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.

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

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

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging

PubMed Central

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

2017-01-01

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

Investigating the neural basis for functional and effective connectivity. Application to fMRI

PubMed Central

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

2005-01-01

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

Detecting nonlinear dynamics of functional connectivity

NASA Astrophysics Data System (ADS)

LaConte, Stephen M.; Peltier, Scott J.; Kadah, Yasser; Ngan, Shing-Chung; Deshpande, Gopikrishna; Hu, Xiaoping

2004-04-01

Functional magnetic resonance imaging (fMRI) is a technique that is sensitive to correlates of neuronal activity. The application of fMRI to measure functional connectivity of related brain regions across hemispheres (e.g. left and right motor cortices) has great potential for revealing fundamental physiological brain processes. Primarily, functional connectivity has been characterized by linear correlations in resting-state data, which may not provide a complete description of its temporal properties. In this work, we broaden the measure of functional connectivity to study not only linear correlations, but also those arising from deterministic, non-linear dynamics. Here the delta-epsilon approach is extended and applied to fMRI time series. The method of delays is used to reconstruct the joint system defined by a reference pixel and a candidate pixel. The crux of this technique relies on determining whether the candidate pixel provides additional information concerning the time evolution of the reference. As in many correlation-based connectivity studies, we fix the reference pixel. Every brain location is then used as a candidate pixel to estimate the spatial pattern of deterministic coupling with the reference. Our results indicate that measured connectivity is often emphasized in the motor cortex contra-lateral to the reference pixel, demonstrating the suitability of this approach for functional connectivity studies. In addition, discrepancies with traditional correlation analysis provide initial evidence for non-linear dynamical properties of resting-state fMRI data. Consequently, the non-linear characterization provided from our approach may provide a more complete description of the underlying physiology and brain function measured by this type of data.

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

ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks.

PubMed

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

2017-12-01

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

Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?

PubMed

Varoquaux, G; Gramfort, A; Poline, J B; Thirion, B

2012-01-01

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems. Copyright © 2012 Elsevier Ltd. All rights reserved.

Impact of Autocorrelation on Functional Connectivity

PubMed Central

Arbabshirani, Mohammad R.; Damaraju, Eswar; Phlypo, Ronald; Plis, Sergey; Allen, Elena; Ma, Sai; Mathalon, Daniel; Preda, Adrian; Vaidya, Jatin G.; Adali, Tülay; Calhoun, Vince D.

2014-01-01

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in “spurious” correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies. PMID:25072392

fMRI during natural sleep as a method to study brain function during early childhood.

PubMed

Redcay, Elizabeth; Kennedy, Daniel P; Courchesne, Eric

2007-12-01

Many techniques to study early functional brain development lack the whole-brain spatial resolution that is available with fMRI. We utilized a relatively novel method in which fMRI data were collected from children during natural sleep. Stimulus-evoked responses to auditory and visual stimuli as well as stimulus-independent functional networks were examined in typically developing 2-4-year-old children. Reliable fMRI data were collected from 13 children during presentation of auditory stimuli (tones, vocal sounds, and nonvocal sounds) in a block design. Twelve children were presented with visual flashing lights at 2.5 Hz. When analyses combined all three types of auditory stimulus conditions as compared to rest, activation included bilateral superior temporal gyri/sulci (STG/S) and right cerebellum. Direct comparisons between conditions revealed significantly greater responses to nonvocal sounds and tones than to vocal sounds in a number of brain regions including superior temporal gyrus/sulcus, medial frontal cortex and right lateral cerebellum. The response to visual stimuli was localized to occipital cortex. Furthermore, stimulus-independent functional connectivity MRI analyses (fcMRI) revealed functional connectivity between STG and other temporal regions (including contralateral STG) and medial and lateral prefrontal regions. Functional connectivity with an occipital seed was localized to occipital and parietal cortex. In sum, 2-4 year olds showed a differential fMRI response both between stimulus modalities and between stimuli in the auditory modality. Furthermore, superior temporal regions showed functional connectivity with numerous higher-order regions during sleep. We conclude that the use of sleep fMRI may be a valuable tool for examining functional brain organization in young children.

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.

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.

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

Analyzing and Assessing Brain Structure with Graph Connectivity Measures

DTIC Science & Technology

2014-05-09

structural brain networks, i.e. determining which regions of the brain are physically connected. Meanwhile, functional MRI ( fMRI ) yields an image of...produced by fMRI is a map of which parts are of the brain are active and which are not at a given time. In creating functional networks, regions of...the brain which often activitate together, i.e., often show up on fMRI as deoxygenated regions together, are considered connected. DTI allows the

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.

Resting-state fMRI and social cognition: An opportunity to connect.

PubMed

Doruyter, Alex; Groenewold, Nynke A; Dupont, Patrick; Stein, Dan J; Warwick, James M

2017-09-01

Many psychiatric disorders are characterized by altered social cognition. The importance of social cognition has previously been recognized by the National Institute of Mental Health Research Domain Criteria project, in which it features as a core domain. Social task-based functional magnetic resonance imaging (fMRI) currently offers the most direct insight into how the brain processes social information; however, resting-state fMRI may be just as important in understanding the biology and network nature of social processing. Resting-state fMRI allows researchers to investigate the functional relationships between brain regions in a neutral state: so-called resting functional connectivity (RFC). There is evidence that RFC is predictive of how the brain processes information during social tasks. This is important because it shifts the focus from possibly context-dependent aberrations to context-independent aberrations in functional network architecture. Rather than being analysed in isolation, the study of resting-state brain networks shows promise in linking results of task-based fMRI results, structural connectivity, molecular imaging findings, and performance measures of social cognition-which may prove crucial in furthering our understanding of the social brain. Copyright © 2017 John Wiley & Sons, Ltd.

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

A conditional Granger causality model approach for group analysis in functional MRI

PubMed Central

Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

2011-01-01

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve greater accuracy in detecting network connectivity than the widely used pairwise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI. PMID:21232892

Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems

PubMed Central

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

2012-01-01

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

Maintenance and Representation of Mind Wandering during Resting-State fMRI.

PubMed

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

2017-01-12

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

Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI.

PubMed

Cai, Biao; Zille, Pascal; Stephen, Julia M; Wilson, Tony W; Calhoun, Vince D; Wang, Yu Ping

2018-05-01

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session. In this paper, we propose an approach called dynamic sparse connectivity patterns (dSCPs), which takes advantage of both matrix factorization and time-varying fMRI time series to improve the estimation power of FC. The feasibility of analyzing dynamic FC with our model is first validated through simulated experiments. Then, we use our framework to measure the difference between young adults and children with real fMRI data set from the Philadelphia Neurodevelopmental Cohort (PNC). The results from the PNC data set showed significant FC differences between young adults and children in four different states. For instance, young adults had reduced connectivity between the default mode network and other subnetworks, as well as hyperconnectivity within the visual system in states 1 and 3, and hypoconnectivity in state 2. Meanwhile, they exhibited temporal correlation patterns that changed over time within functional subnetworks. In addition, the dSCPs model indicated that older people tend to spend more time within a relatively connected FC pattern. Overall, the proposed method provides a valid means to assess dynamic FC, which could facilitate the study of brain networks.

A METHOD FOR USING BLOCKED AND EVENT-RELATED FMRI DATA TO STUDY “RESTING STATE” FUNCTIONAL CONNECTIVITY

PubMed Central

Fair, Damien A.; Schlaggar, Bradley L.; Cohen B.A., Alexander L.; Miezin, Francis M.; Dosenbach, Nico U.F.; Wenger, Kristin K.; Fox, Michael D.; Snyder, Abraham Z.; Raichle, Marcus E.; Petersen, Steven E.

2007-01-01

Resting state functional connectivity MRI (fcMRI) has become a particularly useful tool for studying regional relationships in typical and atypical populations. Because many investigators have already obtained large datasets of task related fMRI, the ability to use this existing task data for resting state fcMRI is of considerable interest. Two classes of datasets could potentially be modified to emulate resting state data. These datasets include: 1) “interleaved” resting blocks from blocked or mixed blocked/event-related sets, and 2) residual timecourses from event-related sets that lack rest blocks. Using correlation analysis, we compared the functional connectivity of resting epochs taken from a mixed blocked/event-related design fMRI data set and the residuals derived from event-related data with standard continuous resting state data to determine which class of data can best emulate resting state data. We show that despite some differences, the functional connectivity for the interleaved resting periods taken from blocked designs is both qualitatively and quantitatively very similar to that of “continuous” resting state data. In contrast, despite being qualitatively similar to “continuous” resting state data, residuals derived from event-related design data had several distinct quantitative differences. These results suggest that the interleaved resting state data such as those taken from blocked or mixed blocked/event-related fMRI designs are well-suited for resting state functional connectivity analyses. Although using event-related data residuals for resting state functional connectivity may still be useful, results should be interpreted with care. PMID:17239622

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

PubMed Central

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

2017-01-01

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

Test-retest reliability of functional connectivity networks during naturalistic fMRI paradigms.

PubMed

Wang, Jiahui; Ren, Yudan; Hu, Xintao; Nguyen, Vinh Thai; Guo, Lei; Han, Junwei; Guo, Christine Cong

2017-04-01

Functional connectivity analysis has become a powerful tool for probing the human brain function and its breakdown in neuropsychiatry disorders. So far, most studies adopted resting-state paradigm to examine functional connectivity networks in the brain, thanks to its low demand and high tolerance that are essential for clinical studies. However, the test-retest reliability of resting-state connectivity measures is moderate, potentially due to its low behavioral constraint. On the other hand, naturalistic neuroimaging paradigms, an emerging approach for cognitive neuroscience with high ecological validity, could potentially improve the reliability of functional connectivity measures. To test this hypothesis, we characterized the test-retest reliability of functional connectivity measures during a natural viewing condition, and benchmarked it against resting-state connectivity measures acquired within the same functional magnetic resonance imaging (fMRI) session. We found that the reliability of connectivity and graph theoretical measures of brain networks is significantly improved during natural viewing conditions over resting-state conditions, with an average increase of almost 50% across various connectivity measures. Not only sensory networks for audio-visual processing become more reliable, higher order brain networks, such as default mode and attention networks, but also appear to show higher reliability during natural viewing. Our results support the use of natural viewing paradigms in estimating functional connectivity of brain networks, and have important implications for clinical application of fMRI. Hum Brain Mapp 38:2226-2241, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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.

Enhanced Thalamic Functional Connectivity with No fMRI Responses to Affected Forelimb Stimulation in Stroke-Recovered Rats.

PubMed

Shim, Woo H; Suh, Ji-Yeon; Kim, Jeong K; Jeong, Jaeseung; Kim, Young R

2016-01-01

Neurological recovery after stroke has been extensively investigated to provide better understanding of neurobiological mechanism, therapy, and patient management. Recent advances in neuroimaging techniques, particularly functional MRI (fMRI), have widely contributed to unravel the relationship between the altered neural function and stroke-affected brain areas. As results of previous investigations, the plastic reorganization and/or gradual restoration of the hemodynamic fMRI responses to neural stimuli have been suggested as relevant mechanisms underlying the stroke recovery process. However, divergent study results and modality-dependent outcomes have clouded the proper interpretation of variable fMRI signals. Here, we performed both evoked and resting state fMRI (rs-fMRI) to clarify the link between the fMRI phenotypes and post-stroke functional recovery. The experiments were designed to examine the altered neural activity within the contra-lesional hemisphere and other undamaged brain regions using rat models with large unilateral stroke, which despite the severe injury, exhibited nearly full recovery at ∼6 months after stroke. Surprisingly, both blood oxygenation level-dependent and blood volume-weighted (CBVw) fMRI activities elicited by electrical stimulation of the stroke-affected forelimb were completely absent, failing to reveal the neural origin of the behavioral recovery. In contrast, the functional connectivity maps showed highly robust rs-fMRI activity concentrated in the contra-lesional ventromedial nucleus of thalamus (VM). The negative finding in the stimuli-induced fMRI study using the popular rat middle cerebral artery model denotes weak association between the fMRI hemodynamic responses and neurological improvement. The results strongly caution the indiscreet interpretation of stroke-affected fMRI signals and demonstrate rs-fMRI as a complementary tool for efficiently characterizing stroke recovery.

A generative model of whole-brain effective connectivity.

PubMed

Frässle, Stefan; Lomakina, Ekaterina I; Kasper, Lars; Manjaly, Zina M; Leff, Alex; Pruessmann, Klaas P; Buhmann, Joachim M; Stephan, Klaas E

2018-05-25

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data - in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling - for example, for phenotyping individual patients in terms of whole-brain network structure. Copyright © 2018. Published by Elsevier Inc.

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.

Safety of externally stimulated intracranial electrodes during functional MRI at 1.5T.

PubMed

Bhattacharyya, Pallab K; Mullin, Jeffery; Lee, Bryan S; Gonzalez-Martinez, Jorge A; Jones, Stephen E

2017-05-01

Surgical resection of the epileptogenic zone (EZ) is a potential cure for medically refractory focal epilepsy. Proper identification of the EZ is essential for such resection. Synergistic application of functional magnetic resonance imaging (fMRI) simultaneously with stimulation of a single externalized intracranial stereotactic EEG (SEEG) electrode has the potential to improve identification of the EZ. While most EEG-fMRI studies use the electrodes passively to record electrical activity, it is possible to stimulate the brain using the electrodes by connecting them with conducting cables to the stimulation hardware. In this study, we investigated the effect of MRI-induced heating on a single SEEG electrode and its sensitivity to geometry, configuration, and associated connections required for the stimulation. The temperature increase of a single electrode embedded within a gel phantom and connected to an external stimulation system was measured during 1.5T MRI scans using adjacent fluoroptic temperature sensors. A receive-only split-array head coil and a transmit-receive head coil were used for testing. Sequences included a standard localizer, T1-weighted axial fast low-angle shot (FLASH), gradient echo-planar imaging (GE-EPI) axial fMRI, and a high specific absorption rate T2-weighted turbo spin-echo (TSE) axial scan. Variations of the electrode location and connecting cable configuration were tested. No unacceptable heating was observed with the standard sequences used for evaluation of the EZ. Considerable heating (up to 14°C) was observed with the TSE sequence, which is not used clinically. The temperature increase was insignificant (<0.05°C) for electrode contacts closest to the isocenter and connecting cables lying along the isocenter, and varied with configurations of the connecting cable assembly. Simultaneous intracranial electrode stimulation during fMRI using an externalized stimulation system may be safe with strict adherence to settings tested prior to the fMRI. Localizer, FLASH, and GE-EPI fMRI may be safely performed in patients with a single SEEG electrode following the configurations tested in this study, but high SAR TSE scans should not be performed in these patients. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Hippocampal Networks Habituate as Novelty Accumulates

ERIC Educational Resources Information Center

Murty, Vishnu P.; Ballard, Ian C.; Macduffie, Katherine E.; Krebs, Ruth M.; Adcock, R. Alison

2013-01-01

Novelty detection, a critical computation within the medial temporal lobe (MTL) memory system, necessarily depends on prior experience. The current study used functional magnetic resonance imaging (fMRI) in humans to investigate dynamic changes in MTL activation and functional connectivity as experience with novelty accumulates. fMRI data were…

Brain Functional Connectivity in MS: An EEG-NIRS Study

DTIC Science & Technology

2015-10-01

electrical (EEG) and blood volume and blood oxygen-based (NIRS and fMRI ) signals, and to use the results to help optimize blood oxygen level...dependent (BOLD) fMRI analyses of brain activity. Participants will be patients with MS (n=25) and healthy demographically matched controls (n=25) who will...undergo standardized evaluations and imaging using combined EEG-NIRS- fMRI . EEG-NIRS data will be used to construct maps of neurovascular coupling

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

Resting states are resting traits--an FMRI study of sex differences and menstrual cycle effects in resting state cognitive control networks.

PubMed

Hjelmervik, Helene; Hausmann, Markus; Osnes, Berge; Westerhausen, René; Specht, Karsten

2014-01-01

To what degree resting state fMRI is stable or susceptible to internal mind states of the individual is currently an issue of debate. To address this issue, the present study focuses on sex differences and investigates whether resting state fMRI is stable in men and women or changes within relative short-term periods (i.e., across the menstrual cycle). Due to the fact that we recently reported menstrual cycle effects on cognitive control based on data collected during the same sessions, the current study is particularly interested in fronto-parietal resting state networks. Resting state fMRI was measured in sixteen women during three different cycle phases (menstrual, follicular, and luteal). Fifteen men underwent three sessions in corresponding time intervals. We used independent component analysis to identify four fronto-parietal networks. The results showed sex differences in two of these networks with women exhibiting higher functional connectivity in general, including the prefrontal cortex. Menstrual cycle effects on resting states were non-existent. It is concluded that sex differences in resting state fMRI might reflect sexual dimorphisms in the brain rather than transitory activating effects of sex hormones on the functional connectivity in the resting brain.

Resting States Are Resting Traits – An fMRI Study of Sex Differences and Menstrual Cycle Effects in Resting State Cognitive Control Networks

PubMed Central

Hjelmervik, Helene; Hausmann, Markus; Osnes, Berge; Westerhausen, René; Specht, Karsten

2014-01-01

To what degree resting state fMRI is stable or susceptible to internal mind states of the individual is currently an issue of debate. To address this issue, the present study focuses on sex differences and investigates whether resting state fMRI is stable in men and women or changes within relative short-term periods (i.e., across the menstrual cycle). Due to the fact that we recently reported menstrual cycle effects on cognitive control based on data collected during the same sessions, the current study is particularly interested in fronto-parietal resting state networks. Resting state fMRI was measured in sixteen women during three different cycle phases (menstrual, follicular, and luteal). Fifteen men underwent three sessions in corresponding time intervals. We used independent component analysis to identify four fronto-parietal networks. The results showed sex differences in two of these networks with women exhibiting higher functional connectivity in general, including the prefrontal cortex. Menstrual cycle effects on resting states were non-existent. It is concluded that sex differences in resting state fMRI might reflect sexual dimorphisms in the brain rather than transitory activating effects of sex hormones on the functional connectivity in the resting brain. PMID:25057823

Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI.

PubMed

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

2018-03-16

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

Patterns of resting state connectivity in human primary visual cortical areas: a 7T fMRI study.

PubMed

Raemaekers, Mathijs; Schellekens, Wouter; van Wezel, Richard J A; Petridou, Natalia; Kristo, Gert; Ramsey, Nick F

2014-01-01

The nature and origin of fMRI resting state fluctuations and connectivity are still not fully known. More detailed knowledge on the relationship between resting state patterns and brain function may help to elucidate this matter. We therefore performed an in depth study of how resting state fluctuations map to the well known architecture of the visual system. We investigated resting state connectivity at both a fine and large scale within and across visual areas V1, V2 and V3 in ten human subjects using a 7Tesla scanner. We found evidence for several coexisting and overlapping connectivity structures at different spatial scales. At the fine-scale level we found enhanced connectivity between the same topographic locations in the fieldmaps of V1, V2 and V3, enhanced connectivity to the contralateral functional homologue, and to a lesser extent enhanced connectivity between iso-eccentric locations within the same visual area. However, by far the largest proportion of the resting state fluctuations occurred within large-scale bilateral networks. These large-scale networks mapped to some extent onto the architecture of the visual system and could thereby obscure fine-scale connectivity. In fact, most of the fine-scale connectivity only became apparent after the large-scale network fluctuations were filtered from the timeseries. We conclude that fMRI resting state fluctuations in the visual cortex may in fact be a composite signal of different overlapping sources. Isolating the different sources could enhance correlations between BOLD and electrophysiological correlates of resting state activity. © 2013 Elsevier Inc. All rights reserved.

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

A Putative Multiple-Demand System in the Macaque Brain.

PubMed

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

2016-08-17

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

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

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2015-11-01

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. Copyright © 2015 International Federation of Clinical Neurophysiology. All rights reserved.

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.

Constructing fMRI connectivity networks: a whole brain functional parcellation method for node definition.

PubMed

Maggioni, Eleonora; Tana, Maria Gabriella; Arrigoni, Filippo; Zucca, Claudio; Bianchi, Anna Maria

2014-05-15

Functional Magnetic Resonance Imaging (fMRI) is used for exploring brain functionality, and recently it was applied for mapping the brain connection patterns. To give a meaningful neurobiological interpretation to the connectivity network, it is fundamental to properly define the network framework. In particular, the choice of the network nodes may affect the final connectivity results and the consequent interpretation. We introduce a novel method for the intra subject topological characterization of the nodes of fMRI brain networks, based on a whole brain parcellation scheme. The proposed whole brain parcellation algorithm divides the brain into clusters that are homogeneous from the anatomical and functional point of view, each of which constitutes a node. The functional parcellation described is based on the Tononi's cluster index, which measures instantaneous correlation in terms of intrinsic and extrinsic statistical dependencies. The method performance and reliability were first tested on simulated data, then on a real fMRI dataset acquired on healthy subjects during visual stimulation. Finally, the proposed algorithm was applied to epileptic patients' fMRI data recorded during seizures, to verify its usefulness as preparatory step for effective connectivity analysis. For each patient, the nodes of the network involved in ictal activity were defined according to the proposed parcellation scheme and Granger Causality Analysis (GCA) was applied to infer effective connectivity. We showed that the algorithm 1) performed well on simulated data, 2) was able to produce reliable inter subjects results and 3) led to a detailed definition of the effective connectivity pattern. Copyright © 2014 Elsevier B.V. All rights reserved.

Time-frequency dynamics of resting-state brain connectivity measured with fMRI.

PubMed

Chang, Catie; Glover, Gary H

2010-03-01

Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time-frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the "anticorrelated" ("task-positive") network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease.

PubMed

Case, Michelle; Zhang, Huishi; Mundahl, John; Datta, Yvonne; Nelson, Stephen; Gupta, Kalpna; He, Bin

2017-01-01

Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.

Blunted amygdala functional connectivity during a stress task in alcohol dependent individuals: A pilot study.

