Both groups showed a different course of excitability in both M1 and a different course of fMRI activation within the supplementary motor area and M1 left. We suggest the inter-regional functional balance was affected by daily iTBS over dPMC right. Maybe an inter-regional connectivity within a network is differentially balanced. An excitability increase within an inhibitory-balanced network would therefore disturb the underlying network. Copyright © 2014 Elsevier Inc. All rights reserved.

How art changes your brain: differential effects of visual art production and cognitive art evaluation on functional brain connectivity.

PubMed

Bolwerk, Anne; Mack-Andrick, Jessica; Lang, Frieder R; Dörfler, Arnd; Maihöfner, Christian

2014-01-01

Visual art represents a powerful resource for mental and physical well-being. However, little is known about the underlying effects at a neural level. A critical question is whether visual art production and cognitive art evaluation may have different effects on the functional interplay of the brain's default mode network (DMN). We used fMRI to investigate the DMN of a non-clinical sample of 28 post-retirement adults (63.71 years ±3.52 SD) before (T0) and after (T1) weekly participation in two different 10-week-long art interventions. Participants were randomly assigned to groups stratified by gender and age. In the visual art production group 14 participants actively produced art in an art class. In the cognitive art evaluation group 14 participants cognitively evaluated artwork at a museum. The DMN of both groups was identified by using a seed voxel correlation analysis (SCA) in the posterior cingulated cortex (PCC/preCUN). An analysis of covariance (ANCOVA) was employed to relate fMRI data to psychological resilience which was measured with the brief German counterpart of the Resilience Scale (RS-11). We observed that the visual art production group showed greater spatial improvement in functional connectivity of PCC/preCUN to the frontal and parietal cortices from T0 to T1 than the cognitive art evaluation group. Moreover, the functional connectivity in the visual art production group was related to psychological resilience (i.e., stress resistance) at T1. Our findings are the first to demonstrate the neural effects of visual art production on psychological resilience in adulthood.

How Art Changes Your Brain: Differential Effects of Visual Art Production and Cognitive Art Evaluation on Functional Brain Connectivity

PubMed Central

Bolwerk, Anne; Mack-Andrick, Jessica; Lang, Frieder R.; Dörfler, Arnd; Maihöfner, Christian

2014-01-01

Visual art represents a powerful resource for mental and physical well-being. However, little is known about the underlying effects at a neural level. A critical question is whether visual art production and cognitive art evaluation may have different effects on the functional interplay of the brain's default mode network (DMN). We used fMRI to investigate the DMN of a non-clinical sample of 28 post-retirement adults (63.71 years ±3.52 SD) before (T0) and after (T1) weekly participation in two different 10-week-long art interventions. Participants were randomly assigned to groups stratified by gender and age. In the visual art production group 14 participants actively produced art in an art class. In the cognitive art evaluation group 14 participants cognitively evaluated artwork at a museum. The DMN of both groups was identified by using a seed voxel correlation analysis (SCA) in the posterior cingulated cortex (PCC/preCUN). An analysis of covariance (ANCOVA) was employed to relate fMRI data to psychological resilience which was measured with the brief German counterpart of the Resilience Scale (RS-11). We observed that the visual art production group showed greater spatial improvement in functional connectivity of PCC/preCUN to the frontal and parietal cortices from T0 to T1 than the cognitive art evaluation group. Moreover, the functional connectivity in the visual art production group was related to psychological resilience (i.e., stress resistance) at T1. Our findings are the first to demonstrate the neural effects of visual art production on psychological resilience in adulthood. PMID:24983951

Functional mapping of language networks in the normal brain using a word-association task.

PubMed

Ghosh, Shantanu; Basu, Amrita; Kumaran, Senthil S; Khushu, Subash

2010-08-01

Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI) in normal human subjects. Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2*-weighted gradient-echo echo-planar imaging (GE-EPI) sequence (TR 4523 ms, TE 64 ms, flip angle 90°) with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s) with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2) with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD) signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Single subject analysis of the functional data (FWE-corrected, P≤0.001) revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG), superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG), anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001) revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Group data analysis revealed a cerebellar-occipital-fusiform-thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these areas facilitate language comprehension by activating a semantic association network of words processed postlexical access. This finding is important when assessing the extent of cognitive damage and/or recovery and can be used for presurgical planning after optimization.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Identifying dynamic functional connectivity biomarkers using GIG-ICA: application to schizophrenia, schizoaffective disorder and psychotic bipolar disorder

PubMed Central

Du, Yuhui; Pearlson, Godfrey D; Lin, Dongdong; Sui, Jing; Chen, Jiayu; Salman, Mustafa; Tamminga, Carol A.; Ivleva, Elena I.; Sweeney, John A.; Keshavan, Matcheri S.; Clementz, Brett A.; Bustillo, Juan; Calhoun, Vince D.

2017-01-01

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD) and schizophrenia (SZ). We introduce a group information guided independent component analysis (GIG-ICA) procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. PMID:28294459

Evidence for Functional Networks within the Human Brain's White Matter.

PubMed

Peer, Michael; Nitzan, Mor; Bick, Atira S; Levin, Netta; Arzy, Shahar

2017-07-05

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry. SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders. Copyright © 2017 the authors 0270-6474/17/376394-14$15.00/0.

Frontoparietal Priority Maps as Biomarkers for mTBI

DTIC Science & Technology

2015-10-01

preliminary data analysis is underway. 15. SUBJECT TERMS mTBI, fMRI , DTI, psychophysics, vision, convergence insufficiency 16. SECURITY CLASSIFICATION OF...biomarkers and behavioral measures of visual performance in veterans who have and have not experienced mTBI. KEYWORDS mTBI fMRI DTI psychophysics...MRI protocol prepared 8 Delayed to month 15 due to recruitment delays. Major Task 5: acquire MRI measures, which include DTI and fMRI Complete

New Insights into Signed Path Coefficient Granger Causality Analysis

PubMed Central

Zhang, Jian; Li, Chong; Jiang, Tianzi

2016-01-01

Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of “signed path coefficient Granger causality,” a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an “excitatory” or “inhibitory” influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation. PMID:27833547

Attribution of emotions to body postures: an independent component analysis study of functional connectivity in autism.

PubMed

Libero, Lauren E; Stevens, Carl E; Kana, Rajesh K

2014-10-01

The ability to interpret others' body language is a vital skill that helps us infer their thoughts and emotions. However, individuals with autism spectrum disorder (ASD) have been found to have difficulty in understanding the meaning of people's body language, perhaps leading to an overarching deficit in processing emotions. The current fMRI study investigates the functional connectivity underlying emotion and action judgment in the context of processing body language in high-functioning adolescents and young adults with autism, using an independent components analysis (ICA) of the fMRI time series. While there were no reliable group differences in brain activity, the ICA revealed significant involvement of occipital and parietal regions in processing body actions; and inferior frontal gyrus, superior medial prefrontal cortex, and occipital cortex in body expressions of emotions. In a between-group analysis, participants with autism, relative to typical controls, demonstrated significantly reduced temporal coherence in left ventral premotor cortex and right superior parietal lobule while processing emotions. Participants with ASD, on the other hand, showed increased temporal coherence in left fusiform gyrus while inferring emotions from body postures. Finally, a positive predictive relationship was found between empathizing ability and the brain areas underlying emotion processing in ASD participants. These results underscore the differential role of frontal and parietal brain regions in processing emotional body language in autism. Copyright © 2014 Wiley Periodicals, Inc.

Altered Effective Connectivity among Core Neurocognitive Networks in Idiopathic Generalized Epilepsy: An fMRI Evidence

PubMed Central

Wei, Huilin; An, Jie; Shen, Hui; Zeng, Ling-Li; Qiu, Shijun; Hu, Dewen

2016-01-01

Idiopathic generalized epilepsy (IGE) patients with generalized tonic-clonic seizures (GTCS) suffer long-term cognitive impairments, and present a higher incidence of psychosocial and psychiatric disturbances than healthy people. It is possible that the cognitive dysfunctions and higher psychopathological risk in IGE-GTCS derive from disturbed causal relationship among core neurocognitive brain networks. To test this hypothesis, we examined the effective connectivity across the salience network (SN), default mode network (DMN), and central executive network (CEN) using resting-state functional magnetic resonance imaging (fMRI) data collected from 27 IGE-GTCS patients and 29 healthy controls. In the study, a combination framework of time domain and frequency domain multivariate Granger causality analysis was firstly proposed, and proved to be valid and accurate by simulation experiments. Using this method, we then observed significant differences in the effective connectivity graphs between the patient and control groups. Specifically, between-group statistical analysis revealed that relative to the healthy controls, the patients established significantly enhanced Granger causal influence from the dorsolateral prefrontal cortex to the dorsal anterior cingulate cortex, which is coherent both in the time and frequency domains analyses. Meanwhile, time domain analysis also revealed decreased Granger causal influence from the right fronto-insular cortex to the posterior cingulate cortex in the patients. These findings may provide new evidence for functional brain organization disruption underlying cognitive dysfunctions and psychopathological risk in IGE-GTCS. PMID:27656137

Mapping brain functional alterations in betel-quid chewers using resting-state fMRI and network analysis.

PubMed

Weng, Jun-Cheng; Chou, Yu-Syuan; Huang, Guo-Joe; Tyan, Yeu-Sheng; Ho, Ming-Chou

2018-04-01

The World Health Organization regards betel quid (BQ) as a human carcinogen, and DSM-IV and ICD-10 dependence symptoms may develop with its heavy use. BQ's possible effects of an enhanced reward system and disrupted inhibitory control may increase the likelihood of habitual substance use. The current study aimed to employ resting-state fMRI to examine the hypothesized enhanced reward system (e.g., the basal forebrain system) and disrupted inhibitory control (e.g., the prefrontal system) in BQ chewers. The current study recruited three groups of 48 male participants: 16 BQ chewers, 15 tobacco- and alcohol-user controls, and 17 healthy controls. We used functional connectivity (FC), mean fractional amplitude of low-frequency fluctuations (mfALFF), and mean regional homogeneity (mReHo) to evaluate functional alternations in BQ chewers. Graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also performed to identify the functional network differences among the three groups. Our hypothesis was partially supported: the enhanced reward system for the BQ chewers (e.g., habitual drug-seeking behavior) was supported; however, their inhibitory control was relatively preserved. In addition, we reported that the BQ chewers may have enhanced visuospatial processing and decreased local segregation. The current results (showing an enhanced reward system in the chewers) provided the clinicians with important insight for the future development of an effective abstinence treatment.

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

PubMed

Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

2006-07-01

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Cortical representation of facial and tongue movements: a task functional magnetic resonance imaging study.

PubMed

Xiao, Fu-Long; Gao, Pei-Yi; Qian, Tian-Yi; Sui, Bin-Bin; Xue, Jing; Zhou, Jian; Lin, Yan

2017-05-01

Functional magnetic resonance imaging (fMRI) mapping can present the activated cortical area during movement, while little is known about precise location in facial and tongue movements. To investigate the representation of facial and tongue movements by task fMRI. Twenty right-handed healthy subjects were underwent block design task fMRI examination. Task movements included lip pursing, cheek bulging, grinning and vertical tongue excursion. Statistical parametric mapping (SPM8) was applied to analysis the data. One-sample t-test was used to calculate the common activation area between facial and tongue movements. Also, paired t-test was used to test for areas of over- or underactivation in tongue movement compared with each group of facial movements. The common areas within facial and tongue movements suggested the similar motor circuits of activation in both movements. Prior activation in tongue movement was situated laterally and inferiorly in sensorimotor area relative to facial movements. Prior activation of tongue movement was investigated in left superior parietal lobe relative to lip pursing. Also, prior activation in bilateral cuneus lobe in grinning compared with tongue movement was detected. © 2015 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

PubMed

Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

2018-05-15

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

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.

The dynamic imprint of word learning on the dorsal language pathway.

PubMed

Palomar-García, María-Ángeles; Sanjuán, Ana; Bueichekú, Elisenda; Ventura-Campos, Noelia; Ávila, César

2017-10-01

According to Hickok and Poeppel (2007), the acquisition of new vocabulary rests on the dorsal language pathway connecting auditory and motor areas. The present study tested this hypothesis longitudinally by measuring BOLD signal changes during a verbal repetition task and modulation of resting state functional connectivity (rs-FC) in the dorsal stream. Thirty-five healthy participants, divided into trained and control groups, completed fMRI sessions on days 1, 10, and 24. Between days 1 and 10, the trained group learned 84 new pseudowords associated with 84 native words. Task-related fMRI results showed a reduced activity in the IFG and STG while processing the learned vocabulary after training, returning to initial values two weeks later. Moreover, rs-fMRI analysis showed stronger rs-FC between the IFG and STG in the trained group than in the control group after learning, especially on day 24. These neural changes were more evident in participants with a larger vocabulary. Discussion focuses on the prominent role of the dorsal stream in vocabulary acquisition. Even when their meaning was known, newly learned words were again processed through the dorsal stream two weeks after learning, with the increase in rs-FC between auditory and motor areas being a relevant long-term imprint of vocabulary learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Functional Neuroimaging of Emotionally Intense Autobiographical Memories in Post-Traumatic Stress Disorder

PubMed Central

St. Jacques, Peggy L.; Botzung, Anne; Miles, Amanda; Rubin, David C.

2010-01-01

Post-traumatic stress disorder (PTSD) affects regions that support autobiographical memory (AM) retrieval, such as the hippocampus, amygdala and ventral medial prefrontal cortex (PFC). However, it is not well understood how PTSD may impact the neural mechanisms of memory retrieval for the personal past. We used a generic cue method combined with parametric modulation analysis and functional MRI (fMRI) to investigate the neural mechanisms affected by PTSD symptoms during the retrieval of a large sample of emotionally intense AMs. There were three main results. First, the PTSD group showed greater recruitment of the amygdala/hippocampus during the construction of negative versus positive emotionally intense AMs, when compared to controls. Second, across both the construction and elaboration phases of retrieval the PTSD group showed greater recruitment of the ventral medial PFC for negatively intense memories, but less recruitment for positively intense memories. Third, the PTSD group showed greater functional coupling between the ventral medial PFC and the amygdala for negatively intense memories, but less coupling for positively intense memories. In sum, the fMRI data suggest that there was greater recruitment and coupling of emotional brain regions during the retrieval of negatively intense AMs in the PTSD group when compared to controls. PMID:21109253

Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR).

PubMed

Wong, Chung-Ki; Zotev, Vadim; Misaki, Masaya; Phillips, Raquel; Luo, Qingfei; Bodurka, Jerzy

2016-04-01

Head motions during functional magnetic resonance imaging (fMRI) impair fMRI data quality and introduce systematic artifacts that can affect interpretation of fMRI results. Electroencephalography (EEG) recordings performed simultaneously with fMRI provide high-temporal-resolution information about ongoing brain activity as well as head movements. Recently, an EEG-assisted retrospective motion correction (E-REMCOR) method was introduced. E-REMCOR utilizes EEG motion artifacts to correct the effects of head movements in simultaneously acquired fMRI data on a slice-by-slice basis. While E-REMCOR is an efficient motion correction approach, it involves an independent component analysis (ICA) of the EEG data and identification of motion-related ICs. Here we report an automated implementation of E-REMCOR, referred to as aE-REMCOR, which we developed to facilitate the application of E-REMCOR in large-scale EEG-fMRI studies. The aE-REMCOR algorithm, implemented in MATLAB, enables an automated preprocessing of the EEG data, an ICA decomposition, and, importantly, an automatic identification of motion-related ICs. aE-REMCOR has been used to perform retrospective motion correction for 305 fMRI datasets from 16 subjects, who participated in EEG-fMRI experiments conducted on a 3T MRI scanner. Performance of aE-REMCOR has been evaluated based on improvement in temporal signal-to-noise ratio (TSNR) of the fMRI data, as well as correction efficiency defined in terms of spike reduction in fMRI motion parameters. The results show that aE-REMCOR is capable of substantially reducing head motion artifacts in fMRI data. In particular, when there are significant rapid head movements during the scan, a large TSNR improvement and high correction efficiency can be achieved. Depending on a subject's motion, an average TSNR improvement over the brain upon the application of aE-REMCOR can be as high as 27%, with top ten percent of the TSNR improvement values exceeding 55%. The average correction efficiency over the 305 fMRI scans is 18% and the largest achieved efficiency is 71%. The utility of aE-REMCOR on the resting state fMRI connectivity of the default mode network is also examined. The motion-induced position-dependent error in the DMN connectivity analysis is shown to be reduced when aE-REMCOR is utilized. These results demonstrate that aE-REMCOR can be conveniently and efficiently used to improve fMRI motion correction in large clinical EEG-fMRI studies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Functional MRI evidence for fine motor praxis dysfunction in children with persistent speech disorders

PubMed Central

Redle, Erin; Vannest, Jennifer; Maloney, Thomas; Tsevat, Rebecca K.; Eikenberry, Sarah; Lewis, Barbara; Shriberg, Lawrence D.; Tkach, Jean; Holland, Scott K.

2014-01-01

Children with persistent speech disorders (PSD) often present with overt or subtle motor deficits; the possibility that speech disorders and motor deficits could arise from a shared neurological base is currently unknown. Functional MRI (fMRI) was used to examine the brain networks supporting fine motor praxis in children with PSD and without clinically identified fine motor deficits. Methods This case-control study included 12 children with PSD (mean age 7.42 years, 4 female) and 12 controls (mean age 7.44 years, 4 female). Children completed behavioral evaluations using standardized motor assessments and parent reported functional measures. During fMRI scanning, participants completed a cued finger tapping task contrasted passive listening. A general linear model approach identified brain regions associated with finger tapping in each group and regions that differed between groups. The relationship between regional fMRI activation and fine motor skill was assessed using a regression analysis. Results Children with PSD had significantly poorer results for rapid speech production and fine motor praxis skills, but did not differ on classroom functional skills. Functional MRI results showed that children with PSD had significantly more activation in the cerebellum during finger tapping. Positive correlations between performance on a fine motor praxis test and activation multiple cortical regions were noted for children with PSD but not for controls. Conclusions Over-activation in the cerebellum during a motor task may reflect a subtle abnormality in the non-speech motor neural circuitry in children with PSD. PMID:25481413

Combination of DTI and fMRI reveals the white matter changes correlating with the decline of default-mode network activity in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Wu, Xianjun; Di, Qian; Li, Yao; Zhao, Xiaojie

2009-02-01

Recently, evidences from fMRI studies have shown that there was decreased activity among the default-mode network in Alzheimer's disease (AD), and DTI researches also demonstrated that demyelinations exist in white matter of AD patients. Therefore, combining these two MRI methods may help to reveal the relationship between white matter damages and alterations of the resting state functional connectivity network. In the present study, we tried to address this issue by means of correlation analysis between DTI and resting state fMRI images. The default-mode networks of AD and normal control groups were compared to find the areas with significantly declined activity firstly. Then, the white matter regions whose fractional anisotropy (FA) value correlated with this decline were located through multiple regressions between the FA values and the BOLD response of the default networks. Among these correlating white matter regions, those whose FA values also declined were found by a group comparison between AD patients and healthy elderly control subjects. Our results showed that the areas with decreased activity among default-mode network included left posterior cingulated cortex (PCC), left medial temporal gyrus et al. And the damaged white matter areas correlated with the default-mode network alterations were located around left sub-gyral temporal lobe. These changes may relate to the decreased connectivity between PCC and medial temporal lobe (MTL), and thus correlate with the deficiency of default-mode network activity.

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

Effects of haloperidol and aripiprazole on the human mesolimbic motivational system: A pharmacological fMRI study.

PubMed

Bolstad, Ingeborg; Andreassen, Ole A; Groote, Inge; Server, Andres; Sjaastad, Ivar; Kapur, Shitij; Jensen, Jimmy

2015-12-01

The atypical antipsychotic drug aripiprazole is a partial dopamine (DA) D2 receptor agonist, which differentiates it from most other antipsychotics. This study compares the brain activation characteristic produced by aripiprazole with that of haloperidol, a typical D2 receptor antagonist. Healthy participants received an acute oral dose of haloperidol, aripiprazole or placebo, and then performed an active aversive conditioning task with aversive and neutral events presented as sounds, while blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) was carried out. The fMRI task, targeting the mesolimbic motivational system that is thought to be disturbed in psychosis, was based on the conditioned avoidance response (CAR) animal model - a widely used test of therapeutic potential of antipsychotic drugs. In line with the CAR animal model, the present results show that subjects given haloperidol were not able to avoid more aversive than neutral task trials, even though the response times were shorter during aversive events. In the aripiprazole and placebo groups more aversive than neutral events were avoided. Accordingly, the task-related BOLD-fMRI response in the mesolimbic motivational system was diminished in the haloperidol group compared to the placebo group, particularly in the ventral striatum, whereas the aripiprazole group showed task-related activations intermediate of the placebo and haloperidol groups. The current results show differential effects on brain function by aripiprazole and haloperidol, probably related to altered DA transmission. This supports the use of pharmacological fMRI to study antipsychotic properties in humans. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

BOLD fMRI and DTI in strabismic amblyopes following occlusion therapy.

PubMed

Gupta, Shikha; Kumaran, Senthil S; Saxena, Rohit; Gudwani, Sunita; Menon, Vimala; Sharma, Pradeep

2016-08-01

Evaluation of brain cluster activation using the functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) was sought in strabismic amblyopes. In this hospital-based case-control cross-sectional study, fMRI and DTI were conducted in strabismic amblyopes before initiation of any therapy and after visual recovery following the administration of occlusion therapy. FMRI was performed in 10 strabismic amblyopic subjects (baseline group) and in 5 left strabismic amblyopic children post-occlusion therapy after two-line visual improvement. Ten age-matched healthy children with right ocular dominance formed control group. Structural and functional MRI was carried out on 1.5T MR scanner. The visual task consisted of 8 Hz flickering checkerboard with red dot and occasional green dot. Blood-oxygen-level-dependent (BOLD) fMRI was analyzed using statistical parametric mapping and DTI on NordicIce (NordicNeuroLab) softwares. Reduced occipital activation was elicited when viewing with the amblyopic eye in amblyopes. An 'ipsilateral to viewing eye' pattern of calcarine BOLD activation was observed in controls and left amblyopes. Activation of cortical areas associated with visual processing differed in relation to the viewing eye. Following visual recovery on occlusion therapy, enhanced activity in bilateral hemispheres in striate as well as extrastriate regions when viewing with either eye was seen. Improvement in visual acuity following occlusion therapy correlates with hemodynamic activity in amblyopes.

Variability comparison of simultaneous brain near-infrared spectroscopy (NIRS) and functional MRI (fMRI) during visual stimulation

PubMed Central

Minati, Ludovico; Visani, Elisa; Dowell, Nick G; Medford, Nick; Critchley, Hugo D

2011-01-01

Brain near-infrared spectroscopy (NIRS) is emerging as a potential alternative to functional MRI (fMRI). To date, no study has explicitly compared the two techniques in terms of measurement variability, a key parameter dictating attainable statistical power. Here, NIRS and fMRI were simultaneously recorded during event-related visual stimulation. Inter-subject coefficients of variation (CVs) for peak response amplitude were considerably larger for NIRS than fMRI, but inter-subject CVs for response latency and intra-subject CVs for response amplitude were overall comparable. Our results may represent an optimistic estimate of the CVs of NIRS measurements, as optode positioning was guided by structural MRI, which is normally unavailable. We conclude that fMRI may be preferable to NIRS for group comparisons, but NIRS is equally powerful when comparing conditions within participants. The discrepancy between inter- and intra-subject CVs is likely related to variability in head anatomy and tissue properties which may be better accounted for by emerging NIRS technology. PMID:21780948

White versus gray matter: fMRI hemodynamic responses show similar characteristics, but differ in peak amplitude

PubMed Central

2012-01-01

Background There is growing evidence for the idea of fMRI activation in white matter. In the current study, we compared hemodynamic response functions (HRF) in white matter and gray matter using 4 T fMRI. White matter fMRI activation was elicited in the isthmus of the corpus callosum at both the group and individual levels (using an established interhemispheric transfer task). Callosal HRFs were compared to HRFs from cingulate and parietal activation. Results Examination of the raw HRF revealed similar overall response characteristics. Finite impulse response modeling confirmed that the WM HRF characteristics were comparable to those of the GM HRF, but had significantly decreased peak response amplitudes. Conclusions Overall, the results matched a priori expectations of smaller HRF responses in white matter due to the relative drop in cerebral blood flow (CBF) and cerebral blood volume (CBV). Importantly, the findings demonstrate that despite lower CBF and CBV, white matter fMRI activation remained within detectable ranges at 4 T. PMID:22852798

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

PubMed Central

Liu, Zhongming; de Zwart, Jacco A.; Chang, Catie; Duan, Qi; van Gelderen, Peter; Duyn, Jeff H.

2014-01-01

Spontaneous activity in the human brain occurs in complex spatiotemporal patterns that may reflect functionally specialized neural networks. Here, we propose a subspace analysis method to elucidate large-scale networks by the joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The new approach is based on the notion that the neuroelectrical activity underlying the fMRI signal may have EEG spectral features that report on regional neuronal dynamics and interregional interactions. Applying this approach to resting healthy adults, we indeed found characteristic spectral signatures in the EEG correlates of spontaneous fMRI signals at individual brain regions as well as the temporal synchronization among widely distributed regions. These spectral signatures not only allowed us to parcel the brain into clusters that resembled the brain's established functional subdivision, but also offered important clues for disentangling the involvement of individual regions in fMRI network activity. PMID:23796947

Cross multivariate correlation coefficients as screening tool for analysis of concurrent EEG-fMRI recordings.

PubMed

Ji, Hong; Petro, Nathan M; Chen, Badong; Yuan, Zejian; Wang, Jianji; Zheng, Nanning; Keil, Andreas

2018-02-06

Over the past decade, the simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data has garnered growing interest because it may provide an avenue towards combining the strengths of both imaging modalities. Given their pronounced differences in temporal and spatial statistics, the combination of EEG and fMRI data is however methodologically challenging. Here, we propose a novel screening approach that relies on a Cross Multivariate Correlation Coefficient (xMCC) framework. This approach accomplishes three tasks: (1) It provides a measure for testing multivariate correlation and multivariate uncorrelation of the two modalities; (2) it provides criterion for the selection of EEG features; (3) it performs a screening of relevant EEG information by grouping the EEG channels into clusters to improve efficiency and to reduce computational load when searching for the best predictors of the BOLD signal. The present report applies this approach to a data set with concurrent recordings of steady-state-visual evoked potentials (ssVEPs) and fMRI, recorded while observers viewed phase-reversing Gabor patches. We test the hypothesis that fluctuations in visuo-cortical mass potentials systematically covary with BOLD fluctuations not only in visual cortical, but also in anterior temporal and prefrontal areas. Results supported the hypothesis and showed that the xMCC-based analysis provides straightforward identification of neurophysiological plausible brain regions with EEG-fMRI covariance. Furthermore xMCC converged with other extant methods for EEG-fMRI analysis. © 2018 The Authors Journal of Neuroscience Research Published by Wiley Periodicals, Inc.

Combining functional neuroimaging with off-line brain stimulation: modulation of task-related activity in language areas.

PubMed

Andoh, Jamila; Paus, Tomás

2011-02-01

Repetitive TMS (rTMS) provides a noninvasive tool for modulating neural activity in the human brain. In healthy participants, rTMS applied over the language-related areas in the left hemisphere, including the left posterior temporal area of Wernicke (LTMP) and inferior frontal area of Broca, have been shown to affect performance on word recognition tasks. To investigate the neural substrate of these behavioral effects, off-line rTMS was combined with fMRI acquired during the performance of a word recognition task. Twenty right-handed healthy men underwent fMRI scans before and after a session of 10-Hz rTMS applied outside the magnetic resonance scanner. Functional magnetic resonance images were acquired during the performance of a word recognition task that used English or foreign-language words. rTMS was applied over the LTMP in one group of 10 participants (LTMP group), whereas the homologue region in the right hemisphere was stimulated in another group of 10 participants (RTMP group). Changes in task-related fMRI response (English minus foreign languages) and task performances (response time and accuracy) were measured in both groups and compared between pre-rTMS and post-rTMS. Our results showed that rTMS increased task-related fMRI response in the homologue areas contralateral to the stimulated sites. We also found an effect of rTMS on response time for the LTMP group only. These findings provide insights into changes in neural activity in cortical regions connected to the stimulated site and are consistent with a hypothesis raised in a previous review about the role of the homologue areas in the contralateral hemisphere for preserving behavior after neural interference.

Does Right-Sided Language Lateralization on BOLD-fMRI Affect Postoperative Language Outcome for AVM Patients?

PubMed

Liu, Zhi; Deng, Xiaofeng; Cao, Yong; Zhao, Yuanli; Zhao, Jizong; Wang, Shuo

2017-01-01

For cerebral arteriovenous malformations (AVMs) involving language areas, right-sided language lateralization on functional magnetic resonance imaging (fMRI) has been reported, which is regarded as language cortex reorganization. The authors attempted to study if this right-sided language lateralization affects postoperative language outcome. Clinical and imaging data of 43 right-handed AVM patients who underwent preoperative fMRI were retrospectively reviewed. All lesions involved the language cortex, with the Broca area involved in 13 patients and the Wernicke area involved in 30 patients. Lateralization indices (LI) of BOLD signal activations were calculated to determine language lateralization. All patients underwent craniotomy and total resection. Western aphasia battery (WAB) was used to evaluate language functions preoperatively, 1-2 weeks after surgery and 6-30 months after surgery. On preoperative fMRI, right-sided lateralization was observed in 18 patients (41.9%, R Group), including 3 with rightsided lateralization in the Broca area alone, 14 in the Wernicke area alone, and 1 in both areas. The other 25 patients were non-rightsided lateralized (NR Group). One week after surgery, 7 patients in the R Group (38.9%) and 11 patients in the NR Group (44.0%) had language function deterioration, and no significant difference was found (p=0.983). At long-term follow-up, 3 patients in the R Group (16.7%) and 4 patients in the NR Group (16.0%) still had aphasia, and no significant difference was observed (p=1.000). Although right-sided lateralization on fMRI might suggest language cortex reorganization, it is not a factor predicting better postoperative language outcome for AVM patients.

Neural Categorization of Vibrotactile Frequency in Flutter and Vibration Stimulations: An fMRI Study.

PubMed

Kim, Junsuk; Chung, Yoon Gi; Chung, Soon-Cheol; Bulthoff, Heinrich H; Kim, Sung-Phil

2016-01-01

As the use of wearable haptic devices with vibrating alert features is commonplace, an understanding of the perceptual categorization of vibrotactile frequencies has become important. This understanding can be substantially enhanced by unveiling how neural activity represents vibrotactile frequency information. Using functional magnetic resonance imaging (fMRI), this study investigated categorical clustering patterns of the frequency-dependent neural activity evoked by vibrotactile stimuli with gradually changing frequencies from 20 to 200 Hz. First, a searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions exhibiting neural activities associated with frequency information. We found that the contralateral postcentral gyrus (S1) and the supramarginal gyrus (SMG) carried frequency-dependent information. Next, we applied multidimensional scaling (MDS) to find low-dimensional neural representations of different frequencies obtained from the multi-voxel activity patterns within these regions. The clustering analysis on the MDS results showed that neural activity patterns of 20-100 Hz and 120-200 Hz were divided into two distinct groups. Interestingly, this neural grouping conformed to the perceptual frequency categories found in the previous behavioral studies. Our findings therefore suggest that neural activity patterns in the somatosensory cortical regions may provide a neural basis for the perceptual categorization of vibrotactile frequency.

Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease

PubMed Central

Manning, Kathryn Y.; Rajakumar, Nagalingam; Gómez, Francisco A.; Soddu, Andrea; Borrie, Michael J.

2017-01-01

Previous studies have demonstrated altered brain activity in Alzheimer's disease using task based functional MRI (fMRI), network based resting-state fMRI, and glucose metabolism from 18F fluorodeoxyglucose-PET (FDG-PET). Our goal was to define a novel indicator of neuronal activity based on a first-order textural feature of the resting state functional MRI (RS-fMRI) signal. Furthermore, we examined the association between this neuronal activity metric and glucose metabolism from 18F FDG-PET. We studied 15 normal elderly controls (NEC) and 15 probable Alzheimer disease (AD) subjects from the AD Neuroimaging Initiative. An independent component analysis was applied to the RS-fMRI, followed by template matching to identify neuronal components (NC). A regional brain activity measurement was constructed based on the variation of the RS-fMRI signal of these NC. The standardized glucose uptake values of several brain regions relative to the cerebellum (SUVR) were measured from partial volume corrected FDG-PET images. Comparing the AD and NEC groups, the mean brain activity metric was significantly lower in the accumbens, while the glucose SUVR was significantly lower in the amygdala and hippocampus. The RS-fMRI brain activity metric was positively correlated with cognitive measures and amyloid β1–42 cerebral spinal fluid levels; however, these did not remain significant following Bonferroni correction. There was a significant linear correlation between the brain activity metric and the glucose SUVR measurements. This proof of concept study demonstrates that this novel and easy to implement RS-fMRI brain activity metric can differentiate a group of healthy elderly controls from a group of people with AD. PMID:28582450

Extremely preterm children exhibit increased interhemispheric connectivity for language: findings from fMRI-constrained MEG analysis.

PubMed

Barnes-Davis, Maria E; Merhar, Stephanie L; Holland, Scott K; Kadis, Darren S

2018-04-16

Children born extremely preterm are at significant risk for cognitive impairment, including language deficits. The relationship between preterm birth and neurological changes that underlie cognitive deficits is poorly understood. We use a stories-listening task in fMRI and MEG to characterize language network representation and connectivity in children born extremely preterm (n = 15, <28 weeks gestation, ages 4-6 years), and in a group of typically developing control participants (n = 15, term birth, 4-6 years). Participants completed a brief neuropsychological assessment. Conventional fMRI analyses revealed no significant differences in language network representation across groups (p > .05, corrected). The whole-group fMRI activation map was parcellated to define the language network as a set of discrete nodes, and the timecourse of neuronal activity at each position was estimated using linearly constrained minimum variance beamformer in MEG. Virtual timecourses were subjected to connectivity and network-based analyses. We observed significantly increased beta-band functional connectivity in extremely preterm compared to controls (p < .05). Specifically, we observed an increase in connectivity between left and right perisylvian cortex. Subsequent effective connectivity analyses revealed that hyperconnectivity in preterms was due to significantly increased information flux originating from the right hemisphere (p < 0.05). The total strength and density of the language network were not related to language or nonverbal performance, suggesting that the observed hyperconnectivity is a "pure" effect of prematurity. Although our extremely preterm children exhibited typical language network architecture, we observed significantly altered network dynamics, indicating reliance on an alternative neural strategy for the language task. © 2018 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease.

PubMed

Kazemifar, Samaneh; Manning, Kathryn Y; Rajakumar, Nagalingam; Gómez, Francisco A; Soddu, Andrea; Borrie, Michael J; Menon, Ravi S; Bartha, Robert

2017-01-01

Previous studies have demonstrated altered brain activity in Alzheimer's disease using task based functional MRI (fMRI), network based resting-state fMRI, and glucose metabolism from 18F fluorodeoxyglucose-PET (FDG-PET). Our goal was to define a novel indicator of neuronal activity based on a first-order textural feature of the resting state functional MRI (RS-fMRI) signal. Furthermore, we examined the association between this neuronal activity metric and glucose metabolism from 18F FDG-PET. We studied 15 normal elderly controls (NEC) and 15 probable Alzheimer disease (AD) subjects from the AD Neuroimaging Initiative. An independent component analysis was applied to the RS-fMRI, followed by template matching to identify neuronal components (NC). A regional brain activity measurement was constructed based on the variation of the RS-fMRI signal of these NC. The standardized glucose uptake values of several brain regions relative to the cerebellum (SUVR) were measured from partial volume corrected FDG-PET images. Comparing the AD and NEC groups, the mean brain activity metric was significantly lower in the accumbens, while the glucose SUVR was significantly lower in the amygdala and hippocampus. The RS-fMRI brain activity metric was positively correlated with cognitive measures and amyloid β1-42 cerebral spinal fluid levels; however, these did not remain significant following Bonferroni correction. There was a significant linear correlation between the brain activity metric and the glucose SUVR measurements. This proof of concept study demonstrates that this novel and easy to implement RS-fMRI brain activity metric can differentiate a group of healthy elderly controls from a group of people with AD.

Functional cortical and subcortical abnormalities in pedophilia: a combined study using a choice reaction time task and fMRI.

PubMed

Poeppl, Timm B; Nitschke, Joachim; Dombert, Beate; Santtila, Pekka; Greenlee, Mark W; Osterheider, Michael; Mokros, Andreas

2011-06-01

Pedophiles show sexual interest in prepubescent children but not in adults. Research into the neurofunctional mechanisms of paraphilias has gathered momentum over the last years. To elucidate the underlying neural processing of sexual interest among pedophiles and to highlight the differences in comparison with nonparaphilic sexual interest in adults. Nine pedophilic patients and 11 nonpedophilic control subjects underwent functional magnetic resonance imaging (fMRI) while viewing pictures of nude (prepubescents, pubescents, and adults) and neutral content, as well as performing a concomitant choice reaction time task (CRTT). Brain blood oxygen level-dependent (BOLD) signals and response latencies in the CRTT during exposure to each picture category. Analysis of behavioral data showed group differences in reaction times regarding prepubescent and adult but not pubescent stimuli. During stimulation with pictures displaying nude prepubescents, pedophiles showed increased BOLD response in brain areas known to be involved in processing of visual sexual stimuli. Comparison of pedophilic patients with the control group discovered differences in BOLD responses with respect to prepubescent and adult but not to pubescent stimuli. Differential effects in particular occurred in the cingulate gyrus and insular region. The brain response of pedophiles to visual sexual stimulation by images of nude prepubescents is comparable with previously described neural patterns of sexual processing in nonpedophilic human males evoked by visual stimuli depicting nude adults. Nevertheless, group differences found in the cingulate gyrus and the insular region suggest an important role of these brain areas in pedophilic sexual interest. Furthermore, combining attention-based methods like CRTT with fMRI may be a viable option for future diagnostic procedures regarding pedophilia. © 2011 International Society for Sexual Medicine.

Assessment of unconstrained cerebrovascular reactivity marker for large age-range FMRI studies.

PubMed

Kannurpatti, Sridhar S; Motes, Michael A; Biswal, Bharat B; Rypma, Bart

2014-01-01

Breath hold (BH), a commonly used task to measure cerebrovascular reactivity (CVR) in fMRI studies varies in outcome among individuals due to subject-physiology and/or BH-inspiration/expiration differences (i.e., performance). In prior age-related fMRI studies, smaller task-related BOLD response variability is observed among younger than older individuals. Also, a linear CVR versus task relationship exists in younger individuals which maybe useful to test the accuracy of CVR responses in older groups. Hence we hypothesized that subject-related physiological and/or BH differences, if present, may compromise CVR versus task linearity in older individuals. To test the hypothesis, empirical BH versus task relationships from motor and cognitive areas were obtained in younger (mean age = 26 years) and older (mean age = 58 years) human subjects. BH versus task linearity was observed only in the younger group, confirming our hypothesis. Further analysis indicated BH responses and its variability to be similar in both younger and older groups, suggesting that BH may not accurately represent CVR in a large age range. Using the resting state fluctuation of amplitude (RSFA) as an unconstrained alternative to BH, subject-wise correspondence between BH and RSFA was tested. Correlation between BH versus RSFA was significant within the motor but was not significant in the cognitive areas in the younger and was completely disrupted in both areas in the older subjects indicating that BH responses are constrained by subject-related physiology and/or performance-related differences. Contrasting BH to task, RSFA-task relationships were independent of age accompanied by age-related increases in CVR variability as measured by RSFA, not observed with BH. Together the results obtained indicate that RSFA accurately represents CVR in any age range avoiding multiple and yet unknown physiologic and task-related pitfalls of BH.

Functional Connectivity Estimated from Resting-State fMRI Reveals Selective Alterations in Male Adolescents with Pure Conduct Disorder

PubMed Central

Lu, Feng-Mei; Zhou, Jian-Song; Zhang, Jiang; Xiang, Yu-Tao; Zhang, Jian; Liu, Qi; Wang, Xiao-Ping; Yuan, Zhen

2015-01-01

Conduct disorder (CD) is characterized by a persistent pattern of antisocial behavior and aggression in childhood and adolescence. Previous task-based and resting-state functional magnetic resonance imaging (fMRI) studies have revealed widespread brain regional abnormalities in adolescents with CD. However, whether the resting-state networks (RSNs) are altered in adolescents with CD remains unknown. In this study, resting-state fMRI data were first acquired from eighteen male adolescents with pure CD and eighteen age- and gender-matched typically developing (TD) individuals. Independent component analysis (ICA) was implemented to extract nine representative RSNs, and the generated RSNs were then compared to show the differences between the CD and TD groups. Interestingly, it was observed from the brain mapping results that compared with the TD group, the CD group manifested decreased functional connectivity in four representative RSNs: the anterior default mode network (left middle frontal gyrus), which is considered to be correlated with impaired social cognition, the somatosensory network (bilateral supplementary motor area and right postcentral gyrus), the lateral visual network (left superior occipital gyrus), and the medial visual network (right fusiform, left lingual gyrus and right calcarine), which are expected to be relevant to the perceptual systems responsible for perceptual dysfunction in male adolescents with CD. Importantly, the novel findings suggested that male adolescents with pure CD were identified to have dysfunctions in both low-level perceptual networks (the somatosensory network and visual network) and a high-order cognitive network (the default mode network). Revealing the changes in the functional connectivity of these RSNs enhances our understanding of the neural mechanisms underlying the modulation of emotion and social cognition and the regulation of perception in adolescents with CD. PMID:26713867

Consistency and similarity of MEG- and fMRI-signal time courses during movie viewing.

PubMed

Lankinen, Kaisu; Saari, Jukka; Hlushchuk, Yevhen; Tikka, Pia; Parkkonen, Lauri; Hari, Riitta; Koskinen, Miika

2018-06-01

Movie viewing allows human perception and cognition to be studied in complex, real-life-like situations in a brain-imaging laboratory. Previous studies with functional magnetic resonance imaging (fMRI) and with magneto- and electroencephalography (MEG and EEG) have demonstrated consistent temporal dynamics of brain activity across movie viewers. However, little is known about the similarities and differences of fMRI and MEG or EEG dynamics during such naturalistic situations. We thus compared MEG and fMRI responses to the same 15-min black-and-white movie in the same eight subjects who watched the movie twice during both MEG and fMRI recordings. We analyzed intra- and intersubject voxel-wise correlations within each imaging modality as well as the correlation of the MEG envelopes and fMRI signals. The fMRI signals showed voxel-wise within- and between-subjects correlations up to r = 0.66 and r = 0.37, respectively, whereas these correlations were clearly weaker for the envelopes of band-pass filtered (7 frequency bands below 100 Hz) MEG signals (within-subjects correlation r < 0.14 and between-subjects r < 0.05). Direct MEG-fMRI voxel-wise correlations were unreliable. Notably, applying a spatial-filtering approach to the MEG data uncovered consistent canonical variates that showed considerably stronger (up to r = 0.25) between-subjects correlations than the univariate voxel-wise analysis. Furthermore, the envelopes of the time courses of these variates up to about 10 Hz showed association with fMRI signals in a general linear model. Similarities between envelopes of MEG canonical variates and fMRI voxel time-courses were seen mostly in occipital, but also in temporal and frontal brain regions, whereas intra- and intersubject correlations for MEG and fMRI separately were strongest only in the occipital areas. In contrast to the conventional univariate analysis, the spatial-filtering approach was able to uncover associations between the MEG envelopes and fMRI time courses, shedding light on the similarities of hemodynamic and electromagnetic brain activities during movie viewing. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Neural correlates of incidental memory in mild cognitive impairment: an fMRI study.

PubMed

Mandzia, Jennifer L; McAndrews, Mary Pat; Grady, Cheryl L; Graham, Simon J; Black, Sandra E

2009-05-01

Behaviour and fMRI brain activation patterns were compared during encoding and recognition tasks in mild cognitive impairment (MCI) (n=14) and normal controls (NC) (n=14). Deep (natural vs. man-made) and shallow (color vs. black and white) decisions were made at encoding and pictures from each condition were presented for yes/no recognition 20 min later. MCI showed less inferior frontal activation during deep (left only) and superficial encoding (bilaterally) and in both medial temporal lobes (MTL). When performance was equivalent (recognition of words encoded superficially), MTL activation was similar for the two groups, but during recognition testing of deeply encoded items NC showed more activation in both prefrontal and left MTL region. In a region of interest analysis, the extent of activation during deep encoding in the parahippocampi bilaterally and in left hippocampus correlated with subsequent recognition accuracy for those items in controls but not in MCI, which may reflect the heterogeneity of activation responses in conjunction with different degrees of pathology burden and progression status in the MCI group.

Pain when love is near

NASA Astrophysics Data System (ADS)

Tamam, S.; Ahmad, A. H.; Aziz, M. E.; Kamil, W. A.

2017-05-01

The aim of the study is to investigate brain responses to acute laser pain when a loved one is nearby. Laser pain stimuli at individual pain threshold were delivered using Th:YAG laser to 17 female participants. The participants were categorised into two groups, Love Hurts or Love Heals, according to their responses to pain stimulation during the presence of their loved ones. fMRI brain activation was obtained using 3 T Philips Achieva MRI scanner utilising blocked design paradigm comprising 15 blocks of stimulation phase and 15 blocks of no stimulation. fMRI images were analysed using statistical parametric mapping (SPM) focusing on random effects (RFX) analysis. We found that both groups activated pain-related areas such as the thalamus, secondary somatosensory cortex, insula and cingulate cortex. However, Love Hurts showed more activity in thalamus, parahippocampal gyrus and hippocampus; while Love Heals showed more activity in the entire part of cingulate cortex during the presence of their loved ones. In conclusion, there may be specific brain regions responsible for modulation of pain due to the presence of a loved one thus manifesting as Love Hurts or Love Heals.

Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia.

PubMed

Correa, Nicolle M; Li, Yi-Ou; Adalı, Tülay; Calhoun, Vince D

2008-12-01

Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. However, fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. We propose a data fusion scheme at the feature level using canonical correlation analysis (CCA) to determine inter-subject covariations across modalities. As we show both with simulation results and application to real data, multimodal CCA (mCCA) proves to be a flexible and powerful method for discovering associations among various data types. We demonstrate the versatility of the method with application to two datasets, an fMRI and EEG, and an fMRI and sMRI dataset, both collected from patients diagnosed with schizophrenia and healthy controls. CCA results for fMRI and EEG data collected for an auditory oddball task reveal associations of the temporal and motor areas with the N2 and P3 peaks. For the application to fMRI and sMRI data collected for an auditory sensorimotor task, CCA results show an interesting joint relationship between fMRI and gray matter, with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas associated with less gray matter as compared to healthy controls. Additionally, we compare our scheme with an independent component analysis based fusion method, joint-ICA that has proven useful for such a study and note that the two methods provide complementary perspectives on data fusion.

Analysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension.

PubMed

Siyah Mansoory, Meysam; Oghabian, Mohammad Ali; Jafari, Amir Homayoun; Shahbabaie, Alireza

2017-01-01

Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obligatory for graph construction and analysis is consistently underestimated by LC, because not all the bivariate distributions, but only the marginals are Gaussian. In a number of studies, Mutual Information (MI) has been employed, as a similarity measure between each two time series of the brain regions, a pure nonlinear measure. Owing to the complex fractal organization of the brain indicating self-similarity, more information on the brain can be revealed by fMRI Fractal Dimension (FD) analysis. In the present paper, Box-Counting Fractal Dimension (BCFD) is introduced for graph theoretical analysis of fMRI data in 17 methamphetamine drug users and 18 normal controls. Then, BCFD performance was evaluated compared to those of LC and MI methods. Moreover, the global topological graph properties of the brain networks inclusive of global efficiency, clustering coefficient and characteristic path length in addict subjects were investigated too. Compared to normal subjects by using statistical tests (P<0.05), topological graph properties were postulated to be disrupted significantly during the resting-state fMRI. Based on the results, analyzing the graph topological properties (representing the brain networks) based on BCFD is a more reliable method than LC and MI.

fMRI: blood oxygen level-dependent activation during a working memory-selective attention task in children born extremely preterm.

PubMed

Griffiths, Silja Torvik; Gundersen, Hilde; Neto, Emanuel; Elgen, Irene; Markestad, Trond; Aukland, Stein M; Hugdahl, Kenneth

2013-08-01

Extremely preterm (EPT)/extremely low-birth-weight (ELBW) children attaining school age and adolescence often have problems with executive functions such as working memory and selective attention. Our aim was to investigate a hypothesized difference in blood oxygen level-dependent (BOLD) activation during a selective attention-working memory task in EPT/ELBW children as compared with term-born controls. A regional cohort of 28 EPT/ELBW children and 28 term-born controls underwent functional magnetic resonance imaging (fMRI) scanning at 11 y of age while performing a combined Stroop n-back task. Group differences in BOLD activation were analyzed with Statistical Parametric Mapping 8 analysis software package, and reaction times (RTs) and response accuracy (RA) were compared in a multifactorial ANOVA test. The BOLD activation pattern in the preterm group involved the same areas (cingulate, prefrontal, and parietal cortexes), but all areas displayed significantly less activation than those in the control group, particularly when the cognitive load was increased. The RA results corresponded with the activation data in that the preterm group had significantly fewer correct responses. No group difference was found regarding RTs. Children born EPT/ELBW displayed reduced working memory and selective attention capacity as compared with term-born controls. These impairments had neuronal correlates with reduced BOLD activation in areas responsible for online stimulus monitoring, working memory, and cognitive control.

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.

Impaired Long Distance Functional Connectivity and Weighted Network Architecture in Alzheimer's Disease

PubMed Central

Liu, Yong; Yu, Chunshui; Zhang, Xinqing; Liu, Jieqiong; Duan, Yunyun; Alexander-Bloch, Aaron F.; Liu, Bing; Jiang, Tianzi; Bullmore, Ed

2014-01-01

Alzheimer's disease (AD) is increasingly recognized as a disconnection syndrome, which leads to cognitive impairment due to the disruption of functional activity across large networks or systems of interconnected brain regions. We explored abnormal functional magnetic resonance imaging (fMRI) resting-state dynamics, functional connectivity, and weighted functional networks, in a sample of patients with severe AD (N = 18) and age-matched healthy volunteers (N = 21). We found that patients had reduced amplitude and regional homogeneity of low-frequency fMRI oscillations, and reduced the strength of functional connectivity, in several regions previously described as components of the default mode network, for example, medial posterior parietal cortex and dorsal medial prefrontal cortex. In patients with severe AD, functional connectivity was particularly attenuated between regions that were separated by a greater physical distance; and loss of long distance connectivity was associated with less efficient global and nodal network topology. This profile of functional abnormality in severe AD was consistent with the results of a comparable analysis of data on 2 additional groups of patients with mild AD (N = 17) and amnestic mild cognitive impairment (MCI; N = 18). A greater degree of cognitive impairment, measured by the mini-mental state examination across all patient groups, was correlated with greater attenuation of functional connectivity, particularly over long connection distances, for example, between anterior and posterior components of the default mode network, and greater reduction of global and nodal network efficiency. These results indicate that neurodegenerative disruption of fMRI oscillations and connectivity in AD affects long-distance connections to hub nodes, with the consequent loss of network efficiency. This profile was evident also to a lesser degree in the patients with less severe cognitive impairment, indicating that the potential of resting-state fMRI measures as biomarkers or predictors of disease progression in AD. PMID:23314940

Effects of stimulants on brain function in attention-deficit/hyperactivity disorder: a systematic review and meta-analysis.

PubMed

Rubia, Katya; Alegria, Analucia A; Cubillo, Ana I; Smith, Anna B; Brammer, Michael J; Radua, Joaquim

2014-10-15

Psychostimulant medication, most commonly the catecholamine agonist methylphenidate, is the most effective treatment for attention-deficit/hyperactivity disorder (ADHD). However, relatively little is known on the mechanisms of action. Acute effects on brain function can elucidate underlying neurocognitive effects. We tested methylphenidate effects relative to placebo in functional magnetic resonance imaging (fMRI) during three disorder-relevant tasks in medication-naïve ADHD adolescents. In addition, we conducted a systematic review and meta-analysis of the fMRI findings of acute stimulant effects on ADHD brain function. The fMRI study compared 20 adolescents with ADHD under either placebo or methylphenidate in a randomized controlled trial while performing stop, working memory, and time discrimination tasks. The meta-analysis was conducted searching PubMed, ScienceDirect, Web of Knowledge, Google Scholar, and Scopus databases. Peak coordinates of clusters of significant effects of stimulant medication relative to placebo or off medication were extracted for each study. The fMRI analysis showed that methylphenidate significantly enhanced activation in bilateral inferior frontal cortex (IFC)/insula during inhibition and time discrimination but had no effect on working memory networks. The meta-analysis, including 14 fMRI datasets and 212 children with ADHD, showed that stimulants most consistently enhanced right IFC/insula activation, which also remained for a subgroup analysis of methylphenidate effects alone. A more lenient threshold also revealed increased putamen activation. Psychostimulants most consistently increase right IFC/insula activation, which are key areas of cognitive control and also the most replicated neurocognitive dysfunction in ADHD. These neurocognitive effects may underlie their positive clinical effects. © 2013 Society of Biological Psychiatry Published by Society of Biological Psychiatry All rights reserved.

Effects of Stimulants on Brain Function in Attention-Deficit/Hyperactivity Disorder: A Systematic Review and Meta-Analysis

PubMed Central

Rubia, Katya; Alegria, Analucia A.; Cubillo, Ana I.; Smith, Anna B.; Brammer, Michael J.; Radua, Joaquim

2014-01-01

Background Psychostimulant medication, most commonly the catecholamine agonist methylphenidate, is the most effective treatment for attention-deficit/hyperactivity disorder (ADHD). However, relatively little is known on the mechanisms of action. Acute effects on brain function can elucidate underlying neurocognitive effects. We tested methylphenidate effects relative to placebo in functional magnetic resonance imaging (fMRI) during three disorder-relevant tasks in medication-naïve ADHD adolescents. In addition, we conducted a systematic review and meta-analysis of the fMRI findings of acute stimulant effects on ADHD brain function. Methods The fMRI study compared 20 adolescents with ADHD under either placebo or methylphenidate in a randomized controlled trial while performing stop, working memory, and time discrimination tasks. The meta-analysis was conducted searching PubMed, ScienceDirect, Web of Knowledge, Google Scholar, and Scopus databases. Peak coordinates of clusters of significant effects of stimulant medication relative to placebo or off medication were extracted for each study. Results The fMRI analysis showed that methylphenidate significantly enhanced activation in bilateral inferior frontal cortex (IFC)/insula during inhibition and time discrimination but had no effect on working memory networks. The meta-analysis, including 14 fMRI datasets and 212 children with ADHD, showed that stimulants most consistently enhanced right IFC/insula activation, which also remained for a subgroup analysis of methylphenidate effects alone. A more lenient threshold also revealed increased putamen activation. Conclusions Psychostimulants most consistently increase right IFC/insula activation, which are key areas of cognitive control and also the most replicated neurocognitive dysfunction in ADHD. These neurocognitive effects may underlie their positive clinical effects. PMID:24314347

An automated method for identifying artifact in independent component analysis of resting-state FMRI.

PubMed

Bhaganagarapu, Kaushik; Jackson, Graeme D; Abbott, David F

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.

An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State fMRI

PubMed Central

Bhaganagarapu, Kaushik; Jackson, Graeme D.; Abbott, David F.

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available. PMID:23847511

Sparse representation of whole-brain fMRI signals for identification of functional networks.

PubMed

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

2015-02-01

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

Magnetic susceptibility induced echo time shifts: Is there a bias in age-related fMRI studies?

PubMed Central

Ngo, Giang-Chau; Wong, Chelsea N.; Guo, Steve; Paine, Thomas; Kramer, Arthur F.; Sutton, Bradley P.

2016-01-01

Purpose To evaluate the potential for bias in functional MRI (fMRI) aging studies resulting from age-related differences in magnetic field distributions which can impact echo time and functional contrast. Materials and Methods Magnetic field maps were taken on 31 younger adults (age: 22 ± 2.9 years) and 46 older adults (age: 66 ± 4.5 years) on a 3 T scanner. Using the spatial gradients of the magnetic field map for each participant, an echo planar imaging (EPI) trajectory was simulated. The effective echo time, time at which the k-space trajectory is the closest to the center of k-space, was calculated. This was used to examine both within-subject and across-age-group differences in the effective echo time maps. The Blood Oxygenation Level Dependent (BOLD) percent signal change resulting from those echo time shifts was also calculated to determine their impact on fMRI aging studies. Result For a single subject, the effective echo time varied as much as ± 5 ms across the brain. An unpaired t-test between the effective echo time across age group resulted in significant differences in several regions of the brain (p<0.01). The difference in echo time was only approximately 1 ms, however which is not expected to have an important impact on BOLD fMRI percent signal change (< 4%). Conclusion Susceptibility-induced magnetic field gradients induce local echo time shifts in gradient echo fMRI images, which can cause variable BOLD sensitivity across the brain. However, the age-related differences in BOLD signal are expected to be small for an fMRI study at 3 T. PMID:27299727

fMRI response during visual motion stimulation in patients with late whiplash syndrome.

PubMed

Freitag, P; Greenlee, M W; Wachter, K; Ettlin, T M; Radue, E W

2001-01-01

After whiplash trauma, up to one fourth of patients develop chronic symptoms including head and neck pain and cognitive disturbances. Resting perfusion single-photon-emission computed tomography (SPECT) found decreased temporoparietooccipital tracer uptake among these long-term symptomatic patients with late whiplash syndrome. As MT/MST (V5/V5a) are located in that area, this study addressed the question whether these patients show impairments in visual motion perception. We examined five symptomatic patients with late whiplash syndrome, five asymptomatic patients after whiplash trauma, and a control group of seven volunteers without the history of trauma. Tests for visual motion perception and functional magnetic resonance imaging (fMRI) measurements during visual motion stimulation were performed. Symptomatic patients showed a significant reduction in their ability to perceive coherent visual motion compared with controls, whereas the asymptomatic patients did not show this effect. fMRI activation was similar during random dot motion in all three groups, but was significantly decreased during coherent dot motion in the symptomatic patients compared with the other two groups. Reduced psychophysical motion performance and reduced fMRI responses in symptomatic patients with late whiplash syndrome both point to a functional impairment in cortical areas sensitive to coherent motion. Larger studies are needed to confirm these clinical and functional imaging results to provide a possible additional diagnostic criterion for the evaluation of patients with late whiplash syndrome.