PubMed

Wade, Natasha E; Padula, Claudia B; Anthenelli, Robert M; Nelson, Erik; Eliassen, James; Lisdahl, Krista M

2017-12-01

Scant research has been conducted on neural mechanisms underlying stress processing in individuals with alcohol dependence (AD). We examined neural substrates of stress in AD individuals compared with controls using an fMRI task previously shown to induce stress, assessing amygdala functional connectivity to medial prefrontal cortex (mPFC). For this novel pilot study, 10 abstinent AD individuals and 11 controls completed a modified Trier stress task while undergoing fMRI acquisition. The amygdala was used as a seed region for whole-brain seed-based functional connectivity analysis. After controlling for family-wise error (p = 0.05), there was significantly decreased left and right amygdala connectivity with frontal (specifically mPFC), temporal, parietal, and cerebellar regions. Subjective stress, but not craving, increased from pre-to post-task. This study demonstrated decreased connectivity between the amygdala and regions important for stress and emotional processing in long-term abstinent individuals with AD. These results suggest aberrant stress processing in individuals with AD even after lengthy periods of abstinence.

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity.

PubMed

Finn, Emily S; Shen, Xilin; Scheinost, Dustin; Rosenberg, Monica D; Huang, Jessica; Chun, Marvin M; Papademetris, Xenophon; Constable, R Todd

2015-11-01

Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.

Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review

PubMed Central

Hull, Jocelyn V.; Jacokes, Zachary J.; Torgerson, Carinna M.; Irimia, Andrei; Van Horn, John Darrell

2017-01-01

Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives. PMID:28101064

Mapping white-matter functional organization at rest and during naturalistic visual perception.

PubMed

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

2017-02-01

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

Brain correlates of hypnotic paralysis-a resting-state fMRI study.

PubMed

Pyka, M; Burgmer, M; Lenzen, T; Pioch, R; Dannlowski, U; Pfleiderer, B; Ewert, A W; Heuft, G; Arolt, V; Konrad, C

2011-06-15

Hypnotic paralysis has been used since the times of Charcot to study altered states of consciousness; however, the underlying neurobiological correlates are poorly understood. We investigated human brain function during hypnotic paralysis using resting-state functional magnetic resonance imaging (fMRI), focussing on two core regions of the default mode network and the representation of the paralysed hand in the primary motor cortex. Hypnotic suggestion induced an observable left-hand paralysis in 19 participants. Resting-state fMRI at 3T was performed in pseudo-randomised order awake and in the hypnotic condition. Functional connectivity analyses revealed increased connectivity of the precuneus with the right dorsolateral prefrontal cortex, angular gyrus, and a dorsal part of the precuneus. Functional connectivity of the medial frontal cortex and the primary motor cortex remained unchanged. Our results reveal that the precuneus plays a pivotal role during maintenance of an altered state of consciousness. The increased coupling of selective cortical areas with the precuneus supports the concept that hypnotic paralysis may be mediated by a modified representation of the self which impacts motor abilities. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

Inferring multi-scale neural mechanisms with brain network modelling

PubMed Central

Schirner, Michael; McIntosh, Anthony Randal; Jirsa, Viktor; Deco, Gustavo

2018-01-01

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies. PMID:29308767

Decoupling function and anatomy in atlases of functional connectivity patterns: language mapping in tumor patients.

PubMed

Langs, Georg; Sweet, Andrew; Lashkari, Danial; Tie, Yanmei; Rigolo, Laura; Golby, Alexandra J; Golland, Polina

2014-12-01

In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors. Copyright © 2014. Published by Elsevier Inc.

Large-scale DCMs for resting-state fMRI.

PubMed

Razi, Adeel; Seghier, Mohamed L; Zhou, Yuan; McColgan, Peter; Zeidman, Peter; Park, Hae-Jeong; Sporns, Olaf; Rees, Geraint; Friston, Karl J

2017-01-01

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity . This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM-with functional connectivity priors-is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

Transcortical Sensory Aphasia after Left Frontal Lobe Infarction: Loss of Functional Connectivity.

PubMed

Kwon, Miseon; Shim, Woo Hyun; Kim, Sang-Joon; Kim, Jong S

2017-01-01

The underlying mechanism of transcortical sensory aphasia (TSA) caused by lesions occurring in the left frontal lobe remains unclear. We attempted to investigate the mechanism with the use of functional MRI (fMRI). We studied 2 patients with TSA after a left frontal infarction identified by diffusion-weighted MRI. As control subjects, a patient with transcortical motor aphasia and a healthy normal adult were chosen. The Korean version of Western Aphasia Battery was performed initially and at 3 months post stroke. We performed fMRI using verb generation and sentence completion tasks. Resting-state fMRI (rs-fMRI) was also obtained for network-level analysis initially and at 3 months post stroke. The results of diffusion- and perfusion-weighted MRI revealed no diffusion-perfusion mismatch. Initial fMRI in patients with TSA showed no reversed inter-/intrahemispheric activation patterns. rs-fMRI showed significantly decreased resting-state functional connectivity in the language network in patients with TSA compared with the control subjects. Follow-up rs-fMRI studies showed improvement in functional connectivity along with the recovery of patients' language function. Our data showed that the auditory comprehension deficits in patients with frontal lobe infarcts is attributed to difficulty accessing the posterior language area due to functional disconnection between language centers in the acute stage of stroke. © 2017 S. Karger AG, Basel.

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

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.

A preliminary investigation of Stroop-related intrinsic connectivity in cocaine dependence: Associations with treatment outcomes

PubMed Central

Mitchell, Marci R.; Balodis, Iris M.; DeVito, Elise E.; Lacadie, Cheryl M.; Yeston, Jon; Scheinost, Dustin; Constable, R. Todd; Carroll, Kathleen M.; Potenza, Marc N.

2013-01-01

Background Cocaine-dependent individuals demonstrate neural and behavioral differences compared to healthy comparison subjects when performing the Stroop color-word inference test. Stroop measures also relate to treatment outcome for cocaine dependence. Intrinsic connectivity analyses assess the extent to which task-related regional brain activations are related to each other in the absence of defining a priori regions-of-interest. Objective This study examined: 1) the extent to which cocaine-dependent and non-addicted individuals differed on measures of intrinsic connectivity during fMRI Stroop performance; and, 2) the relationships between fMRI Stroop intrinsic connectivity and treatment outcome in cocaine dependence. Methods Sixteen treatment-seeking cocaine-dependent patients and matched non-addicted comparison subjects completed an fMRI Stroop task. Between-group differences in intrinsic connectivity were assessed and related to self-reported and urine-toxicology-based cocaine-abstinence measures. Results Cocaine-dependent patients vs. comparison subjects showed less intrinsic connectivity in cortical and sub-cortical regions. When adjusting for individual degree of intrinsic connectivity, cocaine-dependent vs. comparison subjects showed relatively greater intrinsic connectivity in the ventral striatum, putamen, inferior frontal gyrus, anterior insula, thalamus, and substantia nigra. Non-mean-adjusted intrinsic-connectivity measures in the midbrain, thalamus, ventral striatum, substantia nigra, insula, and hippocampus negatively correlated with measures of cocaine abstinence. Conclusion The diminished intrinsic connectivity in cocaine-dependent vs. comparison subjects suggests poorer communication across brain regions during cognitive-control processes. In mean-adjusted analyses, the cocaine-dependent group displayed relatively greater Stroop-related connectivity in regions implicated in motivational processes in addictions. The relationships between treatment outcomes and connectivity in the midbrain and basal ganglia suggest that connectivity represents a potential treatment target. PMID:24200209

Intrinsic, stimulus-driven and task-dependent connectivity in human auditory cortex.

PubMed

Häkkinen, Suvi; Rinne, Teemu

2018-06-01

A hierarchical and modular organization is a central hypothesis in the current primate model of auditory cortex (AC) but lacks validation in humans. Here we investigated whether fMRI connectivity at rest and during active tasks is informative of the functional organization of human AC. Identical pitch-varying sounds were presented during a visual discrimination (i.e. no directed auditory attention), pitch discrimination, and two versions of pitch n-back memory tasks. Analysis based on fMRI connectivity at rest revealed a network structure consisting of six modules in supratemporal plane (STP), temporal lobe, and inferior parietal lobule (IPL) in both hemispheres. In line with the primate model, in which higher-order regions have more longer-range connections than primary regions, areas encircling the STP module showed the highest inter-modular connectivity. Multivariate pattern analysis indicated significant connectivity differences between the visual task and rest (driven by the presentation of sounds during the visual task), between auditory and visual tasks, and between pitch discrimination and pitch n-back tasks. Further analyses showed that these differences were particularly due to connectivity modulations between the STP and IPL modules. While the results are generally in line with the primate model, they highlight the important role of human IPL during the processing of both task-irrelevant and task-relevant auditory information. Importantly, the present study shows that fMRI connectivity at rest, during presentation of sounds, and during active listening provides novel information about the functional organization of human AC.

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

Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI

PubMed Central

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

2016-01-01

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

FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network

PubMed Central

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

2008-01-01

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

The power of using functional fMRI on small rodents to study brain pharmacology and disease

PubMed Central

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

2015-01-01

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

Sources of group differences in functional connectivity: an investigation applied to autism spectrum disorder.

PubMed

Jones, Tyler B; Bandettini, Peter A; Kenworthy, Lauren; Case, Laura K; Milleville, Shawn C; Martin, Alex; Birn, Rasmus M

2010-01-01

An increasing number of fMRI studies are using the correlation of low-frequency fluctuations between brain regions, believed to reflect synchronized variations in neuronal activity, to infer "functional connectivity". In studies of autism spectrum disorder (ASD), decreases in this measure of connectivity have been found by focusing on the response to task modulation, by using only the rest periods, or by analyzing purely resting-state data. This difference in connectivity, however, could result from a number of different mechanisms--differences in noise, task-related fluctuations, task performance, or spontaneous neuronal activity. In this study, we investigate the difference in functional connectivity between adolescents with high-functioning ASD and typically developing control subjects by examining the residual fluctuations occurring on top of the fMRI response to an overt verbal fluency task. We find decreased correlations of these residuals (a decreased "connectivity") in ASD subjects. Furthermore, we find that this decrease was not due to task-related effects, block-to-block variations in task performance, or increased noise, and the difference was greatest when primarily rest periods are considered. These findings suggest that the estimate of disrupted functional connectivity in ASD is likely driven by differences in task-unrelated neuronal fluctuations.

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.

PubMed

Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z

2015-11-01

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. Copyright © 2015 Elsevier Inc. All rights reserved.

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

PubMed Central

Brier, Matthew R.; Mitra, Anish; McCarthy, John E.; Ances, Beau M.; Snyder, Abraham Z.

2015-01-01

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. PMID:26208872

Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data.

PubMed

Sharaev, Maksim G; Zavyalova, Viktoria V; Ushakov, Vadim L; Kartashov, Sergey I; Velichkovsky, Boris M

2016-01-01

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078-0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p < 0.05). Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain's functioning at resting state.

Amygdala subnuclei response and connectivity during emotional processing.

PubMed

Hrybouski, Stanislau; Aghamohammadi-Sereshki, Arash; Madan, Christopher R; Shafer, Andrea T; Baron, Corey A; Seres, Peter; Beaulieu, Christian; Olsen, Fraser; Malykhin, Nikolai V

2016-06-01

The involvement of the human amygdala in emotion-related processing has been studied using functional magnetic resonance imaging (fMRI) for many years. However, despite the amygdala being comprised of several subnuclei, most studies investigated the role of the entire amygdala in processing of emotions. Here we combined a novel anatomical tracing protocol with event-related high-resolution fMRI acquisition to study the responsiveness of the amygdala subnuclei to negative emotional stimuli and to examine intra-amygdala functional connectivity. The greatest sensitivity to the negative emotional stimuli was observed in the centromedial amygdala, where the hemodynamic response amplitude elicited by the negative emotional stimuli was greater and peaked later than for neutral stimuli. Connectivity patterns converge with extant findings in animals, such that the centromedial amygdala was more connected with the nuclei of the basal amygdala than with the lateral amygdala. Current findings provide evidence of functional specialization within the human amygdala. Copyright © 2016 Elsevier Inc. All rights reserved.

Investigating Focal Connectivity Deficits in Alzheimer's Disease Using Directional Brain Networks Derived from Resting-State fMRI

PubMed Central

Zhao, Sinan; Rangaprakash, D; Venkataraman, Archana; Liang, Peipeng; Deshpande, Gopikrishna

2017-01-01

Connectivity analysis of resting-state fMRI has been widely used to identify biomarkers of Alzheimer's disease (AD) based on brain network aberrations. However, it is not straightforward to interpret such connectivity results since our understanding of brain functioning relies on regional properties (activations and morphometric changes) more than connections. Further, from an interventional standpoint, it is easier to modulate the activity of regions (using brain stimulation, neurofeedback, etc.) rather than connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. In brief, we present a model of directed connectivity (using Granger causality) that characterizes the coupling among different regions in healthy controls and Alzheimer's disease. We then characterized group differences using a (between-subject) generative model of pathology, which generates latent connectivity variables that best explain the (within-subject) directed connectivity. Crucially, our generative model at the second (between-subject) level explains connectivity in terms of local or regionally specific abnormalities. This allows one to explain disconnections among multiple regions in terms of regionally specific pathology; thereby offering a target for therapeutic intervention. Two foci were identified, locus coeruleus in the brain stem and right orbitofrontal cortex. Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. We further partitioned the aberrant connectomic network into four unique sub-networks, which likely leads to symptoms commonly observed in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD. PMID:28729831

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.

Glucose Administration Enhances fMRI Brain Activation and Connectivity Related to Episodic Memory Encoding for Neutral and Emotional Stimuli

ERIC Educational Resources Information Center

Parent, Marise B.; Krebs-Kraft, Desiree L.; Ryan, John P.; Wilson, Jennifer S.; Harenski, Carla; Hamann, Stephan

2011-01-01

Glucose enhances memory in a variety of species. In humans, glucose administration enhances episodic memory encoding, although little is known regarding the neural mechanisms underlying these effects. Here we examined whether elevating blood glucose would enhance functional MRI (fMRI) activation and connectivity in brain regions associated with…

Multimodal mapping of the brain's functional connectivity and the adult outcome of attention deficit hyperactivity disorder.

PubMed

Sudre, Gustavo; Szekely, Eszter; Sharp, Wendy; Kasparek, Steven; Shaw, Philip

2017-10-31

We have a limited understanding of why many children with attention deficit hyperactivity disorder do not outgrow the disorder by adulthood. Around 20-30% retain the full syndrome as young adults, and about 50% show partial, rather than complete, remission. Here, to delineate the neurobiology of this variable outcome, we ask if the persistence of childhood symptoms into adulthood impacts on the brain's functional connectivity. We studied 205 participants followed clinically since childhood. In early adulthood, participants underwent magnetoencephalography (MEG) to measure neuronal activity directly and functional MRI (fMRI) to measure hemodynamic activity during a task-free period (the "resting state"). We found that symptoms of inattention persisting into adulthood were associated with disrupted patterns of typical functional connectivity in both MEG and fMRI. Specifically, those with persistent inattention lost the typical balance of connections within the default mode network (DMN; prominent during introspective thought) and connections between this network and those supporting attention and cognitive control. By contrast, adults whose childhood inattentive symptoms had resolved did not differ significantly from their never-affected peers, both hemodynamically and electrophysiologically. The anomalies in functional connectivity tied to clinically significant inattention centered on midline regions of the DMN in both MEG and fMRI, boosting confidence in a possible pathophysiological role. The findings suggest that the clinical course of this common childhood onset disorder impacts the functional connectivity of the adult brain. Published under the PNAS license.

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.

Hierarchical functional modularity in the resting-state human brain.

PubMed

Ferrarini, Luca; Veer, Ilya M; Baerends, Evelinda; van Tol, Marie-José; Renken, Remco J; van der Wee, Nic J A; Veltman, Dirk J; Aleman, André; Zitman, Frans G; Penninx, Brenda W J H; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, Julien

2009-07-01

Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Copyright 2009 Wiley-Liss, Inc

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

Connectivity changes after laser ablation: Resting-state fMRI.

PubMed

Boerwinkle, Varina L; Vedantam, Aditya; Lam, Sandi; Wilfong, Angus A; Curry, Daniel J

2018-05-01

Resting-state functional magnetic resonance imaging (rsfMRI) is emerging as a useful tool in the multimodal assessment of patients with epilepsy. In pediatric patients who cannot perform task-based fMRI, rsfMRI may present an adjunct and alternative. Although changes in brain activation during task-based fMRI have been described after surgery for epilepsy, there is limited data on the role of postoperative rsfMRI. In this short review, we discuss the role of postoperative rsfMRI after laser ablation of seizure foci. By establishing standardized anesthesia protocols and imaging parameters, we have been able to perform serial rsfMRI at postoperative follow-up. The development of in-house software that can merge rsfMRI images to surgical navigation systems has allowed us to enhance the clinical applications of this technique. Resting-state fMRI after laser ablation has the potential to identify changes in connectivity, localize new seizure foci, and guide antiepileptic therapy. In our experience, rsfMRI complements conventional MR imaging and task-based fMRI for the evaluation of patients with seizure recurrence after laser ablation, and represents a potential noninvasive biomarker for functional connectivity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Findings in resting-state fMRI by differences from K-means clustering.

PubMed

Chyzhyk, Darya; Graña, Manuel

2014-01-01

Resting state fMRI has growing number of studies with diverse aims, always centered on some kind of functional connectivity biomarker obtained from correlation regarding seed regions, or by analytical decomposition of the signal towards the localization of the spatial distribution of functional connectivity patterns. In general, studies are computationally costly and very sensitive to noise and preprocessing of data. In this paper we consider clustering by K-means as a exploratory procedure which can provide some results with little computational effort, due to efficient implementations that are readily available. We demonstrate the approach on a dataset of schizophrenia patients, finding differences between patients with and without auditory hallucinations.

Characterizing Signals Within Lesions and Mapping Brain Network Connectivity After Traumatic Axonal Injury: A 7 Tesla Resting-State FMRI Study.

PubMed

Lee, Seul; Polimeni, Jonathan R; Price, Collin M; Edlow, Brian L; McNab, Jennifer A

2018-06-01

Resting-state functional magnetic resonance imaging (RS-FMRI) has been widely used to map brain functional connectivity, but it is unclear how to probe connectivity within and around lesions. In this study, we characterize RS-FMRI signal time course properties and evaluate different seed placements within and around hemorrhagic traumatic axonal injury (hTAI) lesions. RS-FMRI was performed on a 7 Tesla scanner in a patient who recovered consciousness after traumatic coma and in three healthy controls. Eleven lesions in the patient were characterized in terms of (1) temporal signal-to-noise ratio (tSNR); (2) physiological noise, through comparison of noise regressors derived from the white matter (WM), cerebrospinal fluid (CSF), and gray matter (GM); and (3) seed-based functional connectivity. Temporal SNR at the center of the lesions was 38.3% and 74.1% lower compared with the same region in the contralesional hemisphere of the patient and in the ipsilesional hemispheres of the controls, respectively. Within the lesions, WM noise was more prominent than CSF and GM noise. Lesional seeds did not produce discernable networks, but seeds in the contralesional hemisphere revealed networks whose nodes appeared to be shifted or obscured due to overlapping or nearby lesions. Single-voxel seed analysis demonstrated that placing a seed within a lesion's periphery was necessary to identify networks associated with the lesion region. These findings provide evidence of resting-state network changes in the human brain after recovery from traumatic coma. Furthermore, we show that seed placement within a lesion's periphery or in the contralesional hemisphere may be necessary for network identification in patients with hTAI.

Identifying Individuals with Antisocial Personality Disorder Using Resting-State fMRI

PubMed Central

Tang, Yan; Jiang, Weixiong; Liao, Jian; Wang, Wei; Luo, Aijing

2013-01-01

Antisocial personality disorder (ASPD) is closely connected to criminal behavior. A better understanding of functional connectivity in the brains of ASPD patients will help to explain abnormal behavioral syndromes and to perform objective diagnoses of ASPD. In this study we designed an exploratory data-driven classifier based on machine learning to investigate changes in functional connectivity in the brains of patients with ASPD using resting state functional magnetic resonance imaging (fMRI) data in 32 subjects with ASPD and 35 controls. The results showed that the classifier achieved satisfactory performance (86.57% accuracy, 77.14% sensitivity and 96.88% specificity) and could extract stabile information regarding functional connectivity that could be used to discriminate ASPD individuals from normal controls. More importantly, we found that the greatest change in the ASPD subjects was uncoupling between the default mode network and the attention network. Moreover, the precuneus, superior parietal gyrus and cerebellum exhibited high discriminative power in classification. A voxel-based morphometry analysis was performed and showed that the gray matter volumes in the parietal lobule and white matter volumes in the precuneus were abnormal in ASPD compared to controls. To our knowledge, this study was the first to use resting-state fMRI to identify abnormal functional connectivity in ASPD patients. These results not only demonstrated good performance of the proposed classifier, which can be used to improve the diagnosis of ASPD, but also elucidate the pathological mechanism of ASPD from a resting-state functional integration viewpoint. PMID:23593272

Identifying individuals with antisocial personality disorder using resting-state FMRI.

PubMed

Tang, Yan; Jiang, Weixiong; Liao, Jian; Wang, Wei; Luo, Aijing

2013-01-01

Antisocial personality disorder (ASPD) is closely connected to criminal behavior. A better understanding of functional connectivity in the brains of ASPD patients will help to explain abnormal behavioral syndromes and to perform objective diagnoses of ASPD. In this study we designed an exploratory data-driven classifier based on machine learning to investigate changes in functional connectivity in the brains of patients with ASPD using resting state functional magnetic resonance imaging (fMRI) data in 32 subjects with ASPD and 35 controls. The results showed that the classifier achieved satisfactory performance (86.57% accuracy, 77.14% sensitivity and 96.88% specificity) and could extract stabile information regarding functional connectivity that could be used to discriminate ASPD individuals from normal controls. More importantly, we found that the greatest change in the ASPD subjects was uncoupling between the default mode network and the attention network. Moreover, the precuneus, superior parietal gyrus and cerebellum exhibited high discriminative power in classification. A voxel-based morphometry analysis was performed and showed that the gray matter volumes in the parietal lobule and white matter volumes in the precuneus were abnormal in ASPD compared to controls. To our knowledge, this study was the first to use resting-state fMRI to identify abnormal functional connectivity in ASPD patients. These results not only demonstrated good performance of the proposed classifier, which can be used to improve the diagnosis of ASPD, but also elucidate the pathological mechanism of ASPD from a resting-state functional integration viewpoint.