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

PubMed

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

Dynamic fMRI of a decision-making task

NASA Astrophysics Data System (ADS)

Singh, Manbir; Sungkarat, Witaya

2008-03-01

A novel fMRI technique has been developed to capture the dynamics of the evolution of brain activity during complex tasks such as those designed to evaluate the neural basis of decision-making under different situations. A task called the Iowa Gambling Task was used as an example. Six normal human volunteers were studied. The task was presented inside a 3T MRI and a dynamic fMRI study of the approximately 2s period between the beginning and end of the decision-making period was conducted by employing a series of reference functions, separated by 200 ms, designed to capture activation at different time-points within this period. As decision-making culminates with a button-press, the timing of the button press was chosen as the reference (t=0) and corresponding reference functions were shifted backward in steps of 200ms from this point up to the time when motor activity from the previous button press became predominant. SPM was used to realign, high-pass filter (cutoff 200s), normalize to the Montreal Neurological Institute (MNI) Template using a 12 parameter affine/non-linear transformation, 8mm Gaussian smoothing, and event-related General Linear Model analysis for each of the shifted reference functions. The t-score of each activated voxel was then examined to find its peaking time. A random effect analysis (p<0.05) showed prefrontal, parietal and bi-lateral hippocampal activation peaking at different times during the decision making period in the n=6 group study.

An fMRI Investigation of the Neural Correlates Underlying the Processing of Novel Metaphoric Expressions

ERIC Educational Resources Information Center

Mashal, N.; Faust, M.; Hendler, T.; Jung-Beeman, M.

2007-01-01

The neural networks associated with processing related pairs of words forming literal, novel, and conventional metaphorical expressions and unrelated pairs of words were studied in a group of 15 normal adults using fMRI. Subjects read the four types of linguistic expressions and decided which relation exists between the two words (metaphoric,…

Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event-related fMRI.

PubMed

Asemani, Davud; Morsheddost, Hassan; Shalchy, Mahsa Alizadeh

2017-06-01

Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works. The HRF amplitude peaks are compared for three groups HRF of young, non-demented and demented elderly groups for both vision and motor regions. Prior works often reported significant differences in the HRF peak amplitude between the young and the elderly. The authors' experimentations show that the HRF peaks are not significantly different comparing the young adults with the elderly (either demented or non-demented). It is here demonstrated that the contradictory findings of the previous studies on the HRF peaks for the elderly compared with the young are originated from the noise contribution in fMRI data.

Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm.

PubMed

Belilovsky, Eugene; Gkirtzou, Katerina; Misyrlis, Michail; Konova, Anna B; Honorio, Jean; Alia-Klein, Nelly; Goldstein, Rita Z; Samaras, Dimitris; Blaschko, Matthew B

2015-12-01

We explore various sparse regularization techniques for analyzing fMRI data, such as the ℓ1 norm (often called LASSO in the context of a squared loss function), elastic net, and the recently introduced k-support norm. Employing sparsity regularization allows us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. In this work we consider sparse regularization in both the regression and classification settings. We perform experiments on fMRI scans from cocaine-addicted as well as healthy control subjects. We show that in many cases, use of the k-support norm leads to better predictive performance, solution stability, and interpretability as compared to other standard approaches. We additionally analyze the advantages of using the absolute loss function versus the standard squared loss which leads to significantly better predictive performance for the regularization methods tested in almost all cases. Our results support the use of the k-support norm for fMRI analysis and on the clinical side, the generalizability of the I-RISA model of cocaine addiction. Copyright © 2015 Elsevier Ltd. All rights reserved.

MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

PubMed Central

Plis, Sergey M.; Calhoun, Vince D.; Weisend, Michael P.; Eichele, Tom; Lane, Terran

2010-01-01

The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources. PMID:21120141

Final Report: Computer-aided Human Centric Cyber Situation Awareness

DTIC Science & Technology

2016-03-20

logs, OS audit trails, vulnerability reports, and packet dumps ), weeding out the false positives, grouping the related indicators so that different...short time duration of each visual stimulus in an fMRI study, we have designed “network security analysis cards ” that require the subject to...determine whether alerts in the cards indicate malicious events. Two types of visual displays of alerts (i.e., tabular display and node-link display) are

Sensing the effects of mouth breathing by using 3-tesla MRI

NASA Astrophysics Data System (ADS)

Park, Chan-A.; Kang, Chang-Ki

2017-06-01

We investigated the effects of mouth breathing and typical nasal breathing on brain function by using blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). The study had two parts: the first test was a simple contrast between mouth and nasal breathing, and the second test involved combined breathing modes, e.g., mouth inspiration and nasal expiration. Eleven healthy participants performed the combined breathing task while undergoing 3T fMRI. In the group-level analysis, contrast images acquired by using an individual participantlevel analysis were processed using the one-sample t test. We also conducted a region-of-interest analysis comparing signal intensity changes between the breathing modes; the region was selected using an automated anatomical labeling map. The results demonstrated that the BOLD signal in the hippocampus and brainstem was significantly decreased in mouth breathing relative to nasal breathing. On the other hand, both the precentral and postcentral gyri showed activation that was more significant in mouth breathing compared to nasal breathing. This study suggests that the BOLD activity patterns between mouth and nasal breathing may be induced differently, especially in the hippocampus, which could provide clues to explain the effects on brain cognitive function due to mouth breathing.

Effects of cue-exposure treatment on neural cue reactivity in alcohol dependence: a randomized trial.

PubMed

Vollstädt-Klein, Sabine; Loeber, Sabine; Kirsch, Martina; Bach, Patrick; Richter, Anne; Bühler, Mira; von der Goltz, Christoph; Hermann, Derik; Mann, Karl; Kiefer, Falk

2011-06-01

In alcohol-dependent patients, alcohol-associated cues elicit brain activation in mesocorticolimbic networks involved in relapse mechanisms. Cue-exposure based extinction training (CET) has been shown to be efficacious in the treatment of alcoholism; however, it has remained unexplored whether CET mediates its therapeutic effects via changes of activity in mesolimbic networks in response to alcohol cues. In this study, we assessed CET treatment effects on cue-induced responses using functional magnetic resonance imaging (fMRI). In a randomized controlled trial, abstinent alcohol-dependent patients were randomly assigned to a CET group (n = 15) or a control group (n = 15). All patients underwent an extended detoxification treatment comprising medically supervised detoxification, health education, and supportive therapy. The CET patients additionally received nine CET sessions over 3 weeks, exposing the patient to his/her preferred alcoholic beverage. Cue-induced fMRI activation to alcohol cues was measured at pretreatment and posttreatment. Compared with pretreatment, fMRI cue-reactivity reduction was greater in the CET relative to the control group, especially in the anterior cingulate gyrus and the insula, as well as limbic and frontal regions. Before treatment, increased cue-induced fMRI activation was found in limbic and reward-related brain regions and in visual areas. After treatment, the CET group showed less activation than the control group in the left ventral striatum. The study provides first evidence that an exposure-based psychotherapeutic intervention in the treatment of alcoholism impacts on brain areas relevant for addiction memory and attentional focus to alcohol-associated cues and affects mesocorticolimbic reward pathways suggested to be pathophysiologically involved in addiction. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.

PubMed

Du, Yuhui; Pearlson, Godfrey D; Lin, Dongdong; Sui, Jing; Chen, Jiayu; Salman, Mustafa; Tamminga, Carol A; Ivleva, Elena I; Sweeney, John A; Keshavan, Matcheri S; Clementz, Brett A; Bustillo, Juan; Calhoun, Vince D

2017-05-01

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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.

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.

Advanced MRI in Acute Military TBI

DTIC Science & Technology

2015-11-01

advanced MRI methods, DTI and resting-state fMRI correlation analysis, in military TBI patients acutely after injury and correlate findings with TBI...14 4 Introduction The objective of the project was to test two advanced MRI methods, DTI and resting-state fMRI correlation analysis, in...of Concussion Exam (MACE )(44) were reviewed. This brief cognitive test 279 assesses orientation, immediate verbal memory , concentration, and short

The future of fMRI in cognitive neuroscience.

PubMed

Poldrack, Russell A

2012-08-15

Over the last 20 years, fMRI has revolutionized cognitive neuroscience. Here I outline a vision for what the next 20 years of fMRI in cognitive neuroscience might look like. Some developments that I hope for include increased methodological rigor, an increasing focus on connectivity and pattern analysis as opposed to "blobology", a greater focus on selective inference powered by open databases, and increased use of ontologies and computational models to describe underlying processes. Copyright © 2011 Elsevier Inc. All rights reserved.

An fMRI Study of the Impact of Block Building and Board Games on Spatial Ability

PubMed Central

Newman, Sharlene D.; Hansen, Mitchell T.; Gutierrez, Arianna

2016-01-01

Previous studies have found that block play, board games, and puzzles result in better spatial ability. This study focused on examining the differential impact of structured block play and board games on spatial processing. Two groups of 8-year-old children were studied. One group participated in a five session block play training paradigm and the second group had a similar training protocol but played a word/spelling board game. A mental rotation task was assessed before and after training. The mental rotation task was performed during fMRI to observe the neural changes associated with the two play protocols. Only the block play group showed effects of training for both behavioral measures and fMRI measured brain activation. Behaviorally, the block play group showed improvements in both reaction time and accuracy. Additionally, the block play group showed increased involvement of regions that have been linked to spatial working memory and spatial processing after training. The board game group showed non-significant improvements in mental rotation performance, likely related to practice effects, and no training related brain activation differences. While the current study is preliminary, it does suggest that different “spatial” play activities have differential impacts on spatial processing with structured block play but not board games showing a significant impact on mental rotation performance. PMID:27621714

Intrusive Memories of Distressing Information: An fMRI Study

PubMed Central

Battaglini, Eva; Liddell, Belinda; Das, Pritha; Malhi, Gin; Felmingham, Kim

2016-01-01

Although intrusive memories are characteristic of many psychological disorders, the neurobiological underpinning of these involuntary recollections are largely unknown. In this study we used functional magentic resonance imaging (fMRI) to identify the neural networks associated with encoding of negative stimuli that are subsequently experienced as intrusive memories. Healthy partipants (N = 42) viewed negative and neutral images during a visual/verbal processing task in an fMRI context. Two days later they were assessed on the Impact of Event Scale for occurrence of intrusive memories of the encoded images. A sub-group of participants who reported significant intrusions (n = 13) demonstrated stronger activation in the amygdala, bilateral ACC and parahippocampal gyrus during verbal encoding relative to a group who reported no intrusions (n = 13). Within-group analyses also revealed that the high intrusion group showed greater activity in the dorsomedial (dmPFC) and dorsolateral prefrontal cortex (dlPFC), inferior frontal gyrus and occipital regions during negative verbal processing compared to neutral verbal processing. These results do not accord with models of intrusions that emphasise visual processing of information at encoding but are consistent with models that highlight the role of inhibitory and suppression processes in the formation of subsequent intrusive memories. PMID:27685784

A shift to randomness of brain oscillations in people with autism.

PubMed

Lai, Meng-Chuan; Lombardo, Michael V; Chakrabarti, Bhismadev; Sadek, Susan A; Pasco, Greg; Wheelwright, Sally J; Bullmore, Edward T; Baron-Cohen, Simon; Suckling, John

2010-12-15

Resting-state functional magnetic resonance imaging (fMRI) enables investigation of the intrinsic functional organization of the brain. Fractal parameters such as the Hurst exponent, H, describe the complexity of endogenous low-frequency fMRI time series on a continuum from random (H = .5) to ordered (H = 1). Shifts in fractal scaling of physiological time series have been associated with neurological and cardiac conditions. Resting-state fMRI time series were recorded in 30 male adults with an autism spectrum condition (ASC) and 33 age- and IQ-matched male volunteers. The Hurst exponent was estimated in the wavelet domain and between-group differences were investigated at global and voxel level and in regions known to be involved in autism. Complex fractal scaling of fMRI time series was found in both groups but globally there was a significant shift to randomness in the ASC (mean H = .758, SD = .045) compared with neurotypical volunteers (mean H = .788, SD = .047). Between-group differences in H, which was always reduced in the ASC group, were seen in most regions previously reported to be involved in autism, including cortical midline structures, medial temporal structures, lateral temporal and parietal structures, insula, amygdala, basal ganglia, thalamus, and inferior frontal gyrus. Severity of autistic symptoms was negatively correlated with H in retrosplenial and right anterior insular cortex. Autism is associated with a small but significant shift to randomness of endogenous brain oscillations. Complexity measures may provide physiological indicators for autism as they have done for other medical conditions. Copyright © 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Contrasting brain patterns of writing-related DTI parameters, fMRI connectivity, and DTI-fMRI connectivity correlations in children with and without dysgraphia or dyslexia.

PubMed

Richards, T L; Grabowski, T J; Boord, P; Yagle, K; Askren, M; Mestre, Z; Robinson, P; Welker, O; Gulliford, D; Nagy, W; Berninger, V

2015-01-01

Based on comprehensive testing and educational history, children in grades 4-9 (on average 12 years) were diagnosed with dysgraphia (persisting handwriting impairment) or dyslexia (persisting word spelling/reading impairment) or as typical writers and readers (controls). The dysgraphia group (n = 14) and dyslexia group (n = 17) were each compared to the control group (n = 9) and to each other in separate analyses. Four brain region seed points (left occipital temporal gyrus, supramarginal gyrus, precuneus, and inferior frontal gyrus) were used in these analyses which were shown in a metaanalysis to be related to written word production on four indicators of white matter integrity and fMRI functional connectivity for four tasks (self-guided mind wandering during resting state, writing letter that follows a visually displayed letter in alphabet, writing missing letter to create a correctly spelled real word, and planning for composing after scanning on topic specified by researcher). For those DTI indicators on which the dysgraphic group or dyslexic group differed from the control group (fractional anisotropy, relative anisotropy, axial diffusivity but not radial diffusivity), correlations were computed between the DTI parameter and fMRI functional connectivity for the two writing tasks (alphabet and spelling) by seed points. Analyses, controlled for multiple comparisons, showed that (a) the control group exhibited more white matter integrity than either the dysgraphic or dyslexic group; (b) the dysgraphic and dyslexic groups showed more functional connectivity than the control group but differed in patterns of functional connectivity for task and seed point; and (c) the dysgraphic and dyslexic groups showed different patterns of significant DTI-fMRI connectivity correlations for specific seed points and written language tasks. Thus, dysgraphia and dyslexia differ in white matter integrity, fMRI functional connectivity, and white matter-gray matter correlations. Of clinical relevance, brain differences were observed in dysgraphia and dyslexia on written language tasks yoked to their defining behavioral impairments in handwriting and/or in word spelling and on the cognitive mind wandering rest condition and composition planning.

Contrasting brain patterns of writing-related DTI parameters, fMRI connectivity, and DTI–fMRI connectivity correlations in children with and without dysgraphia or dyslexia

PubMed Central

Richards, T.L.; Grabowski, T.J.; Boord, P.; Yagle, K.; Askren, M.; Mestre, Z.; Robinson, P.; Welker, O.; Gulliford, D.; Nagy, W.; Berninger, V.

2015-01-01

Based on comprehensive testing and educational history, children in grades 4–9 (on average 12 years) were diagnosed with dysgraphia (persisting handwriting impairment) or dyslexia (persisting word spelling/reading impairment) or as typical writers and readers (controls). The dysgraphia group (n = 14) and dyslexia group (n = 17) were each compared to the control group (n = 9) and to each other in separate analyses. Four brain region seed points (left occipital temporal gyrus, supramarginal gyrus, precuneus, and inferior frontal gyrus) were used in these analyses which were shown in a metaanalysis to be related to written word production on four indicators of white matter integrity and fMRI functional connectivity for four tasks (self-guided mind wandering during resting state, writing letter that follows a visually displayed letter in alphabet, writing missing letter to create a correctly spelled real word, and planning for composing after scanning on topic specified by researcher). For those DTI indicators on which the dysgraphic group or dyslexic group differed from the control group (fractional anisotropy, relative anisotropy, axial diffusivity but not radial diffusivity), correlations were computed between the DTI parameter and fMRI functional connectivity for the two writing tasks (alphabet and spelling) by seed points. Analyses, controlled for multiple comparisons, showed that (a) the control group exhibited more white matter integrity than either the dysgraphic or dyslexic group; (b) the dysgraphic and dyslexic groups showed more functional connectivity than the control group but differed in patterns of functional connectivity for task and seed point; and (c) the dysgraphic and dyslexic groups showed different patterns of significant DTI–fMRI connectivity correlations for specific seed points and written language tasks. Thus, dysgraphia and dyslexia differ in white matter integrity, fMRI functional connectivity, and white matter–gray matter correlations. Of clinical relevance, brain differences were observed in dysgraphia and dyslexia on written language tasks yoked to their defining behavioral impairments in handwriting and/or in word spelling and on the cognitive mind wandering rest condition and composition planning. PMID:26106566

RETROSPECTIVE DETECTION OF INTERLEAVED SLICE ACQUISITION PARAMETERS FROM FMRI DATA

PubMed Central

Parker, David; Rotival, Georges; Laine, Andrew; Razlighi, Qolamreza R.

2015-01-01

To minimize slice excitation leakage to adjacent slices, interleaved slice acquisition is nowadays performed regularly in fMRI scanners. In interleaved slice acquisition, the number of slices skipped between two consecutive slice acquisitions is often referred to as the ‘interleave parameter’; the loss of this parameter can be catastrophic for the analysis of fMRI data. In this article we present a method to retrospectively detect the interleave parameter and the axis in which it is applied. Our method relies on the smoothness of the temporal-distance correlation function, which becomes disrupted along the axis on which interleaved slice acquisition is applied. We examined this method on simulated and real data in the presence of fMRI artifacts such as physiological noise, motion, etc. We also examined the reliability of this method in detecting different types of interleave parameters and demonstrated an accuracy of about 94% in more than 1000 real fMRI scans. PMID:26161244

Measuring Asymmetric Interactions in Resting State Brain Networks*

PubMed Central

Joshi, Anand A.; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M.

2015-01-01

Directed graph representations of brain networks are increasingly being used in brain image analysis to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

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.

Corpus Callosum Diffusion and Language Lateralization in Patients with Brain Tumors: A DTI and fMRI Study.

PubMed

Tantillo, Gabriella; Peck, Kyung K; Arevalo-Perez, Julio; Lyo, John K; Chou, Joanne F; Young, Robert J; Brennan, Nicole Petrovich; Holodny, Andrei I

2016-01-01

Examining how left-hemisphere brain tumors might impact both the microstructure of the corpus callosum (CC) as measured by fractional anisotropy (FA) values in diffusion tensor imaging (DTI) as well as cortical language lateralization measured with functional MRI (fMRI). fMRI tasks (phonemic fluency and verb generation) were performed in order to detect activation in Broca's and Wernicke's area. Twenty patients with left-hemisphere brain tumors were investigated. fMRI results were divided into left dominant (LD), right dominant (RD), or codominant (CD) for language function. DTI was performed to generate FA maps in the anterior and posterior CC. FA values were correlated with the degree of language dominance. Patients who were LD or RD for language in Broca's area had lower FA in the anterior CC than those who were CD for language (median for CD = .72, LD = .66, RD = .65, P < .09). Lateralized versus CD group level analysis also showed that CD patients had higher FA in the anterior CC than patients who displayed strong lateralization in either hemisphere (median for CD = .72, lateralized = .65, P < .05). Our preliminary observations indicate that the greater FA in CD patients may reflect a more directional microstructure for the CC in this region, suggesting a greater need for interhemispheric transfer of information. Because brain tumors can cause compensatory codominance, our findings may suggest a mechanism by which interhemispheric transfer is facilitated during plasticity in the presence of a tumor. Copyright © 2015 by the American Society of Neuroimaging.

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.

Association between amygdala response to emotional faces and social anxiety in autism spectrum disorders.

PubMed

Kleinhans, Natalia M; Richards, Todd; Weaver, Kurt; Johnson, L Clark; Greenson, Jessica; Dawson, Geraldine; Aylward, Elizabeth

2010-10-01

Difficulty interpreting facial expressions has been reported in autism spectrum disorders (ASD) and is thought to be associated with amygdala abnormalities. To further explore the neural basis of abnormal emotional face processing in ASD, we conducted an fMRI study of emotional face matching in high-functioning adults with ASD and age, IQ, and gender matched controls. In addition, we investigated whether there was a relationship between self-reported social anxiety and fMRI activation. During fMRI scanning, study participants were instructed to match facial expressions depicting fear or anger. The control condition was a comparable shape-matching task. The control group evidenced significantly increased left prefrontal activation and decreased activation in the occipital lobes compared to the ASD group during emotional face matching. Further, within the ASD group, greater social anxiety was associated with increased activation in right amygdala and left middle temporal gyrus, and decreased activation in the fusiform face area. These results indicate that level of social anxiety mediates the neural response to emotional face perception in ASD. Copyright © 2010 Elsevier Ltd. All rights reserved.

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.

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…

Voluntary Explicit versus Involuntary Conceptual Memory Are Associated with Dissociable fMRI Responses in Hippocampus, Amygdala, and Parietal Cortex for Emotional and Neutral Word Pairs

ERIC Educational Resources Information Center

Ramponi, Cristina; Barnard, Philip J.; Kherif, Ferath; Henson, Richard N.

2011-01-01

Although functional neuroimaging studies have supported the distinction between explicit and implicit forms of memory, few have matched explicit and implicit tests closely, and most of these tested perceptual rather than conceptual implicit memory. We compared event-related fMRI responses during an intentional test, in which a group of…

Functional MRI evidence for fine motor praxis dysfunction in children with persistent speech disorders.

PubMed

Redle, Erin; Vannest, Jennifer; Maloney, Thomas; Tsevat, Rebecca K; Eikenberry, Sarah; Lewis, Barbara; Shriberg, Lawrence D; Tkach, Jean; Holland, Scott K

2015-02-09

Children with persistent speech disorders (PSD) often present with overt or subtle motor deficits; the possibility that speech disorders and motor deficits could arise from a shared neurological base is currently unknown. Functional MRI (fMRI) was used to examine the brain networks supporting fine motor praxis in children with PSD and without clinically identified fine motor deficits. This case-control study included 12 children with PSD (mean age 7.42 years, four female) and 12 controls (mean age 7.44 years, four female). Children completed behavioral evaluations using standardized motor assessments and parent reported functional measures. During fMRI scanning, participants completed a cued finger tapping task contrasted passive listening. A general linear model approach identified brain regions associated with finger tapping in each group and regions that differed between groups. The relationship between regional fMRI activation and fine motor skill was assessed using a regression analysis. Children with PSD had significantly poorer results for rapid speech production and fine motor praxis skills, but did not differ on classroom functional skills. Functional MRI results showed that children with PSD had significantly more activation in the cerebellum during finger tapping. Positive correlations between performance on a fine motor praxis test and activation multiple cortical regions were noted for children with PSD but not for controls. Over-activation in the cerebellum during a motor task may reflect a subtle abnormality in the non-speech motor neural circuitry in children with PSD. Copyright © 2014 Elsevier B.V. All rights reserved.

Presurgical functional magnetic resonance imaging in patients with brain tumors.

PubMed

Ravn, Søren; Holmberg, Mats; Sørensen, Preben; Frokjaer, Jens B; Carl, Jesper

2016-01-01

Clinical functional magnetic resonance imaging (fMRI) is still an upcoming diagnostic tool because it is time-consuming to perform the post-scan calculations and interpretations. A standardized and easily used method for the clinical assessment of fMRI scans could decrease the workload and make fMRI more attractive for clinical use. To evaluate a standardized clinical approach for distance measurement between benign brain tumors and eloquent cortex in terms of the ability to predict pre- and postoperative neurological deficits after intraoperative neuronavigation-assisted surgery. A retrospective study of 34 patients. The fMRI data were reanalyzed using a standardized distance measurement procedure combining data from both fMRI and three-dimensional T1 MRI scans. The pre- and postoperative neurological status of each patient was obtained from hospital records. Data analysis was performed using logistic regression analysis to determine whether the distance measured between the tumor margin and fMRI activity could serve as a predictor for neurological deficits. An odds ratio of 0.89 mm(-1) (P = 0.03) was found between the risk of preoperative neurological motor deficits and the tumor-fMRI distance. An odds ratio of 0.82 mm(-1) (P = 0.04) was found between the risk of additional postoperative neurological motor deficits and the tumor-fMRI distance. The tumor was radically removed in 10 cases; five patients experienced additional postoperative motor deficits (tumor-fMRI distance <18 mm) and five did not (tumor-fMRI distance >18 mm) (P = 0.008). This study indicates that the distance measured between the tumor margin and fMRI activation could serve as a valuable predictor of neurological motor deficits. © The Foundation Acta Radiologica 2014.

Monkey cortex through fMRI glasses

PubMed Central

Vanduffel, Wim; Zhu, Qi; Orban, Guy A.

2015-01-01

In 1998 several groups reported the feasibility of functional magnetic resonance imaging (fMRI) experiments in monkeys, with the goal to bridge the gap between invasive nonhuman primate studies and human functional imaging. These studies yielded critical insights in the neuronal underpinnings of the BOLD signal. Furthermore, the technology has been successful in guiding electrophysiological recordings and identifying focal perturbation targets. Finally, invaluable information was obtained concerning human brain evolution. We here provide a comprehensive overview of awake monkey fMRI studies mainly confined to the visual system. We review the latest insights about the topographic organization of monkey visual cortex and discuss the spatial relationships between retinotopy and category and feature selective clusters. We briefly discuss the functional layout of parietal and frontal cortex and continue with a summary of some fascinating functional and effective connectivity studies. Finally, we review recent comparative fMRI experiments and speculate about the future of nonhuman primate imaging. PMID:25102559

Monkey cortex through fMRI glasses.

PubMed

Vanduffel, Wim; Zhu, Qi; Orban, Guy A

2014-08-06

In 1998 several groups reported the feasibility of fMRI experiments in monkeys, with the goal to bridge the gap between invasive nonhuman primate studies and human functional imaging. These studies yielded critical insights in the neuronal underpinnings of the BOLD signal. Furthermore, the technology has been successful in guiding electrophysiological recordings and identifying focal perturbation targets. Finally, invaluable information was obtained concerning human brain evolution. We here provide a comprehensive overview of awake monkey fMRI studies mainly confined to the visual system. We review the latest insights about the topographic organization of monkey visual cortex and discuss the spatial relationships between retinotopy and category- and feature-selective clusters. We briefly discuss the functional layout of parietal and frontal cortex and continue with a summary of some fascinating functional and effective connectivity studies. Finally, we review recent comparative fMRI experiments and speculate about the future of nonhuman primate imaging. Copyright © 2014 Elsevier Inc. All rights reserved.

Memory Performance and fMRI Signal in Presymptomatic Familial Alzheimer’s Disease

PubMed Central

Braskie, Meredith N.; Medina, Luis D.; Rodriguez-Agudelo, Yaneth; Geschwind, Daniel H.; Macias-Islas, Miguel Angel; Thompson, Paul M.; Cummings, Jeffrey L.; Bookheimer, Susan Y.; Ringman, John M.

2013-01-01

Rare autosomal dominant mutations result in familial Alzheimer’s disease (FAD) with a relatively consistent age of onset within families. This provides an estimate of years until disease onset (relative age) in mutation carriers. Increased AD risk has been associated with differences in functional magnetic resonance imaging (fMRI) activity during memory tasks, but most of these studies have focused on possession of apolipoprotein E allele 4 (APOE4), a risk factor, but not causative variant, of late-onset AD. Evaluation of fMRI activity in presymptomatic FAD mutation carriers versus noncarriers provides insight into preclinical changes in those who will certainly develop AD in a prescribed period of time. Adults from FAD mutation-carrying families (nine mutation carriers, eight noncarriers) underwent fMRI scanning while performing a memory task. We examined fMRI signal differences between carriers and noncarriers, and how signal related to fMRI task performance within mutation status group, controlling for relative age and education. Mutation noncarriers had greater retrieval period activity than carriers in several AD-relevant regions, including the left hippocampus. Better performing noncarriers showed greater encoding period activity including in the parahippocampal gyrus. Poorer performing carriers showed greater retrieval period signal, including in the frontal and temporal lobes, suggesting underlying pathological processes. PMID:22806961

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

PubMed

McDonald, Amalia R; Muraskin, Jordan; Dam, Nicholas T Van; Froehlich, Caroline; Puccio, Benjamin; Pellman, John; Bauer, Clemens C C; Akeyson, Alexis; Breland, Melissa M; Calhoun, Vince D; Carter, Steven; Chang, Tiffany P; Gessner, Chelsea; Gianonne, Alyssa; Giavasis, Steven; Glass, Jamie; Homann, Steven; King, Margaret; Kramer, Melissa; Landis, Drew; Lieval, Alexis; Lisinski, Jonathan; Mackay-Brandt, Anna; Miller, Brittny; Panek, Laura; Reed, Hayley; Santiago, Christine; Schoell, Eszter; Sinnig, Richard; Sital, Melissa; Taverna, Elise; Tobe, Russell; Trautman, Kristin; Varghese, Betty; Walden, Lauren; Wang, Runtang; Waters, Abigail B; Wood, Dylan C; Castellanos, F Xavier; Leventhal, Bennett; Colcombe, Stanley J; LaConte, Stephen; Milham, Michael P; Craddock, R Cameron

2017-02-01

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

A comparison of functional magnetic resonance imaging findings in children with and without a history of early exposure to general anesthesia.

PubMed

Taghon, Thomas A; Masunga, Abigail N; Small, Robert H; Kashou, Nasser H

2015-03-01

Functional magnetic resonance imaging (fMRI) has been used to evaluate the long-term consequences of early exposure to neurotoxic agents. fMRI shows that different patterns of brain activation occur in ethanol-exposed subjects performing a go/no-go response inhibition task. Pharmacologically, ethanol and general anesthetics have similar receptor-level activity in the brain. This study utilizes fMRI to examine brain activation patterns in children exposed to general anesthesia and surgery during early brain development. After obtaining Nationwide Children's Hospital IRB approval, a surgical database was utilized to identify children aged 10-17 years with a history of at least 1 h of exposure to general anesthetics and surgery when they were between 0 and 24 months of age. Age- and gender-matched children without anesthesia exposure were recruited as a control group. All subjects were scanned while being presented with a go/no-go response inhibition task. Reaction time and accuracy data were acquired, and the blood-oxygen-level-dependent (BOLD) fMRI signal was measured as a biomarker for regional neuronal activity. There were no differences in terms of performance accuracy and response time. The analysis did not reveal any significant activation differences in the primary region of interest (prefrontal cortex and caudate nucleus); however, activation differences were seen in other structures, including the cerebellum, cingulate gyrus, and paracentral lobule. Early anesthetic exposure and surgery did not affect accuracy, response time, or activation patterns in the primary region of interest during performance of the task. Intergroup differences in activation patterns in other areas of the brain were observed, and the significance of these findings is unknown. fMRI appears to be a useful tool in evaluating the long-term effects of early exposure to general anesthesia. © 2015 John Wiley & Sons Ltd.

Integrating EEG and fMRI in epilepsy.

PubMed

Formaggio, Emanuela; Storti, Silvia Francesca; Bertoldo, Alessandra; Manganotti, Paolo; Fiaschi, Antonio; Toffolo, Gianna Maria

2011-02-14

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes. Copyright © 2010 Elsevier Inc. All rights reserved.

Hybrid ICA-Bayesian network approach reveals distinct effective connectivity differences in schizophrenia.

PubMed

Kim, D; Burge, J; Lane, T; Pearlson, G D; Kiehl, K A; Calhoun, V D

2008-10-01

We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge, J., Lane, T., Link, H., Qiu, S., Clark, V.P., 2007. Discrete dynamic Bayesian network analysis of fMRI data. Hum Brain Mapp.) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge, J., Lane, T., 2005. Learning Class-Discriminative Dynamic Bayesian Networks. Proceedings of the International Conference on Machine Learning, Bonn, Germany, pp. 97-104.). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, A., 1991. Probability, random variables, and stochastic processes. McGraw-Hill, New York.). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal, frontal, and cerebellar regions during an auditory paradigm.

The functional organization of trial-related activity in lexical processing after early left hemispheric brain lesions: An event-related fMRI study

PubMed Central

Fair, Damien A.; Choi, Alexander H.; Dosenbach, Yannic B.L.; Coalson, Rebecca S.; Miezin, Francis M.; Petersen, Steven E.; Schlaggar, Bradley L.

2009-01-01

Children with congenital left hemisphere damage due to perinatal stroke are capable of acquiring relatively normal language functions despite experiencing a cortical insult that in adults often leads to devastating lifetime disabilities. Although this observed phenomenon accepted, its neurobiological mechanisms are not well characterized. In this paper we examined the functional neuroanatomy of lexical processing in 13 children/adolescents with perinatal left hemispheric damage. In contrast to many previous perinatal infarct fMRI studies, we use an event-related design, which allowed us to isolate trial related activity and examine correct and error trials separately. Using both group and single subject analysis techniques we attempt to address several methodological factors that may contribute to some discrepancies in the perinatal lesion literature. These methodological factors include making direct statistical comparisons, using common stereotactic space, using both single-subject and group analyses, and accounting for performance differences. Our group analysis, investigating correct trial related activity (separately from error trials), showed very few statistical differences in the non-involved right hemisphere between patients and performance matched controls. The single subject analysis revealed atypical regional activation patterns in several patients; however, the location of these regions identified in individual patients often varied across subjects. These results are consistent with the idea that alternative functional organization of trial-related activity after left hemisphere lesions is in large part unique to the individual. In addition, reported differences between results obtained with event-related designs and blocked designs may suggest diverging organizing principles for sustained and trial-related activity after early childhood brain injuries. PMID:19819000

Visual hallucinations are associated with hyperconnectivity between the amygdala and visual cortex in people with a diagnosis of schizophrenia.

PubMed

Ford, Judith M; Palzes, Vanessa A; Roach, Brian J; Potkin, Steven G; van Erp, Theo G M; Turner, Jessica A; Mueller, Bryon A; Calhoun, Vincent D; Voyvodic, Jim; Belger, Aysenil; Bustillo, Juan; Vaidya, Jatin G; Preda, Adrian; McEwen, Sarah C; Mathalon, Daniel H

2015-01-01

While auditory verbal hallucinations (AH) are a cardinal symptom of schizophrenia, people with a diagnosis of schizophrenia (SZ) may also experience visual hallucinations (VH). In a retrospective analysis of a large sample of SZ and healthy controls (HC) studied as part of the functional magnetic resonance imaging (fMRI) Biomedical Informatics Research Network (FBIRN), we asked if SZ who endorsed experiencing VH during clinical interviews had greater connectivity between visual cortex and limbic structures than SZ who did not endorse experiencing VH. We analyzed resting state fMRI data from 162 SZ and 178 age- and gender-matched HC. SZ were sorted into groups according to clinical ratings on AH and VH: SZ with VH (VH-SZ; n = 45), SZ with AH but no VH (AH-SZ; n = 50), and SZ with neither AH nor VH (NoH-SZ; n = 67). Our primary analysis was seed based, extracting connectivity between visual cortex and the amygdala (because of its role in fear and negative emotion) and visual cortex and the hippocampus (because of its role in memory). Compared with the other groups, VH-SZ showed hyperconnectivity between the amygdala and visual cortex, specifically BA18, with no differences in connectivity among the other groups. In a voxel-wise, whole brain analysis comparing VH-SZ with AH-SZ, the amygdala was hyperconnected to left temporal pole and inferior frontal gyrus in VH-SZ, likely due to their more severe thought broadcasting. VH-SZ have hyperconnectivity between subcortical areas subserving emotion and cortical areas subserving higher order visual processing, providing biological support for distressing VH in schizophrenia. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Visual Hallucinations Are Associated With Hyperconnectivity Between the Amygdala and Visual Cortex in People With a Diagnosis of Schizophrenia

PubMed Central

Ford, Judith M.; Palzes, Vanessa A.; Roach, Brian J.; Potkin, Steven G.; van Erp, Theo G. M.; Turner, Jessica A.; Mueller, Bryon A.; Calhoun, Vincent D.; Voyvodic, Jim; Belger, Aysenil; Bustillo, Juan; Vaidya, Jatin G.; Preda, Adrian; McEwen, Sarah C.; Mathalon, Daniel H.

2015-01-01

Introduction: While auditory verbal hallucinations (AH) are a cardinal symptom of schizophrenia, people with a diagnosis of schizophrenia (SZ) may also experience visual hallucinations (VH). In a retrospective analysis of a large sample of SZ and healthy controls (HC) studied as part of the functional magnetic resonance imaging (fMRI) Biomedical Informatics Research Network (FBIRN), we asked if SZ who endorsed experiencing VH during clinical interviews had greater connectivity between visual cortex and limbic structures than SZ who did not endorse experiencing VH. Methods: We analyzed resting state fMRI data from 162 SZ and 178 age- and gender-matched HC. SZ were sorted into groups according to clinical ratings on AH and VH: SZ with VH (VH-SZ; n = 45), SZ with AH but no VH (AH-SZ; n = 50), and SZ with neither AH nor VH (NoH-SZ; n = 67). Our primary analysis was seed based, extracting connectivity between visual cortex and the amygdala (because of its role in fear and negative emotion) and visual cortex and the hippocampus (because of its role in memory). Results: Compared with the other groups, VH-SZ showed hyperconnectivity between the amygdala and visual cortex, specifically BA18, with no differences in connectivity among the other groups. In a voxel-wise, whole brain analysis comparing VH-SZ with AH-SZ, the amygdala was hyperconnected to left temporal pole and inferior frontal gyrus in VH-SZ, likely due to their more severe thought broadcasting. Conclusions: VH-SZ have hyperconnectivity between subcortical areas subserving emotion and cortical areas subserving higher order visual processing, providing biological support for distressing VH in schizophrenia. PMID:24619536

The functional organization of trial-related activity in lexical processing after early left hemispheric brain lesions: An event-related fMRI study.

PubMed

Fair, Damien A; Choi, Alexander H; Dosenbach, Yannic B L; Coalson, Rebecca S; Miezin, Francis M; Petersen, Steven E; Schlaggar, Bradley L

2010-08-01

Children with congenital left hemisphere damage due to perinatal stroke are capable of acquiring relatively normal language functions despite experiencing a cortical insult that in adults often leads to devastating lifetime disabilities. Although this observed phenomenon is accepted, its neurobiological mechanisms are not well characterized. In this paper we examined the functional neuroanatomy of lexical processing in 13 children/adolescents with perinatal left hemispheric damage. In contrast to many previous perinatal infarct fMRI studies, we used an event-related design, which allowed us to isolate trial-related activity and examine correct and error trials separately. Using both group and single subject analysis techniques we attempt to address several methodological factors that may contribute to some discrepancies in the perinatal lesion literature. These methodological factors include making direct statistical comparisons, using common stereotactic space, using both single subject and group analyses, and accounting for performance differences. Our group analysis, investigating correct trial-related activity (separately from error trials), showed very few statistical differences in the non-involved right hemisphere between patients and performance matched controls. The single subject analysis revealed atypical regional activation patterns in several patients; however, the location of these regions identified in individual patients often varied across subjects. These results are consistent with the idea that alternative functional organization of trial-related activity after left hemisphere lesions is in large part unique to the individual. In addition, reported differences between results obtained with event-related designs and blocked designs may suggest diverging organizing principles for sustained and trial-related activity after early childhood brain injuries. 2009 Elsevier Inc. All rights reserved.

Cerebral correlates of heart rate variations during a spontaneous panic attack in the fMRI scanner.

PubMed

Spiegelhalder, Kai; Hornyak, Magdolna; Kyle, Simon David; Paul, Dominik; Blechert, Jens; Seifritz, Erich; Hennig, Jürgen; Tebartz van Elst, Ludger; Riemann, Dieter; Feige, Bernd

2009-12-01

We report the first published case study of a suddenly occurring panic attack in a patient with no prior history of panic disorder during combined functional magnetic resonance imaging (fMRI, 1.5 Tesla) and electrocardiogram (ECG) recording. The single case was a 46-year-old woman who developed a panic attack near the planned end of the fMRI acquisition session, which therefore had to be aborted. Correlational analysis of heart rate fluctuations and fMRI data revealed a significant negative association in the left middle temporal gyrus. Additionally, regions-of-interest (ROI) analyses indicated significant positive associations in the left amygdala, and trends towards significance in the right amygdala and left insula.

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.

The power of emotional valence-from cognitive to affective processes in reading.

PubMed

Altmann, Ulrike; Bohrn, Isabel C; Lubrich, Oliver; Menninghaus, Winfried; Jacobs, Arthur M

2012-01-01

The comprehension of stories requires the reader to imagine the cognitive and affective states of the characters. The content of many stories is unpleasant, as they often deal with conflict, disturbance or crisis. Nevertheless, unpleasant stories can be liked and enjoyed. In this fMRI study, we used a parametric approach to examine (1) the capacity of increasing negative valence of story contents to activate the mentalizing network (cognitive and affective theory of mind, ToM), and (2) the neural substrate of liking negatively valenced narratives. A set of 80 short narratives was compiled, ranging from neutral to negative emotional valence. For each story mean rating values on valence and liking were obtained from a group of 32 participants in a prestudy, and later included as parametric regressors in the fMRI analysis. Another group of 24 participants passively read the narratives in a three Tesla MRI scanner. Results revealed a stronger engagement of affective ToM-related brain areas with increasingly negative story valence. Stories that were unpleasant, but simultaneously liked, engaged the medial prefrontal cortex (mPFC), which might reflect the moral exploration of the story content. Further analysis showed that the more the mPFC becomes engaged during the reading of negatively valenced stories, the more coactivation can be observed in other brain areas related to the neural processing of affective ToM and empathy.

The power of emotional valence—from cognitive to affective processes in reading

PubMed Central

Altmann, Ulrike; Bohrn, Isabel C.; Lubrich, Oliver; Menninghaus, Winfried; Jacobs, Arthur M.

2012-01-01

The comprehension of stories requires the reader to imagine the cognitive and affective states of the characters. The content of many stories is unpleasant, as they often deal with conflict, disturbance or crisis. Nevertheless, unpleasant stories can be liked and enjoyed. In this fMRI study, we used a parametric approach to examine (1) the capacity of increasing negative valence of story contents to activate the mentalizing network (cognitive and affective theory of mind, ToM), and (2) the neural substrate of liking negatively valenced narratives. A set of 80 short narratives was compiled, ranging from neutral to negative emotional valence. For each story mean rating values on valence and liking were obtained from a group of 32 participants in a prestudy, and later included as parametric regressors in the fMRI analysis. Another group of 24 participants passively read the narratives in a three Tesla MRI scanner. Results revealed a stronger engagement of affective ToM-related brain areas with increasingly negative story valence. Stories that were unpleasant, but simultaneously liked, engaged the medial prefrontal cortex (mPFC), which might reflect the moral exploration of the story content. Further analysis showed that the more the mPFC becomes engaged during the reading of negatively valenced stories, the more coactivation can be observed in other brain areas related to the neural processing of affective ToM and empathy. PMID:22754519

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

A New Paradigm for Individual Subject Language Mapping: Movie-Watching fMRI.

PubMed

Tie, Yanmei; Rigolo, Laura; Ozdemir Ovalioglu, Aysegul; Olubiyi, Olutayo; Doolin, Kelly L; Mukundan, Srinivasan; Golby, Alexandra J

2015-01-01

Functional MRI (fMRI) based on language tasks has been used in presurgical language mapping in patients with lesions in or near putative language areas. However, if patients have difficulty performing the tasks due to neurological deficits, it leads to unreliable or noninterpretable results. In this study, we investigate the feasibility of using a movie-watching fMRI for language mapping. A 7-minute movie clip with contrasting speech and nonspeech segments was shown to 22 right-handed healthy subjects. Based on all subjects' language functional regions-of-interest, 6 language response areas were defined, within which a language response model (LRM) was derived by extracting the main temporal activation profile. Using a leave-one-out procedure, individuals' language areas were identified as the areas that expressed highly correlated temporal responses with the LRM derived from an independent group of subjects. Compared with an antonym generation task-based fMRI, the movie-watching fMRI generated language maps with more localized activations in the left frontal language area, larger activations in the left temporoparietal language area, and significant activations in their right-hemisphere homologues. Results of 2 brain tumor patients' movie-watching fMRI using the LRM derived from the healthy subjects indicated its ability to map putative language areas; while their task-based fMRI maps were less robust and noisier. These results suggest that it is feasible to use this novel "task-free" paradigm as a complementary tool for fMRI language mapping when patients cannot perform the tasks. Its deployment in more neurosurgical patients and validation against gold-standard techniques need further investigation. Copyright © 2015 by the American Society of Neuroimaging.

A new paradigm for individual subject language mapping: Movie-watching fMRI

PubMed Central

Tie, Yanmei; Rigolo, Laura; Ovalioglu, Aysegul Ozdemir; Olubiyi, Olutayo; Doolin, Kelly L.; Mukundan, Srinivasan; Golby, Alexandra J.

2015-01-01

Background Functional MRI (fMRI) based on language tasks has been used in pre-surgical language mapping in patients with lesions in or near putative language areas. However, if the patients have difficulty performing the tasks due to neurological deficits, it leads to unreliable or non-interpretable results. In this study, we investigate the feasibility of using a movie-watching fMRI for language mapping. Methods A 7-min movie clip with contrasting speech and non-speech segments was shown to 22 right-handed healthy subjects. Based on all subjects' language functional regions-of-interest, six language response areas were defined, within which a language response model (LRM) was derived by extracting the main temporal activation profile. Using a leave-one-out procedure, individuals' language areas were identified as the areas that expressed highly correlated temporal responses with the LRM derived from an independent group of subjects. Results Compared with an antonym generation task-based fMRI, the movie-watching fMRI generated language maps with more localized activations in the left frontal language area, larger activations in the left temporoparietal language area, and significant activations in their right-hemisphere homologues. Results of two brain tumor patients' movie-watching fMRI using the LRM derived from the healthy subjects indicated its ability to map putative language areas; while their task-based fMRI maps were less robust and noisier. Conclusions These results suggest that it is feasible to use this novel “task-free” paradigm as a complementary tool for fMRI language mapping when patients cannot perform the tasks. Its deployment in more neurosurgical patients and validation against gold-standard techniques need further investigation. PMID:25962953

Relationship between fMRI response during a nonverbal memory task and marijuana use in college students.

PubMed

Dager, Alecia D; Tice, Madelynn R; Book, Gregory A; Tennen, Howard; Raskin, Sarah A; Austad, Carol S; Wood, Rebecca M; Fallahi, Carolyn R; Hawkins, Keith A; Pearlson, Godfrey D

2018-04-26

Marijuana (MJ) is widely used among college students, with peak use between ages 18-22. Research suggests memory dysfunction in adolescent and young adult MJ users, but the neural correlates are unclear. We examined functional magnetic resonance imaging (fMRI) response during a memory task among college students with varying degrees of MJ involvement. Participants were 64 college students, ages 18-20, who performed a visual encoding and recognition task during fMRI. MJ use was ascertained for 3 months prior to scanning; 27 individuals reported past 3-month MJ use, and 33 individuals did not. fMRI response was modeled during encoding based on whether targets were subsequently recognized (correct encoding), and during recognition based on target identification (hits). fMRI response in left and right inferior frontal gyrus (IFG) and hippocampal regions of interest was examined between MJ users and controls. There were no group differences between MJ users and controls on fMRI response during encoding, although single sample t-tests revealed that MJ users failed to activate the hippocampus. During recognition, MJ users showed less fMRI response than controls in right hippocampus (Cohen's d = 0.55), left hippocampus (Cohen's d = 0.67) and left IFG (Cohen's d = 0.61). Heavier MJ involvement was associated with lower fMRI response in left hippocampus and left IFG. This study provides evidence of MJ-related prefrontal and hippocampal dysfunction during recognition memory in college students. These findings may contribute to our previously identified decrements in academic performance in college MJ users and could have substantial implications for academic and occupational functioning. Copyright © 2018 Elsevier B.V. All rights reserved.

Functional Connectivity Mapping in the Animal Model: Principles and Applications of Resting-State fMRI

PubMed Central

Gorges, Martin; Roselli, Francesco; Müller, Hans-Peter; Ludolph, Albert C.; Rasche, Volker; Kassubek, Jan

2017-01-01

“Resting-state” fMRI has substantially contributed to the understanding of human and non-human functional brain organization by the analysis of correlated patterns in spontaneous activity within dedicated brain systems. Spontaneous neural activity is indirectly measured from the blood oxygenation level-dependent signal as acquired by echo planar imaging, when subjects quietly “resting” in the scanner. Animal models including disease or knockout models allow a broad spectrum of experimental manipulations not applicable in humans. The non-invasive fMRI approach provides a promising tool for cross-species comparative investigations. This review focuses on the principles of “resting-state” functional connectivity analysis and its applications to living animals. The translational aspect from in vivo animal models toward clinical applications in humans is emphasized. We introduce the fMRI-based investigation of the non-human brain’s hemodynamics, the methodological issues in the data postprocessing, and the functional data interpretation from different abstraction levels. The longer term goal of integrating fMRI connectivity data with structural connectomes obtained with tracing and optical imaging approaches is presented and will allow the interrogation of fMRI data in terms of directional flow of information and may identify the structural underpinnings of observed functional connectivity patterns. PMID:28539914

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data

PubMed Central

Calhoun, Vince D.; Liu, Jingyu; Adalı, Tülay

2009-01-01

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain. PMID:19059344

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.

PubMed

Calhoun, Vince D; Liu, Jingyu; Adali, Tülay

2009-03-01

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain.

Neck Collar with Mild Jugular Vein Compression Ameliorates Brain Activation Changes during a Working Memory Task after a Season of High School Football.

PubMed

Yuan, Weihong; Leach, James; Maloney, Thomas; Altaye, Mekibib; Smith, David; Gubanich, Paul J; Barber Foss, Kim D; Thomas, Staci; DiCesare, Christopher A; Kiefer, Adam W; Myer, Gregory D

2017-08-15

Emerging evidence indicates that repetitive head impacts, even at a sub-concussive level, may result in exacerbated or prolonged neurological deficits in athletes. This study aimed to: 1) quantify the effect of repetitive head impacts on the alteration of neuronal activity based on functional magnetic resonance imaging (fMRI) of working memory after a high school football season; and 2) determine whether a neck collar that applies mild jugular vein compression designed to reduce brain energy absorption in head impact through "slosh" mitigation can ameliorate the altered fMRI activation during a working memory task. Participants were recruited from local high school football teams with 27 and 25 athletes assigned to the non-collar and collar group, respectively. A standard N-Back task was used to engage working memory in the fMRI at both pre- and post-season. The two study groups experienced similar head impact frequency and magnitude during the season (all p > 0.05). fMRI blood oxygen level dependent (BOLD) signal response (a reflection of the neuronal activity level) during the working memory task increased significantly from pre- to post-season in the non-collar group (corrected p < 0.05), but not in the collar group. Areas displaying less activation change in the collar group (corrected p < 0.05) included the precuneus, inferior parietal cortex, and dorsal lateral prefrontal cortex. Additionally, BOLD response in the non-collar group increased significantly in direct association with the total number of impacts and total g-force (p < 0.05). Our data provide initial neuroimaging evidence for the effect of repetitive head impacts on the working memory related brain activity, as well as a potential protective effect that resulted from the use of the purported brain slosh reducing neck collar in contact sports.

Reward circuitry in resilience to severe trauma: An fMRI investigation of resilient special forces soldiers

PubMed Central

Vythilingam, Meena; Nelson, Eric E.; Scaramozza, Matthew; Waldeck, Tracy; Hazlett, Gary; Southwick, Steven M.; Pine, Daniel S.; Drevets, Wayne; Charney, Dennis S.; Ernst, Monique

2008-01-01

Enhanced brain reward function could contribute to resilience to trauma. Reward circuitry in active duty, resilient special forces (SF) soldiers was evaluated using fMRI during a monetary incentive delay task. Findings in this group of resilient individuals revealed unique patterns of activation during expectation of reward in the subgenual prefrontal cortex and nucleus accumbens area; regions pivotal to reward processes. PMID:19243926

fMRI response to spatial working memory in adolescents with comorbid marijuana and alcohol use disorders☆

PubMed Central

Schweinsburg, Alecia D.; Schweinsburg, Brian C.; Cheung, Erick H.; Brown, Gregory G.; Brown, Sandra A.; Tapert, Susan F.

2008-01-01

Alcohol and marijuana use are prevalent in adolescence, yet the neural impact of concomitant use remains unclear. We previously demonstrated functional magnetic resonance imaging (fMRI) response to spatial working memory (SWM) among teens with alcohol use disorders (AUD) compared to controls, and predicted that adolescents with marijuana and alcohol use disorders would show additional abnormalities. Participants were three groups of 15−17-year-olds: 19 non-abusing controls, 15 AUD teens with limited exposure to drugs, and 15 teens with comorbid marijuana and alcohol use disorders (MAUD) and minimal other drug experience. After >2 days’ abstinence, participants performed a SWM task during fMRI acquisition. fMRI brain response patterns differed between groups, despite similar performance on the task. MAUD youths showed less activation in inferior frontal and temporal regions than controls, and more response in other prefrontal regions. Compared to AUD teens, MAUD youths also showed less inferior frontal and temporal activation, but more medial frontal response. Overall, MAUD youths showed different brain response abnormalities than teens with AUD alone, despite relatively short histories of substance involvement. This pattern could suggest compensation for marijuana-related attention and working memory deficits. However, relatively recent use and premorbid features may influence results, and should be examined in future studies. PMID:16002029

Olfactory Deficit Detected by fMRI in Early Alzheimer’s Disease

PubMed Central

Wang, Jianli; Eslinger, Paul J.; Doty, Richard L.; Zimmerman, Erin K.; Grunfeld, Robert; Sun, Xiaoyu; Connor, James R.; Price, Joseph L.; Smith, Michael B.; Yang, Qing X.

2012-01-01

Alzheimer’s disease (AD) is accompanied by smell dysfunction, as measured by psychophysical tests. Currently it is unknown whether AD-related alterations in central olfactory system neural activity, as measured by functional magnetic resonance imaging (fMRI), are detectable beyond those observed in healthy elderly. Moreover, it is not known whether such changes are correlated with indices of odor perception and dementia. To investigate these issues, twelve early stage AD patients and thirteen non-demented controls underwent fMRI while being exposed to each of three concentrations of lavender oil odorant. All participants were administered the University of Pennsylvania Smell Identification Test (UPSIT), the Mini-Mental State Examination (MMSE), the Mattis Dementia Rating Scale-2 (DRS-2), and the Clinical Dementia Rating Scale (CDR). The Blood oxygen level-dependent (BOLD) signal at primary olfactory cortex (POC) was weaker in AD than in HC subjects. At the lowest odorant concentration, the BOLD signals within POC, hippocampus, and insula were significantly correlated with UPSIT, MMSE, DRS-2, and CDR scores. The BOLD signal intensity and activation volume within the POC increased significantly as a function of odorant concentration in the AD group, but not in the control group. These findings demonstrate that olfactory fMRI is sensitive to the AD-related olfactory and functional cognitive decline. PMID:20709038

Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures

PubMed Central

Cetin, Mustafa S.; Houck, Jon M.; Rashid, Barnaly; Agacoglu, Oktay; Stephen, Julia M.; Sui, Jing; Canive, Jose; Mayer, Andy; Aine, Cheryl; Bustillo, Juan R.; Calhoun, Vince D.

2016-01-01

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of “synchronicity” of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone. PMID:27807403

Computing moment to moment BOLD activation for real-time neurofeedback

PubMed Central

Hinds, Oliver; Ghosh, Satrajit; Thompson, Todd W.; Yoo, Julie J.; Whitfield-Gabrieli, Susan; Triantafyllou, Christina; Gabrieli, John D.E.

2013-01-01

Estimating moment to moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment to moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment to moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI timeseries, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method. PMID:20682350

Are batterers different from other criminals? An fMRI study

PubMed Central

Verdejo-Román, Juan; Contreras-Rodríguez, Oren; Carmona-Perera, Martina; Pérez-García, Miguel; Hidalgo-Ruzzante, Natalia

2016-01-01

Abstract Intimate partner violence (IPV) is a complex and global phenomenon that requires a multi-perspective analysis. Nevertheless, the number of neuroscientific studies conducted on this issue is scarce as compared with studies of other types of violence, and no neuroimaging studies comparing batterers to other criminals have been conducted. Thus, the main aim of this study was to compare the brain functioning of batterers to that of other criminals when they are exposed to IPV or general violence pictures. An fMRI study was conducted in 21 batterers and 20 other criminals while they observed IPV images (IPVI), general violence images (GVI) and neutral images (NI). Results demonstrated that batterers, compared with other criminals, exhibited a higher activation in the anterior and posterior cingulate cortex and in the middle prefrontal cortex and a decreased activation in the superior prefrontal cortex to IPVI compared to NI. The paired t-test comparison between IPVI and GVI for each group showed engagement of the medial prefrontal cortex, the posterior cingulate and the left angular cortices to IPVI in the batterer group only. These results could have important implications for a better understanding of the IPV phenomenon. PMID:26884544

Disrupted functional connectivity of the hippocampus in patients with hyperthyroidism: evidence from resting-state fMRI.

PubMed

Zhang, Wei; Liu, Xianjun; Zhang, Yi; Song, Lingheng; Hou, Jingming; Chen, Bing; He, Mei; Cai, Ping; Lii, Haitao

2014-10-01

The hippocampus expresses high levels of thyroid hormone receptors, suggesting that hippocampal functions, including cognition and regulation of mood, can be disrupted by thyroid pathology. Indeed, structural and functional alterations within the hippocampus have been observed in hyperthyroid patients. In addition to internal circuitry, hippocampal processing is dependent on extensive connections with other limbic and neocortical structures, but the effects of hyperthyroidism on functional connectivity (FC) with these areas have not been studied. The purpose of this study was to investigate possible abnormalities in the FC between the hippocampus and other neural structures in hyperthyroid patients using resting-state fMRI. Seed-based correlation analysis was performed on resting-state fMRI data to reveal possible differences in hippocampal FC between hyperthyroid patients and healthy controls. Correlation analysis was used to investigate the relationships between the strength of FC in regions showing significant group differences and clinical variables. Compared to controls, hyperthyroid patients showed weaker FC between the bilateral hippocampus and both the bilateral anterior cingulate cortex (ACC) and bilateral posterior cingulate cortex (PCC), as well as between the right hippocampus and right medial orbitofrontal cortex (mOFC). Disease duration was negatively correlated with FC strength between the bilateral hippocampus and bilateral ACC and PCC. Levels of depression and anxiety were negatively correlated with FC strength between the bilateral hippocampus and bilateral ACC. Decreased functional connectivity between the hippocampus and bilateral ACC, PCC, and right mOFC may contribute to the emotional and cognitive dysfunction associated with hyperthyroidism. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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.

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.

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.