Neuropsychology and cognitive neuroscience in the fMRI era: A recapitulation of localizationist and connectionist views.

PubMed

Sutterer, Matthew J; Tranel, Daniel

2017-11-01

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

Review of thalamocortical resting-state fMRI studies in schizophrenia

PubMed Central

Giraldo-Chica, Monica; Woodward, Neil D.

2017-01-01

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

Dynamic connectivity regression: Determining state-related changes in brain connectivity

PubMed Central

Cribben, Ivor; Haraldsdottir, Ragnheidur; Atlas, Lauren Y.; Wager, Tor D.; Lindquist, Martin A.

2014-01-01

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N=26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method’s ability to observe how the networks between different brain regions changed with subjects’ emotional state. PMID:22484408

Early life social stress and resting state functional connectivity in postpartum rat anterior cingulate circuits.

PubMed

Nephew, Benjamin C; Febo, Marcelo; Huang, Wei; Colon-Perez, Luis M; Payne, Laurellee; Poirier, Guillaume L; Greene, Owen; King, Jean A

2018-03-15

Continued development and refinement of resting state functional connectivity (RSFC) fMRI techniques in both animal and clinical studies has enhanced our comprehension of the adverse effects of stress on psychiatric health. The objective of the current study was to assess both maternal behavior and resting state functional connectivity (RSFC) changes in these animals when they were dams caring for their own young. It was hypothesized that ECSS exposed dams would express depressed maternal care and exhibit similar (same networks), yet different specific changes in RSFC (different individual nuclei) than reported when they were adult females. We have developed an ethologically relevant transgenerational model of the role of chronic social stress (CSS) in the etiology of postpartum depression and anxiety. Initial fMRI investigation of the CSS model indicates that early life exposure to CSS (ECSS) induces long term changes in functional connectivity in adult nulliparous female F1 offspring. ECSS in F1 dams resulted in depressed maternal care specifically during early lactation, consistent with previous CSS studies, and induced changes in functional connectivity in regions associated with sensory processing, maternal and emotional responsiveness, memory, and the reward pathway, with robust changes in anterior cingulate circuits. The sample sizes for the fMRI groups were low, limiting statistical power. This behavioral and functional neuroanatomical foundation can now be used to enhance our understanding of the neural etiology of early life stress associated disorders and test preventative measures and treatments for stress related disorders. Copyright © 2018 Elsevier B.V. All rights reserved.

A wavelet-based estimator of the degrees of freedom in denoised fMRI time series for probabilistic testing of functional connectivity and brain graphs.

PubMed

Patel, Ameera X; Bullmore, Edward T

2016-11-15

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

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.

Combined DTI Tractography and Functional MRI Study of the Language Connectome in Healthy Volunteers: Extensive Mapping of White Matter Fascicles and Cortical Activations.

PubMed

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

2016-01-01

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

Improvement of white matter and functional connectivity abnormalities by repetitive transcranial magnetic stimulation in crossed aphasia in dextral.

PubMed

Lu, Haitao; Wu, Haiyan; Cheng, Hewei; Wei, Dongjie; Wang, Xiaoyan; Fan, Yong; Zhang, Hao; Zhang, Tong

2014-01-01

As a special aphasia, the occurrence of crossed aphasia in dextral (CAD) is unusual. This study aims to improve the language ability by applying 1 Hz repetitive transcranial magnetic stimulation (rTMS). We studied multiple modality imaging of structural connectivity (diffusion tensor imaging), functional connectivity (resting fMRI), PET, and neurolinguistic analysis on a patient with CAD. Furthermore, we applied rTMS of 1 Hz for 40 times and observed the language function improvement. The results indicated that a significantly reduced structural and function connectivity was found in DTI and fMRI data compared with the control. The PET imaging showed hypo-metabolism in right hemisphere and left cerebellum. In conclusion, one of the mechanisms of CAD is that right hemisphere is the language dominance. Stimulating left Wernicke area could improve auditory comprehension, stimulating left Broca's area could enhance expression, and the results outlasted 6 months by 1 Hz rTMS balancing the excitability inter-hemisphere in CAD.

Different Resting-State Functional Connectivity Alterations in Smokers and Nonsmokers with Internet Gaming Addiction

PubMed Central

Chen, Xue; Wang, Yao; Zhou, Yan; Sun, Yawen; Ding, Weina; Zhuang, Zhiguo; Xu, Jianrong; Du, Yasong

2014-01-01

This study investigated changes in resting-state functional connectivity (rsFC) of posterior cingulate cortex (PCC) in smokers and nonsmokers with Internet gaming addiction (IGA). Twenty-nine smokers with IGA, 22 nonsmokers with IGA, and 30 healthy controls (HC group) underwent a resting-state fMRI scan. PCC connectivity was determined in all subjects by investigating synchronized low-frequency fMRI signal fluctuations using a temporal correlation method. Compared with the nonsmokers with IGA, the smokers with IGA exhibited decreased rsFC with PCC in the right rectus gyrus. Left middle frontal gyrus exhibited increased rsFC. The PCC connectivity with the right rectus gyrus was found to be negatively correlated with the CIAS scores in the smokers with IGA before correction. Our results suggested that smokers with IGA had functional changes in brain areas related to motivation and executive function compared with the nonsmokers with IGA. PMID:25506057

Acupuncture Modulates Resting State Connectivity in Default and Sensorimotor Brain Networks

PubMed Central

Dhond, Rupali P.; Yeh, Calvin; Park, Kyungmo; Kettner, Norman; Napadow, Vitaly

2008-01-01

Previous studies have defined low-frequency, spatially consistent networks in resting fMRI data which may reflect functional connectivity. We sought to explore how a complex somatosensory stimulation, acupuncture, influences intrinsic connectivity in two of these networks: the default mode network (DMN) and sensorimotor network (SMN). We analyzed resting fMRI data taken before and after verum and sham acupuncture. Electrocardiography data was used to infer autonomic modulation through measures of heart rate variability (HRV). Probabilistic independent component analysis was used to separate resting fMRI data into DMN and SMN components. Following verum, but not sham, acupuncture there was increased DMN connectivity with pain (anterior cingulate cortex (ACC), periaqueductal gray), affective (amygdala, ACC), and memory (hippocampal formation, middle temporal gyrus) related brain regions. Furthermore, increased DMN connectivity with the hippocampal formation, a region known to support memory and interconnected with autonomic brain regions, was negatively correlated with acupuncture-induced increase in a sympathetic related HRV metric (LFu), and positively correlated with a parasympathetic related metric (HFu). Following verum, but not sham, acupuncture there was also increased SMN connectivity with pain related brain regions (ACC, cerebellum). We attribute differences between verum and sham acupuncture to more varied and stronger sensations evoked by verum acupuncture. Our results demonstrate for the first time that acupuncture can enhance the post-stimulation spatial extent of resting brain networks to include anti-nociceptive, memory, and affective brain regions. This modulation and sympathovagal response may relate to acupuncture analgesia and other potential therapeutic effects. PMID:18337009

Recovery of directed intracortical connectivity from fMRI data

NASA Astrophysics Data System (ADS)

Gilson, Matthieu; Ritter, Petra; Deco, Gustavo

2016-06-01

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

[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.

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

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.

Experimentally induced thyrotoxicosis leads to increased connectivity in temporal lobe structures: a resting state fMRI study.

PubMed

Göttlich, Martin; Heldmann, Marcus; Göbel, Anna; Dirk, Anna-Luise; Brabant, Georg; Münte, Thomas F

2015-06-01

Adult onset hyperthyroidism may impact on different cognitive domains, including attention and concentration, memory, perceptual function, language and executive function. Previous PET studies implicated changed functionality of limbic regions, the temporal and frontal lobes in hyperthyroidism, whereas it is unknown whether cognitive effects of hyperthyroidism may be due to changed brain connectivity. This study aimed to investigate the effect of experimentally induced short-term hyperthyroidism thyrotoxicosis on resting-state functional connectivity using functional magnetic resonance imaging. Twenty-nine healthy male right-handed subjects were examined twice, once prior and once after 8 weeks of oral administration of 250 μg levothyroxine per day. Resting-state fMRI was subjected to graph-theory based analysis methods to investigate whole-brain intrinsic functional connectivity. Despite a lack of subjective changes noticed by the subjects significant thyrotoxicosis was confirmed in all subjects. This induced a significant increase in resting-state functional connectivity specifically in the rostral temporal lobes (0.05 FDR corrected at the cluster level), which is caused by an increased connectivity to the cognitive control network. The increased connectivity between temporal poles and the cognitive control network shown here under experimental conditions supports an important function of thyroid hormones in the regulation of paralimbic structures. Copyright © 2015 Elsevier Ltd. All rights reserved.

The effect of constraining eye-contact during dynamic emotional face perception—an fMRI study

PubMed Central

Zurcher, Nicole R.; Lassalle, Amandine; Hippolyte, Loyse; Ward, Noreen; Johnels, Jakob Åsberg

2017-01-01

Abstract Eye-contact modifies how we perceive emotions and modulates activity in the social brain network. Here, using fMRI, we demonstrate that adding a fixation cross in the eye region of dynamic facial emotional stimuli significantly increases activation in the social brain of healthy, neurotypical participants when compared with activation for the exact same stimuli observed in a free-viewing mode. In addition, using PPI analysis, we show that the degree of amygdala connectivity with the rest of the brain is enhanced for the constrained view for all emotions tested except for fear, and that anxiety and alexithymia modulate the strength of amygdala connectivity for each emotion differently. Finally, we show that autistic traits have opposite effects on amygdala connectivity for fearful and angry emotional expressions, suggesting that these emotions should be treated separately in studies investigating facial emotion processing. PMID:28402536

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.

A Longitudinal Study on Resting State Functional Connectivity in Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease.

PubMed

Hafkemeijer, Anne; Möller, Christiane; Dopper, Elise G P; Jiskoot, Lize C; van den Berg-Huysmans, Annette A; van Swieten, John C; van der Flier, Wiesje M; Vrenken, Hugo; Pijnenburg, Yolande A L; Barkhof, Frederik; Scheltens, Philip; van der Grond, Jeroen; Rombouts, Serge A R B

2017-01-01

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. We applied longitudinal resting state functional magnetic resonance imaging (fMRI) to delineate functional brain connections relevant for disease progression and diagnostic accuracy. We used two-center resting state fMRI data of 20 AD patients (65.1±8.0 years), 12 bvFTD patients (64.7±5.4 years), and 22 control subjects (63.8±5.0 years) at baseline and 1.8-year follow-up. We used whole-network and voxel-based network-to-region analyses to study group differences in functional connectivity at baseline and follow-up, and longitudinal changes in connectivity within and between groups. At baseline, connectivity between paracingulate gyrus and executive control network, between cuneal cortex and medial visual network, and between paracingulate gyrus and salience network was higher in AD compared with controls. These differences were also present after 1.8 years. At follow-up, connectivity between angular gyrus and right frontoparietal network, and between paracingulate gyrus and default mode network was lower in bvFTD compared with controls, and lower compared with AD between anterior cingulate gyrus and executive control network, and between lateral occipital cortex and medial visual network. Over time, connectivity decreased in AD between precuneus and right frontoparietal network and in bvFTD between inferior frontal gyrus and left frontoparietal network. Longitudinal changes in connectivity between supramarginal gyrus and right frontoparietal network differ between both patient groups and controls. We found disease-specific brain regions with longitudinal connectivity changes. This suggests the potential of longitudinal resting state fMRI to delineate regions relevant for disease progression and for diagnostic accuracy, although no group differences in longitudinal changes in the direct comparison of AD and bvFTD were found.

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.

MDMA (Ecstasy) association with impaired fMRI BOLD thalamic coherence and functional connectivity*

PubMed Central

Salomon, Ronald M.; Karageorgiou, John; Dietrich, Mary S.; McLellan, Jessica Y.; Charboneau, Evonne J.; Blackford, Jennifer U.; Cowan, Ronald L.

2011-01-01

Background MDMA exposure is associated with chronic serotonergic dysfunction in preclinical and clinical studies. A recent functional magnetic resonance imaging (fMRI) comparison of past MDMA users to non-MDMA-using controls revealed increased spatial extent and amplitude of activation in the supplementary motor area during motor tasks (Karageorgiou et al., 2009). Blood oxygenation level dependent (BOLD) data from that study were reanalyzed for intraregional coherence and for inter-regional temporal correlations between time series, as functional connectivity. Methods Fourteen MDMA users and ten controls reporting similar non-MDMA abuse performed finger taps during fMRI. Fourteen motor pathway regions plus a pontine raphé region were examined. Coherence was expressed as percent of voxels positively correlated with an intraregional index voxel. Functional connectivity was determined using wavelet correlations. Results Intraregional thalamic coherence was significantly diminished at low frequencies in MDMA users compared to controls (p=0.009). Inter-regional functional connectivity was significantly weaker for right thalamo - left caudate (p=0.002), right thalamo - left thalamus (p=0.007), right caudate - right postcentral (p=0.007) and right supplementary motor area - right precentral gyrus (p=0.011) region pairs compared to controls. When stratified by lifetime exposure, significant negative associations were observed between cumulative MDMA use and functional connectivity in seven other region-pairs, while only one region-pair showed a positive association. Conclusions Reported prior MDMA use was associated with deficits in BOLD intraregional coherence and inter-regional functional connectivity, even among functionally robust pathways involving motor regions. This suggests that MDMA use is associated with long-lasting effects on brain neurophysiology beyond the cognitive domain. PMID:21807471

Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study.

PubMed

Wu, Lei; Eichele, Tom; Calhoun, Vince D

2010-10-01

Concurrent EEG-fMRI studies have provided increasing details of the dynamics of intrinsic brain activity during the resting state. Here, we investigate a prominent effect in EEG during relaxed resting, i.e. the increase of the alpha power when the eyes are closed compared to when the eyes are open. This phenomenon is related to changes in thalamo-cortical and cortico-cortical synchronization. In order to investigate possible changes to EEG-fMRI coupling and fMRI functional connectivity during the two states we adopted a data-driven approach that fuses the multimodal data on the basis of parallel ICA decompositions of the fMRI data in the spatial domain and of the EEG data in the spectral domain. The power variation of a posterior alpha component was used as a reference function to deconvolve the hemodynamic responses from occipital, frontal, temporal, and subcortical fMRI components. Additionally, we computed the functional connectivity between these components. The results showed widespread alpha hemodynamic responses and high functional connectivity during eyes-closed (EC) rest, while eyes open (EO) resting abolished many of the hemodynamic responses and markedly decreased functional connectivity. These data suggest that generation of local hemodynamic responses is highly sensitive to state changes that do not involve changes of mental effort or awareness. They also indicate the localized power differences in posterior alpha between EO and EC in resting state data are accompanied by spatially widespread amplitude changes in hemodynamic responses and inter-regional functional connectivity, i.e. low frequency hemodynamic signals display an equivalent of alpha reactivity. Copyright 2010 Elsevier Inc. All rights reserved.

Caffeine reduces resting-state BOLD functional connectivity in the motor cortex.

PubMed

Rack-Gomer, Anna Leigh; Liau, Joy; Liu, Thomas T

2009-05-15

In resting-state functional magnetic resonance imaging (fMRI), correlations between spontaneous low-frequency fluctuations in the blood oxygenation level dependent (BOLD) signal are used to assess functional connectivity between different brain regions. Changes in resting-state BOLD connectivity measures are typically interpreted as changes in coherent neural activity across spatially distinct brain regions. However, this interpretation can be complicated by the complex dependence of the BOLD signal on both neural and vascular factors. For example, prior studies have shown that vasoactive agents that alter baseline cerebral blood flow, such as caffeine and carbon dioxide, can significantly alter the amplitude and dynamics of the task-related BOLD response. In this study, we examined the effect of caffeine (200 mg dose) on resting-state BOLD connectivity in the motor cortex across a sample of healthy young subjects (N=9). We found that caffeine significantly (p<0.05) reduced measures of resting-state BOLD connectivity in the motor cortex. Baseline cerebral blood flow and spectral energy in the low-frequency BOLD fluctuations were also significantly decreased by caffeine. These results suggest that caffeine usage should be carefully considered in the design and interpretation of resting-state BOLD fMRI studies.

Reduced amygdala-orbitofrontal connectivity during moral judgments in youths with disruptive behavior disorders and psychopathic traits

PubMed Central

Marsh, Abigail A.; Finger, Elizabeth C.; Fowler, Katherine A.; Jurkowitz, Ilana T.N.; Schechter, Julia C.; Yu, Henry H.; Pine, Daniel S.; Blair, R. J. R.

2011-01-01

We used functional magnetic resonance imaging (fMRI) to investigate dysfunction in the amygdala and orbitofrontal cortex in adolescents with disruptive behavior disorders and psychopathic traits during a moral judgment task. Fourteen adolescents with psychopathic traits and 14 healthy controls were assessed using fMRI while they categorized illegal and legal behaviors in a moral judgment implicit association task. fMRI data were then analyzed using random-effects analysis of variance and functional connectivity. Youths with psychopathic traits showed reduced amygdala activity when making judgments about legal actions and reduced functional connectivity between the amygdala and orbitofrontal cortex during task performance. These results suggest that psychopathic traits are associated with amygdala and orbitofrontal cortex dysfunction. This dysfunction may relate to previous findings of disrupted moral judgment in this population. PMID:22047730

Functional MRI and Multivariate Autoregressive Models

PubMed Central

Rogers, Baxter P.; Katwal, Santosh B.; Morgan, Victoria L.; Asplund, Christopher L.; Gore, John C.

2010-01-01

Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays, and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series, and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI. PMID:20444566

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

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.

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

Functional connectivity associated with social networks in older adults: A resting-state fMRI study.

PubMed

Pillemer, Sarah; Holtzer, Roee; Blumen, Helena M

2017-06-01

Poor social networks and decreased levels of social support are associated with worse mood, health, and cognition in younger and older adults. Yet, we know very little about the brain substrates associated with social networks and social support, particularly in older adults. This study examined functional brain substrates associated with social networks using the Social Network Index (SNI) and resting-state functional magnetic resonance imaging (fMRI). Resting-state fMRI data from 28 non-demented older adults were analyzed with independent components analyses. As expected, four established resting-state networks-previously linked to motor, vision, speech, and other language functions-correlated with the quality (SNI-1: total number of high-contact roles of a respondent) and quantity (SNI-2: total number of individuals in a respondent's social network) of social networks: a sensorimotor, a visual, a vestibular/insular, and a left frontoparietal network. Moreover, SNI-1 was associated with greater functional connectivity in the lateral prefrontal regions of the left frontoparietal network, while SNI-2 was associated with greater functional connectivity in the medial prefrontal regions of this network. Thus, lateral prefrontal regions may be particularly linked to the quality of social networks while medial prefrontal regions may be particularly linked to the quantity of social networks.

The neural correlates of risk propensity in males and females using resting-state fMRI

PubMed Central

Zhou, Yuan; Li, Shu; Dunn, John; Li, Huandong; Qin, Wen; Zhu, Maohu; Rao, Li-Lin; Song, Ming; Yu, Chunshui; Jiang, Tianzi

2014-01-01

Men are more risk prone than women, but the underlying basis remains unclear. To investigate this question, we developed a trait-like measure of risk propensity which we correlated with resting-state functional connectivity to identify sex differences. Specifically, we used short- and long-range functional connectivity densities to identify associated brain regions and examined their functional connectivities in resting-state functional magnetic resonance imaging (fMRI) data collected from a large sample of healthy young volunteers. We found that men had a higher level of general risk propensity (GRP) than women. At the neural level, although they shared a common neural correlate of GRP in a network centered at the right inferior frontal gyrus, men and women differed in a network centered at the right secondary somatosensory cortex, which included the bilateral dorsal anterior/middle insular cortices and the dorsal anterior cingulate cortex. In addition, men and women differed in a local network centered at the left inferior orbitofrontal cortex. Most of the regions identified by this resting-state fMRI study have been previously implicated in risk processing when people make risky decisions. This study provides a new perspective on the brain-behavioral relationships in risky decision making and contributes to our understanding of sex differences in risk propensity. PMID:24478649

Addiction Related Alteration in Resting-state Brain Connectivity

PubMed Central

Ma, Ning; Liu, Ying; Li, Nan; Wang, Chang-Xin; Zhang, Hao; Jiang, Xiao-Feng; Xu, Hu-Sheng; Fu, Xian-Ming; Hu, Xiaoping; Zhang, Da-Ren

2009-01-01

It is widely accepted that addictive drug use is related to abnormal functional organization in the user’s brain. The present study aimed to identify this type of abnormality within the brain networks implicated in addiction by resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI). With fMRI data acquired during resting state from 14 chronic heroin users (12 of whom were being treated with methadone) and 13 non-addicted controls, we investigated the addiction related alteration in functional connectivity between the regions in the circuits implicated in addiction with seed-based correlation analysis. Compared with controls, chronic heroin users showed increased functional connectivity between nucleus accumbens and ventral/rostral anterior cingulate cortex (ACC), and orbital frontal cortex (OFC), between amygdala and OFC; and reduced functional connectivity between prefrontal cortex and OFC, and ACC. These observations of altered resting-state functional connectivity suggested abnormal functional organization in the addicted brain and may provide additional evidence supporting the theory of addiction that emphasizes enhanced salience value of a drug and its related cues but weakened cognitive control in the addictive state. PMID:19703568

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

PubMed Central

Yu, Qingbao; Erhardt, Erik B.; Sui, Jing; Du, Yuhui; He, Hao; Hjelm, Devon; Cetin, Mustafa S.; Rachakonda, Srinivas; Miller, Robyn L.; Pearlson, Godfrey; Calhoun, Vince D.

2014-01-01

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. PMID:25514514

Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data.