Neuroethics and fMRI: Mapping a Fledgling Relationship

PubMed Central

Garnett, Alex; Whiteley, Louise; Piwowar, Heather; Rasmussen, Edie; Illes, Judy

2011-01-01

Human functional magnetic resonance imaging (fMRI) informs the understanding of the neural basis of mental function and is a key domain of ethical enquiry. It raises questions about the practice and implications of research, and reflexively informs ethics through the empirical investigation of moral judgments. It is at the centre of debate surrounding the importance of neuroscience findings for concepts such as personhood and free will, and the extent of their practical consequences. Here, we map the landscape of fMRI and neuroethics, using citation analysis to uncover salient topics. We find that this landscape is sparsely populated: despite previous calls for debate, there are few articles that discuss both fMRI and ethical, legal, or social implications (ELSI), and even fewer direct citations between the two literatures. Recognizing that practical barriers exist to integrating ELSI discussion into the research literature, we argue nonetheless that the ethical challenges of fMRI, and controversy over its conceptual and practical implications, make this essential. PMID:21526115

Functional abnormalities in normally appearing athletes following mild traumatic brain injury: a functional MRI study

PubMed Central

Slobounov, Semyon M.; Zhang, K.; Pennell, D.; Ray, W.; Johnson, B.; Sebastianelli, W.

2010-01-01

Memory problems are one of the most common symptoms of sport-related mild traumatic brain injury (MTBI), known as concussion. Surprisingly, little research has examined spatial memory in concussed athletes given its importance in athletic environments. Here, we combine functional magnetic resonance imaging (fMRI) with a virtual reality (VR) paradigm designed to investigate the possibility of residual functional deficits in recently concussed but asymptomatic individuals. Specifically, we report performance of spatial memory navigation tasks in a VR environment and fMRI data in 15 athletes suffering from MTBI and 15 neurologically normal, athletically active age matched controls. No differences in performance were observed between these two groups of subjects in terms of success rate (94 and 92%) and time to complete the spatial memory navigation tasks (mean = 19.5 and 19.7 s). Whole brain analysis revealed that similar brain activation patterns were observed during both encoding and retrieval among the groups. However, concussed athletes showed larger cortical networks with additional increases in activity outside of the shared region of interest (ROI) during encoding. Quantitative analysis of blood oxygen level dependent (BOLD) signal revealed that concussed individuals had a significantly larger cluster size during encoding at parietal cortex, right dorsolateral prefrontal cortex, and right hippocampus. In addition, there was a significantly larger BOLD signal percent change at the right hippocampus. Neither cluster size nor BOLD signal percent change at shared ROIs was different between groups during retrieval. These major findings are discussed with respect to current hypotheses regarding the neural mechanism responsible for alteration of brain functions in a clinical setting. PMID:20039023

A Hierarchical Model for Simultaneous Detection and Estimation in Multi-subject fMRI Studies

PubMed Central

Degras, David; Lindquist, Martin A.

2014-01-01

In this paper we introduce a new hierarchical model for the simultaneous detection of brain activation and estimation of the shape of the hemodynamic response in multi-subject fMRI studies. The proposed approach circumvents a major stumbling block in standard multi-subject fMRI data analysis, in that it both allows the shape of the hemodynamic response function to vary across region and subjects, while still providing a straightforward way to estimate population-level activation. An e cient estimation algorithm is presented, as is an inferential framework that not only allows for tests of activation, but also for tests for deviations from some canonical shape. The model is validated through simulations and application to a multi-subject fMRI study of thermal pain. PMID:24793829

Perceptual Measurement in Schizophrenia: Promising Electrophysiology and Neuroimaging Paradigms From CNTRICS

PubMed Central

Butler, Pamela D.; Chen, Yue; Ford, Judith M.; Geyer, Mark A.; Silverstein, Steven M.; Green, Michael F.

2012-01-01

The sixth meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) focused on selecting promising imaging paradigms for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of perception, the 2 constructs of interest were “gain control” and “visual integration.” CNTRICS received 6 task nominations for imaging paradigms for gain control and 3 task nominations for integration. The breakout group for perception evaluated the degree to which each of these tasks met prespecified criteria. For gain control, the breakout group believed that one task (mismatch negativity) was already mature and was being incorporated into multisite clinical trials. The breakout group recommended that 1 visual task (steady-state visual evoked potentials to magnocellular- vs parvocellular-biased stimuli) and 2 auditory measures (an event-related potential (ERP) measure of corollary discharge and a functional magnetic resonance imaging (fMRI) version of prepulse inhibition of startle) be adapted for use in clinical trials in schizophrenia research. For visual integration, the breakout group recommended that fMRI and ERP versions of a contour integration test and an fMRI version of a coherent motion test be adapted for use in clinical trials. This manuscript describes the ways in which each of these tasks met the criteria used in the breakout group to evaluate and recommend tasks for further development. PMID:21890745

R6/2 Huntington's disease mice develop early and progressive abnormal brain metabolism and seizures.

PubMed

Cepeda-Prado, Efrain; Popp, Susanna; Khan, Usman; Stefanov, Dimitre; Rodríguez, Jorge; Menalled, Liliana B; Dow-Edwards, Diana; Small, Scott A; Moreno, Herman

2012-05-09

A hallmark feature of Huntington's disease pathology is the atrophy of brain regions including, but not limited to, the striatum. Though MRI studies have identified structural CNS changes in several Huntington's disease (HD) mouse models, the functional consequences of HD pathology during the progression of the disease have yet to be investigated using in vivo functional MRI (fMRI). To address this issue, we first established the structural and functional MRI phenotype of juvenile HD mouse model R6/2 at early and advanced stages of disease. Significantly higher fMRI signals [relative cerebral blood volumes (rCBVs)] and atrophy were observed in both age groups in specific brain regions. Next, fMRI results were correlated with electrophysiological analysis, which showed abnormal increases in neuronal activity in affected brain regions, thus identifying a mechanism accounting for the abnormal fMRI findings. [(14)C] 2-deoxyglucose maps to investigate patterns of glucose utilization were also generated. An interesting mismatch between increases in rCBV and decreases in glucose uptake was observed. Finally, we evaluated the sensitivity of this mouse line to audiogenic seizures early in the disease course. We found that R6/2 mice had an increased susceptibility to develop seizures. Together, these findings identified seizure activity in R6/2 mice and show that neuroimaging measures sensitive to oxygen metabolism can be used as in vivo biomarkers, preceding the onset of an overt behavioral phenotype. Since fMRI-rCBV can also be obtained in patients, we propose that it may serve as a translational tool to evaluate therapeutic responses in humans and HD mouse models.

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

PubMed

Dey, Soumyabrata; Rao, A Ravishankar; Shah, Mubarak

2014-01-01

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

Neural substrates of smoking cue reactivity: A meta-analysis of fMRI studies

PubMed Central

Engelmann, Jeffrey M.; Versace, Francesco; Robinson, Jason D.; Minnix, Jennifer A.; Lam, Cho Y.; Cui, Yong; Brown, Victoria L.; Cinciripini, Paul M.

2012-01-01

Reactivity to smoking-related cues may be an important factor that precipitates relapse in smokers who are trying to quit. The neurobiology of smoking cue reactivity has been investigated in several fMRI studies. We combined the results of these studies using activation likelihood estimation, a meta-analytic technique for fMRI data. Results of the meta-analysis indicated that smoking cues reliably evoke larger fMRI responses than neutral cues in the extended visual system, precuneus, posterior cingulate gyrus, anterior cingulate gyrus, dorsal and medial prefrontal cortex, insula, and dorsal striatum. Subtraction meta-analyses revealed that parts of the extended visual system and dorsal prefrontal cortex are more reliably responsive to smoking cues in deprived smokers than in non-deprived smokers, and that short-duration cues presented in event-related designs produce larger responses in the extended visual system than long-duration cues presented in blocked designs. The areas that were found to be responsive to smoking cues agree with theories of the neurobiology of cue reactivity, with two exceptions. First, there was a reliable cue reactivity effect in the precuneus, which is not typically considered a brain region important to addiction. Second, we found no significant effect in the nucleus accumbens, an area that plays a critical role in addiction, but this effect may have been due to technical difficulties associated with measuring fMRI data in that region. The results of this meta-analysis suggest that the extended visual system should receive more attention in future studies of smoking cue reactivity. PMID:22206965

Strategies for reducing large fMRI data sets for independent component analysis.

PubMed

Wang, Ze; Wang, Jiongjiong; Calhoun, Vince; Rao, Hengyi; Detre, John A; Childress, Anna R

2006-06-01

In independent component analysis (ICA), principal component analysis (PCA) is generally used to reduce the raw data to a few principal components (PCs) through eigenvector decomposition (EVD) on the data covariance matrix. Although this works for spatial ICA (sICA) on moderately sized fMRI data, it is intractable for temporal ICA (tICA), since typical fMRI data have a high spatial dimension, resulting in an unmanageable data covariance matrix. To solve this problem, two practical data reduction methods are presented in this paper. The first solution is to calculate the PCs of tICA from the PCs of sICA. This approach works well for moderately sized fMRI data; however, it is highly computationally intensive, even intractable, when the number of scans increases. The second solution proposed is to perform PCA decomposition via a cascade recursive least squared (CRLS) network, which provides a uniform data reduction solution for both sICA and tICA. Without the need to calculate the covariance matrix, CRLS extracts PCs directly from the raw data, and the PC extraction can be terminated after computing an arbitrary number of PCs without the need to estimate the whole set of PCs. Moreover, when the whole data set becomes too large to be loaded into the machine memory, CRLS-PCA can save data retrieval time by reading the data once, while the conventional PCA requires numerous data retrieval steps for both covariance matrix calculation and PC extractions. Real fMRI data were used to evaluate the PC extraction precision, computational expense, and memory usage of the presented methods.

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.

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.

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.

Assessment of Unconstrained Cerebrovascular Reactivity Marker for Large Age-Range fMRI Studies

PubMed Central

Kannurpatti, Sridhar S.; Motes, Michael A.; Biswal, Bharat B.; Rypma, Bart

2014-01-01

Breath hold (BH), a commonly used task to measure cerebrovascular reactivity (CVR) in fMRI studies varies in outcome among individuals due to subject-physiology and/or BH-inspiration/expiration differences (i.e., performance). In prior age-related fMRI studies, smaller task-related BOLD response variability is observed among younger than older individuals. Also, a linear CVR versus task relationship exists in younger individuals which maybe useful to test the accuracy of CVR responses in older groups. Hence we hypothesized that subject-related physiological and/or BH differences, if present, may compromise CVR versus task linearity in older individuals. To test the hypothesis, empirical BH versus task relationships from motor and cognitive areas were obtained in younger (mean age = 26 years) and older (mean age = 58 years) human subjects. BH versus task linearity was observed only in the younger group, confirming our hypothesis. Further analysis indicated BH responses and its variability to be similar in both younger and older groups, suggesting that BH may not accurately represent CVR in a large age range. Using the resting state fluctuation of amplitude (RSFA) as an unconstrained alternative to BH, subject-wise correspondence between BH and RSFA was tested. Correlation between BH versus RSFA was significant within the motor but was not significant in the cognitive areas in the younger and was completely disrupted in both areas in the older subjects indicating that BH responses are constrained by subject-related physiology and/or performance-related differences. Contrasting BH to task, RSFA-task relationships were independent of age accompanied by age-related increases in CVR variability as measured by RSFA, not observed with BH. Together the results obtained indicate that RSFA accurately represents CVR in any age range avoiding multiple and yet unknown physiologic and task-related pitfalls of BH. PMID:24551151

Multifaceted Genomic Risk for Brain Function in Schizophrenia

PubMed Central

Chen, Jiayu; Calhoun, Vince D.; Pearlson, Godfrey D.; Ehrlich, Stefan; Turner, Jessica A.; Ho, Beng-Choon; Wassink, Thomas H.; Michael, Andrew M; Liu, Jingyu

2012-01-01

Recently, deriving candidate endophenotypes from brain imaging data has become a valuable approach to study genetic influences on schizophrenia (SZ), whose pathophysiology remains unclear. In this work we utilized a multivariate approach, parallel independent component analysis, to identify genomic risk components associated with brain function abnormalities in SZ. 5157 candidate single nucleotide polymorphisms (SNPs) were derived from genome-wide array based on their possible connections with SZ and further investigated for their associations with brain activations captured with functional magnetic resonance imaging (fMRI) during a sensorimotor task. Using data from 92 SZ patients and 116 healthy controls, we detected a significant correlation (r= 0.29; p= 2.41×10−5) between one fMRI component and one SNP component, both of which significantly differentiated patients from controls. The fMRI component mainly consisted of precentral and postcentral gyri, the major activated regions in the motor task. On average, higher activation in these regions was observed in participants with higher loadings of the linked SNP component, predominantly contributed to by 253 SNPs. 138 identified SNPs were from known coding regions of 100 unique genes. 31 identified SNPs did not differ between groups, but moderately correlated with some other group-discriminating SNPs, indicating interactions among alleles contributing towards elevated SZ susceptibility. The genes associated with the identified SNPs participated in four neurotransmitter pathways: GABA receptor signaling, dopamine receptor signaling, neuregulin signaling and glutamate receptor signaling. In summary, our work provides further evidence for the complexity of genomic risk to the functional brain abnormality in SZ and suggests a pathological role of interactions between SNPs, genes and multiple neurotransmitter pathways. PMID:22440650

Effects of carbamazepine and lamotrigine on functional magnetic resonance imaging cognitive networks.

PubMed

Xiao, Fenglai; Caciagli, Lorenzo; Wandschneider, Britta; Sander, Josemir W; Sidhu, Meneka; Winston, Gavin; Burdett, Jane; Trimmel, Karin; Hill, Andrea; Vollmar, Christian; Vos, Sjoerd B; Ourselin, Sebastien; Thompson, Pamela J; Zhou, Dong; Duncan, John S; Koepp, Matthias J

2018-06-13

To investigate the effects of sodium channel-blocking antiepileptic drugs (AEDs) on functional magnetic resonance imaging (fMRI) language network activations in patients with focal epilepsy. In a retrospective study, we identified patients who were treated at the time of language fMRI scanning with either carbamazepine (CBZ; n = 42) or lamotrigine (LTG; n = 42), but not another sodium channel-blocking AED. We propensity-matched 42 patients taking levetiracetam (LEV) as "patient-controls" and included further 42 age- and gender-matched healthy controls. After controlling for age, age at onset of epilepsy, gender, and antiepileptic comedications, we compared verbal fluency fMRI activations between groups and out-of-scanner psychometric measures of verbal fluency. Patients on CBZ performed less well on a verbal fluency tests than those taking LTG or LEV. Compared to either LEV-treated patients or controls, patients taking CBZ showed decreased activations in left inferior frontal gyrus and patients on LTG showed abnormal deactivations in frontal and parietal default mode areas. All patient groups showed fewer activations in the putamen bilaterally compared to controls. In a post hoc analysis, out-of-scanner fluency scores correlated positively with left putamen activation. Our study provides evidence of AED effects on the functional neuroanatomy of language, which might explain subtle language deficits in patients taking otherwise well-tolerated sodium channel-blocking agents. Patients on CBZ showed dysfunctional frontal activation and more pronounced impairment of performance than patients taking LTG, which was associated only with failure to deactivate task-negative networks. As previously shown for working memory, LEV treatment did not affect functional language networks. © 2018 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Nicotine-induced activation of caudate and anterior cingulate cortex in response to errors in schizophrenia.

PubMed

Moran, Lauren V; Stoeckel, Luke E; Wang, Kristina; Caine, Carolyn E; Villafuerte, Rosemond; Calderon, Vanessa; Baker, Justin T; Ongur, Dost; Janes, Amy C; Evins, A Eden; Pizzagalli, Diego A

2018-03-01

Nicotine improves attention and processing speed in individuals with schizophrenia. Few studies have investigated the effects of nicotine on cognitive control. Prior functional magnetic resonance imaging (fMRI) research demonstrates blunted activation of dorsal anterior cingulate cortex (dACC) and rostral anterior cingulate cortex (rACC) in response to error and decreased post-error slowing in schizophrenia. Participants with schizophrenia (n = 13) and healthy controls (n = 12) participated in a randomized, placebo-controlled, crossover study of the effects of transdermal nicotine on cognitive control. For each drug condition, participants underwent fMRI while performing the stop signal task where participants attempt to inhibit prepotent responses to "go (motor activation)" signals when an occasional "stop (motor inhibition)" signal appears. Error processing was evaluated by comparing "stop error" trials (failed response inhibition) to "go" trials. Resting-state fMRI data were collected prior to the task. Participants with schizophrenia had increased nicotine-induced activation of right caudate in response to errors compared to controls (DRUG × GROUP effect: p corrected  < 0.05). Both groups had significant nicotine-induced activation of dACC and rACC in response to errors. Using right caudate activation to errors as a seed for resting-state functional connectivity analysis, relative to controls, participants with schizophrenia had significantly decreased connectivity between the right caudate and dACC/bilateral dorsolateral prefrontal cortices. In sum, we replicated prior findings of decreased post-error slowing in schizophrenia and found that nicotine was associated with more adaptive (i.e., increased) post-error reaction time (RT). This proof-of-concept pilot study suggests a role for nicotinic agents in targeting cognitive control deficits in schizophrenia.

Occupational solvent exposure and brain function: an fMRI study.

PubMed

Tang, Cheuk Ying; Carpenter, David M; Eaves, Emily L; Ng, Johnny; Ganeshalingam, Nimalya; Weisel, Clifford; Qian, Hua; Lange, Gudrun; Fiedler, Nancy L

2011-07-01

Deficits in cognitive function have been demonstrated among workers chronically exposed to solvents, but the neural basis for these deficits has not been shown. We used functional magnetic resonance imaging (fMRI) to compare pathophysiological changes in brain function between solvent-exposed and control workers. Painters, drywall tapers, and carpenters were recruited from the International Union of Painters and Allied Trades, District Council 9 in New York City and District Council 21 in Philadelphia, Pennsylvania, and from the Carpenters Union in New Jersey. Twenty-seven solvent-exposed and 27 control subjects of similar age, education, and occupational status completed the N-Back working memory test during fMRI. After controlling for confounders (age; lifetime marijuana, cocaine, and alcohol use; blood lead; symptoms of depression; verbal intelligence), voxelwise group analysis and regional activation levels were compared and then correlated with an index of lifetime solvent exposure. Solvent-exposed workers' performance on the N-Back was significantly worse than that of controls. Activation of the anterior cingulate, prefrontal, and parietal cortices--areas serving working memory function and attention--was also significantly lower for solvent-exposed workers relative to controls. After controlling for confounders, we observed a negative correlation between lifetime solvent exposure and activation in these same regions among the solvent-exposed workers. This study is one of the few to document neural structures affected by exposure to solvents. Our findings provide a biological mechanism for the neurobehavioral deficits in working memory and attention that have previously been reported by other groups studying the effects of chronic exposure to solvents. These imaging markers, which are consistent with the neurobehavioral measures in our subject population, are consistent with altered brain pathology caused by prolonged exposure to solvent mixtures during construction work.

Occupational Solvent Exposure and Brain Function: An fMRI Study

PubMed Central

Carpenter, David M.; Eaves, Emily L.; Ng, Johnny; Ganeshalingam, Nimalya; Weisel, Clifford; Qian, Hua; Lange, Gudrun; Fiedler, Nancy L.

2011-01-01

Background: Deficits in cognitive function have been demonstrated among workers chronically exposed to solvents, but the neural basis for these deficits has not been shown. Objectives: We used functional magnetic resonance imaging (fMRI) to compare pathophysiological changes in brain function between solvent-exposed and control workers. Methods: Painters, drywall tapers, and carpenters were recruited from the International Union of Painters and Allied Trades, District Council 9 in New York City and District Council 21 in Philadelphia, Pennsylvania, and from the Carpenters Union in New Jersey. Twenty-seven solvent-exposed and 27 control subjects of similar age, education, and occupational status completed the N-Back working memory test during fMRI. After controlling for confounders (age; lifetime marijuana, cocaine, and alcohol use; blood lead; symptoms of depression; verbal intelligence), voxelwise group analysis and regional activation levels were compared and then correlated with an index of lifetime solvent exposure. Results: Solvent-exposed workers’ performance on the N-Back was significantly worse than that of controls. Activation of the anterior cingulate, prefrontal, and parietal cortices—areas serving working memory function and attention—was also significantly lower for solvent-exposed workers relative to controls. After controlling for confounders, we observed a negative correlation between lifetime solvent exposure and activation in these same regions among the solvent-exposed workers. Conclusions: This study is one of the few to document neural structures affected by exposure to solvents. Our findings provide a biological mechanism for the neurobehavioral deficits in working memory and attention that have previously been reported by other groups studying the effects of chronic exposure to solvents. These imaging markers, which are consistent with the neurobehavioral measures in our subject population, are consistent with altered brain pathology caused by prolonged exposure to solvent mixtures during construction work. PMID:21296712

[Functional connectivity of temporal parietal junction in online game addicts:a resting-state functional magnetic resonance imaging study].

PubMed

Yuan, Ji; Qian, Ruobing; Lin, Bin; Fu, Xianming; Wei, Xiangpin; Weng, Chuanbo; Niu, Chaoshi; Wang, Yehan

2014-02-11

To explore the functions of temporal parietal junction (TPJ) as parts of attention networks in the pathogenesis of online game addiction using resting-state functional magnetic resonance imaging (fMRI). A total of 17 online game addicts (OGA) were recruited as OGA group and 17 healthy controls during the same period were recruited as CON group. The neuropsychological tests were performed for all of them to compare the inter-group differences in the results of Internet Addiction Test (IAT) and attention functions. All fMRI data were preprocessed after resting-state fMRI scanning. Then left and right TPJ were selected as regions of interest (ROIs) to calculate the linear correlation between TPJ and entire brain to compare the inter-group differences. Obvious differences existed between OGA group (71 ± 5 scores) and CON group (19 ± 7 scores) in the IAT results and attention function (P < 0.05). Compared with the controls, right TPJ in online game addicts showed decreased functional connectivity with bilateral ventromedial prefrontal cortex (VMPFC), bilateral hippocampal gyrus and bilateral amygdaloid nucleus, but increased functional connectivity with right cuneus.However, left TPJ demonstrated decreased functional connectivity with bilateral superior frontal gyrus and bilateral middle frontal gyrus, but increased functional connectivity with bilateral cuneus (P < 0.05). Altered functional connectivity of TPJ reflected its dysfunction in online game addicts.It suggests that TPJ is an important component of attention networks participating in the generation of online game addiction.

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

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.

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.

Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in A Multimodal CCA+ Joint ICA Model

PubMed Central

Sui, Jing; Pearlson, Godfrey; Adali, Tülay; Kiehl, Kent A.; Caprihan, Arvind; Liu, Jingyu; Yamamoto, Jeremy; Calhoun, Vince D.

2011-01-01

Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, “multimodal CCA+ joint ICA’, which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into “features” (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function-structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms. PMID:21640835

PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data.

PubMed

Mejia, Amanda F; Nebel, Mary Beth; Eloyan, Ani; Caffo, Brian; Lindquist, Martin A

2017-07-01

Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical research. However, one source of HD data that has received relatively little attention is functional magnetic resonance images (fMRI), which consists of hundreds of thousands of measurements sampled at hundreds of time points. At a time when the availability of fMRI data is rapidly growing-primarily through large, publicly available grassroots datasets-automated quality control and outlier detection methods are greatly needed. We propose principal components analysis (PCA) leverage and demonstrate how it can be used to identify outlying time points in an fMRI run. Furthermore, PCA leverage is a measure of the influence of each observation on the estimation of principal components, which are often of interest in fMRI data. We also propose an alternative measure, PCA robust distance, which is less sensitive to outliers and has controllable statistical properties. The proposed methods are validated through simulation studies and are shown to be highly accurate. We also conduct a reliability study using resting-state fMRI data from the Autism Brain Imaging Data Exchange and find that removal of outliers using the proposed methods results in more reliable estimation of subject-level resting-state networks using independent components analysis. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Improving Functional Magnetic Resonance Imaging Motor Studies Through Simultaneous Electromyography Recordings

PubMed Central

MacIntosh, Bradley J.; Baker, S. Nicole; Mraz, Richard; Ives, John R.; Martel, Anne L.; McIlroy, William E.; Graham, Simon J.

2016-01-01

Specially designed optoelectronic and data postprocessing methods are described that permit electromyography (EMG) of muscle activity simultaneous with functional MRI (fMRI). Hardware characterization and validation included simultaneous EMG and event-related fMRI in 17 healthy participants during either ankle (n = 12), index finger (n = 3), or wrist (n = 2) contractions cued by visual stimuli. Principal component analysis (PCA) and independent component analysis (ICA) were evaluated for their ability to remove residual fMRI gradient-induced signal contamination in EMG data. Contractions of ankle tibialis anterior and index finger abductor were clearly distinguishable, although observing contractions from the wrist flexors proved more challenging. To demonstrate the potential utility of simultaneous EMG and fMRI, data from the ankle experiments were analyzed using two approaches: 1) assuming contractions coincided precisely with visual cues, and 2) using EMG to time the onset and offset of muscle contraction precisely for each participant. Both methods produced complementary activation maps, although the EMG-guided approach recovered more active brain voxels and revealed activity better in the basal ganglia and cerebellum. Furthermore, numerical simulations confirmed that precise knowledge of behavioral responses, such as those provided by EMG, are much more important for event-related experimental designs compared to block designs. This simultaneous EMG and fMRI methodology has important applications where the amplitude or timing of motor output is impaired, such as after stroke. PMID:17133382

Improving functional magnetic resonance imaging motor studies through simultaneous electromyography recordings.

PubMed

MacIntosh, Bradley J; Baker, S Nicole; Mraz, Richard; Ives, John R; Martel, Anne L; McIlroy, William E; Graham, Simon J

2007-09-01

Specially designed optoelectronic and data postprocessing methods are described that permit electromyography (EMG) of muscle activity simultaneous with functional MRI (fMRI). Hardware characterization and validation included simultaneous EMG and event-related fMRI in 17 healthy participants during either ankle (n = 12), index finger (n = 3), or wrist (n = 2) contractions cued by visual stimuli. Principal component analysis (PCA) and independent component analysis (ICA) were evaluated for their ability to remove residual fMRI gradient-induced signal contamination in EMG data. Contractions of ankle tibialis anterior and index finger abductor were clearly distinguishable, although observing contractions from the wrist flexors proved more challenging. To demonstrate the potential utility of simultaneous EMG and fMRI, data from the ankle experiments were analyzed using two approaches: 1) assuming contractions coincided precisely with visual cues, and 2) using EMG to time the onset and offset of muscle contraction precisely for each participant. Both methods produced complementary activation maps, although the EMG-guided approach recovered more active brain voxels and revealed activity better in the basal ganglia and cerebellum. Furthermore, numerical simulations confirmed that precise knowledge of behavioral responses, such as those provided by EMG, are much more important for event-related experimental designs compared to block designs. This simultaneous EMG and fMRI methodology has important applications where the amplitude or timing of motor output is impaired, such as after stroke. (c) 2006 Wiley-Liss, Inc.

fMRI and EEG responses to periodic visual stimulation.

PubMed

Guy, C N; ffytche, D H; Brovelli, A; Chumillas, J

1999-08-01

EEG/VEP and fMRI responses to periodic visual stimulation are reported. The purpose of these experiments was to look for similar patterns in the time series produced by each method to help understand the relationship between the two. The stimulation protocol was the same for both sets of experiments and consisted of five complete cycles of checkerboard pattern reversal at 1.87 Hz for 30 s followed by 30 s of a stationary checkerboard. The fMRI data was analyzed using standard methods, while the EEG was analyzed with a new measurement of activation-the VEPEG. Both VEPEG and fMRI time series contain the fundamental frequency of the stimulus and quasi harmonic components-an unexplained double frequency commonly found in fMRI data. These results have prompted a reappraisal of the methods for analyzing fMRI data and have suggested a connection between our findings and much older published invasive electrophysiological measurements of blood flow and the partial pressures of oxygen and carbon dioxide. Overall our new analysis suggests that fMRI signals are strongly dependant on hydraulic blood flow effects. We distinguish three categories of fMRI signal corresponding to: focal activated regions of brain tissue; diffuse nonspecific regions of steal; and major cerebral vessels of arterial supply or venous drainage. Each category of signal has its own finger print in frequency, amplitude, and phase. Finally, we put forward the hypothesis that modulations in blood flow are not only the consequence but are also the cause of modulations in functional activity. Copyright 1999 Academic Press.

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Real-time fMRI processing with physiological noise correction - Comparison with off-line analysis.

PubMed

Misaki, Masaya; Barzigar, Nafise; Zotev, Vadim; Phillips, Raquel; Cheng, Samuel; Bodurka, Jerzy

2015-12-30

While applications of real-time functional magnetic resonance imaging (rtfMRI) are growing rapidly, there are still limitations in real-time data processing compared to off-line analysis. We developed a proof-of-concept real-time fMRI processing (rtfMRIp) system utilizing a personal computer (PC) with a dedicated graphic processing unit (GPU) to demonstrate that it is now possible to perform intensive whole-brain fMRI data processing in real-time. The rtfMRIp performs slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis with multiple noise regressors including physiological noise modeled with cardiac (RETROICOR) and respiration volume per time (RVT). The whole-brain data analysis with more than 100,000voxels and more than 250volumes is completed in less than 300ms, much faster than the time required to acquire the fMRI volume. Real-time processing implementation cannot be identical to off-line analysis when time-course information is used, such as in slice-timing correction, signal scaling, and GLM. We verified that reduced slice-timing correction for real-time analysis had comparable output with off-line analysis. The real-time GLM analysis, however, showed over-fitting when the number of sampled volumes was small. Our system implemented real-time RETROICOR and RVT physiological noise corrections for the first time and it is capable of processing these steps on all available data at a given time, without need for recursive algorithms. Comprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM. Copyright © 2015 Elsevier B.V. All rights reserved.

Two types of exercise-induced neuroplasticity in congenital hemiparesis: a transcranial magnetic stimulation, functional MRI, and magnetoencephalography study.

PubMed

Juenger, Hendrik; Kuhnke, Nicola; Braun, Christoph; Ummenhofer, Frank; Wilke, Marko; Walther, Michael; Koerte, Inga; Delvendahl, Igor; Jung, Nikolai H; Berweck, Steffen; Staudt, Martin; Mall, Volker

2013-10-01

Early unilateral brain lesions can lead to a persistence of ipsilateral corticospinal projections from the contralesional hemisphere, which can enable the contralesional hemisphere to exert motor control over the paretic hand. In contrast to the primary motor representation (M1), the primary somatosensory representation (S1) of the paretic hand always remains in the lesioned hemisphere. Here, we report on differences in exercise-induced neuroplasticity between individuals with such ipsilateral motor projections (ipsi) and individuals with early unilateral lesions but 'healthy' contralateral motor projections (contra). Sixteen children and young adults with congenital hemiparesis participated in the study (contralateral [Contra] group: n=7, four females, three males; age range 10-30y, median age 16y; ipsilateral [Ipsi] group: n=9, four females, five males; age range 11-31y, median age 12y; Manual Ability Classification System levels I to II in all individuals in both groups). The participants underwent a 12-day intervention of constraint-induced movement therapy (CIMT), consisting of individual training (2h/d) and group training (8h/d). Before and after CIMT, hand function was tested using the Wolf Motor Function Test (WMFT) and diverging neuroplastic effects were observed by transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). Statistical analysis of TMS data was performed using the non-parametric Wilcoxon signed-rank test for pair-wise comparison; for fMRI standard statistical parametric and non-parametric mapping (SPM5, SnPM3) procedures (first level/second level) were carried out. Statistical analyses of MEG data involved analyses of variance (ANOVA) and t-tests. While MEG demonstrated a significant increase in S1 activation in both groups (p=0.012), TMS showed a decrease in M1 excitability in the Ipsi group (p=0.036), but an increase in M1 excitability in the Contra group (p=0.043). Similarly, fMRI showed a decrease in M1 activation in the Ipsi group, but an increase in activation in the M1-S1 region in the Contra group (for both groups p<0.001 [SnPM3] within the search volume). Different patterns of sensorimotor (re)organization in individuals with early unilateral lesions show, on a cortical level, different patterns of exercise-induced neuroplasticity. The findings help to improve the understanding of the general principles of sensorimotor learning and will help to develop more specific therapies for different pathologies in congenital hemiparesis. © 2013 Mac Keith Press.

Treatment with olanzapine is associated with modulation of the default mode network in patients with Schizophrenia.

PubMed

Sambataro, Fabio; Blasi, Giuseppe; Fazio, Leonardo; Caforio, Grazia; Taurisano, Paolo; Romano, Raffaella; Di Giorgio, Annabella; Gelao, Barbara; Lo Bianco, Luciana; Papazacharias, Apostolos; Popolizio, Teresa; Nardini, Marcello; Bertolino, Alessandro

2010-03-01

Earlier studies have shown widespread alterations of functional connectivity of various brain networks in schizophrenia, including the default mode network (DMN). The DMN has also an important role in the performance of cognitive tasks. Furthermore, treatment with second-generation antipsychotic drugs may ameliorate to some degree working memory (WM) deficits and related brain activity. The aim of this study was to evaluate the effects of treatment with olanzapine monotherapy on functional connectivity among brain regions of the DMN during WM. Seventeen patients underwent an 8-week prospective study and completed two functional magnetic resonance imaging (fMRI) scans at 4 and 8 weeks of treatment during the performance of the N-back WM task. To control for potential repetition effects, 19 healthy controls also underwent two fMRI scans at a similar time interval. We used spatial group-independent component analysis (ICA) to analyze fMRI data. Relative to controls, patients with schizophrenia had reduced connectivity strength within the DMN in posterior cingulate, whereas it was greater in precuneus and inferior parietal lobule. Treatment with olanzapine was associated with increases in DMN connectivity with ventromedial prefrontal cortex, but not in posterior regions of DMN. These results suggest that treatment with olanzapine is associated with the modulation of DMN connectivity in schizophrenia. In addition, our findings suggest critical functional differences in the regions of DMN.

Treatment with Olanzapine is Associated with Modulation of the Default Mode Network in Patients with Schizophrenia

PubMed Central

Sambataro, Fabio; Blasi, Giuseppe; Fazio, Leonardo; Caforio, Grazia; Taurisano, Paolo; Romano, Raffaella; Di Giorgio, Annabella; Gelao, Barbara; Lo Bianco, Luciana; Papazacharias, Apostolos; Popolizio, Teresa; Nardini, Marcello; Bertolino, Alessandro

2010-01-01

Earlier studies have shown widespread alterations of functional connectivity of various brain networks in schizophrenia, including the default mode network (DMN). The DMN has also an important role in the performance of cognitive tasks. Furthermore, treatment with second-generation antipsychotic drugs may ameliorate to some degree working memory (WM) deficits and related brain activity. The aim of this study was to evaluate the effects of treatment with olanzapine monotherapy on functional connectivity among brain regions of the DMN during WM. Seventeen patients underwent an 8-week prospective study and completed two functional magnetic resonance imaging (fMRI) scans at 4 and 8 weeks of treatment during the performance of the N-back WM task. To control for potential repetition effects, 19 healthy controls also underwent two fMRI scans at a similar time interval. We used spatial group-independent component analysis (ICA) to analyze fMRI data. Relative to controls, patients with schizophrenia had reduced connectivity strength within the DMN in posterior cingulate, whereas it was greater in precuneus and inferior parietal lobule. Treatment with olanzapine was associated with increases in DMN connectivity with ventromedial prefrontal cortex, but not in posterior regions of DMN. These results suggest that treatment with olanzapine is associated with the modulation of DMN connectivity in schizophrenia. In addition, our findings suggest critical functional differences in the regions of DMN. PMID:19956088

Assessment of lexical semantic judgment abilities in alcohol-dependent subjects: an fMRI study.

PubMed

Bagga, D; Singh, N; Modi, S; Kumar, P; Bhattacharya, D; Garg, M L; Khushu, S

2013-12-01

Neuropsychological studies have shown that alcohol dependence is associated with neurocognitive deficits in tasks requiring memory, perceptual motor skills, abstraction and problem solving, whereas language skills are relatively spared in alcoholics despite structural abnormalities in the language-related brain regions. To investigate the preserved mechanisms of language processing in alcohol-dependents, functional brain imaging was undertaken in healthy controls (n=18) and alcohol-dependents (n=16) while completing a lexical semantic judgment task in a 3 T MR scanner. Behavioural data indicated that alcohol-dependents took more time than controls for performing the task but there was no significant difference in their response accuracy. fMRI data analysis revealed that while performing the task, the alcoholics showed enhanced activations in left supramarginal gyrus, precuneus bilaterally, left angular gyrus, and left middle temporal gyrus as compared to control subjects. The extensive activations observed in alcoholics as compared to controls suggest that alcoholics recruit additional brain areas to meet the behavioural demands for equivalent task performance. The results are consistent with previous fMRI studies suggesting compensatory mechanisms for the execution of task for showing an equivalent performance or decreased neural efficiency of relevant brain networks. However, on direct comparison of the two groups, the results did not survive correction for multiple comparisons; therefore, the present findings need further exploration.

Subliminal and supraliminal processing of reward-related stimuli in anorexia nervosa.

PubMed

Boehm, I; King, J A; Bernardoni, F; Geisler, D; Seidel, M; Ritschel, F; Goschke, T; Haynes, J-D; Roessner, V; Ehrlich, S

2018-04-01

Previous studies have highlighted the role of the brain reward and cognitive control systems in the etiology of anorexia nervosa (AN). In an attempt to disentangle the relative contribution of these systems to the disorder, we used functional magnetic resonance imaging (fMRI) to investigate hemodynamic responses to reward-related stimuli presented both subliminally and supraliminally in acutely underweight AN patients and age-matched healthy controls (HC). fMRI data were collected from a total of 35 AN patients and 35 HC, while they passively viewed subliminally and supraliminally presented streams of food, positive social, and neutral stimuli. Activation patterns of the group × stimulation condition × stimulus type interaction were interrogated to investigate potential group differences in processing different stimulus types under the two stimulation conditions. Moreover, changes in functional connectivity were investigated using generalized psychophysiological interaction analysis. AN patients showed a generally increased response to supraliminally presented stimuli in the inferior frontal junction (IFJ), but no alterations within the reward system. Increased activation during supraliminal stimulation with food stimuli was observed in the AN group in visual regions including superior occipital gyrus and the fusiform gyrus/parahippocampal gyrus. No group difference was found with respect to the subliminal stimulation condition and functional connectivity. Increased IFJ activation in AN during supraliminal stimulation may indicate hyperactive cognitive control, which resonates with clinical presentation of excessive self-control in AN patients. Increased activation to food stimuli in visual regions may be interpreted in light of an attentional food bias in AN.

Intrinsic spontaneous brain activity predicts individual variability in associative memory in older adults.

PubMed

Zheng, Zhiwei; Li, Rui; Xiao, Fengqiu; He, Rongqiao; Zhang, Shouzi; Li, Juan

2018-06-01

Older adults demonstrate notable individual differences in associative memory. Here, resting-state functional magnetic resonance imaging (rsfMRI) was used to investigate whether intrinsic brain activity at rest could predict individual differences in associative memory among cognitively healthy older adults. Regional amplitude of low-frequency fluctuations (ALFF) analysis and a correlation-based resting-state functional connectivity (RSFC) approach were used to analyze data acquired from 102 cognitively normal elderly who completed the paired-associative learning test (PALT) and underwent fMRI scans. Participants were divided into two groups based on the retrospective self-reports on whether or not they utilized encoding strategies during the PALT. The behavioral results revealed better associative memory performance in the participants who reported utilizing memory strategies compared with participants who reported not doing so. The fMRI results showed that higher associative memory performance was associated with greater functional connectivity between the right superior frontal gyrus and the right posterior cerebellum lobe in the strategy group. The regional ALFF values in the right superior frontal gyrus were linked to associative memory performance in the no-strategy group. These findings suggest that the regional spontaneous fluctuations and functional connectivity during rest may subserve the individual differences in the associative memory in older adults, and that this is modulated by self-initiated memory strategy use. © 2018 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Head motion parameters in fMRI differ between patients with mild cognitive impairment and Alzheimer disease versus elderly control subjects.

PubMed

Haller, Sven; Monsch, Andreas U; Richiardi, Jonas; Barkhof, Frederik; Kressig, Reto W; Radue, Ernst W

2014-11-01

Motion artifacts are a well-known and frequent limitation during neuroimaging workup of cognitive decline. While head motion typically deteriorates image quality, we test the hypothesis that head motion differs systematically between healthy controls (HC), amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD) and consequently might contain diagnostic information. This prospective study was approved by the local ethics committee and includes 28 HC (age 71.0 ± 6.9 years, 18 females), 15 aMCI (age 67.7 ± 10.9 years, 9 females) and 20 AD (age 73.4 ± 6.8 years, 10 females). Functional magnetic resonance imaging (fMRI) at 3T included a 9 min echo-planar imaging sequence with 180 repetitions. Cumulative average head rotation and translation was estimated based on standard fMRI preprocessing and compared between groups using receiver operating characteristic statistics. Global cumulative head rotation discriminated aMCI from controls [p < 0.01, area under curve (AUC) 0.74] and AD from controls (p < 0.01, AUC 0.73). The ratio of rotation z versus y discriminated AD from controls (p < 0.05, AUC 0.71) and AD from aMCI (p < 0.05, AUC of 0.75). Head motion systematically differs between aMCI/AD and controls. Since motion is not random but convoluted with diagnosis, the higher amount of motion in aMCI and AD as compared to controls might be a potential confounding factor for fMRI group comparisons. Additionally, head motion not only deteriorates image quality, yet also contains useful discriminatory information and is available for free as a "side product" of fMRI data preprocessing.

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

Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis

DTIC Science & Technology

2017-10-13

AWARD NUMBER: W81XWH-15-2-0032 TITLE: Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis...TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-15-2-0032 5b. GRANT NUMBER Identifying Subgroups of Tinnitus Using Novel Resting State fMRI...Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The subject of the project is FY14 PRMRP Topic Area – Tinnitus . The broad goal is

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,…

Developmental Meta-Analysis of the Functional Neural Correlates of Autism Spectrum Disorders

ERIC Educational Resources Information Center

Dickstein, Daniel P.; Pescosolido, Matthew F.; Reidy, Brooke L.; Galvan, Thania; Kim, Kerri L.; Seymour, Karen E.; Laird, Angela R.; Di Martino, Adriana; Barrett, Rowland P.

2013-01-01

Objective: There is a pressing need to elucidate the brain-behavior interactions underlying autism spectrum disorders (ASD) given the marked rise in ASD diagnosis over the past decade. Functional magnetic resonance imaging (fMRI) has begun to address this need, but few fMRI studies have evaluated age-related changes in ASD. Therefore, we conducted…

Neural correlates of improved recognition of happy faces after erythropoietin treatment in bipolar disorder.

PubMed

Miskowiak, K W; Petersen, N A; Harmer, C J; Ehrenreich, E; Kessing, L V; Vinberg, M; Macoveanu, J; Siebner, H R

2018-06-07

Bipolar disorder is associated with impairments in social cognition including the recognition of happy faces. This is accompanied by imbalanced cortico-limbic response to emotional faces. We found that EPO improved the recognition of happy faces in patients with bipolar disorder. This randomized, controlled, longitudinal fMRI study explores the neuronal underpinnings of this effect. Forty-four patients with bipolar disorder in full or partial remission were randomized to eight weekly erythropoietin (EPO; 40 000 IU) or saline (NaCl 0.9%) infusions in a double-blind, parallel-group design. Participants underwent whole-brain fMRI at 3T, mood ratings and blood tests at baseline and week 14. During fMRI, participants viewed happy and fearful faces and performed a gender discrimination task. Thirty-four patients had complete pre- and post-treatment fMRI data (EPO: N = 18, saline: N = 16). Erythropoietin vs. saline increased right superior frontal response to happy vs. fearful faces. This correlated with improved happiness recognition in the EPO group. Erythropoietin also enhanced gender discrimination accuracy for happy faces. These effects were not influenced by medication, mood, red blood cells or blood pressure. Together with previous findings, the present observation suggests that increased dorsal prefrontal attention control is a common mechanism of EPO-associated improvements across several cognitive domains. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Influence of Acoustic Overstimulation on the Central Auditory System: An Functional Magnetic Resonance Imaging (fMRI) Study.

PubMed

Wolak, Tomasz; Cieśla, Katarzyna; Rusiniak, Mateusz; Piłka, Adam; Lewandowska, Monika; Pluta, Agnieszka; Skarżyński, Henryk; Skarżyński, Piotr H

2016-11-28

BACKGROUND The goal of the fMRI experiment was to explore the involvement of central auditory structures in pathomechanisms of a behaviorally manifested auditory temporary threshold shift in humans. MATERIAL AND METHODS The material included 18 healthy volunteers with normal hearing. Subjects in the exposure group were presented with 15 min of binaural acoustic overstimulation of narrowband noise (3 kHz central frequency) at 95 dB(A). The control group was not exposed to noise but instead relaxed in silence. Auditory fMRI was performed in 1 session before and 3 sessions after acoustic overstimulation and involved 3.5-4.5 kHz sweeps. RESULTS The outcomes of the study indicate a possible effect of acoustic overstimulation on central processing, with decreased brain responses to auditory stimulation up to 20 min after exposure to noise. The effect can be seen already in the primary auditory cortex. Decreased BOLD signal change can be due to increased excitation thresholds and/or increased spontaneous activity of auditory neurons throughout the auditory system. CONCLUSIONS The trial shows that fMRI can be a valuable tool in acoustic overstimulation studies but has to be used with caution and considered complimentary to audiological measures. Further methodological improvements are needed to distinguish the effects of TTS and neuronal habituation to repetitive stimulation.

Influence of Acoustic Overstimulation on the Central Auditory System: An Functional Magnetic Resonance Imaging (fMRI) Study

PubMed Central

Wolak, Tomasz; Cieśla, Katarzyna; Rusiniak, Mateusz; Piłka, Adam; Lewandowska, Monika; Pluta, Agnieszka; Skarżyński, Henryk; Skarżyński, Piotr H.

2016-01-01

Background The goal of the fMRI experiment was to explore the involvement of central auditory structures in pathomechanisms of a behaviorally manifested auditory temporary threshold shift in humans. Material/Methods The material included 18 healthy volunteers with normal hearing. Subjects in the exposure group were presented with 15 min of binaural acoustic overstimulation of narrowband noise (3 kHz central frequency) at 95 dB(A). The control group was not exposed to noise but instead relaxed in silence. Auditory fMRI was performed in 1 session before and 3 sessions after acoustic overstimulation and involved 3.5–4.5 kHz sweeps. Results The outcomes of the study indicate a possible effect of acoustic overstimulation on central processing, with decreased brain responses to auditory stimulation up to 20 min after exposure to noise. The effect can be seen already in the primary auditory cortex. Decreased BOLD signal change can be due to increased excitation thresholds and/or increased spontaneous activity of auditory neurons throughout the auditory system. Conclusions The trial shows that fMRI can be a valuable tool in acoustic overstimulation studies but has to be used with caution and considered complimentary to audiological measures. Further methodological improvements are needed to distinguish the effects of TTS and neuronal habituation to repetitive stimulation. PMID:27893698

fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

NASA Astrophysics Data System (ADS)

Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

2010-03-01

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

Reducing Individual Variation for fMRI Studies in Children by Minimizing Template Related Errors

PubMed Central

Weng, Jian; Dong, Shanshan; He, Hongjian; Chen, Feiyan; Peng, Xiaogang

2015-01-01

Spatial normalization is an essential process for group comparisons in functional MRI studies. In practice, there is a risk of normalization errors particularly in studies involving children, seniors or diseased populations and in regions with high individual variation. One way to minimize normalization errors is to create a study-specific template based on a large sample size. However, studies with a large sample size are not always feasible, particularly for children studies. The performance of templates with a small sample size has not been evaluated in fMRI studies in children. In the current study, this issue was encountered in a working memory task with 29 children in two groups. We compared the performance of different templates: a study-specific template created by the experimental population, a Chinese children template and the widely used adult MNI template. We observed distinct differences in the right orbitofrontal region among the three templates in between-group comparisons. The study-specific template and the Chinese children template were more sensitive for the detection of between-group differences in the orbitofrontal cortex than the MNI template. Proper templates could effectively reduce individual variation. Further analysis revealed a correlation between the BOLD contrast size and the norm index of the affine transformation matrix, i.e., the SFN, which characterizes the difference between a template and a native image and differs significantly across subjects. Thereby, we proposed and tested another method to reduce individual variation that included the SFN as a covariate in group-wise statistics. This correction exhibits outstanding performance in enhancing detection power in group-level tests. A training effect of abacus-based mental calculation was also demonstrated, with significantly elevated activation in the right orbitofrontal region that correlated with behavioral response time across subjects in the trained group. PMID:26207985

Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?

PubMed

Emmert, Kirsten; Kopel, Rotem; Sulzer, James; Brühl, Annette B; Berman, Brian D; Linden, David E J; Horovitz, Silvina G; Breimhorst, Markus; Caria, Andrea; Frank, Sabine; Johnston, Stephen; Long, Zhiying; Paret, Christian; Robineau, Fabien; Veit, Ralf; Bartsch, Andreas; Beckmann, Christian F; Van De Ville, Dimitri; Haller, Sven

2016-01-01

An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on the regulated region(s), little is known about the target-independent mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the network involved in self-regulation in general. In an effort to detect regions that are responsible for the act of brain regulation, we performed a post-hoc analysis of data involving different target regions based on studies from different research groups. We included twelve suitable studies that examined nine different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first- (single subject, extracting main paradigm) and second-level (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation. The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies. The anterior insula has been implicated in interoceptive awareness of the body and cognitive control. Basal ganglia are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporo-occipital junction. In conclusion, we demonstrate that several key regions, such as the anterior insula and the basal ganglia, are consistently activated during self-regulation in real-time fMRI neurofeedback independent of the targeted region-of-interest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the anterior insula, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

A longitudinal model for functional connectivity networks using resting-state fMRI.

PubMed

Hart, Brian; Cribben, Ivor; Fiecas, Mark

2018-06-04

Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current fMRI literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC model using a variance components approach. First, for all subjects' visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the baseline FC strength, and 3) the FC's longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI time series data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in the baseline FC network and change in FC over longitudinal time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, we found no difference in the global FC network between Alzheimer's disease patients and healthy controls, but did find differing local aging patterns in the FC between the left hippocampus and the posterior cingulate cortex. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

Language Lateralization in Children Aged 10 to 11 Years: A Combined fMRI and Dichotic Listening Study

PubMed Central

Norrelgen, Fritjof; Lilja, Anders; Ingvar, Martin; Gisselgård, Jens; Fransson, Peter

2012-01-01

Objective The aims of this study were to develop and assess a method to map language networks in children with two auditory fMRI protocols in combination with a dichotic listening task (DL). The method is intended for pediatric patients prior to epilepsy surgery. To evaluate the potential clinical usefulness of the method we first wanted to assess data from a group of healthy children. Methods In a first step language test materials were developed, intended for subsequent implementation in fMRI protocols. An evaluation of this material was done in 30 children with typical development, 10 from the 1st, 4th and the 7th grade, respectively. The language test material was then adapted and implemented in two fMRI protocols intended to target frontal and posterior language networks. In a second step language lateralization was assessed in 17 typical 10–11 year olds with fMRI and DL. To reach a conclusion about language lateralization, firstly, quantitative analyses of the index data from the two fMRI tasks and the index data from the DL task were done separately. In a second step a set of criteria were applied to these results to reach a conclusion about language lateralization. The steps of these analyses are described in detail. Results The behavioral assessment of the language test material showed that it was well suited for typical children. The results of the language lateralization assessments, based on fMRI data and DL data, showed that for 15 of the 17 subjects (88%) a conclusion could be reached about hemispheric language dominance. In 2 cases (12%) DL provided critical data. Conclusions The employment of DL combined with language mapping using fMRI for assessing hemispheric language dominance is novel and it was deemed valuable since it provided additional information compared to the results gained from each method individually. PMID:23284796

Reliability of Task-Based fMRI for Preoperative Planning: A Test-Retest Study in Brain Tumor Patients and Healthy Controls

PubMed Central

Morrison, Melanie A.; Churchill, Nathan W.; Cusimano, Michael D.; Schweizer, Tom A.; Das, Sunit; Graham, Simon J.

2016-01-01

Background Functional magnetic resonance imaging (fMRI) continues to develop as a clinical tool for patients with brain cancer, offering data that may directly influence surgical decisions. Unfortunately, routine integration of preoperative fMRI has been limited by concerns about reliability. Many pertinent studies have been undertaken involving healthy controls, but work involving brain tumor patients has been limited. To develop fMRI fully as a clinical tool, it will be critical to examine these reliability issues among patients with brain tumors. The present work is the first to extensively characterize differences in activation map quality between brain tumor patients and healthy controls, including the effects of tumor grade and the chosen behavioral testing paradigm on reliability outcomes. Method Test-retest data were collected for a group of low-grade (n = 6) and high-grade glioma (n = 6) patients, and for matched healthy controls (n = 12), who performed motor and language tasks during a single fMRI session. Reliability was characterized by the spatial overlap and displacement of brain activity clusters, BOLD signal stability, and the laterality index. Significance testing was performed to assess differences in reliability between the patients and controls, and low-grade and high-grade patients; as well as between different fMRI testing paradigms. Results There were few significant differences in fMRI reliability measures between patients and controls. Reliability was significantly lower when comparing high-grade tumor patients to controls, or to low-grade tumor patients. The motor task produced more reliable activation patterns than the language tasks, as did the rhyming task in comparison to the phonemic fluency task. Conclusion In low-grade glioma patients, fMRI data are as reliable as healthy control subjects. For high-grade glioma patients, further investigation is required to determine the underlying causes of reduced reliability. To maximize reliability outcomes, testing paradigms should be carefully selected to generate robust activation patterns. PMID:26894279

Cerebral responses to innocuous somatic pressure stimulation following aerobic exercise rehabilitation in chronic pain patients: a functional magnetic resonance imaging study

PubMed Central

Micalos, Peter S; Korgaonkar, Mayuresh S; Drinkwater, Eric J; Cannon, Jack; Marino, Frank E

2014-01-01

Objective The purpose of this research was to assess the functional brain activity and perceptual rating of innocuous somatic pressure stimulation before and after exercise rehabilitation in patients with chronic pain. Materials and methods Eleven chronic pain patients and eight healthy pain-free controls completed 12 weeks of supervised aerobic exercise intervention. Perceptual rating of standardized somatic pressure stimulation (2 kg) on the right anterior mid-thigh and brain responses during functional magnetic resonance imaging (fMRI) were assessed at pre- and postexercise rehabilitation. Results There was a significant difference in the perceptual rating of innocuous somatic pressure stimulation between the chronic pain and control groups (P=0.02) but no difference following exercise rehabilitation. Whole brain voxel-wise analysis with correction for multiple comparisons revealed trends for differences in fMRI responses between the chronic pain and control groups in the superior temporal gyrus (chronic pain > control, corrected P=0.30), thalamus, and caudate (control > chronic, corrected P=0.23). Repeated measures of the regions of interest (5 mm radius) for blood oxygen level-dependent signal response revealed trend differences for superior temporal gyrus (P=0.06), thalamus (P=0.04), and caudate (P=0.21). Group-by-time interactions revealed trend differences in the caudate (P=0.10) and superior temporal gyrus (P=0.29). Conclusion Augmented perceptual and brain responses to innocuous somatic pressure stimulation were shown in the chronic pain group compared to the control group; however, 12-weeks of exercise rehabilitation did not significantly attenuate these responses. PMID:25210471

Right and left amygdalae activation in patients with major depression receiving antidepressant treatment, as revealed by fMRI.

PubMed

Chen, Yen-Ting; Huang, Min-Wei; Hung, I-Chung; Lane, Hsien-Yuan; Hou, Chun-Ju

2014-10-08

A differential contribution of the right and left amygdalae to affective information processing has been proposed. However, the direction of this lateralization has not been confirmed. In this study, we used a pre- and post-treatment (escitalopram) design to analyze the relative differences between neural activity in the right and left amygdalae during exposure to emotional stimuli in currently depressed patients. To the best of our knowledge, this study is to compare neural activity between the left and right amygdalae in people with depression. Our findings could lead to the development of parameters or biomarkers for depressive symptoms and treatment response. We used a pre-post-test design without a control group. Twenty currently depressed participants underwent an emotion processing task during fMRI. These participants were then treated with an antidepressant for 6 weeks. We used amygdala region-of-interest analysis to evaluate the hemodynamic response during exposure to colored emotional pictures. In total, thirteen of the 20 participants were placed into a separate group based on degree of response to antidepressants. The partial response group had an averaged HDRS score of 10.75 ± 2.25 and an averaged DBOLDLR signal of 189.18 ± 140.23 (m1 = 8), and the remitted group had an averaged HDRS score of 4.80 ± 1.64 and an averaged DBOLDLR signal of 421.26 ± 109.19 (m2 = 5). Each individual had lateralized amygdala activity, and the direction of asymmetry persisted following treatment. Amygdala responses to four types of emotional stimuli did not significantly change (p > 0.05) with treatment in either the right or the left amygdala. However, the difference in neural activity between the right and left amygdalae was greater after treatment, and the variation in neural activity was larger in the left amygdala. We found that the response between the right and left amygdala did not differ in terms of time series, although activity increased after pharmaceutical treatment for each emotion tested. In the future, changes in BOLD signals as revealed by fMRI might be useful in evaluating the clinical manifestation of major depression.

N1 Magnitude of Auditory Evoked Potentials and Spontaneous Functional Connectivity Between Bilateral Heschl's Gyrus Are Coupled at Interindividual Level.

PubMed

Tan, Ao; Hu, Li; Tu, Yiheng; Chen, Rui; Hung, Yeung Sam; Zhang, Zhiguo

2016-07-01

N1 component of auditory evoked potentials is extensively used to investigate the propagation and processing of auditory inputs. However, the substantial interindividual variability of N1 could be a possible confounding factor when comparing different individuals or groups. Therefore, identifying the neuronal mechanism and origin of the interindividual variability of N1 is crucial in basic research and clinical applications. This study is aimed to use simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data to investigate the coupling between N1 and spontaneous functional connectivity (FC). EEG and fMRI data were simultaneously collected from a group of healthy individuals during a pure-tone listening task. Spontaneous FC was estimated from spontaneous blood oxygenation level-dependent (BOLD) signals that were isolated by regressing out task evoked BOLD signals from raw BOLD signals and then was correlated to N1 magnitude across individuals. It was observed that spontaneous FC between bilateral Heschl's gyrus was significantly and positively correlated with N1 magnitude across individuals (Spearman's R = 0.829, p < 0.001). The specificity of this observation was further confirmed by two whole-brain voxelwise analyses (voxel-mirrored homotopic connectivity analysis and seed-based connectivity analysis). These results enriched our understanding of the functional significance of the coupling between event-related brain responses and spontaneous brain connectivity, and hold the potential to increase the applicability of brain responses as a probe to the mechanism underlying pathophysiological conditions.