PubMed

Power, Jonathan D; Plitt, Mark; Gotts, Stephen J; Kundu, Prantik; Voon, Valerie; Bandettini, Peter A; Martin, Alex

2018-02-27

"Functional connectivity" techniques are commonplace tools for studying brain organization. A critical element of these analyses is to distinguish variance due to neurobiological signals from variance due to nonneurobiological signals. Multiecho fMRI techniques are a promising means for making such distinctions based on signal decay properties. Here, we report that multiecho fMRI techniques enable excellent removal of certain kinds of artifactual variance, namely, spatially focal artifacts due to motion. By removing these artifacts, multiecho techniques reveal frequent, large-amplitude blood oxygen level-dependent (BOLD) signal changes present across all gray matter that are also linked to motion. These whole-brain BOLD signals could reflect widespread neural processes or other processes, such as alterations in blood partial pressure of carbon dioxide (pCO 2 ) due to ventilation changes. By acquiring multiecho data while monitoring breathing, we demonstrate that whole-brain BOLD signals in the resting state are often caused by changes in breathing that co-occur with head motion. These widespread respiratory fMRI signals cannot be isolated from neurobiological signals by multiecho techniques because they occur via the same BOLD mechanism. Respiratory signals must therefore be removed by some other technique to isolate neurobiological covariance in fMRI time series. Several methods for removing global artifacts are demonstrated and compared, and were found to yield fMRI time series essentially free of motion-related influences. These results identify two kinds of motion-associated fMRI variance, with different physical mechanisms and spatial profiles, each of which strongly and differentially influences functional connectivity patterns. Distance-dependent patterns in covariance are nearly entirely attributable to non-BOLD artifacts.

Short-term memory deficits correlate with hippocampal-thalamic functional connectivity alterations following acute sleep restriction.

PubMed

Chengyang, Li; Daqing, Huang; Jianlin, Qi; Haisheng, Chang; Qingqing, Meng; Jin, Wang; Jiajia, Liu; Enmao, Ye; Yongcong, Shao; Xi, Zhang

2017-08-01

Acute sleep restriction heavily influences cognitive function, affecting executive processes such as attention, response inhibition, and memory. Previous neuroimaging studies have suggested a link between hippocampal activity and short-term memory function. However, the specific contribution of the hippocampus to the decline of short-term memory following sleep restriction has yet to be established. In the current study, we utilized resting-state functional magnetic resonance imaging (fMRI) to examine the association between hippocampal functional connectivity (FC) and the decline of short-term memory following total sleep deprivation (TSD). Twenty healthy adult males aged 20.9 ± 2.3 years (age range, 18-24 years) were enrolled in a within-subject crossover study. Short-term memory and FC were assessed using a Delay-matching short-term memory test and a resting-state fMRI scan before and after TSD. Seed-based correlation analysis was performed using fMRI data for the left and right hippocampus to identify differences in hippocampal FC following TSD. Subjects demonstrated reduced alertness and a decline in short-term memory performance following TSD. Moreover, fMRI analysis identified reduced hippocampal FC with the superior frontal gyrus (SFG), temporal regions, and supplementary motor area. In addition, an increase in FC between the hippocampus and bilateral thalamus was observed, the extent of which correlated with short-term memory performance following TSD. Our findings indicate that the disruption of hippocampal-cortical connectivity is linked to the decline in short-term memory observed after acute sleep restriction. Such results provide further evidence that support the cognitive impairment model of sleep deprivation.

Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset.

PubMed

Bulgarelli, Chiara; Blasi, Anna; Arridge, Simon; Powell, Samuel; de Klerk, Carina C J M; Southgate, Victoria; Brigadoi, Sabrina; Penny, William; Tak, Sungho; Hamilton, Antonia

2018-04-12

Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies. fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Regional homogeneity of fMRI time series in autism spectrum disorders.

PubMed

Shukla, Dinesh K; Keehn, Brandon; Müller, Ralph Axel

2010-05-26

Functional magnetic resonance imaging (fMRI) and functional connectivity MRI (fcMRI) studies of autism spectrum disorders (ASD) have suggested atypical patterns of activation and long-distance connectivity for diverse tasks and networks in ASD. We explored the regional homogeneity (ReHo) approach in ASD, which is analogous to conventional fcMRI, but focuses on local connectivity. FMRI data of 26 children with ASD and 29 typically developing (TD) children were acquired during continuous task performance (visual search). Effects of motion and task were removed and Kendall's coefficient of concordance (KCC) was computed, based on the correlation of the blood oxygen level dependent (BOLD) time series for each voxel and its six nearest neighbors. ReHo was lower in the ASD than the TD group in superior parietal and anterior prefrontal regions. Inverse effects of greater ReHo in the ASD group were detected in lateral and medial temporal regions, predominantly in the right hemisphere. Our findings suggest that ReHo is a sensitive measure for detecting cortical abnormalities in autism. However, impact of methodological factors (such as spatial resolution) on ReHo require further investigation. Published by Elsevier Ireland Ltd.

Oscillations, networks, and their development: MEG connectivity changes with age.

PubMed

Schäfer, Carmen B; Morgan, Benjamin R; Ye, Annette X; Taylor, Margot J; Doesburg, Sam M

2014-10-01

Magnetoencephalographic (MEG) investigations of inter-regional amplitude correlations have yielded new insights into the organization and neurophysiology of resting-state networks (RSNs) first identified using fMRI. Inter-regional MEG amplitude correlations in adult RSNs have been shown to be most prominent in alpha and beta frequency ranges and to express strong congruence with RSN topologies found using fMRI. Despite such advances, little is known about how oscillatory connectivity in RSNs develops throughout childhood and adolescence. This study used a novel fMRI-guided MEG approach to investigate the maturation of resting-state amplitude correlations in physiologically relevant frequency ranges within and among six RSNs in 59 participants, aged 6-34 years. We report age-related increases in inter-regional amplitude correlations that were largest in alpha and beta frequency bands. In contrast to fMRI reports, these changes were observed both within and between the various RSNs analyzed. Our results provide the first evidence of developmental changes in spontaneous neurophysiological connectivity in source-resolved RSNs, which indicate increasing integration within and among intrinsic functional brain networks throughout childhood, adolescence, and early adulthood. Copyright © 2014 Wiley Periodicals, Inc.

The Effects of Pharmacological Opioid Blockade on Neural Measures of Drug Cue-Reactivity in Humans.

PubMed

Courtney, Kelly E; Ghahremani, Dara G; Ray, Lara A

2016-11-01

Interactions between dopaminergic and opioidergic systems have been implicated in the reinforcing properties of drugs of abuse. The present study investigated the effects of opioid blockade, via naltrexone, on functional magnetic resonance imaging (fMRI) measures during methamphetamine cue-reactivity to elucidate the role of endogenous opioids in the neural systems underlying drug craving. To investigate this question, non-treatment seeking individuals with methamphetamine use disorder (N=23; 74% male, mean age=34.70 (SD=8.95)) were recruited for a randomized, placebo controlled, within-subject design and underwent a visual methamphetamine cue-reactivity task during two blood-oxygen-level dependent (BOLD) fMRI sessions following 3 days of naltrexone (50 mg) and matched time for placebo. fMRI analyses tested naltrexone-induced differences in BOLD activation and functional connectivity during cue processing. The results showed that naltrexone administration reduced cue-reactivity in sensorimotor regions and related to altered functional connectivity of dorsal striatum, ventral tegmental area, and precuneus with frontal, visual, sensory, and motor-related regions. Naltrexone also weakened the associations between subjective craving and precuneus functional connectivity with sensorimotor regions and strengthened the associations between subjective craving and dorsal striatum and precuneus connectivity with frontal regions. In conclusion, this study provides the first evidence that opioidergic blockade alters neural responses to drug cues in humans with methamphetamine addiction and suggests that naltrexone may be reducing drug cue salience by decreasing the involvement of sensorimotor regions and by engaging greater frontal regulation over salience attribution.

Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity.

PubMed

Glerean, Enrico; Salmi, Juha; Lahnakoski, Juha M; Jääskeläinen, Iiro P; Sams, Mikko

2012-01-01

Functional brain activity and connectivity have been studied by calculating intersubject and seed-based correlations of hemodynamic data acquired with functional magnetic resonance imaging (fMRI). To inspect temporal dynamics, these correlation measures have been calculated over sliding time windows with necessary restrictions on the length of the temporal window that compromises the temporal resolution. Here, we show that it is possible to increase temporal resolution by using instantaneous phase synchronization (PS) as a measure of dynamic (time-varying) functional connectivity. We applied PS on an fMRI dataset obtained while 12 healthy volunteers watched a feature film. Narrow frequency band (0.04-0.07 Hz) was used in the PS analysis to avoid artifactual results. We defined three metrics for computing time-varying functional connectivity and time-varying intersubject reliability based on estimation of instantaneous PS across the subjects: (1) seed-based PS, (2) intersubject PS, and (3) intersubject seed-based PS. Our findings show that these PS-based metrics yield results consistent with both seed-based correlation and intersubject correlation methods when inspected over the whole time series, but provide an important advantage of maximal single-TR temporal resolution. These metrics can be applied both in studies with complex naturalistic stimuli (e.g., watching a movie or listening to music in the MRI scanner) and more controlled (e.g., event-related or blocked design) paradigms. A MATLAB toolbox FUNPSY ( http://becs.aalto.fi/bml/software.html ) is openly available for using these metrics in fMRI data analysis.

Levodopa response differs in Parkinson's motor subtypes: A task-based effective connectivity study.

PubMed

Mohl, Brianne; Berman, Brian D; Shelton, Erika; Tanabe, Jody

2017-06-15

Parkinson's disease (PD) is a circuit-level disorder with clinically-determined motor subtypes. Despite evidence suggesting each subtype may have different pathophysiology, few neuroimaging studies have examined levodopa-induced differences in neural activation between tremor dominant (TD) and postural instability/gait difficulty (PIGD) subtype patients during a motor task. The goal of this functional MRI (fMRI) study was to examine task-induced activation and connectivity in the cortico-striatal-thalamo-cortical motor circuit in healthy controls, TD patients, and PIGD patients before and after levodopa administration. Fourteen TD and 12 PIGD cognitively-intact patients and 21 age- and sex-matched healthy controls completed a right-hand, paced tapping fMRI paradigm. Collectively, PD patients off medication (OFF) showed hypoactivation of the motor cortex relative to healthy controls, even when controlling for performance. After levodopa intake, the PIGD patients had significantly increased activation in the left putamen compared with TD patients and healthy controls. Psychophysiological interaction analysis revealed that levodopa increased effective connectivity between the posterior putamen and other areas of the motor circuit during tapping in TD patients, but not in PIGD patients. This novel, levodopa-induced difference in the neural responses between PD motor subtypes may have significant implications for elucidating the mechanisms underlying the distinct phenotypic manifestations and enabling the classification of motor subtypes objectively using fMRI. © 2017 Wiley Periodicals, Inc.

Effective connectivity of facial expression network by using Granger causality analysis

NASA Astrophysics Data System (ADS)

Zhang, Hui; Li, Xiaoting

2013-10-01

Functional magnetic resonance imaging (fMRI) is an advanced non-invasive data acquisition technique to investigate the neural activity in human brain. In addition to localize the functional brain regions that is activated by specific cognitive task, fMRI can also be utilized to measure the task-related functional interactions among the active regions of interest (ROI) in the brain. Among the variety of analysis tools proposed for modeling the connectivity of brain regions, Granger causality analysis (GCA) measure the directions of information interactions by looking for the lagged effect among the brain regions. In this study, we use fMRI and Granger Causality analysis to investigate the effective connectivity of brain network induced by viewing several kinds of expressional faces. We focus on four kinds of facial expression stimuli: fearful, angry, happy and neutral faces. Five face selective regions of interest are localized and the effective connectivity within these regions is measured for the expressional faces. Our result based on 8 subjects showed that there is significant effective connectivity from STS to amygdala, from amygdala to OFA, aFFA and pFFA, from STS to aFFA and from pFFA to aFFA. This result suggested that there is an information flow from the STS to the amygdala when perusing expressional faces. This emotional expressional information flow that is conveyed by STS and amygdala, flow back to the face selective regions in occipital-temporal lobes, which constructed a emotional face processing network.

Resting state activity in patients with disorders of consciousness

PubMed Central

Soddu, Andrea; Vanhaudenhuyse, Audrey; Demertzi, Athena; Bruno, Marie-Aurélie; Tshibanda, Luaba; Di, Haibo; Boly, Mélanie; Papa, Michele; Laureys, Steven; Noirhomme, Quentin

Summary Recent advances in the study of spontaneous brain activity have demonstrated activity patterns that emerge with no task performance or sensory stimulation; these discoveries hold promise for the study of higher-order associative network functionality. Additionally, such advances are argued to be relevant in pathological states, such as disorders of consciousness (DOC), i.e., coma, vegetative and minimally conscious states. Recent studies on resting state activity in DOC, measured with functional magnetic resonance imaging (fMRI) techniques, show that functional connectivity is disrupted in the task-negative or the default mode network. However, the two main approaches employed in the analysis of resting state functional connectivity data (i.e., hypothesis-driven seed-voxel and data-driven independent component analysis) present multiple methodological difficulties, especially in non-collaborative DOC patients. Improvements in motion artifact removal and spatial normalization are needed before fMRI resting state data can be used as proper biomarkers in severe brain injury. However, we anticipate that such developments will boost clinical resting state fMRI studies, allowing for easy and fast acquisitions and ultimately improve the diagnosis and prognosis in the absence of DOC patients’ active collaboration in data acquisition. PMID:21693087

Structural connectivity patterns associated with the putative visual word form area and children's reading ability.

PubMed

Fan, Qiuyun; Anderson, Adam W; Davis, Nicole; Cutting, Laurie E

2014-10-24

With the advent of neuroimaging techniques, especially functional MRI (fMRI), studies have mapped brain regions that are associated with good and poor reading, most centrally a region within the left occipito-temporal/fusiform region (L-OT/F) often referred to as the visual word form area (VWFA). Despite an abundance of fMRI studies of the putative VWFA, research about its structural connectivity has just started. Provided that the putative VWFA may be connected to distributed regions in the brain, it remains unclear how this network is engaged in constituting a well-tuned reading circuitry in the brain. Here we used diffusion MRI to study the structural connectivity patterns of the putative VWFA and surrounding areas within the L-OT/F in children with typically developing (TD) reading ability and with word recognition deficits (WRD; sometimes referred to as dyslexia). We found that L-OT/F connectivity varied along a posterior-anterior gradient, with specific structural connectivity patterns related to reading ability in the ROIs centered upon the putative VWFA. Findings suggest that the architecture of the putative VWFA connectivity is fundamentally different between TD and WRD, with TD showing greater connectivity to linguistic regions than WRD, and WRD showing greater connectivity to visual and parahippocampal regions than TD. Findings thus reveal clear structural abnormalities underlying the functional abnormalities in the putative VWFA in WRD. Copyright © 2014 Elsevier B.V. All rights reserved.

Reduced amygdala-orbitofrontal connectivity during moral judgments in youths with disruptive behavior disorders and psychopathic traits.

PubMed

Marsh, Abigail A; Finger, Elizabeth C; Fowler, Katherine A; Jurkowitz, Ilana T N; Schechter, Julia C; Yu, Henry H; Pine, Daniel S; Blair, R J R

2011-12-30

We used functional magnetic resonance imaging (fMRI) to investigate dysfunction in the amygdala and orbitofrontal cortex in adolescents with disruptive behavior disorders and psychopathic traits during a moral judgment task. Fourteen adolescents with psychopathic traits and 14 healthy controls were assessed using fMRI while they categorized illegal and legal behaviors in a moral judgment implicit association task. fMRI data were then analyzed using random-effects analysis of variance and functional connectivity. Youths with psychopathic traits showed reduced amygdala activity when making judgments about legal actions and reduced functional connectivity between the amygdala and orbitofrontal cortex during task performance. These results suggest that psychopathic traits are associated with amygdala and orbitofrontal cortex dysfunction. This dysfunction may relate to previous findings of disrupted moral judgment in this population. 2011 Elsevier Ireland Ltd. All rights reserved.

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

Combined Functional and Causal Connectivity Analyses of Language Networks in Children: A Feasibility Study

ERIC Educational Resources Information Center

Wilke, Marko; Lidzba, Karen; Krageloh-Mann, Ingeborg

2009-01-01

Instead of assessing activation in distinct brain regions, approaches to investigating the networks underlying distinct brain functions have come into the focus of neuroscience research. Here, we provide a completely data-driven framework for assessing functional and causal connectivity in functional magnetic resonance imaging (fMRI) data,…

Cortical connective field estimates from resting state fMRI activity.

PubMed

Gravel, Nicolás; Harvey, Ben; Nordhjem, Barbara; Haak, Koen V; Dumoulin, Serge O; Renken, Remco; Curčić-Blake, Branislava; Cornelissen, Frans W

2014-01-01

One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective field (CF) modeling to estimate the spatial profile of functional connectivity in the early visual cortex during resting state functional magnetic resonance imaging (RS-fMRI). This model-based analysis estimates the spatial integration between blood-oxygen level dependent (BOLD) signals in distinct cortical visual field maps using fMRI. Just as population receptive field (pRF) mapping predicts the collective neural activity in a voxel as a function of response selectivity to stimulus position in visual space, CF modeling predicts the activity of voxels in one visual area as a function of the aggregate activity in voxels in another visual area. In combination with pRF mapping, CF locations on the cortical surface can be interpreted in visual space, thus enabling reconstruction of visuotopic maps from resting state data. We demonstrate that V1 ➤ V2 and V1 ➤ V3 CF maps estimated from resting state fMRI data show visuotopic organization. Therefore, we conclude that-despite some variability in CF estimates between RS scans-neural properties such as CF maps and CF size can be derived from resting state data.

Changes in Resting State Effective Connectivity in the Motor Network Following Rehabilitation of Upper Extremity Poststroke Paresis

PubMed Central

James, G. Andrew; Lu, Zhong-Lin; VanMeter, John W.; Sathian, K.; Hu, Xiaoping P.; Butler, Andrew J.

2013-01-01

Background A promising paradigm in human neuroimaging is the study of slow (<0.1 Hz) spontaneous fluctuations in the hemodynamic response measured by functional magnetic resonance imaging (fMRI). Spontaneous activity (i.e., resting state) refers to activity that cannot be attributed to specific inputs or outputs, that is, activity intrinsically generated by the brain. Method This article presents pilot data examining neural connectivity in patients with poststroke hemiparesis before and after 3 weeks of upper extremity rehabilitation in the Accelerated Skill Acquisition Program (ASAP). Resting-state fMRI data acquired pre and post therapy were analyzed using an exploratory adaptation of structural equation modeling (SEM) to evaluate therapy-related changes in motor network effective connectivity. Results Each ASAP patient showed behavioral improvement. ASAP patients also showed increased influence of the affected hemisphere premotor cortex (a-PM) upon the unaffected hemisphere premotor cortex (u-PM) following therapy. The influence of a-PM on affected hemisphere primary motor cortex (a-M1) also increased with therapy for 3 of 5 patients, including those with greatest behavioral improvement. Conclusions Our findings suggest that network analyses of resting-state fMRI constitute promising tools for functional characterization of functional brain disorders, for intergroup comparisons, and potentially for assessing effective connectivity within single subjects; all of which have important implications for stroke rehabilitation. PMID:19740732

Working memory capacity and the functional connectome - insights from resting-state fMRI and voxelwise centrality mapping.

PubMed

Markett, Sebastian; Reuter, Martin; Heeren, Behrend; Lachmann, Bernd; Weber, Bernd; Montag, Christian

2018-02-01

The functional connectome represents a comprehensive network map of functional connectivity throughout the human brain. To date, the relationship between the organization of functional connectivity and cognitive performance measures is still poorly understood. In the present study we use resting-state functional magnetic resonance imaging (fMRI) data to explore the link between the functional connectome and working memory capacity in an individual differences design. Working memory capacity, which refers to the maximum amount of context information that an individual can retain in the absence of external stimulation, was assessed outside the MRI scanner and estimated based on behavioral data from a change detection task. Resting-state time series were analyzed by means of voxelwise degree and eigenvector centrality mapping, which are data-driven network analytic approaches for the characterization of functional connectivity. We found working memory capacity to be inversely correlated with both centrality in the right intraparietal sulcus. Exploratory analyses revealed that this relationship was putatively driven by an increase in negative connectivity strength of the structure. This resting-state connectivity finding fits previous task based activation studies that have shown that this area responds to manipulations of working memory load.

A study of structural and functional connectivity in early Alzheimer's disease using rest fMRI and diffusion tensor imaging.

PubMed

Balachandar, R; John, J P; Saini, J; Kumar, K J; Joshi, H; Sadanand, S; Aiyappan, S; Sivakumar, P T; Loganathan, S; Varghese, M; Bharath, S

2015-05-01

Alzheimer's disease (AD) is a progressive neurodegenerative condition where in early diagnosis and interventions are key policy priorities in dementia services and research. We studied the functional and structural connectivity in mild AD to determine the nature of connectivity changes that coexist with neurocognitive deficits in the early stages of AD. Fifteen mild AD subjects and 15 cognitively healthy controls (CHc) matched for age and gender, underwent detailed neurocognitive assessment and magnetic resonance imaging (MRI) of resting state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI). Rest fMRI was analyzed using dual regression approach and DTI by voxel wise statistics. Patients with mild AD had significantly lower functional connectivity (FC) within the default mode network and increased FC within the executive network. The mild AD group scored significantly lower in all domains of cognition compared with CHc. But fractional anisotropy did not significantly (p < 0.05) differ between the groups. Resting state functional connectivity alterations are noted during initial stages of cognitive decline in AD, even when there are no significant white matter microstructural changes. Copyright © 2014 John Wiley & Sons, Ltd.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models

PubMed Central

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

2013-01-01

The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258

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.