Altered spontaneous brain activity in adolescent boys with pure conduct disorder revealed by regional homogeneity analysis.

PubMed

Wu, Qiong; Zhang, Xiaocui; Dong, Daifeng; Wang, Xiang; Yao, Shuqiao

2017-07-01

Functional magnetic resonance imaging (fMRI) studies have revealed abnormal neural activity in several brain regions of adolescents with conduct disorder (CD) performing various tasks. However, little is known about the spontaneous neural activity in people with CD in a resting state. The aims of this study were to investigate CD-associated regional activity abnormalities and to explore the relationship between behavioral impulsivity and regional activity abnormalities. Resting-state fMRI (rs-fMRI) scans were administered to 28 adolescents with CD and 28 age-, gender-, and IQ-matched healthy controls (HCs). The rs-fMRI data were subjected to regional homogeneity (ReHo) analysis. ReHo can demonstrate the temporal synchrony of regional blood oxygen level-dependent signals and reflect the coordination of local neuronal activity facilitating similar goals or representations. Compared to HCs, the CD group showed increased ReHo bilaterally in the insula as well as decreased ReHo in the right inferior parietal lobule, right middle temporal gyrus and right fusiform gyrus, left anterior cerebellum anterior, and right posterior cerebellum. In the CD group, mean ReHo values in the left and the right insula correlated positively with Barratt Impulsivity Scale (BIS) total scores. The results suggest that CD is associated with abnormal intrinsic brain activity, mainly in the cerebellum and temporal-parietal-limbic cortices, regions that are related to emotional and cognitive processing. BIS scores in adolescents with CD may reflect severity of abnormal neuronal synchronization in the insula.

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

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.

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.

Gender differences in brain regional homogeneity of healthy subjects after normal sleep and after sleep deprivation: a resting-state fMRI study.

PubMed

Dai, Xi-Jian; Gong, Hong-Han; Wang, Yi-Xiang; Zhou, Fu-Qing; Min, You-Jiang; Zhao, Feng; Wang, Si-Yong; Liu, Bi-Xia; Xiao, Xiang-Zuo

2012-06-01

To explore the gender differences of brain regional homogeneity (ReHo) in healthy subjects during the resting-state, after normal sleep, and after sleep deprivation (SD) using functional magnetic resonance imaging (fMRI) and the ReHo method. Sixteen healthy subjects (eight males and eight females) each underwent the resting-state fMRI exams twice, i.e., once after normal sleep and again after 24h's SD. According to the gender and sleep, 16 subjects were all measured twice and divided into four groups: the male control group (MC), female control group (FC), male SD group (MSD), and female SD group (FSD). The ReHo method was used to calculate and analyze the data, SPM5 software was used to perform a two-sample T-test and a two-pair T-test with a P value <0.001, and cluster volume ≥ 270 mm(3) was used to determine statistical significance. Compared with the MC, the MSD showed significantly higher ReHo in the right paracentral lobule (BA3/6), but in no obviously lower regions. Compared with the FC, the FSD showed significantly higher ReHo in bilateral parietal lobes (BA2/3), bilateral vision-related regions of occipital lobes (BA17/18/19), right frontal lobe (BA4/6), and lower ReHo in the right frontal lobe. Compared with the FC, the MC showed significantly higher ReHo in the left occipital lobe (BA18/19), and left temporal lobe (BA21), left frontal lobe, and lower ReHo in the right insula and in the left parietal lobe. Compared with the FSD, the MSD showed significantly higher ReHo in the left cerebellum posterior lobe (uvula/declive of vermis), left parietal lobe, and bilateral frontal lobes, and lower ReHo in the right occipital lobe (BA17) and right frontal lobe (BA4). The differences of brain activity in the resting state can be widely found not only between the control and SD group in a same gender group, but also between the male group and female group. Thus, we should take the gender differences into consideration in future fMRI studies, especially the treatment of brain-related diseases (e.g., depression). Copyright © 2012 Elsevier B.V. All rights reserved.

Functional brain networks reconstruction using group sparsity-regularized learning.

PubMed

Zhao, Qinghua; Li, Will X Y; Jiang, Xi; Lv, Jinglei; Lu, Jianfeng; Liu, Tianming

2018-06-01

Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction. To address this problem, in this paper, we propose to reconstruct brain networks utilizing the structure guided group sparse regression (S2GSR) in which 116 anatomical regions from the AAL template, as prior knowledge, are employed to guide the network reconstruction when performing sparse representation of whole-brain fMRI data. Specifically, we extract fMRI signals from standard space aligned with the AAL template. Then by learning a global over-complete dictionary, with the learned dictionary as a set of features (regressors), the group structured regression employs anatomical structures as group information to regress whole brain signals. Finally, the decomposition coefficients matrix is mapped back to the brain volume to represent functional brain networks and patterns. We use the publicly available Human Connectome Project (HCP) Q1 dataset as the test bed, and the experimental results indicate that the proposed anatomically guided structure sparse representation is effective in reconstructing concurrent functional brain networks.

Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

NASA Astrophysics Data System (ADS)

Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong

2010-03-01

The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.

Functional connectivity analysis in resting state fMRI with echo-state networks and non-metric clustering for network structure recovery

NASA Astrophysics Data System (ADS)

Wismüller, Axel; DSouza, Adora M.; Abidin, Anas Z.; Wang, Xixi; Hobbs, Susan K.; Nagarajan, Mahesh B.

2015-03-01

Echo state networks (ESN) are recurrent neural networks where the hidden layer is replaced with a fixed reservoir of neurons. Unlike feed-forward networks, neuron training in ESN is restricted to the output neurons alone thereby providing a computational advantage. We demonstrate the use of such ESNs in our mutual connectivity analysis (MCA) framework for recovering the primary motor cortex network associated with hand movement from resting state functional MRI (fMRI) data. Such a framework consists of two steps - (1) defining a pair-wise affinity matrix between different pixel time series within the brain to characterize network activity and (2) recovering network components from the affinity matrix with non-metric clustering. Here, ESNs are used to evaluate pair-wise cross-estimation performance between pixel time series to create the affinity matrix, which is subsequently subject to non-metric clustering with the Louvain method. For comparison, the ground truth of the motor cortex network structure is established with a task-based fMRI sequence. Overlap between the primary motor cortex network recovered with our model free MCA approach and the ground truth was measured with the Dice coefficient. Our results show that network recovery with our proposed MCA approach is in close agreement with the ground truth. Such network recovery is achieved without requiring low-pass filtering of the time series ensembles prior to analysis, an fMRI preprocessing step that has courted controversy in recent years. Thus, we conclude our MCA framework can allow recovery and visualization of the underlying functionally connected networks in the brain on resting state fMRI.

PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI.

PubMed

Huang, Huiyuan; Ding, Zhongxiang; Mao, Dewang; Yuan, Jianhua; Zhu, Fangmei; Chen, Shuda; Xu, Yan; Lou, Lin; Feng, Xiaoyan; Qi, Le; Qiu, Wusi; Zhang, Han; Zang, Yu-Feng

2016-10-01

The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.

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

Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers

PubMed Central

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-01-01

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL. PMID:24389422

A Selective Review of Simulated Driving Studies: Combining Naturalistic and Hybrid Paradigms, Analysis Approaches, and Future Directions

PubMed Central

Calhoun, V. D.; Pearlson, G. D.

2011-01-01

Naturalistic paradigms such as movie watching or simulated driving that mimic closely real-world complex activities are becoming more widely used in functional magnetic resonance imaging (fMRI) studies both because of their ability to robustly stimulate brain connectivity and the availability of analysis methods which are able to capitalize on connectivity within and among intrinsic brain networks identified both during a task and in resting fMRI data. In this paper we review over a decade of work from our group and others on the use of simulated driving paradigms to study both the healthy brain as well as the effects of acute alcohol administration on functional connectivity during such paradigms. We briefly review our initial work focused on the configuration of the driving simulator and the analysis strategies. We then describe in more detail several recent studies from our group including a hybrid study examining distracted driving and compare resulting data with those from a separate visual oddball task. The analysis of these data were performed primarily using a combination of group independent component analysis (ICA) and the general linear model (GLM) and in the various studies we highlight novel findings which result from an analysis of either 1) within-network connectivity, 2) inter-network connectivity, also called functional network connectivity, or 3) the degree to which the modulation of the various intrinsic networks were associated with the alcohol administration and the task context. Despite the fact that the behavioral effects of alcohol intoxication are relatively well known, there is still much to discover on how acute alcohol exposure modulates brain function in a selective manner, associated with behavioral alterations. Through the above studies, we have learned more regarding the impact of acute alcohol intoxication on organization of the brain’s intrinsic connectivity networks during performance of a complex, real-world cognitive operation. Lessons learned from the above studies have broader applicability to designing ecologically valid, complex, functional MRI cognitive paradigms and incorporating pharmacologic challenges into such studies. Overall, the use of hybrid driving studies is a particularly promising area of neuroscience investigation. PMID:21718791

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.

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

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition

PubMed Central

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-01-01

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition. PMID:24961428

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition.

PubMed

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-05-28

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition.

Multistimulation group therapy in Alzheimer's disease promotes changes in brain functioning.

PubMed

Baglio, Francesca; Griffanti, Ludovica; Saibene, Francesca Lea; Ricci, Cristian; Alberoni, Margherita; Critelli, Raffaella; Villanelli, Fabiana; Fioravanti, Raffaella; Mantovani, Federica; D'amico, Alessandra; Cabinio, Monia; Preti, Maria Giulia; Nemni, Raffaello; Farina, Elisabetta

2015-01-01

Background. The growing social emergency represented by Alzheimer's disease (AD) and the lack of medical treatments able to modify the disease course have kindled the interest in nonpharmacological therapies. Objective. We introduced a novel nonpharmacological approach for people with AD (PWA) named Multidimensional Stimulation group Therapy (MST) to improve PWA condition in different disease domains: cognition, behavior, and motor functioning. Methods. Enrolling 60 PWA in a mild to moderate stage of the disease, we evaluated the efficacy of MST with a randomized-controlled study. Neuropsychological and neurobehavioral measures and functional magnetic resonance imaging (fMRI) data were considered as outcome measures. Results. The following significant intervention-related changes were observed: reduction in Neuropsychiatric Inventory scale score, improvement in language and memory subscales of Alzheimer's Disease Assessment Scale-Cognitive subscale, and increased fMRI activations in temporal brain areas, right insular cortex, and thalamus. Conclusions. Cognitive-behavioral and fMRI results support the notion that MST has significant effects in improving PWA cognitive-behavioral status by restoring neural functioning. © The Author(s) 2014.

Delineating potential epileptogenic areas utilizing resting functional magnetic resonance imaging (fMRI) in epilepsy patients.

PubMed

Pizarro, Ricardo; Nair, Veena; Meier, Timothy; Holdsworth, Ryan; Tunnell, Evelyn; Rutecki, Paul; Sillay, Karl; Meyerand, Mary E; Prabhakaran, Vivek

2016-08-01

Seizure localization includes neuroimaging like electroencephalogram, and magnetic resonance imaging (MRI) with limited ability to characterize the epileptogenic network. Temporal clustering analysis (TCA) characterizes epileptogenic network congruent with interictal epileptiform discharges by clustering together voxels with transient signals. We generated epileptogenic areas for 12 of 13 epilepsy patients with TCA, congruent with different areas of seizure onset. Resting functional MRI (fMRI) scans are noninvasive, and can be acquired quickly, in patients with different levels of severity and function. Analyzing resting fMRI data using TCA is quick and can complement clinical methods to characterize the epileptogenic network.

Neural and Behavioral Sequelae of Blast-Related Traumatic Brain Injury

DTIC Science & Technology

2012-11-01

testing and advanced MRI techniques [task-activated functional MRI (fMRI) and diffusion tensor imaging ( DTI )] to gain a comprehensive understanding of... DTI fiber tracking) and neurobehavioral testing (computerized assessment and standard neuropsychological testing) on 60 chronic trauma patients: 15...data analysis. 15. SUBJECT TERMS Blast-related traumatic brain injury (TBI), fMRI, DTI , cognition 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-Site Experimental fMRI Study

ERIC Educational Resources Information Center

Zhou, Bo; Konstorum, Anna; Duong, Thao; Tieu, Kinh H.; Wells, William M.; Brown, Gregory G.; Stern, Hal S.; Shahbaba, Babak

2013-01-01

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model…

When We like What We Know--A Parametric fMRI Analysis of Beauty and Familiarity

ERIC Educational Resources Information Center

Bohrn, Isabel C.; Altmann, Ulrike; Lubrich, Oliver; Menninghaus, Winfried; Jacobs, Arthur M.

2013-01-01

This paper presents a neuroscientific study of aesthetic judgments on written texts. In an fMRI experiment participants read a number of proverbs without explicitly evaluating them. In a post-scan rating they rated each item for familiarity and beauty. These individual ratings were correlated with the functional data to investigate the neural…

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

Physiogenomic analysis of localized FMRI brain activity in schizophrenia.

PubMed

Windemuth, Andreas; Calhoun, Vince D; Pearlson, Godfrey D; Kocherla, Mohan; Jagannathan, Kanchana; Ruaño, Gualberto

2008-06-01

The search for genetic factors associated with disease is complicated by the complexity of the biological pathways linking genotype and phenotype. This analytical complexity is particularly concerning in diseases historically lacking reliable diagnostic biological markers, such as schizophrenia and other mental disorders. We investigate the use of functional magnetic resonance imaging (fMRI) as an intermediate phenotype (endophenotype) to identify physiogenomic associations to schizophrenia. We screened 99 subjects, 30 subjects diagnosed with schizophrenia, 13 unaffected relatives of schizophrenia patients, and 56 unrelated controls, for gene polymorphisms associated with fMRI activation patterns at two locations in temporal and frontal lobes previously implied in schizophrenia. A total of 22 single nucleotide polymorphisms (SNPs) in 15 genes from the dopamine and serotonin neurotransmission pathways were genotyped in all subjects. We identified three SNPs in genes that are significantly associated with fMRI activity. SNPs of the dopamine beta-hydroxylase (DBH) gene and of the dopamine receptor D4 (DRD4) were associated with activity in the temporal and frontal lobes, respectively. One SNP of serotonin-3A receptor (HTR3A) was associated with temporal lobe activity. The results of this study support the physiogenomic analysis of neuroimaging data to discover associations between genotype and disease-related phenotypes.

Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Heute, U; Deuschl, G; Raethjen, J; Muthuraman, Muthuraman

2016-09-01

Recently, interest has been growing to understand the underlying dynamic directional relationship between simultaneously activated regions of the brain during motor task performance. Such directionality analysis (or effective connectivity analysis), based on non-invasive electrophysiological (electroencephalography-EEG) and hemodynamic (functional near infrared spectroscopy-fNIRS; and functional magnetic resonance imaging-fMRI) neuroimaging modalities can provide an estimate of the motor task-related information flow from one brain region to another. Since EEG, fNIRS and fMRI modalities achieve different spatial and temporal resolutions of motor-task related activation in the brain, the aim of this study was to determine the effective connectivity of cortico-cortical sensorimotor networks during finger movement tasks measured by each neuroimaging modality. Nine healthy subjects performed right hand finger movement tasks of different complexity (simple finger tapping-FT, simple finger sequence-SFS, and complex finger sequence-CFS). We focused our observations on three cortical regions of interest (ROIs), namely the contralateral sensorimotor cortex (SMC), the contralateral premotor cortex (PMC) and the contralateral dorsolateral prefrontal cortex (DLPFC). We estimated the effective connectivity between these ROIs using conditional Granger causality (GC) analysis determined from the time series signals measured by fMRI (blood oxygenation level-dependent-BOLD), fNIRS (oxygenated-O2Hb and deoxygenated-HHb hemoglobin), and EEG (scalp and source level analysis) neuroimaging modalities. The effective connectivity analysis showed significant bi-directional information flow between the SMC, PMC, and DLPFC as determined by the EEG (scalp and source), fMRI (BOLD) and fNIRS (O2Hb and HHb) modalities for all three motor tasks. However the source level EEG GC values were significantly greater than the other modalities. In addition, only the source level EEG showed a significantly greater forward than backward information flow between the ROIs. This simultaneous fMRI, fNIRS and EEG study has shown through independent GC analysis of the respective time series that a bi-directional effective connectivity occurs within a cortico-cortical sensorimotor network (SMC, PMC and DLPFC) during finger movement tasks.

Chinese Character and English Word processing in children's ventral occipitotemporal cortex: fMRI evidence for script invariance.

PubMed

Krafnick, Anthony J; Tan, Li-Hai; Flowers, D Lynn; Luetje, Megan M; Napoliello, Eileen M; Siok, Wai-Ting; Perfetti, Charles; Eden, Guinevere F

2016-06-01

Learning to read is thought to involve the recruitment of left hemisphere ventral occipitotemporal cortex (OTC) by a process of "neuronal recycling", whereby object processing mechanisms are co-opted for reading. Under the same theoretical framework, it has been proposed that the visual word form area (VWFA) within OTC processes orthographic stimuli independent of culture and writing systems, suggesting that it is universally involved in written language. However, this "script invariance" has yet to be demonstrated in monolingual readers of two different writing systems studied under the same experimental conditions. Here, using functional magnetic resonance imaging (fMRI), we examined activity in response to English Words and Chinese Characters in 1st graders in the United States and China, respectively. We examined each group separately and found the readers of English as well as the readers of Chinese to activate the left ventral OTC for their respective native writing systems (using both a whole-brain and a bilateral OTC-restricted analysis). Critically, a conjunction analysis of the two groups revealed significant overlap between them for native writing system processing, located in the VWFA and therefore supporting the hypothesis of script invariance. In the second part of the study, we further examined the left OTC region responsive to each group's native writing system and found that it responded equally to Object stimuli (line drawings) in the Chinese-reading children. In English-reading children, the OTC responded much more to Objects than to English Words. Together, these results support the script invariant role of the VWFA and also support the idea that the areas recruited for character or word processing are rooted in object processing mechanisms of the left OTC. Copyright © 2016 Elsevier Inc. All rights reserved.

[Functional magnetic resonance imaging: a critical analysis of its technical, statistical and theoretical implications in human neuroscience].

PubMed

González-García, C; Tudela, P; Ruz, M

2014-04-01

The use of functional magnetic resonance imaging (fMRI) has represented an important step forward for the neurosciences. Nevertheless, it has also been subject to rather a lot of criticism. To study the most widespread criticism against fMRI, so that researchers who are starting to use it may know the different elements that must be taken into account to be able to take a suitable approach to this technique. The fact that fMRI allows brain activity to be observed makes it a very attractive and useful tool, and its use has grown exponentially since the last decade of the 20th century. At the same time, criticism against its use has become especially fierce. Most of this scepticism can be classified into aspects related with the technique and physiology, the analysis of data and their theoretical interpretation. In this study we will review the main arguments defended in each of these three areas, as well as looking at whether they are well-founded or not. Additionally, this work is also intended as a reference for novel researchers when it comes to identifying elements that must be taken into account as they approach fMRI. Despite the fact that fMRI is one of the most interesting options for observing the brain available today, its correct utilisation requires a great deal of control and knowledge. Even so, today most of the criticism it receives no longer has any solid foundation on which to stand.

Brain plasticity in Parkinson's disease with freezing of gait induced by action observation training.

PubMed

Agosta, Federica; Gatti, Roberto; Sarasso, Elisabetta; Volonté, Maria Antonietta; Canu, Elisa; Meani, Alessandro; Sarro, Lidia; Copetti, Massimiliano; Cattrysse, Erik; Kerckhofs, Eric; Comi, Giancarlo; Falini, Andrea; Filippi, Massimo

2017-01-01

Gait disorders represent a therapeutic challenge in Parkinson's disease (PD). This study investigated the efficacy of 4-week action observation training (AOT) on disease severity, freezing of gait and motor abilities in PD, and evaluated treatment-related brain functional changes. 25 PD patients with freezing of gait were randomized into two groups: AOT (action observation combined with practicing the observed actions) and "Landscape" (same physical training combined with landscape-videos observation). At baseline and 4-week, patients underwent clinical evaluation and fMRI. Clinical assessment was repeated at 8-week. At 4-week, both groups showed reduced freezing of gait severity, improved walking speed and quality of life. Moreover, AOT was associated with reduced motor disability and improved balance. AOT group showed a sustained positive effect on motor disability, walking speed, balance and quality of life at 8-week, with a trend toward a persisting reduced freezing of gait severity. At 4-week vs. baseline, AOT group showed increased recruitment of fronto-parietal areas during fMRI tasks, while the Landscape group showed a reduced fMRI activity of the left postcentral and inferior parietal gyri and right rolandic operculum and supramarginal gyrus. In AOT group, functional brain changes were associated with clinical improvements at 4-week and predicted clinical evolution at 8-week. AOT has a more lasting effect in improving motor function, gait and quality of life in PD patients relative to physical therapy alone. AOT-related performance gains are associated with an increased recruitment of motor regions and fronto-parietal mirror neuron and attentional control areas.

Oxytocin and brain activity in humans: A systematic review and coordinate-based meta-analysis of functional MRI studies.

PubMed

Grace, Sally A; Rossell, Susan L; Heinrichs, Markus; Kordsachia, Catarina; Labuschagne, Izelle

2018-05-24

Oxytocin (OXT) is a neuropeptide which has a critical role in human social behaviour and cognition. Research investigating the role of OXT on functional brain changes in humans has often used task paradigms that probe socioemotional processes. Preliminary evidence suggests a central role of the amygdala in the social cognitive effects of intranasal OXT (IN-OXT), however, inconsistencies in task-design and analysis methods have led to inconclusive findings regarding a cohesive model of the neural mechanisms underlying OXT's actions. The aim of this meta-analysis was to systematically investigate these findings. A systematic search of PubMed, PsycINFO, and Scopus databases was conducted for fMRI studies which compared IN-OXT to placebo in humans. First, we systematically reviewed functional magnetic resonance imaging (fMRI) studies of IN-OXT, including studies of healthy humans, those with clinical disorders, and studies examining resting-state fMRI (rsfMRI). Second, we employed a coordinate-based meta-analysis for task-based neuroimaging literature using activation likelihood estimation (ALE), whereby, coordinates were extracted from clusters with significant differences in IN-OXT versus placebo in healthy adults. Data were included for 39 fMRI studies that reported a total of 374 distinct foci. The meta-analysis identified task-related IN-OXT increases in activity within a cluster of the left superior temporal gyrus during tasks of emotion processing. These findings are important as they implicate regions beyond the amygdala in the neural effects of IN-OXT. The outcomes from this meta-analysis can guide a priori predictions for future OXT research, and provide an avenue for targeted treatment interventions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Regional autonomy changes in resting-state functional MRI in patients with HIV associated neurocognitive disorder

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Chockanathan, Udaysankar; Wismüller, Axel

2018-03-01

In this study, we investigate whether there are discernable changes in influence that brain regions have on themselves once patients show symptoms of HIV Associated Neurocognitive Disorder (HAND) using functional MRI (fMRI). Simple functional connectivity measures, such as correlation cannot reveal such information. To this end, we use mutual connectivity analysis (MCA) with Local Models (LM), which reveals a measure of influence in terms of predictability. Once such measures of interaction are obtained, we train two classifiers to characterize difference in patterns of regional self-influence between healthy subjects and subjects presenting with HAND symptoms. The two classifiers we use are Support Vector Machines (SVM) and Localized Generalized Matrix Learning Vector Quantization (LGMLVQ). Performing machine learning on fMRI connectivity measures is popularly known as multi-voxel pattern analysis (MVPA). By performing such an analysis, we are interested in studying the impact HIV infection has on an individual's brain. The high area under receiver operating curve (AUC) and accuracy values for 100 different train/test separations using MCA-LM self-influence measures (SVM: mean AUC=0.86, LGMLVQ: mean AUC=0.88, SVM and LGMLVQ: mean accuracy=0.78) compared with standard MVPA analysis using cross-correlation between fMRI time-series (SVM: mean AUC=0.58, LGMLVQ: mean AUC=0.57), demonstrates that self-influence features can be more discriminative than measures of interaction between time-series pairs. Furthermore, our results suggest that incorporating measures of self-influence in MVPA analysis used commonly in fMRI analysis has the potential to provide a performance boost and indicate important changes in dynamics of regions in the brain as a consequence of HIV infection.

Frontolimbic dysfunction in response to facial emotion in borderline personality disorder: an event-related fMRI study

PubMed Central

Minzenberg, Michael J.; Fan, Jin; New, Antonia S.; Tang, Cheuk Y.; Siever, Larry J.

2007-01-01

Clinical hallmarks of borderline personality disorder (BPD) include social and emotional dysregulation. We tested a model of frontolimbic dysfunction in facial emotion processing in BPD. Groups of 12 unmedicated adults with BPD by DSM-IV and 12 demographically-matched healthy controls (HC) viewed facial expressions (Conditions) of neutral emotion, fear and anger, and made gender discriminations during rapid event-related functional magnetic resonance imaging (fMRI). Analysis of variance of Region of Interest signal change revealed a statistically significant effect of the Group-by-Region-by-Condition interaction. This was due to the BPD group exhibiting a significantly larger magnitude of deactivation (relative to HC) in the bilateral rostral/subgenual anterior cingulate cortex (ACC) to fear and in the left ACC to fear minus neutral; and significantly greater activation in the right amygdala to fear minus neutral. There were no significant between-group differences in ROI signal change in response to anger. In voxel-wise analyses constrained within these ROIs, the BPD group exhibited significant changes in the fear minus neutral contrast, with relatively less activation in the bilateral rostral/subgenual ACC, and greater activation in the right amygdala. In the anger minus neutral contrast this pattern was reversed, with the BPD group showing greater activation in the bilateral rostral/subgenual ACC and less activation in the bilateral amygdala. We conclude that adults with BPD exhibit changes in fronto-limbic activity in the processing of fear stimuli, with exaggerated amygdala response and impaired emotion-modulation of ACC activity. The neural substrates underlying processing of anger may also be altered. These changes may represent an expression of the volumetric and serotonergic deficits observed in these brain areas in BPD. PMID:17601709

Fronto-limbic dysfunction in response to facial emotion in borderline personality disorder: an event-related fMRI study.

PubMed

Minzenberg, Michael J; Fan, Jin; New, Antonia S; Tang, Cheuk Y; Siever, Larry J

2007-08-15

Clinical hallmarks of borderline personality disorder (BPD) include social and emotional dysregulation. We tested a model of fronto-limbic dysfunction in facial emotion processing in BPD. Groups of 12 unmedicated adults with BPD by DSM-IV and 12 demographically-matched healthy controls (HC) viewed facial expressions (Conditions) of neutral emotion, fear and anger, and made gender discriminations during rapid event-related functional magnetic resonance imaging (fMRI). Analysis of variance of Region of Interest signal change revealed a statistically significant effect of the Group-by-Region-by-Condition interaction. This was due to the BPD group exhibiting a significantly larger magnitude of deactivation (relative to HC) in the bilateral rostral/subgenual anterior cingulate cortex (ACC) to fear and in the left ACC to fear minus neutral; and significantly greater activation in the right amygdala to fear minus neutral. There were no significant between-group differences in ROI signal change in response to anger. In voxel-wise analyses constrained within these ROIs, the BPD group exhibited significant changes in the fear minus neutral contrast, with relatively less activation in the bilateral rostral/subgenual ACC, and greater activation in the right amygdala. In the anger minus neutral contrast this pattern was reversed, with the BPD group showing greater activation in the bilateral rostral/subgenual ACC and less activation in the bilateral amygdala. We conclude that adults with BPD exhibit changes in fronto-limbic activity in the processing of fear stimuli, with exaggerated amygdala response and impaired emotion-modulation of ACC activity. The neural substrates underlying processing of anger may also be altered. These changes may represent an expression of the volumetric and serotonergic deficits observed in these brain areas in BPD.

An fMRI and effective connectivity study investigating miss errors during advice utilization from human and machine agents.

PubMed

Goodyear, Kimberly; Parasuraman, Raja; Chernyak, Sergey; de Visser, Ewart; Madhavan, Poornima; Deshpande, Gopikrishna; Krueger, Frank

2017-10-01

As society becomes more reliant on machines and automation, understanding how people utilize advice is a necessary endeavor. Our objective was to reveal the underlying neural associations during advice utilization from expert human and machine agents with fMRI and multivariate Granger causality analysis. During an X-ray luggage-screening task, participants accepted or rejected good or bad advice from either the human or machine agent framed as experts with manipulated reliability (high miss rate). We showed that the machine-agent group decreased their advice utilization compared to the human-agent group and these differences in behaviors during advice utilization could be accounted for by high expectations of reliable advice and changes in attention allocation due to miss errors. Brain areas involved with the salience and mentalizing networks, as well as sensory processing involved with attention, were recruited during the task and the advice utilization network consisted of attentional modulation of sensory information with the lingual gyrus as the driver during the decision phase and the fusiform gyrus as the driver during the feedback phase. Our findings expand on the existing literature by showing that misses degrade advice utilization, which is represented in a neural network involving salience detection and self-processing with perceptual integration.

Are batterers different from other criminals? An fMRI study.

PubMed

Bueso-Izquierdo, Natalia; Verdejo-Román, Juan; Contreras-Rodríguez, Oren; Carmona-Perera, Martina; Pérez-García, Miguel; Hidalgo-Ruzzante, Natalia

2016-05-01

Intimate partner violence (IPV) is a complex and global phenomenon that requires a multi-perspective analysis. Nevertheless, the number of neuroscientific studies conducted on this issue is scarce as compared with studies of other types of violence, and no neuroimaging studies comparing batterers to other criminals have been conducted. Thus, the main aim of this study was to compare the brain functioning of batterers to that of other criminals when they are exposed to IPV or general violence pictures. An fMRI study was conducted in 21 batterers and 20 other criminals while they observed IPV images (IPVI), general violence images (GVI) and neutral images (NI). Results demonstrated that batterers, compared with other criminals, exhibited a higher activation in the anterior and posterior cingulate cortex and in the middle prefrontal cortex and a decreased activation in the superior prefrontal cortex to IPVI compared to NI. The paired t-test comparison between IPVI and GVI for each group showed engagement of the medial prefrontal cortex, the posterior cingulate and the left angular cortices to IPVI in the batterer group only. These results could have important implications for a better understanding of the IPV phenomenon. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Development from childhood to adulthood increases morphological and functional inter-individual variability in the right superior temporal cortex.

PubMed

Bonte, Milene; Frost, Martin A; Rutten, Sanne; Ley, Anke; Formisano, Elia; Goebel, Rainer

2013-12-01

We study the developmental trajectory of morphology and function of the superior temporal cortex (STC) in children (8-9 years), adolescents (14-15 years) and young adults. We analyze cortical surface landmarks and functional MRI (fMRI) responses to voices, other natural categories and tones and examine how hemispheric asymmetry and inter-subject variability change across age. Our results show stable morphological asymmetries across age groups, including a larger left planum temporale and a deeper right superior temporal sulcus. fMRI analyses show that a rightward lateralization for voice-selective responses is present in all groups but decreases with age. Furthermore, STC responses to voices change from being less selective and more spatially diffuse in children to highly selective and focal in adults. Interestingly, the analysis of morphological landmarks reveals that inter-subject variability increases during development in the right--but not in the left--STC. Similarly, inter-subject variability of cortically-realigned functional responses to voices, other categories and tones increases with age in the right STC. Our findings reveal asymmetric developmental changes in brain regions crucial for auditory and voice perception. The age-related increase of inter-subject variability in right STC suggests that anatomy and function of this region are shaped by unique individual developmental experiences. © 2013.

Temporal lobe abnormalities in semantic processing by criminal psychopaths as revealed by functional magnetic resonance imaging.

PubMed

Kiehl, Kent A; Smith, Andra M; Mendrek, Adrianna; Forster, Bruce B; Hare, Robert D; Liddle, Peter F

2004-04-30

We tested the hypothesis that psychopathy is associated with abnormalities in semantic processing of linguistic information. Functional magnetic resonance imaging (fMRI) was used to elucidate and characterize the neural architecture underlying lexico-semantic processes in criminal psychopathic individuals and in a group of matched control participants. Participants performed a lexical decision task in which blocks of linguistic stimuli alternated with a resting baseline condition. In each lexical decision block, the stimuli were either concrete words and pseudowords or abstract words and pseudowords. Consistent with our hypothesis, psychopathic individuals, relative to controls, showed poorer behavioral performance for processing abstract words. Analysis of the fMRI data for both groups indicated that processing of word stimuli, compared with the resting baseline condition, was associated with neural activation in bilateral fusiform gyrus, anterior cingulate, left middle temporal gyrus, right posterior superior temporal gyrus, and left and right inferior frontal gyrus. Analyses confirmed our prediction that psychopathic individuals would fail to show the appropriate neural differentiation between abstract and concrete stimuli in the right anterior temporal gyrus and surrounding cortex. The results are consistent with other studies of semantic processing in psychopathy and support the theory that psychopathy is associated with right hemisphere abnormalities for processing conceptually abstract material.

Temporal lobe abnormalities in semantic processing by criminal psychopaths as revealed by functional magnetic resonance imaging.

PubMed

Kiehl, Kent A; Smith, Andra M; Mendrek, Adrianna; Forster, Bruce B; Hare, Robert D; Liddle, Peter F

2004-01-15

We tested the hypothesis that psychopathy is associated with abnormalities in semantic processing of linguistic information. Functional magnetic resonance imaging (fMRI) was used to elucidate and characterize the neural architecture underlying lexico-semantic processes in criminal psychopathic individuals and in a group of matched control participants. Participants performed a lexical decision task in which blocks of linguistic stimuli alternated with a resting baseline condition. In each lexical decision block, the stimuli were either concrete words and pseudowords or abstract words and pseudowords. Consistent with our hypothesis, psychopathic individuals, relative to controls, showed poorer behavioral performance for processing abstract words. Analysis of the fMRI data for both groups indicated that processing of word stimuli, compared with the resting baseline condition, was associated with neural activation in bilateral fusiform gyrus, anterior cingulate, left middle temporal gyrus, right posterior superior temporal gyrus, and left and right inferior frontal gyrus. Analyses confirmed our prediction that psychopathic individuals would fail to show the appropriate neural differentiation between abstract and concrete stimuli in the right anterior temporal gyrus and surrounding cortex. The results are consistent with other studies of semantic processing in psychopathy and support the theory that psychopathy is associated with right hemisphere abnormalities for processing conceptually abstract material.

Weak task-related modulation and stimulus representations during arithmetic problem solving in children with developmental dyscalculia

PubMed Central

Ashkenazi, Sarit; Rosenberg-Lee, Miriam; Tenison, Caitlin; Menon, Vinod

2015-01-01

Developmental dyscalculia (DD) is a disability that impacts math learning and skill acquisition in school-age children. Here we investigate arithmetic problem solving deficits in young children with DD using univariate and multivariate analysis of fMRI data. During fMRI scanning, 17 children with DD (ages 7–9, grades 2 and 3) and 17 IQ- and reading ability-matched typically developing (TD) children performed complex and simple addition problems which differed only in arithmetic complexity. While the TD group showed strong modulation of brain responses with increasing arithmetic complexity, children with DD failed to show such modulation. Children with DD showed significantly reduced activation compared to TD children in the intraparietal sulcus, superior parietal lobule, supramarginal gyrus and bilateral dorsolateral prefrontal cortex in relation to arithmetic complexity. Critically, multivariate representational similarity revealed that brain response patterns to complex and simple problems were less differentiated in the DD group in bilateral anterior IPS, independent of overall differences in signal level. Taken together, these results show that children with DD not only under-activate key brain regions implicated in mathematical cognition, but they also fail to generate distinct neural responses and representations for different arithmetic problems. Our findings provide novel insights into the neural basis of DD. PMID:22682904

Weak task-related modulation and stimulus representations during arithmetic problem solving in children with developmental dyscalculia.

PubMed

Ashkenazi, Sarit; Rosenberg-Lee, Miriam; Tenison, Caitlin; Menon, Vinod

2012-02-15

Developmental dyscalculia (DD) is a disability that impacts math learning and skill acquisition in school-age children. Here we investigate arithmetic problem solving deficits in young children with DD using univariate and multivariate analysis of fMRI data. During fMRI scanning, 17 children with DD (ages 7-9, grades 2 and 3) and 17 IQ- and reading ability-matched typically developing (TD) children performed complex and simple addition problems which differed only in arithmetic complexity. While the TD group showed strong modulation of brain responses with increasing arithmetic complexity, children with DD failed to show such modulation. Children with DD showed significantly reduced activation compared to TD children in the intraparietal sulcus, superior parietal lobule, supramarginal gyrus and bilateral dorsolateral prefrontal cortex in relation to arithmetic complexity. Critically, multivariate representational similarity revealed that brain response patterns to complex and simple problems were less differentiated in the DD group in bilateral anterior IPS, independent of overall differences in signal level. Taken together, these results show that children with DD not only under-activate key brain regions implicated in mathematical cognition, but they also fail to generate distinct neural responses and representations for different arithmetic problems. Our findings provide novel insights into the neural basis of DD. Copyright © 2011 Elsevier Ltd. All rights reserved.

Test-retest and between-site reliability in a multicenter fMRI study.

PubMed

Friedman, Lee; Stern, Hal; Brown, Gregory G; Mathalon, Daniel H; Turner, Jessica; Glover, Gary H; Gollub, Randy L; Lauriello, John; Lim, Kelvin O; Cannon, Tyrone; Greve, Douglas N; Bockholt, Henry Jeremy; Belger, Aysenil; Mueller, Bryon; Doty, Michael J; He, Jianchun; Wells, William; Smyth, Padhraic; Pieper, Steve; Kim, Seyoung; Kubicki, Marek; Vangel, Mark; Potkin, Steven G

2008-08-01

In the present report, estimates of test-retest and between-site reliability of fMRI assessments were produced in the context of a multicenter fMRI reliability study (FBIRN Phase 1, www.nbirn.net). Five subjects were scanned on 10 MRI scanners on two occasions. The fMRI task was a simple block design sensorimotor task. The impulse response functions to the stimulation block were derived using an FIR-deconvolution analysis with FMRISTAT. Six functionally-derived ROIs covering the visual, auditory and motor cortices, created from a prior analysis, were used. Two dependent variables were compared: percent signal change and contrast-to-noise-ratio. Reliability was assessed with intraclass correlation coefficients derived from a variance components analysis. Test-retest reliability was high, but initially, between-site reliability was low, indicating a strong contribution from site and site-by-subject variance. However, a number of factors that can markedly improve between-site reliability were uncovered, including increasing the size of the ROIs, adjusting for smoothness differences, and inclusion of additional runs. By employing multiple steps, between-site reliability for 3T scanners was increased by 123%. Dropping one site at a time and assessing reliability can be a useful method of assessing the sensitivity of the results to particular sites. These findings should provide guidance toothers on the best practices for future multicenter studies.

Long-lasting changes in brain activation induced by a single REAC technology pulse in Wi-Fi bands. Randomized double-blind fMRI qualitative study.

PubMed

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

2014-07-11

The aim of this randomized double-blind study was to evaluate in healthy adult subjects, with functional magnetic resonance imaging (fMRI), long lasting changes in brain activation patterns following administration of a single, 250 milliseconds pulse emitted with radio-electric asymmetric conveyer (REAC) technology in the Wi-Fi bands. The REAC impulse was not administered during the scan, but after this, according to a protocol that has previously been demonstrated to be effective in improving motor control and postural balance, in healthy subjects and patients. The study was conducted on 33 healthy volunteers, performed with a 1.5 T unit while operating a motor block task involving cyclical and alternating flexion and extension of one leg. Subsequently subjects were randomly divided into a treatment and a sham treatment control group. Repeated fMRI examinations were performed following the administration of the REAC pulse or sham treatment. The Treated group showed cerebellar and ponto-mesencephalic activation components that disappeared in the second scan, while these activation components persisted in the Sham group. This study shows that a very weak signal, such as 250 milliseconds Wi-Fi pulse, administered with REAC technology, could lead to lasting effects on brain activity modification.

Pitfalls in Fractal Time Series Analysis: fMRI BOLD as an Exemplary Case

PubMed Central

Eke, Andras; Herman, Peter; Sanganahalli, Basavaraju G.; Hyder, Fahmeed; Mukli, Peter; Nagy, Zoltan

2012-01-01

This article will be positioned on our previous work demonstrating the importance of adhering to a carefully selected set of criteria when choosing the suitable method from those available ensuring its adequate performance when applied to real temporal signals, such as fMRI BOLD, to evaluate one important facet of their behavior, fractality. Earlier, we have reviewed on a range of monofractal tools and evaluated their performance. Given the advance in the fractal field, in this article we will discuss the most widely used implementations of multifractal analyses, too. Our recommended flowchart for the fractal characterization of spontaneous, low frequency fluctuations in fMRI BOLD will be used as the framework for this article to make certain that it will provide a hands-on experience for the reader in handling the perplexed issues of fractal analysis. The reason why this particular signal modality and its fractal analysis has been chosen was due to its high impact on today’s neuroscience given it had powerfully emerged as a new way of interpreting the complex functioning of the brain (see “intrinsic activity”). The reader will first be presented with the basic concepts of mono and multifractal time series analyses, followed by some of the most relevant implementations, characterization by numerical approaches. The notion of the dichotomy of fractional Gaussian noise and fractional Brownian motion signal classes and their impact on fractal time series analyses will be thoroughly discussed as the central theme of our application strategy. Sources of pitfalls and way how to avoid them will be identified followed by a demonstration on fractal studies of fMRI BOLD taken from the literature and that of our own in an attempt to consolidate the best practice in fractal analysis of empirical fMRI BOLD signals mapped throughout the brain as an exemplary case of potentially wide interest. PMID:23227008

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

Functional changes in people with different hearing status and experiences of using Chinese sign language: an fMRI study.

PubMed

Li, Qiang; Xia, Shuang; Zhao, Fei; Qi, Ji

2014-01-01

The purpose of this study was to assess functional changes in the cerebral cortex in people with different sign language experience and hearing status whilst observing and imitating Chinese Sign Language (CSL) using functional magnetic resonance imaging (fMRI). 50 participants took part in the study, and were divided into four groups according to their hearing status and experience of using sign language: prelingual deafness signer group (PDS), normal hearing non-signer group (HnS), native signer group with normal hearing (HNS), and acquired signer group with normal hearing (HLS). fMRI images were scanned from all subjects when they performed block-designed tasks that involved observing and imitating sign language stimuli. Nine activation areas were found in response to undertaking either observation or imitation CSL tasks and three activated areas were found only when undertaking the imitation task. Of those, the PDS group had significantly greater activation areas in terms of the cluster size of the activated voxels in the bilateral superior parietal lobule, cuneate lobe and lingual gyrus in response to undertaking either the observation or the imitation CSL task than the HnS, HNS and HLS groups. The PDS group also showed significantly greater activation in the bilateral inferior frontal gyrus which was also found in the HNS or the HLS groups but not in the HnS group. This indicates that deaf signers have better sign language proficiency, because they engage more actively with the phonetic and semantic elements. In addition, the activations of the bilateral superior temporal gyrus and inferior parietal lobule were only found in the PDS group and HNS group, and not in the other two groups, which indicates that the area for sign language processing appears to be sensitive to the age of language acquisition. After reading this article, readers will be able to: discuss the relationship between sign language and its neural mechanisms. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

A method for multitask fMRI data fusion applied to schizophrenia.

PubMed

Calhoun, Vince D; Adali, Tulay; Kiehl, Kent A; Astur, Robert; Pekar, James J; Pearlson, Godfrey D

2006-07-01

It is becoming common to collect data from multiple functional magnetic resonance imaging (fMRI) paradigms on a single individual. The data from these experiments are typically analyzed separately and sometimes directly subtracted from one another on a voxel-by-voxel basis. These comparative approaches, although useful, do not directly attempt to examine potential commonalities between tasks and between voxels. To remedy this we propose a method to extract maximally spatially independent maps for each task that are "coupled" together by a shared loading parameter. We first compute an activation map for each task and each individual as "features," which are then used to perform joint independent component analysis (jICA) on the group data. We demonstrate our approach on a data set derived from healthy controls and schizophrenia patients, each of which carried out an auditory oddball task and a Sternberg working memory task. Our analysis approach revealed two interesting findings in the data that were missed with traditional analyses. First, consistent with our hypotheses, schizophrenia patients demonstrate "decreased" connectivity in a joint network including portions of regions implicated in two prevalent models of schizophrenia. A second finding is that for the voxels identified by the jICA analysis, the correlation between the two tasks was significantly higher in patients than in controls. This finding suggests that schizophrenia patients activate "more similarly" for both tasks than do controls. A possible synthesis of both findings is that patients are activating less, but also activating with a less-unique set of regions for these very different tasks. Both of the findings described support the claim that examination of joint activation across multiple tasks can enable new questions to be posed about fMRI data. Our approach can also be applied to data using more than two tasks. It thus provides a way to integrate and probe brain networks using a variety of tasks and may increase our understanding of coordinated brain networks and the impact of pathology upon them. 2005 Wiley-Liss, Inc.

PR01 - The Effects of Total Sleep Deprivation and Recovery Sleep on Cognitive Performance and Brain Function

DTIC Science & Technology

2006-08-01

characterizing brain areas using fMRI activation during parametric variations of attentional load.Neuron, 2001, 32: 737–745. Doran, S . M., Van Dongen...four nouns. Three images collected at the begin- ning of each run were omitted form the analysis. The entire task lasted 300 s . Data Analysis fMRI data...The views, opinions and/or findings contained in this report are those of the author( s ) and should not be construed as an official Department of the

Neural correlates of free recall of "famous events" in a "hypermnestic" individual as compared to an age- and education-matched reference group.

PubMed

Fehr, Thorsten; Staniloiu, Angelica; Markowitsch, Hans J; Erhard, Peter; Herrmann, Manfred

2018-06-19

Memory performance of an individual (within the age range: 50-55 years old) showing superior memory abilities (protagonist PR) was compared to an age- and education-matched reference group in a historical facts ("famous events") retrieval task. Contrasting task versus baseline performance both PR and the reference group showed fMRI activation patterns in parietal and occipital brain regions. The reference group additionally demonstrated activation patterns in cingulate gyrus, whereas PR showed additional widespread activation patterns comprising frontal and cerebellar brain regions. The direct comparison between PR and the reference group revealed larger fMRI contrasts for PR in right frontal, superior temporal and cerebellar brain regions. It was concluded that PR generally recruits brain regions as normal memory performers do, but in a more elaborate way, and furthermore, that he applied a memory-strategy that potentially includes executively driven multi-modal transcoding of information and recruitment of implicit memory resources.

Enhancing Motor Network Activity Using Real-Time Functional MRI Neurofeedback of Left Premotor Cortex

PubMed Central

Marins, Theo F.; Rodrigues, Erika C.; Engel, Annerose; Hoefle, Sebastian; Basílio, Rodrigo; Lent, Roberto; Moll, Jorge; Tovar-Moll, Fernanda

2015-01-01

Neurofeedback by functional magnetic resonance imaging (fMRI) is a technique of potential therapeutic relevance that allows individuals to be aware of their own neurophysiological responses and to voluntarily modulate the activity of specific brain regions, such as the premotor cortex (PMC), important for motor recovery after brain injury. We investigated (i) whether healthy human volunteers are able to up-regulate the activity of the left PMC during a right hand finger tapping motor imagery (MI) task while receiving continuous fMRI-neurofeedback, and (ii) whether successful modulation of brain activity influenced non-targeted motor control regions. During the MI task, participants of the neurofeedback group (NFB) received ongoing visual feedback representing the level of fMRI responses within their left PMC. Control (CTL) group participants were shown similar visual stimuli, but these were non-contingent on brain activity. Both groups showed equivalent levels of behavioral ratings on arousal and MI, before and during the fMRI protocol. In the NFB, but not in CLT group, brain activation during the last run compared to the first run revealed increased activation in the left PMC. In addition, the NFB group showed increased activation in motor control regions extending beyond the left PMC target area, including the supplementary motor area, basal ganglia and cerebellum. Moreover, in the last run, the NFB group showed stronger activation in the left PMC/inferior frontal gyrus when compared to the CTL group. Our results indicate that modulation of PMC and associated motor control areas can be achieved during a single neurofeedback-fMRI session. These results contribute to a better understanding of the underlying mechanisms of MI-based neurofeedback training, with direct implications for rehabilitation strategies in severe brain disorders, such as stroke. PMID:26733832

CNS BOLD fMRI effects of sham-controlled transcutaneous electrical nerve stimulation in the left outer auditory canal - a pilot study.

PubMed

Kraus, Thomas; Kiess, Olga; Hösl, Katharina; Terekhin, Pavel; Kornhuber, Johannes; Forster, Clemens

2013-09-01

It has recently been shown that electrical stimulation of sensory afferents within the outer auditory canal may facilitate a transcutaneous form of central nervous system stimulation. Functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) effects in limbic and temporal structures have been detected in two independent studies. In the present study, we investigated BOLD fMRI effects in response to transcutaneous electrical stimulation of two different zones in the left outer auditory canal. It is hypothesized that different central nervous system (CNS) activation patterns might help to localize and specifically stimulate auricular cutaneous vagal afferents. 16 healthy subjects aged between 20 and 37 years were divided into two groups. 8 subjects were stimulated in the anterior wall, the other 8 persons received transcutaneous vagus nervous stimulation (tVNS) at the posterior side of their left outer auditory canal. For sham control, both groups were also stimulated in an alternating manner on their corresponding ear lobe, which is generally known to be free of cutaneous vagal innervation. Functional MR data from the cortex and brain stem level were collected and a group analysis was performed. In most cortical areas, BOLD changes were in the opposite direction when comparing anterior vs. posterior stimulation of the left auditory canal. The only exception was in the insular cortex, where both stimulation types evoked positive BOLD changes. Prominent decreases of the BOLD signals were detected in the parahippocampal gyrus, posterior cingulate cortex and right thalamus (pulvinar) following anterior stimulation. In subcortical areas at brain stem level, a stronger BOLD decrease as compared with sham stimulation was found in the locus coeruleus and the solitary tract only during stimulation of the anterior part of the auditory canal. The results of the study are in line with previous fMRI studies showing robust BOLD signal decreases in limbic structures and the brain stem during electrical stimulation of the left anterior auditory canal. BOLD signal decreases in the area of the nuclei of the vagus nerve may indicate an effective stimulation of vagal afferences. In contrast, stimulation at the posterior wall seems to lead to unspecific changes of the BOLD signal within the solitary tract, which is a key relay station of vagal neurotransmission. The results of the study show promise for a specific novel method of cranial nerve stimulation and provide a basis for further developments and applications of non-invasive transcutaneous vagus stimulation in psychiatric patients. Copyright © 2013 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.

Abnormal functional connectivity and cortical integrity influence dominant hand motor disability in multiple sclerosis: a multimodal analysis.

PubMed

Zhong, Jidan; Nantes, Julia C; Holmes, Scott A; Gallant, Serge; Narayanan, Sridar; Koski, Lisa

2016-12-01

Functional reorganization and structural damage occur in the brains of people with multiple sclerosis (MS) throughout the disease course. However, the relationship between resting-state functional connectivity (FC) reorganization in the sensorimotor network and motor disability in MS is not well understood. This study used resting-state fMRI, T1-weighted and T2-weighted, and magnetization transfer (MT) imaging to investigate the relationship between abnormal FC in the sensorimotor network and upper limb motor disability in people with MS, as well as the impact of disease-related structural abnormalities within this network. Specifically, the differences in FC of the left hemisphere hand motor region between MS participants with preserved (n = 17) and impaired (n = 26) right hand function, compared with healthy controls (n = 20) was investigated. Differences in brain atrophy and MT ratio measured at the global and regional levels were also investigated between the three groups. Motor preserved MS participants had stronger FC in structurally intact visual information processing regions relative to motor impaired MS participants. Motor impaired MS participants showed weaker FC in the sensorimotor and somatosensory association cortices and more severe structural damage throughout the brain compared with the other groups. Logistic regression analysis showed that regional MTR predicted motor disability beyond the impact of global atrophy whereas regional grey matter volume did not. More importantly, as the first multimodal analysis combining resting-state fMRI, T1-weighted, T2-weighted and MTR images in MS, we demonstrate how a combination of structural and functional changes may contribute to motor impairment or preservation in MS. Hum Brain Mapp 37:4262-4275, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Self-blame-Selective Hyperconnectivity Between Anterior Temporal and Subgenual Cortices and Prediction of Recurrent Depressive Episodes.

PubMed

Lythe, Karen E; Moll, Jorge; Gethin, Jennifer A; Workman, Clifford I; Green, Sophie; Lambon Ralph, Matthew A; Deakin, John F W; Zahn, Roland

2015-11-01

Patients with remitted major depressive disorder (MDD) were previously found to display abnormal functional magnetic resonance imaging connectivity (fMRI) between the right superior anterior temporal lobe (RSATL) and the subgenual cingulate cortex and adjacent septal region (SCSR) when experiencing self-blaming emotions relative to emotions related to blaming others (eg, "indignation or anger toward others"). This finding provided the first neural signature of biases toward overgeneralized self-blaming emotions (eg, "feeling guilty for everything"), known to have a key role in cognitive vulnerability to MDD. It is unknown whether this neural signature predicts risk of recurrence, a crucial step in establishing its potential as a prognostic biomarker, which is urgently needed for stratification into pathophysiologically more homogeneous subgroups and for novel treatments. To use fMRI in remitted MDD at baseline to test the hypothesis that RSATL-SCSR connectivity for self-blaming relative to other-blaming emotions predicts subsequent recurrence of depressive episodes. A prospective cohort study from June 16, 2011, to October 10, 2014, in a clinical research facility completed by 75 psychotropic medication-free patients with remitted MDD and no relevant comorbidity. In total, 31 remained in stable remission, and 25 developed a recurring episode over the 14 months of clinical follow-up and were included in the primary analysis. Thirty-nine control participants with no personal or family history of MDD were recruited for further comparison. Between-group difference (recurring vs stable MDD) in RSATL connectivity, with an a priori SCSR region of interest for self-blaming vs other-blaming emotions. We corroborated our hypothesis that during the experience of self-blaming vs other-blaming emotions, RSATL-SCSR connectivity predicted risk of subsequent recurrence. The recurring MDD group showed higher connectivity than the stable MDD group (familywise error-corrected P < .05 over the a priori SCSR region of interest) and the control group. In addition, the recurring MDD group also exhibited RSATL hyperconnectivity with the right ventral putamen and claustrum and the temporoparietal junction. Together, these regions predicted recurrence with 75% accuracy. To our knowledge, this study is the first to provide a robust demonstration of an fMRI signature of recurrence risk in remitted MDD. Additional studies are needed for its further optimization and validation as a prognostic biomarker.

Effects of chondroitin sulfate on brain response to painful stimulation in knee osteoarthritis patients. A randomized, double-blind, placebo-controlled functional magnetic resonance imaging study.

PubMed

Monfort, Jordi; Pujol, Jesús; Contreras-Rodríguez, Oren; Llorente-Onaindia, Jone; López-Solà, Marina; Blanco-Hinojo, Laura; Vergés, Josep; Herrero, Marta; Sánchez, Laura; Ortiz, Hector; Montañés, Francisco; Deus, Joan; Benito, Pere

2017-06-21

Knee osteoarthritis is causing pain and functional disability. One of the inherent problems with efficacy assessment of pain medication was the lack of objective pain measurements, but functional magnetic resonance imaging (fMRI) has emerged as a useful means to objectify brain response to painful stimulation. We have investigated the effect of chondroitin sulfate (CS) on brain response to knee painful stimulation in patients with knee osteoarthritis using fMRI. Twenty-two patients received CS (800mg/day) and 27 patients placebo, and were assessed at baseline and after 4 months of treatment. Two fMRI tests were conducted in each session by applying painful pressure on the knee interline and on the patella surface. The outcome measurement was attenuation of the response evoked by knee painful stimulation in the brain. fMRI of patella pain showed significantly greater activation reduction under CS compared with placebo in the region of the mesencephalic periaquecductal gray. The CS group, additionally showed pre/post-treatment activation reduction in the cortical representation of the leg. No effects of CS were detected using the interline pressure test. fMRI was sensitive to objectify CS effects on brain response to painful pressure on patellofemoral cartilage, which is consistent with the known CS action on chondrocyte regeneration. The current work yields further support to the utility of fMRI to objectify treatment effects on osteoarthritis pain. Copyright © 2017 Elsevier España, S.L.U. 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.

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.

A receptor-based model for dopamine-induced fMRI signal

PubMed Central

Mandeville, Joseph. B.; Sander, Christin Y. M.; Jenkins, Bruce G.; Hooker, Jacob M.; Catana, Ciprian; Vanduffel, Wim; Alpert, Nathaniel M.; Rosen, Bruce R.; Normandin, Marc D.

2013-01-01

This report describes a multi-receptor physiological model of the fMRI temporal response and signal magnitude evoked by drugs that elevate synaptic dopamine in basal ganglia. The model is formulated as a summation of dopamine’s effects at D1-like and D2-like receptor families, which produce functional excitation and inhibition, respectively, as measured by molecular indicators like adenylate cyclase or neuroimaging techniques like fMRI. Functional effects within the model are described in terms of relative changes in receptor occupancies scaled by receptor densities and neuro-vascular coupling constants. Using literature parameters, the model reconciles many discrepant observations and interpretations of pre-clinical data. Additionally, we present data showing that amphetamine stimulation produces fMRI inhibition at low doses and a biphasic response at higher doses in the basal ganglia of non-human primates (NHP), in agreement with model predictions based upon the respective levels of evoked dopamine. Because information about dopamine release is required to inform the fMRI model, we simultaneously acquired PET 11C-raclopride data in several studies to evaluate the relationship between raclopride displacement and assumptions about dopamine release. At high levels of dopamine release, results suggest that refinements of the model will be required to consistently describe the PET and fMRI data. Overall, the remarkable success of the model in describing a wide range of preclinical fMRI data indicate that this approach will be useful for guiding the design and analysis of basic science and clinical investigations and for interpreting the functional consequences of dopaminergic stimulation in normal subjects and in populations with dopaminergic neuroadaptations. PMID:23466936

Recovering task fMRI signals from highly under-sampled data with low-rank and temporal subspace constraints.

PubMed

Chiew, Mark; Graedel, Nadine N; Miller, Karla L

2018-07-01

Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis. We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R = 16 in a radial-Cartesian acquisition. We show that this approach works with approximate, or not perfectly informative constraints, where the derived benefit is commensurate with the information content contained in the constraints. The proposed method extends low-rank approximation methods for under-sampled fMRI data acquisition by leveraging knowledge of expected task-based variance in the data, enabling improvements in the speed and efficiency of fMRI data acquisition without the loss of subtle features. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.