Changes in Thalamic Connectivity in the Early and Late Stages of Amnestic Mild Cognitive Impairment: A Resting-State Functional Magnetic Resonance Study from ADNI

PubMed Central

Cai, Suping; Huang, Liyu; Zou, Jia; Jing, Longlong; Zhai, Buzhong; Ji, Gongjun; von Deneen, Karen M.; Ren, Junchan; Ren, Aifeng

2015-01-01

We used resting-state functional magnetic resonance imaging (fMRI) to investigate changes in the thalamus functional connectivity in early and late stages of amnestic mild cognitive impairment. Data of 25 late stages of amnestic mild cognitive impairment (LMCI) patients, 30 early stages of amnestic mild cognitive impairment (EMCI) patients and 30 well-matched healthy controls (HC) were analyzed from the Alzheimer’s disease Neuroimaging Initiative (ADNI). We focused on the correlation between low frequency fMRI signal fluctuations in the thalamus and those in all other brain regions. Compared to healthy controls, we found functional connectivity between the left/right thalamus and a set of brain areas was decreased in LMCI and/or EMCI including right fusiform gyrus (FG), left and right superior temporal gyrus, left medial frontal gyrus extending into supplementary motor area, right insula, left middle temporal gyrus (MTG) extending into middle occipital gyrus (MOG). We also observed increased functional connectivity between the left/right thalamus and several regions in LMCI and/or EMCI including left FG, right MOG, left and right precuneus, right MTG and left inferior temporal gyrus. In the direct comparison between the LMCI and EMCI groups, we obtained several brain regions showed thalamus-seeded functional connectivity differences such as the precentral gyrus, hippocampus, FG and MTG. Briefly, these brain regions mentioned above were mainly located in the thalamo-related networks including thalamo-hippocampus, thalamo-temporal, thalamo-visual, and thalamo-default mode network. The decreased functional connectivity of the thalamus might suggest reduced functional integrity of thalamo-related networks and increased functional connectivity indicated that aMCI patients could use additional brain resources to compensate for the loss of cognitive function. Our study provided a new sight to understand the two important states of aMCI and revealed resting-state fMRI is an appropriate method for exploring pathophysiological changes in aMCI. PMID:25679386

Changes in thalamic connectivity in the early and late stages of amnestic mild cognitive impairment: a resting-state functional magnetic resonance study from ADNI.

PubMed

Cai, Suping; Huang, Liyu; Zou, Jia; Jing, Longlong; Zhai, Buzhong; Ji, Gongjun; von Deneen, Karen M; Ren, Junchan; Ren, Aifeng

2015-01-01

We used resting-state functional magnetic resonance imaging (fMRI) to investigate changes in the thalamus functional connectivity in early and late stages of amnestic mild cognitive impairment. Data of 25 late stages of amnestic mild cognitive impairment (LMCI) patients, 30 early stages of amnestic mild cognitive impairment (EMCI) patients and 30 well-matched healthy controls (HC) were analyzed from the Alzheimer's disease Neuroimaging Initiative (ADNI). We focused on the correlation between low frequency fMRI signal fluctuations in the thalamus and those in all other brain regions. Compared to healthy controls, we found functional connectivity between the left/right thalamus and a set of brain areas was decreased in LMCI and/or EMCI including right fusiform gyrus (FG), left and right superior temporal gyrus, left medial frontal gyrus extending into supplementary motor area, right insula, left middle temporal gyrus (MTG) extending into middle occipital gyrus (MOG). We also observed increased functional connectivity between the left/right thalamus and several regions in LMCI and/or EMCI including left FG, right MOG, left and right precuneus, right MTG and left inferior temporal gyrus. In the direct comparison between the LMCI and EMCI groups, we obtained several brain regions showed thalamus-seeded functional connectivity differences such as the precentral gyrus, hippocampus, FG and MTG. Briefly, these brain regions mentioned above were mainly located in the thalamo-related networks including thalamo-hippocampus, thalamo-temporal, thalamo-visual, and thalamo-default mode network. The decreased functional connectivity of the thalamus might suggest reduced functional integrity of thalamo-related networks and increased functional connectivity indicated that aMCI patients could use additional brain resources to compensate for the loss of cognitive function. Our study provided a new sight to understand the two important states of aMCI and revealed resting-state fMRI is an appropriate method for exploring pathophysiological changes in aMCI.

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

PubMed Central

Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works. PMID:26977400

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

PubMed

Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

Statistical testing and power analysis for brain-wide association study.

PubMed

Gong, Weikang; Wan, Lin; Lu, Wenlian; Ma, Liang; Cheng, Fan; Cheng, Wei; Grünewald, Stefan; Feng, Jianfeng

2018-04-05

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method bypasses the need of non-parametric permutation to correct for multiple comparison, thus, it can efficiently tackle large datasets with high resolution fMRI images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods fail. A software package is available at https://github.com/weikanggong/BWAS. Copyright © 2018 Elsevier B.V. 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.

Semi-supervised clustering for parcellating brain regions based on resting state fMRI data

NASA Astrophysics Data System (ADS)

Cheng, Hewei; Fan, Yong

2014-03-01

Many unsupervised clustering techniques have been adopted for parcellating brain regions of interest into functionally homogeneous subregions based on resting state fMRI data. However, the unsupervised clustering techniques are not able to take advantage of exiting knowledge of the functional neuroanatomy readily available from studies of cytoarchitectonic parcellation or meta-analysis of the literature. In this study, we propose a semi-supervised clustering method for parcellating amygdala into functionally homogeneous subregions based on resting state fMRI data. Particularly, the semi-supervised clustering is implemented under the framework of graph partitioning, and adopts prior information and spatial consistent constraints to obtain a spatially contiguous parcellation result. The graph partitioning problem is solved using an efficient algorithm similar to the well-known weighted kernel k-means algorithm. Our method has been validated for parcellating amygdala into 3 subregions based on resting state fMRI data of 28 subjects. The experiment results have demonstrated that the proposed method is more robust than unsupervised clustering and able to parcellate amygdala into centromedial, laterobasal, and superficial parts with improved functionally homogeneity compared with the cytoarchitectonic parcellation result. The validity of the parcellation results is also supported by distinctive functional and structural connectivity patterns of the subregions and high consistency between coactivation patterns derived from a meta-analysis and functional connectivity patterns of corresponding subregions.

A method for functional network connectivity among spatially independent resting-state components in schizophrenia.

PubMed

Jafri, Madiha J; Pearlson, Godfrey D; Stevens, Michael; Calhoun, Vince D

2008-02-15

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.

A Method for Functional Network Connectivity Among Spatially Independent Resting-State Components in Schizophrenia

PubMed Central

Jafri, Madiha J; Pearlson, Godfrey D; Stevens, Michael; Calhoun, Vince D

2011-01-01

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in patients versus controls. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject’s ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients. PMID:18082428

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

PubMed Central

Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen

2014-01-01

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922

Altered resting-state functional connectivity in post-traumatic stress disorder: a perfusion MRI study

NASA Astrophysics Data System (ADS)

Li, Baojuan; Liu, Jian; Liu, Yang; Lu, Hong-Bing; Yin, Hong

2013-03-01

The majority of studies on posttraumatic stress disorder (PTSD) so far have focused on delineating patterns of activations during cognitive processes. Recently, more and more researches have started to investigate functional connectivity in PTSD subjects using BOLD-fMRI. Functional connectivity analysis has been demonstrated as a powerful approach to identify biomarkers of different brain diseases. This study aimed to detect resting-state functional connectivity abnormities in patients with PTSD using arterial spin labeling (ASL) fMRI. As a completely non-invasive technique, ASL allows quantitative estimates of cerebral blood flow (CBF). Compared with BOLD-fMRI, ASL fMRI has many advantages, including less low-frequency signal drifts, superior functional localization, etc. In the current study, ASL images were collected from 10 survivors in mining disaster with recent onset PTSD and 10 survivors without PTSD. Decreased regional CBF in the right middle temporal gyrus, lingual gyrus, and postcentral gyrus was detected in the PTSD patients. Seed-based resting-state functional connectivity analysis was performed using an area in the right middle temporal gyrus as region of interest. Compared with the non-PTSD group, the PTSD subjects demonstrated increased functional connectivity between the right middle temporal gyrus and the right superior temporal gyrus, the left middle temporal gyrus. Meanwhile, decreased functional connectivity between the right middle temporal gyrus and the right postcentral gyrus, the right superior parietal lobule was also found in the PTSD patients. This is the first study which investigated resting-state functional connectivity in PTSD using ASL images. The results may provide new insight into the neural substrates of PTSD.

Network discovery with DCM

PubMed Central

Friston, Karl J.; Li, Baojuan; Daunizeau, Jean; Stephan, Klaas E.

2011-01-01

This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series. The implicit adjacency matrix specifies the form of the network (e.g., cyclic or acyclic) and its graph-theoretical attributes (e.g., degree distribution). The scheme is illustrated using functional magnetic resonance imaging (fMRI) time series to discover functional brain networks. Crucially, it can be applied to experimentally evoked responses (activation studies) or endogenous activity in task-free (resting state) fMRI studies. Unlike conventional approaches to network discovery, DCM permits the analysis of directed and cyclic graphs. Furthermore, it eschews (implausible) Markovian assumptions about the serial independence of random fluctuations. The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks. The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions. We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies. PMID:21182971

Environmental factors linked to depression vulnerability are associated with altered cerebellar resting-state synchronization.

PubMed

Córdova-Palomera, Aldo; Tornador, Cristian; Falcón, Carles; Bargalló, Nuria; Brambilla, Paolo; Crespo-Facorro, Benedicto; Deco, Gustavo; Fañanás, Lourdes

2016-11-28

Hosting nearly eighty percent of all human neurons, the cerebellum is functionally connected to large regions of the brain. Accumulating data suggest that some cerebellar resting-state alterations may constitute a key candidate mechanism for depressive psychopathology. While there is some evidence linking cerebellar function and depression, two topics remain largely unexplored. First, the genetic or environmental roots of this putative association have not been elicited. Secondly, while different mathematical representations of resting-state fMRI patterns can embed diverse information of relevance for health and disease, many of them have not been studied in detail regarding the cerebellum and depression. Here, high-resolution fMRI scans were examined to estimate functional connectivity patterns across twenty-six cerebellar regions in a sample of 48 identical twins (24 pairs) informative for depression liability. A network-based statistic approach was employed to analyze cerebellar functional networks built using three methods: the conventional approach of filtered BOLD fMRI time-series, and two analytic components of this oscillatory activity (amplitude envelope and instantaneous phase). The findings indicate that some environmental factors may lead to depression vulnerability through alterations of the neural oscillatory activity of the cerebellum during resting-state. These effects may be observed particularly when exploring the amplitude envelope of fMRI oscillations.

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.

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.

The Effects of Pharmacological Opioid Blockade on Neural Measures of Drug Cue-Reactivity in Humans

PubMed Central

Courtney, Kelly E; Ghahremani, Dara G; Ray, Lara A

2016-01-01

Interactions between dopaminergic and opioidergic systems have been implicated in the reinforcing properties of drugs of abuse. The present study investigated the effects of opioid blockade, via naltrexone, on functional magnetic resonance imaging (fMRI) measures during methamphetamine cue-reactivity to elucidate the role of endogenous opioids in the neural systems underlying drug craving. To investigate this question, non-treatment seeking individuals with methamphetamine use disorder (N=23; 74% male, mean age=34.70 (SD=8.95)) were recruited for a randomized, placebo controlled, within-subject design and underwent a visual methamphetamine cue-reactivity task during two blood-oxygen-level dependent (BOLD) fMRI sessions following 3 days of naltrexone (50 mg) and matched time for placebo. fMRI analyses tested naltrexone-induced differences in BOLD activation and functional connectivity during cue processing. The results showed that naltrexone administration reduced cue-reactivity in sensorimotor regions and related to altered functional connectivity of dorsal striatum, ventral tegmental area, and precuneus with frontal, visual, sensory, and motor-related regions. Naltrexone also weakened the associations between subjective craving and precuneus functional connectivity with sensorimotor regions and strengthened the associations between subjective craving and dorsal striatum and precuneus connectivity with frontal regions. In conclusion, this study provides the first evidence that opioidergic blockade alters neural responses to drug cues in humans with methamphetamine addiction and suggests that naltrexone may be reducing drug cue salience by decreasing the involvement of sensorimotor regions and by engaging greater frontal regulation over salience attribution. PMID:27312405

A validation of dynamic causal modelling for 7T fMRI.

PubMed

Tak, S; Noh, J; Cheong, C; Zeidman, P; Razi, A; Penny, W D; Friston, K J

2018-07-15

There is growing interest in ultra-high field magnetic resonance imaging (MRI) in cognitive and clinical neuroscience studies. However, the benefits offered by higher field strength have not been evaluated in terms of effective connectivity and dynamic causal modelling (DCM). In this study, we address the validity of DCM for 7T functional MRI data at two levels. First, we evaluate the predictive validity of DCM estimates based upon 3T and 7T in terms of reproducibility. Second, we assess improvements in the efficiency of DCM estimates at 7T, in terms of the entropy of the posterior distribution over model parameters (i.e., information gain). Using empirical data recorded during fist-closing movements with 3T and 7T fMRI, we found a high reproducibility of average connectivity and condition-specific changes in connectivity - as quantified by the intra-class correlation coefficient (ICC = 0.862 and 0.936, respectively). Furthermore, we found that the posterior entropy of 7T parameter estimates was substantially less than that of 3T parameter estimates; suggesting the 7T data are more informative - and furnish more efficient estimates. In the framework of DCM, we treated field-dependent parameters for the BOLD signal model as free parameters, to accommodate fMRI data at 3T and 7T. In addition, we made the resting blood volume fraction a free parameter, because different brain regions can differ in their vascularization. In this paper, we showed DCM enables one to infer changes in effective connectivity from 7T data reliably and efficiently. Copyright © 2018 Elsevier B.V. All rights reserved.

Altered basal ganglia-cortical functional connections in frontal lobe epilepsy: A resting-state fMRI study.

PubMed

Dong, Li; Wang, Pu; Peng, Rui; Jiang, Sisi; Klugah-Brown, Benjamin; Luo, Cheng; Yao, Dezhong

2016-12-01

The purpose of this study was to investigate alterations of basal ganglia-cortical functional connections in patients with frontal lobe epilepsy (FLE). Resting-state functional magnetic resonance imaging (fMRI) data were gathered from 19 FLE patients and 19 age- and gender-matched healthy controls. Functional connectivity (FC) analysis was used to assess the functional connections between basal ganglia and cerebral cortex. Regions of interest, including the left/right caudate, putamen, pallidum and thalamus, were selected as the seeds. Two sample t-test was used to determine the difference between patients and controls, while controlling the age, gender and head motions. Compared with controls, FLE patients demonstrated increased FCs between basal ganglia and regions including the right fusiform gyrus, the bilateral cingulate gyrus, the precuneus and anterior cingulate gyrus. Reduced FCs were mainly located in a range of brain regions including the bilateral middle occipital gyrus, the ventral frontal lobe, the right putamen, the left fusiform gyrus and right rolandic operculum. In addition, the relationships between basal ganglia-cingulate connections and durations of epilepsy were also found. The alterations of functional integrity within the basal ganglia, as well as its connections to limbic and ventral frontal areas, indicate the important roles of the basal ganglia-cortical functional connections in FLE, and provide new insights in the pathophysiological mechanism of FLE. Copyright © 2016 Elsevier B.V. All rights reserved.

The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI.

PubMed

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

2017-06-15

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

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.

In vivo functional connectome of human brainstem nuclei of the ascending arousal, autonomic, and motor systems by high spatial resolution 7-Tesla fMRI.

PubMed

Bianciardi, Marta; Toschi, Nicola; Eichner, Cornelius; Polimeni, Jonathan R; Setsompop, Kawin; Brown, Emery N; Hämäläinen, Matti S; Rosen, Bruce R; Wald, Lawrence L

2016-06-01

Our aim was to map the in vivo human functional connectivity of several brainstem nuclei with the rest of the brain by using seed-based correlation of ultra-high magnetic field functional magnetic resonance imaging (fMRI) data. We used the recently developed template of 11 brainstem nuclei derived from multi-contrast structural MRI at 7 Tesla as seed regions to determine their connectivity to the rest of the brain. To achieve this, we used the increased contrast-to-noise ratio of 7-Tesla fMRI compared with 3 Tesla and time-efficient simultaneous multi-slice imaging to cover the brain with high spatial resolution (1.1-mm isotropic nominal resolution) while maintaining a short repetition time (2.5 s). The delineated Pearson's correlation-based functional connectivity diagrams (connectomes) of 11 brainstem nuclei of the ascending arousal, motor, and autonomic systems from 12 controls are presented and discussed in the context of existing histology and animal work. Considering that the investigated brainstem nuclei play a crucial role in several vital functions, the delineated preliminary connectomes might prove useful for future in vivo research and clinical studies of human brainstem function and pathology, including disorders of consciousness, sleep disorders, autonomic disorders, Parkinson's disease, and other motor disorders.

Hubs of Anticorrelation in High-Resolution Resting-State Functional Connectivity Network Architecture.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Cabanban, Romeo; Crosson, Bruce A

2015-06-01

A major focus of brain research recently has been to map the resting-state functional connectivity (rsFC) network architecture of the normal brain and pathology through functional magnetic resonance imaging. However, the phenomenon of anticorrelations in resting-state signals between different brain regions has not been adequately examined. The preponderance of studies on resting-state fMRI (rsFMRI) have either ignored anticorrelations in rsFC networks or adopted methods in data analysis, which have rendered anticorrelations in rsFC networks uninterpretable. The few studies that have examined anticorrelations in rsFC networks using conventional methods have found anticorrelations to be weak in strength and not very reproducible across subjects. Anticorrelations in rsFC network architecture could reflect mechanisms that subserve a number of important brain processes. In this preliminary study, we examined the properties of anticorrelated rsFC networks by systematically focusing on negative cross-correlation coefficients (CCs) among rsFMRI voxel time series across the brain with graph theory-based network analysis. A number of methods were implemented to enhance the neuronal specificity of resting-state functional connections that yield negative CCs, although at the cost of decreased sensitivity. Hubs of anticorrelation were seen in a number of cortical and subcortical brain regions. Examination of the anticorrelation maps of these hubs indicated that negative CCs in rsFC network architecture highlight a number of regulatory interactions between brain networks and regions, including reciprocal modulations, suppression, inhibition, and neurofeedback.

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.

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

PubMed

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

2017-11-01

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

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

PubMed Central

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

2017-01-01

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

Hyperconnectivity is a fundamental response to neurological disruption.

PubMed

Hillary, Frank G; Roman, Cristina A; Venkatesan, Umesh; Rajtmajer, Sarah M; Bajo, Ricardo; Castellanos, Nazareth D

2015-01-01

In the cognitive and clinical neurosciences, the past decade has been marked by dramatic growth in a literature examining brain "connectivity" using noninvasive methods. We offer a critical review of the blood oxygen level dependent functional MRI (BOLD fMRI) literature examining neural connectivity changes in neurological disorders with focus on brain injury and dementia. The goal is to demonstrate that there are identifiable shifts in local and large-scale network connectivity that can be predicted by the degree of pathology. We anticipate that the most common network response to neurological insult is hyperconnectivity but that this response depends upon demand and resource availability. To examine this hypothesis, we initially reviewed the results from 1,426 studies examining functional brain connectivity in individuals diagnosed with multiple sclerosis, traumatic brain injury, mild cognitive impairment, and Alzheimer's disease. Based upon inclusionary criteria, 126 studies were included for detailed analysis. RESULTS from 126 studies examining local and whole brain connectivity demonstrated increased connectivity in traumatic brain injury and multiple sclerosis. This finding is juxtaposed with findings in mild cognitive impairment and Alzheimer's disease where there is a shift to diminished connectivity as degeneration progresses. This summary of the functional imaging literature using fMRI methods reveals that hyperconnectivity is a common response to neurological disruption and that it may be differentially observable across brain regions. We discuss the factors contributing to both hyper- and hypoconnectivity results after neurological disruption and the implications these findings have for network plasticity. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Altered default network resting-state functional connectivity in adolescents with Internet gaming addiction.

PubMed

Ding, Wei-na; Sun, Jin-hua; Sun, Ya-wen; Zhou, Yan; Li, Lei; Xu, Jian-rong; Du, Ya-song

2013-01-01

Excessive use of the Internet has been linked to a variety of negative psychosocial consequences. This study used resting-state functional magnetic resonance imaging (fMRI) to investigate whether functional connectivity is altered in adolescents with Internet gaming addiction (IGA). Seventeen adolescents with IGA and 24 normal control adolescents underwent a 7.3 minute resting-state fMRI scan. Posterior cingulate cortex (PCC) connectivity was determined in all subjects by investigating synchronized low-frequency fMRI signal fluctuations using a temporal correlation method. To assess the relationship between IGA symptom severity and PCC connectivity, contrast images representing areas correlated with PCC connectivity were correlated with the scores of the 17 subjects with IGA on the Chen Internet Addiction Scale (CIAS) and Barratt Impulsiveness Scale-11 (BIS-11) and their hours of Internet use per week. There were no significant differences in the distributions of the age, gender, and years of education between the two groups. The subjects with IGA showed longer Internet use per week (hours) (p<0.0001) and higher CIAS (p<0.0001) and BIS-11 (p = 0.01) scores than the controls. Compared with the control group, subjects with IGA exhibited increased functional connectivity in the bilateral cerebellum posterior lobe and middle temporal gyrus. The bilateral inferior parietal lobule and right inferior temporal gyrus exhibited decreased connectivity. Connectivity with the PCC was positively correlated with CIAS scores in the right precuneus, posterior cingulate gyrus, thalamus, caudate, nucleus accumbens, supplementary motor area, and lingual gyrus. It was negatively correlated with the right cerebellum anterior lobe and left superior parietal lobule. Our results suggest that adolescents with IGA exhibit different resting-state patterns of brain activity. As these alterations are partially consistent with those in patients with substance addiction, they support the hypothesis that IGA as a behavioral addiction that may share similar neurobiological abnormalities with other addictive disorders.

Altered Default Network Resting-State Functional Connectivity in Adolescents with Internet Gaming Addiction

PubMed Central

Li, Lei; Xu, Jian-rong

2013-01-01

Purpose Excessive use of the Internet has been linked to a variety of negative psychosocial consequences. This study used resting-state functional magnetic resonance imaging (fMRI) to investigate whether functional connectivity is altered in adolescents with Internet gaming addiction (IGA). Methods Seventeen adolescents with IGA and 24 normal control adolescents underwent a 7.3 minute resting-state fMRI scan. Posterior cingulate cortex (PCC) connectivity was determined in all subjects by investigating synchronized low-frequency fMRI signal fluctuations using a temporal correlation method. To assess the relationship between IGA symptom severity and PCC connectivity, contrast images representing areas correlated with PCC connectivity were correlated with the scores of the 17 subjects with IGA on the Chen Internet Addiction Scale (CIAS) and Barratt Impulsiveness Scale-11 (BIS-11) and their hours of Internet use per week. Results There were no significant differences in the distributions of the age, gender, and years of education between the two groups. The subjects with IGA showed longer Internet use per week (hours) (p<0.0001) and higher CIAS (p<0.0001) and BIS-11 (p = 0.01) scores than the controls. Compared with the control group, subjects with IGA exhibited increased functional connectivity in the bilateral cerebellum posterior lobe and middle temporal gyrus. The bilateral inferior parietal lobule and right inferior temporal gyrus exhibited decreased connectivity. Connectivity with the PCC was positively correlated with CIAS scores in the right precuneus, posterior cingulate gyrus, thalamus, caudate, nucleus accumbens, supplementary motor area, and lingual gyrus. It was negatively correlated with the right cerebellum anterior lobe and left superior parietal lobule. Conclusion Our results suggest that adolescents with IGA exhibit different resting-state patterns of brain activity. As these alterations are partially consistent with those in patients with substance addiction, they support the hypothesis that IGA as a behavioral addiction that may share similar neurobiological abnormalities with other addictive disorders. PMID:23555827

Utility of functional MRI in pediatric neurology.

PubMed

Freilich, Emily R; Gaillard, William D

2010-01-01

Functional MRI (fMRI), a tool increasingly used to study cognitive function, is also an important tool for understanding not only normal development in healthy children, but also abnormal development, as seen in children with epilepsy, attention-deficit/hyperactivity disorder, and autism. Since its inception almost 15 years ago, fMRI has seen an explosion in its use and applications in the adult literature. However, only recently has it found a home in pediatric neurology. New adaptations in study design and technologic advances, especially the study of resting state functional connectivity as well as the use of passive task design in sedated children, have increased the utility of functional imaging in pediatrics to help us gain understanding into the developing brain at work. This article reviews the background of fMRI in pediatrics and highlights the most recent literature and clinical applications.

Variability in Cumulative Habitual Sleep Duration Predicts Waking Functional Connectivity.

PubMed

Khalsa, Sakh; Mayhew, Stephen D; Przezdzik, Izabela; Wilson, Rebecca; Hale, Joanne; Goldstone, Aimee; Bagary, Manny; Bagshaw, Andrew P

2016-01-01

We examined whether interindividual differences in habitual sleep patterns, quantified as the cumulative habitual total sleep time (cTST) over a 2-w period, were reflected in waking measurements of intranetwork and internetwork functional connectivity (FC) between major nodes of three intrinsically connected networks (ICNs): default mode network (DMN), salience network (SN), and central executive network (CEN). Resting state functional magnetic resonance imaging (fMRI) study using seed-based FC analysis combined with 14-d wrist actigraphy, sleep diaries, and subjective questionnaires (N = 33 healthy adults, mean age 34.3, standard deviation ± 11.6 y). Data were statistically analyzed using multiple linear regression. Fourteen consecutive days of wrist actigraphy in participant's home environment and fMRI scanning on day 14 at the Birmingham University Imaging Centre. Seed-based FC analysis on ICNs from resting-state fMRI data and multiple linear regression analysis performed for each ICN seed and target. cTST was used to predict FC (controlling for age). cTST was specific predictor of intranetwork FC when the mesial prefrontal cortex (MPFC) region of the DMN was used as a seed for FC, with a positive correlation between FC and cTST observed. No significant relationship between FC and cTST was seen for any pair of nodes not including the MPFC. Internetwork FC between the DMN (MPFC) and SN (right anterior insula) was also predicted by cTST, with a negative correlation observed between FC and cTST. This study improves understanding of the relationship between intranetwork and internetwork functional connectivity of intrinsically connected networks (ICNs) in relation to habitual sleep quality and duration. The cumulative amount of sleep that participants achieved over a 14-d period was significantly predictive of intranetwork and inter-network functional connectivity of ICNs, an observation that may underlie the link between sleep status and cognitive performance. © 2016 Associated Professional Sleep Societies, LLC.

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.

Effects of propofol anesthesia on the processing of noxious stimuli in the spinal cord and the brain.

PubMed

Lichtner, Gregor; Auksztulewicz, Ryszard; Kirilina, Evgeniya; Velten, Helena; Mavrodis, Dionysios; Scheel, Michael; Blankenburg, Felix; von Dincklage, Falk

2018-05-15

Drug-induced unconsciousness is an essential component of general anesthesia, commonly attributed to attenuation of higher-order processing of external stimuli and a resulting loss of information integration capabilities of the brain. In this study, we investigated how the hypnotic drug propofol at doses comparable to those in clinical practice influences the processing of somatosensory stimuli in the spinal cord and in primary and higher-order cortices. Using nociceptive reflexes, somatosensory evoked potentials and functional magnet resonance imaging (fMRI), we found that propofol abolishes the processing of innocuous and moderate noxious stimuli at low to medium concentration levels, but that intense noxious stimuli evoked spinal and cerebral responses even during deep propofol anesthesia that caused profound electroencephalogram (EEG) burst suppression. While nociceptive reflexes and somatosensory potentials were affected only in a minor way by further increasing doses of propofol after the loss of consciousness, fMRI showed that increasing propofol concentration abolished processing of intense noxious stimuli in the insula and secondary somatosensory cortex and vastly increased processing in the frontal cortex. As the fMRI functional connectivity showed congruent changes with increasing doses of propofol - namely the temporal brain areas decreasing their connectivity with the bilateral pre-/postcentral gyri and the supplementary motor area, while connectivity of the latter with frontal areas is increased - we conclude that the changes in processing of noxious stimuli during propofol anesthesia might be related to changes in functional connectivity. Copyright © 2018 Elsevier Inc. All rights reserved.

Abnormalities of Intrinsic Functional Connectivity in Autism Spectrum Disorders

PubMed Central

Monk, Christopher S.; Peltier, Scott J.; Wiggins, Jillian Lee; Weng, Shih-Jen; Carrasco, Melisa; Risi, Susan; Lord, Catherine

2009-01-01

Autism spectrum disorders (ASD) impact social functioning and communication, and individuals with these disorders often have restrictive and repetitive behaviors. Accumulating data indicate that ASD is associated with alterations of neural circuitry. Functional MRI (FMRI) studies have focused on connectivity in the context of psychological tasks. However, even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic or resting connectivity. Notably, the default network, which includes the posterior cingulate cortex, retro-splenial, lateral parietal cortex/angular gyrus, medial prefrontal cortex, superior frontal gyrus, temporal lobe, and parahippocampal gyrus, is strongly active when there is no task. Altered intrinsic connectivity within the default network may underlie offline processing that may actuate ASD impairments. Using FMRI, we sought to evaluate intrinsic connectivity within the default network in ASD. Relative to controls, the ASD group showed weaker connectivity between the posterior cingulate cortex and superior frontal gyrus and stronger connectivity between the posterior cingulate cortex and both the right temporal lobe and right parahippocampal gyrus. Moreover, poorer social functioning in the ASD group was correlated with weaker connectivity between the posterior cingulate cortex and the superior frontal gyrus. In addition, more severe restricted and repetitive behaviors in ASD were correlated with stronger connectivity between the posterior cingulate cortex and right parahippocampal gyrus. These findings indicate that ASD subjects show altered intrinsic connectivity within the default network, and connectivity between these structures is associated with specific ASD symptoms. PMID:19409498

Disruptions in Functional Network Connectivity during Alcohol Intoxicated Driving

PubMed Central

Rzepecki-Smith, Catherine I.; Meda, Shashwath A.; Calhoun, Vince D.; Stevens, Michael C.; Jafri, Madiha J.; Astur, Robert S.; Pearlson, Godfrey D.

2009-01-01

Background: Driving while under the influence of alcohol is a major public health problem whose neural basis is not well understood. In a recently published fMRI study (Meda et al, 2009), our group identified five, independent critical driving-associated brain circuits whose inter-regional connectivity was disrupted by alcohol intoxication. However, the functional connectivity between these circuits has not yet been explored in order to determine how these networks communicate with each other during sober and alcohol-intoxicated states. Methods: In the current study, we explored such differences in connections between the above brain circuits and driving behavior, under the influence of alcohol versus placebo. Forty social drinkers who drove regularly underwent fMRI scans during virtual reality driving simulations following two alcohol doses, placebo and an individualized dose producing blood alcohol concentrations (BACs) of 0.10%. Results: At the active dose, we found specific disruptions of functional network connectivity between the frontal-temporal-basal ganglia and the cerebellar circuits. The temporal connectivity between these two circuits was found to be less correlated (p <0.05) when driving under the influence of alcohol. This disconnection was also associated with an abnormal driving behavior (unstable motor vehicle steering). Conclusions: Connections between frontal-temporal-basal ganglia and cerebellum have recently been explored; these may be responsible in part for maintaining normal motor behavior by integrating their overlapping motor control functions. These connections appear to be disrupted by alcohol intoxication, in turn associated with an explicit type of impaired driving behavior. PMID:20028354

Differences on Brain Connectivity in Adulthood Are Present in Subjects with Iron Deficiency Anemia in Infancy

PubMed Central

Algarin, Cecilia; Karunakaran, Keerthana Deepti; Reyes, Sussanne; Morales, Cristian; Lozoff, Betsy; Peirano, Patricio; Biswal, Bharat

2017-01-01

Iron deficiency continues to be the most prevalent micronutrient deficit worldwide. Since iron is involved in several processes including myelination, dopamine neurotransmission and neuronal metabolism, the presence of iron deficiency anemia (IDA) in infancy relates to long-lasting neurofunctional effects. There is scarce data regarding whether these effects would extend to former iron deficient anemic human adults. Resting state functional magnetic resonance imaging (fMRI) is a novel technique to explore patterns of functional connectivity. Default Mode Network (DMN), one of the resting state networks, is deeply involved in memory, social cognition and self-referential processes. The four core regions consistently identified in the DMN are the medial prefrontal cortex, posterior cingulate/retrosplenial cortex and left and right inferior parietal cortex. Therefore to investigate the DMN in former iron deficient anemic adults is a particularly useful approach to elucidate de long term effects on functional brain. We conducted this research to explore the connection between IDA in infancy and altered patterns of resting state brain functional networks in young adults. Resting-state fMRI studies were performed to 31 participants that belong to a follow-up study since infancy. Of them, 14 participants were former iron deficient anemic in infancy and 17 were controls, with mean age of 21.5 years (±1.5) and 54.8% were males. Resting-state fMRI protocol was used and the data was analyzed using the seed based connectivity statistical analysis to assess the DMN. We found that compared to controls, former iron deficient anemic subjects showed posterior DMN decreased connectivity to the left posterior cingulate cortex (PCC), whereas they exhibited increased anterior DMN connectivity to the right PCC. Differences between groups were also apparent in the left medial frontal gyrus, with former iron deficient anemic participants having increased connectivity with areas included in DMN and dorsal attention networks. These preliminary results suggest different patterns of functional connectivity between former iron deficient anemic and control young adults. Indeed, IDA in infancy, a common nutritional problem among human infants, may turn out to be important for understanding the mechanisms of cognitive alterations, common in adulthood. PMID:28326037

On consciousness, resting state fMRI, and neurodynamics

PubMed Central

2010-01-01

Background During the last years, functional magnetic resonance imaging (fMRI) of the brain has been introduced as a new tool to measure consciousness, both in a clinical setting and in a basic neurocognitive research. Moreover, advanced mathematical methods and theories have arrived the field of fMRI (e.g. computational neuroimaging), and functional and structural brain connectivity can now be assessed non-invasively. Results The present work deals with a pluralistic approach to "consciousness'', where we connect theory and tools from three quite different disciplines: (1) philosophy of mind (emergentism and global workspace theory), (2) functional neuroimaging acquisitions, and (3) theory of deterministic and statistical neurodynamics – in particular the Wilson-Cowan model and stochastic resonance. Conclusions Based on recent experimental and theoretical work, we believe that the study of large-scale neuronal processes (activity fluctuations, state transitions) that goes on in the living human brain while examined with functional MRI during "resting state", can deepen our understanding of graded consciousness in a clinical setting, and clarify the concept of "consiousness" in neurocognitive and neurophilosophy research. PMID:20522270

Radiation-induced functional connectivity alterations in nasopharyngeal carcinoma patients with radiotherapy.

PubMed

Ma, Qiongmin; Wu, Donglin; Zeng, Ling-Li; Shen, Hui; Hu, Dewen; Qiu, Shijun

2016-07-01

The study aims to investigate the radiation-induced brain functional alterations in nasopharyngeal carcinoma (NPC) patients who received radiotherapy (RT) using functional magnetic resonance imaging (fMRI) and statistic scale.The fMRI data of 35 NPC patients with RT and 24 demographically matched untreated NPC patients were acquired. Montreal Cognitive Assessment (MoCA) was also measured to evaluate their global cognition performance. Multivariate pattern analysis was performed to find the significantly altered functional connections between these 2 groups, while the linear correlation level was detected between the altered functional connections and the MoCA scores.Forty-five notably altered functional connections were found, which were mainly located between 3 brain networks, the cerebellum, sensorimotor, and cingulo-opercular. With strictly false discovery rate correction, 5 altered functional connections were shown to have significant linear correlations with the MoCA scores, that is, the connections between the vermis and hippocampus, cerebellum lobule VI and dorsolateral prefrontal cortex, precuneus and dorsal frontal cortex, cuneus and middle occipital lobe, and insula and cuneus. Besides, the connectivity between the vermis and hippocampus was also significantly correlated with the attention score, 1 of the 7 subscores of the MoCA.The present study provides new insights into the radiation-induced functional connectivity impairments in NPC patients. The results showed that the RT may induce the cognitive impairments, especially the attention alterations. The 45 altered functional connections, especially the 5 altered functional connections that were significantly correlated to the MoCA scores, may serve as the potential biomarkers of the RT-induced brain functional impairments and provide valuable targets for further functional recovery treatment.

Reduced Hippocampal Functional Connectivity During Episodic Memory Retrieval in Autism

PubMed Central

Cooper, Rose A.; Richter, Franziska R.; Bays, Paul M.; Plaisted-Grant, Kate C.; Baron-Cohen, Simon

2017-01-01

Abstract Increasing recent research has sought to understand the recollection impairments experienced by individuals with autism spectrum disorder (ASD). Here, we tested whether these memory deficits reflect a reduction in the probability of retrieval success or in the precision of memory representations. We also used functional magnetic resonance imaging (fMRI) to study the neural mechanisms underlying memory encoding and retrieval in ASD, focusing particularly on the functional connectivity of core episodic memory networks. Adults with ASD and typical control participants completed a memory task that involved studying visual displays and subsequently using a continuous dial to recreate their appearance. The ASD group exhibited reduced retrieval success, but there was no evidence of a difference in retrieval precision. fMRI data revealed similar patterns of brain activity and functional connectivity during memory encoding in the 2 groups, though encoding-related lateral frontal activity predicted subsequent retrieval success only in the control group. During memory retrieval, the ASD group exhibited attenuated lateral frontal activity and substantially reduced hippocampal connectivity, particularly between hippocampus and regions of the fronto-parietal control network. These findings demonstrate notable differences in brain function during episodic memory retrieval in ASD and highlight the importance of functional connectivity to understanding recollection-related retrieval deficits in this population. PMID:28057726

The effects of alcohol on the nonhuman primate brain: a network science approach to neuroimaging.

PubMed

Telesford, Qawi K; Laurienti, Paul J; Friedman, David P; Kraft, Robert A; Daunais, James B

2013-11-01

Animal studies have long been an important tool for basic research as they offer a degree of control often lacking in clinical studies. Of particular value is the use of nonhuman primates (NHPs) for neuroimaging studies. Currently, studies have been published using functional magnetic resonance imaging (fMRI) to understand the default-mode network in the NHP brain. Network science provides an alternative approach to neuroimaging allowing for evaluation of whole-brain connectivity. In this study, we used network science to build NHP brain networks from fMRI data to understand the basic functional organization of the NHP brain. We also explored how the brain network is affected following an acute ethanol (EtOH) pharmacological challenge. Baseline resting-state fMRI was acquired in an adult male rhesus macaque (n = 1) and a cohort of vervet monkeys (n = 10). A follow-up scan was conducted in the rhesus macaque to assess network variability and to assess the effects of an acute EtOH challenge on the brain network. The most connected regions in the resting-state networks were similar across species and matched regions identified as the default-mode network in previous NHP fMRI studies. Under an acute EtOH challenge, the functional organization of the brain was significantly impacted. Network science offers a great opportunity to understand the brain as a complex system and how pharmacological conditions can affect the system globally. These models are sensitive to changes in the brain and may prove to be a valuable tool in long-term studies on alcohol exposure. Copyright © 2013 by the Research Society on Alcoholism.

The effect of motivation on working memory: an fMRI and SEM study.

PubMed

Szatkowska, Iwona; Bogorodzki, Piotr; Wolak, Tomasz; Marchewka, Artur; Szeszkowski, Wojciech

2008-09-01

This study investigated the effective connectivity between prefrontal regions of human brain supporting motivational influence on working memory. Functional magnetic resonance imaging (fMRI) and structural equation modeling (SEM) were used to examine the interaction between the lateral orbitofrontal (OFC), medial OFC, and dorsolateral prefrontal (DLPFC) regions in the left and right hemisphere during performance of the verbal 2-back working memory task under two reinforcement conditions. The "low-motivation" condition was not associated with monetary reinforcement, while the "high-motivation" condition involved the probability of winning a certain amount of money. In the "low-motivation" condition, the OFC regions in both hemispheres positively influenced the left DLPFC activity. In the "high-motivation" condition, the connectivity in the network including the right OFC regions and left DLPFC changed from positive to negative, whereas the positive connectivity in the network composed of the left OFC and left DLPFC became slightly enhanced compared with the "low-motivation" condition. However, only the connection between the right lateral OFC and left DLPFC showed a significant condition-dependent change in the strength of influence conveyed through the pathway. This change appears to be the functional correlate of motivational influence on verbal working memory.

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

PubMed Central

Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J.

2016-01-01

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology. PMID:26827811

Functional Imaging and Migraine: New Connections?

PubMed Central

Schwedt, Todd J.; Chong, Catherine D.

2015-01-01

Purpose of Review Over the last several years, a growing number of brain functional imaging studies have provided insights into mechanisms underlying migraine. This manuscript reviews the recent migraine functional neuroimaging literature and provides recommendations for future studies that will help fill knowledge gaps. Recent Findings Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies have identified brain regions that might be responsible for mediating the onset of a migraine attack and those associated with migraine symptoms. Enhanced activation of brain regions that facilitate processing of sensory stimuli suggests a mechanism by which migraineurs are hypersensitive to visual, olfactory, and cutaneous stimuli. Resting state functional connectivity MRI studies have identified numerous brain regions and functional networks with atypical functional connectivity in migraineurs, suggesting that migraine is associated with aberrant brain functional organization. Summary fMRI and PET studies that have identified brain regions and brain networks that are atypical in migraine have helped to describe the neurofunctional basis for migraine symptoms. Future studies should compare functional imaging findings in migraine to other headache and pain disorders and should explore the utility of functional imaging data as biomarkers for diagnostic and treatment purposes. PMID:25887764

Resting-state functional connectivity and motor imagery brain activation

PubMed Central

Saiote, Catarina; Tacchino, Andrea; Brichetto, Giampaolo; Roccatagliata, Luca; Bommarito, Giulia; Cordano, Christian; Battaglia, Mario; Mancardi, Giovanni Luigi; Inglese, Matilde

2016-01-01

Motor imagery (MI) relies on the mental simulation of an action without any overt motor execution (ME), and can facilitate motor learning and enhance the effect of rehabilitation in patients with neurological conditions. While functional magnetic resonance imaging (fMRI) during MI and ME reveals shared cortical representations, the role and functional relevance of the resting-state functional connectivity (RSFC) of brain regions involved in MI is yet unknown. Here, we performed resting-state fMRI followed by fMRI during ME and MI with the dominant hand. We used a behavioral chronometry test to measure ME and MI movement duration and compute an index of performance (IP). Then, we analyzed the voxel-matched correlation between the individual MI parameter estimates and seed-based RSFC maps in the MI network to measure the correspondence between RSFC and MI fMRI activation. We found that inter-individual differences in intrinsic connectivity in the MI network predicted several clusters of activation. Taken together, present findings provide first evidence that RSFC within the MI network is predictive of the activation of MI brain regions, including those associated with behavioral performance, thus suggesting a role for RSFC in obtaining a deeper understanding of neural substrates of MI and of MI ability. PMID:27273577

Encoding the local connectivity patterns of fMRI for cognitive task and state classification.

PubMed

Onal Ertugrul, Itir; Ozay, Mete; Yarman Vural, Fatos T

2018-06-15

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

Network analysis of EEG related functional MRI changes due to medication withdrawal in focal epilepsy

PubMed Central

Hermans, Kees; Ossenblok, Pauly; van Houdt, Petra; Geerts, Liesbeth; Verdaasdonk, Rudolf; Boon, Paul; Colon, Albert; de Munck, Jan C.

2015-01-01

Anti-epileptic drugs (AEDs) have a global effect on the neurophysiology of the brain which is most likely reflected in functional brain activity recorded with EEG and fMRI. These effects may cause substantial inter-subject variability in studies where EEG correlated functional MRI (EEG–fMRI) is used to determine the epileptogenic zone in patients who are candidate for epilepsy surgery. In the present study the effects on resting state fMRI are quantified in conditions with AED administration and after withdrawal of AEDs. EEG–fMRI data were obtained from 10 patients in the condition that the patient was on the steady-state maintenance doses of AEDs as prescribed (condition A) and after withdrawal of AEDs (condition B), at the end of a clinically standard pre-surgical long term video-EEG monitoring session. Resting state networks (RSN) were extracted from fMRI. The epileptic component (ICE) was identified by selecting the RSN component with the largest overlap with the EEG–fMRI correlation pattern. Changes in RSN functional connectivity between conditions A and B were quantified. EEG–fMRI correlation analysis was successful in 30% and 100% of the cases in conditions A and B, respectively. Spatial patterns of ICEs are comparable in conditions A and B, except for one patient for whom it was not possible to identify the ICE in condition A. However, the resting state functional connectivity is significantly increased in the condition after withdrawal of AEDs (condition B), which makes resting state fMRI potentially a new tool to study AED effects. The difference in sensitivity of EEG–fMRI in conditions A and B, which is not related to the number of epileptic EEG events occurring during scanning, could be related to the increased functional connectivity in condition B. PMID:26137444

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

Altered interhemispheric functional connectivity in patients with anisometropic and strabismic amblyopia: a resting-state fMRI study.

PubMed

Liang, Minglong; Xie, Bing; Yang, Hong; Yin, Xuntao; Wang, Hao; Yu, Longhua; He, Sheng; Wang, Jian

2017-05-01

Altered brain functional connectivity has been reported in patients with amblyopia by recent neuroimaging studies. However, relatively little is known about the alterations in interhemispheric functional connectivity in amblyopia. The present study aimed to investigate the functional connectivity patterns between homotopic regions across hemispheres in patients with anisometropic and strabismic amblyopia under resting state. Nineteen monocular anisometropic amblyopia (AA), 18 strabismic amblyopia (SA), and 20 normal-sight controls (NC) were enrolled in this study. After a comprehensive ophthalmologic examination, resting-state fMRI scanning was performed in all participants. The pattern of the interhemispheric functional connectivity was measured with the voxel-mirrored homotopic connectivity (VMHC) approach. VMHC values differences within and between three groups were compared, and correlations between VMHC values and each the clinical variable were also analyzed. Altered VMHC was observed in AA and SA patients in lingual gyrus and fusiform gyrus compared with NC subjects. The altered VMHC of lingual gyrus showed a pattern of AA > SA > NC, while the altered VMHC of fusiform gyrus showed a pattern of AA > NC > SA. Moreover, the VMHC values of lingual gyrus were positively correlated with the stereoacuity both in AA and SA patients, and the VMHC values of fusiform gyrus were positively correlated with the amount of anisometropia just in AA patients. These findings suggest that interhemispheric functional coordination between several homotopic visual-related brain regions is impaired both in AA and SA patients under resting state and revealed the similarities and differences in interhemispheric functional connectivity between the anisometropic and strabismic amblyopia.

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.

Association Between Brain Activation and Functional Connectivity.

PubMed

Tomasi, Dardo; Volkow, Nora D

2018-04-13

The origin of the "resting-state" brain activity recorded with functional magnetic resonance imaging (fMRI) is still uncertain. Here we provide evidence for the neurovascular origins of the amplitude of the low-frequency fluctuations (ALFF) and the local functional connectivity density (lFCD) by comparing them with task-induced blood-oxygen level dependent (BOLD) responses, which are considered a proxy for neuronal activation. Using fMRI data for 2 different tasks (Relational and Social) collected by the Human Connectome Project in 426 healthy adults, we show that ALFF and lFCD have linear associations with the BOLD response. This association was significantly attenuated by a novel task signal regression (TSR) procedure, indicating that task performance enhances lFCD and ALFF in activated regions. We also show that lFCD predicts BOLD activation patterns, as was recently shown for other functional connectivity metrics, which corroborates that resting functional connectivity architecture impacts brain activation responses. Thus, our findings indicate a common source for BOLD responses, ALFF and lFCD, which is consistent with the neurovascular origin of local hemodynamic synchrony presumably reflecting coordinated fluctuations in neuronal activity. This study also supports the development of task-evoked functional connectivity density mapping.

Causal mapping of emotion networks in the human brain: Framework and initial findings.

PubMed

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

2017-11-13

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

Direction of Amygdala-Neocortex Interaction During Dynamic Facial Expression Processing.

PubMed

Sato, Wataru; Kochiyama, Takanori; Uono, Shota; Yoshikawa, Sakiko; Toichi, Motomi

2017-03-01

Dynamic facial expressions of emotion strongly elicit multifaceted emotional, perceptual, cognitive, and motor responses. Neuroimaging studies revealed that some subcortical (e.g., amygdala) and neocortical (e.g., superior temporal sulcus and inferior frontal gyrus) brain regions and their functional interaction were involved in processing dynamic facial expressions. However, the direction of the functional interaction between the amygdala and the neocortex remains unknown. To investigate this issue, we re-analyzed functional magnetic resonance imaging (fMRI) data from 2 studies and magnetoencephalography (MEG) data from 1 study. First, a psychophysiological interaction analysis of the fMRI data confirmed the functional interaction between the amygdala and neocortical regions. Then, dynamic causal modeling analysis was used to compare models with forward, backward, or bidirectional effective connectivity between the amygdala and neocortical networks in the fMRI and MEG data. The results consistently supported the model of effective connectivity from the amygdala to the neocortex. Further increasing time-window analysis of the MEG demonstrated that this model was valid after 200 ms from the stimulus onset. These data suggest that emotional processing in the amygdala rapidly modulates some neocortical processing, such as perception, recognition, and motor mimicry, when observing dynamic facial expressions of emotion. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Second language lexical development and cognitive control: A longitudinal fMRI study.

PubMed

Grant, Angela M; Fang, Shin-Yi; Li, Ping

2015-05-01

In this paper we report a longitudinal functional magnetic resonance imaging (fMRI) study that tested contrasting predictions about the time course of cognitive control in second language (L2) acquisition. We examined the neural correlates of lexical processing in L2 learners twice over the course of one academic year. Specifically, while in the scanner, participants were asked to judge the language membership of unambiguous first and second language words, as well as interlingual homographs. Our ROI and connectivity analyses reveal that with increased exposure to the L2, overall activation in control areas such as the anterior cingulate cortex decrease while connectivity with semantic processing regions such as the middle temporal gyrus increase. These results suggest that cognitive control is more important initially in L2 acquisition, and have significant implications for understanding developmental and neurocognitive models of second language lexical processing. Copyright © 2015 Elsevier Inc. All rights reserved.

Sparse dictionary learning of resting state fMRI networks.

PubMed

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

Individual Functional ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

PubMed Central

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

2013-01-01

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

Neural network alterations across eating disorders: a narrative review of fMRI studies.

PubMed

Steward, Trevor; Menchón, José M; Jiménez-Murcia, Susana; Soriano-Mas, Carles; Fernández-Aranda, Fernando

2017-10-17

Functional magnetic resonance imaging (fMRI) has provided insight on how neural abnormalities are related to the symptomatology of the eating disorders (EDs): anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). More specifically, an increasingly growing number of brain imaging studies has shed light on how functionally connected brain networks contribute not only to disturbed eating behavior, but also to transdiagnostic alterations in body/interoceptive perception, reward processing and executive functions. This narrative review aims to summarize recent advances in fMRI studies of patients with EDs by highlighting studies investigating network alterations that are shared across EDs. Findings on reward processing in both AN and BN patients point to the presence of altered sensitivity to salient food stimuli in striatal regions and to the possibility of hypothalamic inputs being overridden by top-down cognitive control regions. Additionally, innovative new lines of research suggest that increased activations in fronto-striatal circuits are strongly associated with the maintenance of restrictive eating habits in AN patients. Although significantly fewer studies have been carried out in patients with BN and BED, aberrant neural responses to both food cues and anticipated food receipt appear to occur in these populations. These altered responses, coupled with diminished recruitment of prefrontal cognitive control circuitry, are believed to contribute to the binge eating of palatable foods. Results from functional network connectivity studies are diverse, but findings tend to converge on indicating disrupted resting-state connectivity in executive networks, the default-mode network and the salience network across EDs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Control networks and hubs.

PubMed

Gratton, Caterina; Sun, Haoxin; Petersen, Steven E

2018-03-01

Executive control functions are associated with frontal, parietal, cingulate, and insular brain regions that interact through distributed large-scale networks. Here, we discuss how fMRI functional connectivity can shed light on the organization of control networks and how they interact with other parts of the brain. In the first section of our review, we present convergent evidence from fMRI functional connectivity, activation, and lesion studies that there are multiple dissociable control networks in the brain with distinct functional properties. In the second section, we discuss how graph theoretical concepts can help illuminate the mechanisms by which control networks interact with other brain regions to carry out goal-directed functions, focusing on the role of specialized hub regions for mediating cross-network interactions. Again, we use a combination of functional connectivity, lesion, and task activation studies to bolster this claim. We conclude that a large-scale network perspective provides important neurobiological constraints on the neural underpinnings of executive control, which will guide future basic and translational research into executive function and its disruption in disease. © 2017 Society for Psychophysiological Research.

Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

PubMed Central

Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

2013-01-01

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

Unravelling the Intrinsic Functional Organization of the Human Striatum: A Parcellation and Connectivity Study Based on Resting-State fMRI

PubMed Central

Jung, Wi Hoon; Jang, Joon Hwan; Park, Jin Woo; Kim, Euitae; Goo, Eun-Hoe; Im, Oh-Soo; Kwon, Jun Soo

2014-01-01

As the main input hub of the basal ganglia, the striatum receives projections from the cerebral cortex. Many studies have provided evidence for multiple parallel corticostriatal loops based on the structural and functional connectivity profiles of the human striatum. A recent resting-state fMRI study revealed the topography of striatum by assigning each voxel in the striatum to its most strongly correlated cortical network among the cognitive, affective, and motor networks. However, it remains unclear what patterns of striatal parcellation would result from performing the clustering without subsequent assignment to cortical networks. Thus, we applied unsupervised clustering algorithms to parcellate the human striatum based on its functional connectivity patterns to other brain regions without any anatomically or functionally defined cortical targets. Functional connectivity maps of striatal subdivisions, identified through clustering analyses, were also computed. Our findings were consistent with recent accounts of the functional distinctions of the striatum as well as with recent studies about its functional and anatomical connectivity. For example, we found functional connections between dorsal and ventral striatal clusters and the areas involved in cognitive and affective processes, respectively, and between rostral and caudal putamen clusters and the areas involved in cognitive and motor processes, respectively. This study confirms prior findings, showing similar striatal parcellation patterns between the present and prior studies. Given such striking similarity, it is suggested that striatal subregions are functionally linked to cortical networks involving specific functions rather than discrete portions of cortical regions. Our findings also demonstrate that the clustering of functional connectivity patterns is a reliable feature in parcellating the striatum into anatomically and functionally meaningful subdivisions. The striatal subdivisions identified here may have important implications for understanding the relationship between corticostriatal dysfunction and various neurodegenerative and psychiatric disorders. PMID:25203441

Anterior cingulate cortex-related connectivity in first-episode schizophrenia: a spectral dynamic causal modeling study with functional magnetic resonance imaging

PubMed Central

Cui, Long-Biao; Liu, Jian; Wang, Liu-Xian; Li, Chen; Xi, Yi-Bin; Guo, Fan; Wang, Hua-Ning; Zhang, Lin-Chuan; Liu, Wen-Ming; He, Hong; Tian, Ping; Yin, Hong; Lu, Hongbing

2015-01-01

Understanding the neural basis of schizophrenia (SZ) is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and medial prefrontal cortex (MPFC) have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI). Forty-nine patients with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs) were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM) to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA) in addition to classical inference (t-test). In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, spectral DCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions. PMID:26578933

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.

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

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.

Prolonged fasting impairs neural reactivity to visual stimulation.

PubMed

Kohn, N; Wassenberg, A; Toygar, T; Kellermann, T; Weidenfeld, C; Berthold-Losleben, M; Chechko, N; Orfanos, S; Vocke, S; Laoutidis, Z G; Schneider, F; Karges, W; Habel, U

2016-01-01

Previous literature has shown that hypoglycemia influences the intensity of the BOLD signal. A similar but smaller effect may also be elicited by low normal blood glucose levels in healthy individuals. This may not only confound the BOLD signal measured in fMRI, but also more generally interact with cognitive processing, and thus indirectly influence fMRI results. Here we show in a placebo-controlled, crossover, double-blind study on 40 healthy subjects, that overnight fasting and low normal levels of glucose contrasted to an activated, elevated glucose condition have an impact on brain activation during basal visual stimulation. Additionally, functional connectivity of the visual cortex shows a strengthened association with higher-order attention-related brain areas in an elevated blood glucose condition compared to the fasting condition. In a fasting state visual brain areas show stronger coupling to the inferior temporal gyrus. Results demonstrate that prolonged overnight fasting leads to a diminished BOLD signal in higher-order occipital processing areas when compared to an elevated blood glucose condition. Additionally, functional connectivity patterns underscore the modulatory influence of fasting on visual brain networks. Patterns of brain activation and functional connectivity associated with a broad range of attentional processes are affected by maturation and aging and associated with psychiatric disease and intoxication. Thus, we conclude that prolonged fasting may decrease fMRI design sensitivity in any task involving attentional processes when fasting status or blood glucose is not controlled.

Pharmacological Modulation of Noradrenergic Arousal Circuitry Disrupts Functional Connectivity of the Locus Ceruleus in Humans

PubMed Central

Song, Andrew H.

2017-01-01

State-dependent activity of locus ceruleus (LC) neurons has long suggested a role for noradrenergic modulation of arousal. However, in vivo insights into noradrenergic arousal circuitry have been constrained by the fundamental inaccessibility of the human brain for invasive studies. Functional magnetic resonance imaging (fMRI) studies performed during site-specific pharmacological manipulations of arousal levels may be used to study brain arousal circuitry. Dexmedetomidine is an anesthetic that alters the level of arousal by selectively targeting α2 adrenergic receptors on LC neurons, resulting in reduced firing rate and norepinephrine release. Thus, we hypothesized that dexmedetomidine-induced altered arousal would manifest with reduced functional connectivity between the LC and key brain regions involved in the regulation of arousal. To test this hypothesis, we acquired resting-state fMRI data in right-handed healthy volunteers 18–36 years of age (n = 15, 6 males) at baseline, during dexmedetomidine-induced altered arousal, and recovery states. As previously reported, seed-based resting-state fMRI analyses revealed that the LC was functionally connected to a broad network of regions including the reticular formation, basal ganglia, thalamus, posterior cingulate cortex (PCC), precuneus, and cerebellum. Functional connectivity of the LC to only a subset of these regions (PCC, thalamus, and caudate nucleus) covaried with the level of arousal. Functional connectivity of the PCC to the ventral tegmental area/pontine reticular formation and thalamus, in addition to the LC, also covaried with the level of arousal. We propose a framework in which the LC, PCC, thalamus, and basal ganglia comprise a functional arousal circuitry. SIGNIFICANCE STATEMENT Electrophysiological studies of locus ceruleus (LC) neurons have long suggested a role for noradrenergic mechanisms in mediating arousal. However, the fundamental inaccessibility of the human brain for invasive studies has limited a precise understanding of putative brain regions that integrate with the LC to regulate arousal. Our results suggest that the PCC, thalamus, and basal ganglia are key components of a LC-noradrenergic arousal circuit. PMID:28626012

Self-government of complex reading and writing brains informed by cingulo-opercular network for adaptive control and working memory components for language learning.

PubMed

Richards, Todd L; Abbott, Robert D; Yagle, Kevin; Peterson, Dan; Raskind, Wendy; Berninger, Virginia W

2017-01-01

To understand mental self-government of the developing reading and writing brain, correlations of clustering coefficients on fMRI reading or writing tasks with BASC 2 Adaptivity ratings (time 1 only) or working memory components (time 1 before and time 2 after instruction previously shown to improve achievement and change magnitude of fMRI connectivity) were investigated in 39 students in grades 4 to 9 who varied along a continuum of reading and writing skills. A Philips 3T scanner measured connectivity during six leveled fMRI reading tasks (subword-letters and sounds, word-word-specific spellings or affixed words, syntax comprehension-with and without homonym foils or with and without affix foils, and text comprehension) and three fMRI writing tasks-writing next letter in alphabet, adding missing letter in word spelling, and planning for composing. The Brain Connectivity Toolbox generated clustering coefficients based on the cingulo-opercular (CO) network; after controlling for multiple comparisons and movement, significant fMRI connectivity clustering coefficients for CO were identified in 8 brain regions bilaterally (cingulate gyrus, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, superior temporal gyrus, insula, cingulum-cingulate gyrus, and cingulum-hippocampus). BASC2 Parent Ratings for Adaptivity were correlated with CO clustering coefficients on three reading tasks (letter-sound, word affix judgments and sentence comprehension) and one writing task (writing next letter in alphabet). Before instruction, each behavioral working memory measure (phonology, orthography, morphology, and syntax coding, phonological and orthographic loops for integrating internal language and output codes, and supervisory focused and switching attention) correlated significantly with at least one CO clustering coefficient. After instruction, the patterning of correlations changed with new correlations emerging. Results show that the reading and writing brain's mental government, supported by both CO Adaptive Control and multiple working memory components, had changed in response to instruction during middle childhood/early adolescence.

Sexually dimorphic functional connectivity in response to high vs. low energy-dense food cues in obese humans: an fMRI study.

PubMed

Atalayer, Deniz; Pantazatos, Spiro P; Gibson, Charlisa D; McOuatt, Haley; Puma, Lauren; Astbury, Nerys M; Geliebter, Allan

2014-10-15

Sexually-dimorphic behavioral and biological aspects of human eating have been described. Using psychophysiological interaction (PPI) analysis, we investigated sex-based differences in functional connectivity with a key emotion-processing region (amygdala, AMG) and a key reward-processing area (ventral striatum, VS) in response to high vs. low energy-dense (ED) food images using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in obese persons in fasted and fed states. When fed, in response to high vs. low-ED food cues, obese men (vs. women) had greater functional connectivity with AMG in right subgenual anterior cingulate, whereas obese women had greater functional connectivity with AMG in left angular gyrus and right primary motor areas. In addition, when fed, AMG functional connectivity with pre/post-central gyrus was more associated with BMI in women (vs. men). When fasted, obese men (vs. women) had greater functional connectivity with AMG in bilateral supplementary frontal and primary motor areas, left precuneus, and right cuneus, whereas obese women had greater functional connectivity with AMG in left inferior frontal gyrus, right thalamus, and dorsomedial prefrontal cortex. When fed, greater functional connectivity with VS was observed in men in bilateral supplementary and primary motor areas, left postcentral gyrus, and left precuneus. These sex-based differences in functional connectivity in response to visual food cues may help partly explain differential eating behavior, pathology prevalence, and outcomes in men and women. Published by Elsevier Inc.

Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks

PubMed Central

Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

2016-01-01

We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

Radiation-induced functional connectivity alterations in nasopharyngeal carcinoma patients with radiotherapy

PubMed Central

Ma, Qiongmin; Wu, Donglin; Zeng, Ling-Li; Shen, Hui; Hu, Dewen; Qiu, Shijun

2016-01-01

Abstract The study aims to investigate the radiation-induced brain functional alterations in nasopharyngeal carcinoma (NPC) patients who received radiotherapy (RT) using functional magnetic resonance imaging (fMRI) and statistic scale. The fMRI data of 35 NPC patients with RT and 24 demographically matched untreated NPC patients were acquired. Montreal Cognitive Assessment (MoCA) was also measured to evaluate their global cognition performance. Multivariate pattern analysis was performed to find the significantly altered functional connections between these 2 groups, while the linear correlation level was detected between the altered functional connections and the MoCA scores. Forty-five notably altered functional connections were found, which were mainly located between 3 brain networks, the cerebellum, sensorimotor, and cingulo-opercular. With strictly false discovery rate correction, 5 altered functional connections were shown to have significant linear correlations with the MoCA scores, that is, the connections between the vermis and hippocampus, cerebellum lobule VI and dorsolateral prefrontal cortex, precuneus and dorsal frontal cortex, cuneus and middle occipital lobe, and insula and cuneus. Besides, the connectivity between the vermis and hippocampus was also significantly correlated with the attention score, 1 of the 7 subscores of the MoCA. The present study provides new insights into the radiation-induced functional connectivity impairments in NPC patients. The results showed that the RT may induce the cognitive impairments, especially the attention alterations. The 45 altered functional connections, especially the 5 altered functional connections that were significantly correlated to the MoCA scores, may serve as the potential biomarkers of the RT-induced brain functional impairments and provide valuable targets for further functional recovery treatment. PMID:27442663

Dynamic functional connectivity: Promise, issues, and interpretations

PubMed Central

Hutchison, R. Matthew; Womelsdorf, Thilo; Allen, Elena A.; Bandettini, Peter A.; Calhoun, Vince D.; Corbetta, Maurizio; Penna, Stefania Della; Duyn, Jeff H.; Glover, Gary H.; Gonzalez-Castillo, Javier; Handwerker, Daniel A.; Keilholz, Shella; Kiviniemi, Vesa; Leopold, David A.; de Pasquale, Francesco; Sporns, Olaf; Walter, Martin; Chang, Catie

2013-01-01

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations. PMID:23707587

Brain networks during free viewing of complex erotic movie: new insights on psychogenic erectile dysfunction.

PubMed

Cera, Nicoletta; Di Pierro, Ezio Domenico; Ferretti, Antonio; Tartaro, Armando; Romani, Gian Luca; Perrucci, Mauro Gianni

2014-01-01

Psychogenic erectile dysfunction (ED) is defined as a male sexual dysfunction characterized by a persistent or recurrent inability to attain adequate penile erection due predominantly or exclusively to psychological or interpersonal factors. Previous fMRI studies were based on the common occurrence in the male sexual behaviour represented by the sexual arousal and penile erection related to viewing of erotic movies. However, there is no experimental evidence of altered brain networks in psychogenic ED patients (EDp). Some studies showed that fMRI activity collected during non sexual movie viewing can be analyzed in a reliable manner with independent component analysis (ICA) and that the resulting brain networks are consistent with previous resting state neuroimaging studies. In the present study, we investigated the modification of the brain networks in EDp compared to healthy controls (HC), using whole-brain fMRI during free viewing of an erotic video clip. Sixteen EDp and nineteen HC were recruited after RigiScan evaluation, psychiatric, and general medical evaluations. The performed ICA showed that visual network (VN), default-mode network (DMN), fronto-parietal network (FPN) and salience network (SN) were spatially consistent across EDp and HC. However, between-group differences in functional connectivity were observed in the DMN and in the SN. In the DMN, EDp showed decreased connectivity values in the inferior parietal lobes, posterior cingulate cortex and medial prefrontal cortex, whereas in the SN decreased and increased connectivity was observed in the right insula and in the anterior cingulate cortex respectively. The decreased levels of intrinsic functional connectivity principally involved the subsystem of DMN relevant for the self relevant mental simulation that concerns remembering of past experiences, thinking to the future and conceiving the viewpoint of the other's actions. Moreover, the between group differences in the SN nodes suggested a decreased recognition of autonomical and sexual arousal changes in EDp.

Brain Networks during Free Viewing of Complex Erotic Movie: New Insights on Psychogenic Erectile Dysfunction

PubMed Central

Cera, Nicoletta; Di Pierro, Ezio Domenico; Ferretti, Antonio; Tartaro, Armando; Romani, Gian Luca; Perrucci, Mauro Gianni

2014-01-01

Psychogenic erectile dysfunction (ED) is defined as a male sexual dysfunction characterized by a persistent or recurrent inability to attain adequate penile erection due predominantly or exclusively to psychological or interpersonal factors. Previous fMRI studies were based on the common occurrence in the male sexual behaviour represented by the sexual arousal and penile erection related to viewing of erotic movies. However, there is no experimental evidence of altered brain networks in psychogenic ED patients (EDp). Some studies showed that fMRI activity collected during non sexual movie viewing can be analyzed in a reliable manner with independent component analysis (ICA) and that the resulting brain networks are consistent with previous resting state neuroimaging studies. In the present study, we investigated the modification of the brain networks in EDp compared to healthy controls (HC), using whole-brain fMRI during free viewing of an erotic video clip. Sixteen EDp and nineteen HC were recruited after RigiScan evaluation, psychiatric, and general medical evaluations. The performed ICA showed that visual network (VN), default-mode network (DMN), fronto-parietal network (FPN) and salience network (SN) were spatially consistent across EDp and HC. However, between-group differences in functional connectivity were observed in the DMN and in the SN. In the DMN, EDp showed decreased connectivity values in the inferior parietal lobes, posterior cingulate cortex and medial prefrontal cortex, whereas in the SN decreased and increased connectivity was observed in the right insula and in the anterior cingulate cortex respectively. The decreased levels of intrinsic functional connectivity principally involved the subsystem of DMN relevant for the self relevant mental simulation that concerns remembering of past experiences, thinking to the future and conceiving the viewpoint of the other’s actions. Moreover, the between group differences in the SN nodes suggested a decreased recognition of autonomical and sexual arousal changes in EDp. PMID:25126947

FIACH: A biophysical model for automatic retrospective noise control in fMRI.

PubMed

Tierney, Tim M; Weiss-Croft, Louise J; Centeno, Maria; Shamshiri, Elhum A; Perani, Suejen; Baldeweg, Torsten; Clark, Christopher A; Carmichael, David W

2016-01-01

Different noise sources in fMRI acquisition can lead to spurious false positives and reduced sensitivity. We have developed a biophysically-based model (named FIACH: Functional Image Artefact Correction Heuristic) which extends current retrospective noise control methods in fMRI. FIACH can be applied to both General Linear Model (GLM) and resting state functional connectivity MRI (rs-fcMRI) studies. FIACH is a two-step procedure involving the identification and correction of non-physiological large amplitude temporal signal changes and spatial regions of high temporal instability. We have demonstrated its efficacy in a sample of 42 healthy children while performing language tasks that include overt speech with known activations. We demonstrate large improvements in sensitivity when FIACH is compared with current methods of retrospective correction. FIACH reduces the confounding effects of noise and increases the study's power by explaining significant variance that is not contained within the commonly used motion parameters. The method is particularly useful in detecting activations in inferior temporal regions which have proven problematic for fMRI. We have shown greater reproducibility and robustness of fMRI responses using FIACH in the context of task induced motion. In a clinical setting this will translate to increasing the reliability and sensitivity of fMRI used for the identification of language lateralisation and eloquent cortex. FIACH can benefit studies of cognitive development in young children, patient populations and older adults. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Probabilistic fiber tracking of the language and motor white matter pathways of the supplementary motor area (SMA) in patients with brain tumors.

PubMed

Jenabi, Mehrnaz; Peck, Kyung K; Young, Robert J; Brennan, Nicole; Holodny, Andrei I

2014-12-01

Accurate localization of anatomically and functionally separate SMA tracts is important to improve planning prior to neurosurgery. Using fMRI and probabilistic DTI techniques, we assessed the connectivity between the frontal language area (Broca's area) and the rostral pre-SMA (language SMA) and caudal SMA proper (motor SMA). Twenty brain tumor patients completed motor and language fMRI paradigms and DTI. Peaks of functional activity in the language SMA, motor SMA and Broca's area were used to define seed regions for probabilistic tractography. fMRI and probabilistic tractography identified separate and unique pathways connecting the SMA to Broca's area - the language SMA pathway and the motor SMA pathway. For all subjects, the language SMA pathway had a larger number of voxels (P<0.0001) and higher connectivity (P<0.0001) to Broca's area than did the motor SMA pathway. In each patient, the number of voxels was greater in the language and motor SMA pathways than in background pathways (P<0.0001). No differences were found between patients with ipsilateral and those with contralateral tumors for either the language SMA pathway (degree of connectivity: P<0.36; number of voxels: 0.35) or the motor SMA pathway (degree of connectivity, P<0.28; number of voxels, P<0.74). Probabilistic tractography can identify unique white matter tracts that connect language SMA and motor SMA to Broca's area. The language SMA is more significantly connected to Broca's area than is the motor subdivision of the SMA proper. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: functional connectivity-based parcellation method.

PubMed

Kim, Jae-Hun; Lee, Jong-Min; Jo, Hang Joon; Kim, Sook Hui; Lee, Jung Hee; Kim, Sung Tae; Seo, Sang Won; Cox, Robert W; Na, Duk L; Kim, Sun I; Saad, Ziad S

2010-02-01

Noninvasive parcellation of the human cerebral cortex is an important goal for understanding and examining brain functions. Recently, the patterns of anatomical connections using diffusion tensor imaging (DTI) have been used to parcellate brain regions. Here, we present a noninvasive parcellation approach that uses "functional fingerprints" obtained by correlation measures on resting state functional magnetic resonance imaging (fMRI) data to parcellate brain regions. In other terms, brain regions are parcellated based on the similarity of their connection--as reflected by correlation during resting state--to the whole brain. The proposed method was used to parcellate the medial frontal cortex (MFC) into supplementary motor areas (SMA) and pre-SMA subregions. In agreement with anatomical landmark-based parcellation, we find that functional fingerprint clustering of the MFC results in anterior and posterior clusters. The probabilistic maps from 12 subjects showed that the anterior cluster is mainly located rostral to the vertical commissure anterior (VCA) line, whereas the posterior cluster is mainly located caudal to VCA line, suggesting the homologues of pre-SMA and SMA. The functional connections from the putative pre-SMA cluster were connected to brain regions which are responsible for complex/cognitive motor control, whereas those from the putative SMA cluster were connected to brain regions which are related to the simple motor control. These findings demonstrate the feasibility of the functional connectivity-based parcellation of the human cerebral cortex using resting state fMRI. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Errors on interrupter tasks presented during spatial and verbal working memory performance are linearly linked to large-scale functional network connectivity in high temporal resolution resting state fMRI.

PubMed

Magnuson, Matthew Evan; Thompson, Garth John; Schwarb, Hillary; Pan, Wen-Ju; McKinley, Andy; Schumacher, Eric H; Keilholz, Shella Dawn

2015-12-01

The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task presented during a spatial working memory paradigm and decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during a verbal working memory paradigm. A trend for increased DMN resting state connectivity to correlate to measures of fluid intelligence was also observed. These results provide additional evidence of the relationship between resting state networks and behavioral performance, and show that such results can be observed with high temporal resolution fMRI. Because cognitive scores and functional connectivity were collected on nonconsecutive days, these results highlight the stability of functional connectivity/cognitive performance coupling.

Functional network centrality in obesity: A resting-state and task fMRI study.

PubMed

García-García, Isabel; Jurado, María Ángeles; Garolera, Maite; Marqués-Iturria, Idoia; Horstmann, Annette; Segura, Bàrbara; Pueyo, Roser; Sender-Palacios, María José; Vernet-Vernet, Maria; Villringer, Arno; Junqué, Carme; Margulies, Daniel S; Neumann, Jane

2015-09-30

Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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.

Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease.

PubMed

Wolf, R C; Sambataro, F; Vasic, N; Depping, M S; Thomann, P A; Landwehrmeyer, G B; Süssmuth, S D; Orth, M

2014-11-01

Functional magnetic resonance imaging (fMRI) of multiple neural networks during the brain's 'resting state' could facilitate biomarker development in patients with Huntington's disease (HD) and may provide new insights into the relationship between neural dysfunction and clinical symptoms. To date, however, very few studies have examined the functional integrity of multiple resting state networks (RSNs) in manifest HD, and even less is known about whether concomitant brain atrophy affects neural activity in patients. Using MRI, we investigated brain structure and RSN function in patients with early HD (n = 20) and healthy controls (n = 20). For resting-state fMRI data a group-independent component analysis identified spatiotemporally distinct patterns of motor and prefrontal RSNs of interest. We used voxel-based morphometry to assess regional brain atrophy, and 'biological parametric mapping' analyses to investigate the impact of atrophy on neural activity. Compared with controls, patients showed connectivity changes within distinct neural systems including lateral prefrontal, supplementary motor, thalamic, cingulate, temporal and parietal regions. In patients, supplementary motor area and cingulate cortex connectivity indices were associated with measures of motor function, whereas lateral prefrontal connectivity was associated with cognition. This study provides evidence for aberrant connectivity of RSNs associated with motor function and cognition in early manifest HD when controlling for brain atrophy. This suggests clinically relevant changes of RSN activity in the presence of HD-associated cortical and subcortical structural abnormalities.

Changes in alcohol-related brain networks across the first year of college: a prospective pilot study using fMRI effective connectivity mapping.

PubMed

Beltz, Adriene M; Gates, Kathleen M; Engels, Anna S; Molenaar, Peter C M; Pulido, Carmen; Turrisi, Robert; Berenbaum, Sheri A; Gilmore, Rick O; Wilson, Stephen J

2013-04-01

The upsurge in alcohol use that often occurs during the first year of college has been convincingly linked to a number of negative psychosocial consequences and may negatively affect brain development. In this longitudinal functional magnetic resonance imaging (fMRI) pilot study, we examined changes in neural responses to alcohol cues across the first year of college in a normative sample of late adolescents. Participants (N=11) were scanned three times across their first year of college (summer, first semester, second semester), while completing a go/no-go task in which images of alcoholic and non-alcoholic beverages were the response cues. A state-of-the-art effective connectivity mapping technique was used to capture spatiotemporal relations among brain regions of interest (ROIs) at the level of the group and the individual. Effective connections among ROIs implicated in cognitive control were greatest at the second assessment (when negative consequences of alcohol use increased), and effective connections among ROIs implicated in emotion processing were lower (and response times were slower) when participants were instructed to respond to alcohol cues compared to non-alcohol cues. These preliminary findings demonstrate the value of a prospective effective connectivity approach for understanding adolescent changes in alcohol-related neural processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Frequency distribution of causal connectivity in rat sensorimotor network: resting-state fMRI analyses.

PubMed

Shim, Woo H; Baek, Kwangyeol; Kim, Jeong Kon; Chae, Yongwook; Suh, Ji-Yeon; Rosen, Bruce R; Jeong, Jaeseung; Kim, Young R

2013-01-01

Resting-state functional MRI (fMRI) has emerged as an important method for assessing neural networks, enabling extensive connectivity analyses between multiple brain regions. Among the analysis techniques proposed, partial directed coherence (PDC) provides a promising tool to unveil causal connectivity networks in the frequency domain. Using the MRI time series obtained from the rat sensorimotor system, we applied PDC analysis to determine the frequency-dependent causality networks. In particular, we compared in vivo and postmortem conditions to establish the statistical significance of directional PDC values. Our results demonstrate that two distinctive frequency populations drive the causality networks in rat; significant, high-frequency causal connections clustered in the range of 0.2-0.4 Hz, and the frequently documented low-frequency connections <0.15 Hz. Frequency-dependence and directionality of the causal connection are characteristic between sensorimotor regions, implying the functional role of frequency bands to transport specific resting-state signals. In particular, whereas both intra- and interhemispheric causal connections between heterologous sensorimotor regions are robust over all frequency levels, the bilaterally homologous regions are interhemispherically linked mostly via low-frequency components. We also discovered a significant, frequency-independent, unidirectional connection from motor cortex to thalamus, indicating dominant cortical inputs to the thalamus in the absence of external stimuli. Additionally, to address factors underlying the measurement error, we performed signal simulations and revealed that the interactive MRI system noise alone is a likely source of the inaccurate PDC values. This work demonstrates technical basis for the PDC analysis of resting-state fMRI time series and the presence of frequency-dependent causality networks in the sensorimotor system.

Working memory load modulation of parieto-frontal connections: evidence from dynamic causal modeling

PubMed Central

Ma, Liangsuo; Steinberg, Joel L.; Hasan, Khader M.; Narayana, Ponnada A.; Kramer, Larry A.; Moeller, F. Gerard

2011-01-01

Previous neuroimaging studies have shown that working memory load has marked effects on regional neural activation. However, the mechanism through which working memory load modulates brain connectivity is still unclear. In this study, this issue was addressed using dynamic causal modeling (DCM) based on functional magnetic resonance imaging (fMRI) data. Eighteen normal healthy subjects were scanned while they performed a working memory task with variable memory load, as parameterized by two levels of memory delay and three levels of digit load (number of digits presented in each visual stimulus). Eight regions of interest, i.e., bilateral middle frontal gyrus (MFG), anterior cingulate cortex (ACC), inferior frontal cortex (IFC), and posterior parietal cortex (PPC), were chosen for DCM analyses. Analysis of the behavioral data during the fMRI scan revealed that accuracy decreased as digit load increased. Bayesian inference on model structure indicated that a bilinear DCM in which memory delay was the driving input to bilateral PPC and in which digit load modulated several parieto-frontal connections was the optimal model. Analysis of model parameters showed that higher digit load enhanced connection from L PPC to L IFC, and lower digit load inhibited connection from R PPC to L ACC. These findings suggest that working memory load modulates brain connectivity in a parieto-frontal network, and may reflect altered neuronal processes, e.g., information processing or error monitoring, with the change in working memory load. PMID:21692148

Plastic modulation of PTSD resting-state networks by EEG neurofeedback

PubMed Central

Kluetsch, Rosemarie C.; Ros, Tomas; Théberge, Jean; Frewen, Paul A.; Calhoun, Vince D.; Schmahl, Christian; Jetly, Rakesh; Lanius, Ruth A.

2015-01-01

Objective Electroencephalographic (EEG) neurofeedback training has been shown to produce plastic modulations in salience network and default mode network functional connectivity in healthy individuals. In this study, we investigated whether a single session of neurofeedback training aimed at the voluntary reduction of alpha rhythm (8–12 Hz) amplitude would be related to differences in EEG network oscillations, functional MRI (fMRI) connectivity, and subjective measures of state anxiety and arousal in a group of individuals with PTSD. Method 21 individuals with PTSD related to childhood abuse underwent 30 minutes of EEG neurofeedback training preceded and followed by a resting-state fMRI scan. Results Alpha desynchronizing neurofeedback was associated with decreased alpha amplitude during training, followed by a significant increase (‘rebound’) in resting-state alpha synchronization. This rebound was linked to increased calmness, greater salience network connectivity with the right insula, and enhanced default mode network connectivity with bilateral posterior cingulate, right middle frontal gyrus, and left medial prefrontal cortex. Conclusion Our study represents a first step in elucidating the potential neurobehavioral mechanisms mediating the effects of neurofeedback treatment on regulatory systems in PTSD. Moreover, it documents for the first time a spontaneous EEG ‘rebound’ after neurofeedback, pointing to homeostatic/compensatory mechanisms operating in the brain. PMID:24266644

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.

PubMed

Deligianni, Fani; Centeno, Maria; Carmichael, David W; Clayden, Jonathan D

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

PubMed Central

Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

Left Posterior Orbitofrontal Cortex Is Associated With Odor-Induced Autobiographical Memory: An fMRI Study.

PubMed

Watanabe, Keiko; Masaoka, Yuri; Kawamura, Mitsuru; Yoshida, Masaki; Koiwa, Nobuyoshi; Yoshikawa, Akira; Kubota, Satomi; Ida, Masahiro; Ono, Kenjiro; Izumizaki, Masahiko

2018-01-01

Autobiographical odor memory (AM-odor) accompanied by a sense of realism of a specific memory elicits strong emotions. AM-odor differs from memory triggered by other sensory modalities, possibly because olfaction involves a unique sensory process. Here, we examined the orbitofrontal cortex (OFC), using functional magnetic resonance imaging (fMRI) to determine which OFC subregions are related to AM-odor. Both AM-odor and a control odor successively increased subjective ratings of comfortableness and pleasantness. Importantly, AM-odor also increased arousal levels and the vividness of memories, and was associated with a deep and slow breathing pattern. fMRI analysis indicated robust activation in the left posterior OFC (L-POFC). Connectivity between the POFC and whole brain regions was estimated using psychophysiological interaction analysis (PPI). We detected several trends in connectivity between L-POFC and bilateral precuneus, bilateral rostral dorsal anterior cingulate cortex (rdACC), and left parahippocampus, which will be useful for targeting our hypotheses for future investigations. The slow breathing observed in AM-odor was correlated with rdACC activation. Odor associated with emotionally significant autobiographical memories was accompanied by slow and deep breathing, possibly involving rdACC processing.

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.

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.

Default mode network connectivity indicates episodic memory capacity in mesial temporal lobe epilepsy.

PubMed

McCormick, Cornelia; Quraan, Maher; Cohn, Melanie; Valiante, Taufik A; McAndrews, Mary Pat

2013-05-01

The clinical relevance of resting state functional connectivity in neurologic disorders, including mesial temporal lobe epilepsy (mTLE), remains unclear. This study investigated how connectivity in the default mode network changes with unilateral damage to one of its nodes, the hippocampus (HC), and how such connectivity can be exploited clinically to characterize memory deficits and indicate postsurgical memory change. Functional magnetic resonance imaging (fMRI) resting state scans and neuropsychological memory assessments (Warrington Recognition Tests for Words and Faces) were performed on 19 healthy controls, 20 patients with right mTLE, and 18 patients with left mTLE. In addition, postsurgical fMRI resting state and memory change (postsurgical memory performance-presurgical memory performance) data were available for half of these patients. Patients with mTLE showed reduced connectivity from the posterior cingulate cortex (PCC) to the epileptogenic HC and increased PCC connectivity to the contralateral HC. Stronger PCC connectivity to the epileptogenic HC was associated with better presurgical memory and with greater postsurgical memory decline. Stronger PCC connectivity to the contralateral HC was associated with less postsurgical memory decline. Following surgery, PCC connectivity to the remaining HC increased from presurgical values and showed enhanced correlation with postsurgical memory function. It is notable that this index was superior to others (hippocampal volume, preoperative memory scores) in explaining variance in memory change following surgery. Our results demonstrate the striking clinical significance of the brain's intrinsic connectivity in evaluating cognitive capacity and indicating the potential of postsurgical cognitive morbidity in patients with mTLE. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

Ventral Striatum Functional Connectivity as a Predictor of Adolescent Depressive Disorder in a Longitudinal Community-Based Sample.

PubMed

Pan, Pedro Mario; Sato, João R; Salum, Giovanni A; Rohde, Luis A; Gadelha, Ary; Zugman, Andre; Mari, Jair; Jackowski, Andrea; Picon, Felipe; Miguel, Eurípedes C; Pine, Daniel S; Leibenluft, Ellen; Bressan, Rodrigo A; Stringaris, Argyris

2017-11-01

Previous studies have implicated aberrant reward processing in the pathogenesis of adolescent depression. However, no study has used functional connectivity within a distributed reward network, assessed using resting-state functional MRI (fMRI), to predict the onset of depression in adolescents. This study used reward network-based functional connectivity at baseline to predict depressive disorder at follow-up in a community sample of adolescents. A total of 637 children 6-12 years old underwent resting-state fMRI. Discovery and replication analyses tested intrinsic functional connectivity (iFC) among nodes of a putative reward network. Logistic regression tested whether striatal node strength, a measure of reward-related iFC, predicted onset of a depressive disorder at 3-year follow-up. Further analyses investigated the specificity of this prediction. Increased left ventral striatum node strength predicted increased risk for future depressive disorder (odds ratio=1.54, 95% CI=1.09-2.18), even after excluding participants who had depressive disorders at baseline (odds ratio=1.52, 95% CI=1.05-2.20). Among 11 reward-network nodes, only the left ventral striatum significantly predicted depression. Striatal node strength did not predict other common adolescent psychopathology, such as anxiety, attention deficit hyperactivity disorder, and substance use. Aberrant ventral striatum functional connectivity specifically predicts future risk for depressive disorder. This finding further emphasizes the need to understand how brain reward networks contribute to youth depression.

Stimulating neural plasticity with real-time fMRI neurofeedback in Huntington's disease: A proof of concept study.

PubMed

Papoutsi, Marina; Weiskopf, Nikolaus; Langbehn, Douglas; Reilmann, Ralf; Rees, Geraint; Tabrizi, Sarah J

2018-03-01

Novel methods that stimulate neuroplasticity are increasingly being studied to treat neurological and psychiatric conditions. We sought to determine whether real-time fMRI neurofeedback training is feasible in Huntington's disease (HD), and assess any factors that contribute to its effectiveness. In this proof-of-concept study, we used this technique to train 10 patients with HD to volitionally regulate the activity of their supplementary motor area (SMA). We collected detailed behavioral and neuroimaging data before and after training to examine changes of brain function and structure, and cognitive and motor performance. We found that patients overall learned to increase activity of the target region during training with variable effects on cognitive and motor behavior. Improved cognitive and motor performance after training predicted increases in pre-SMA grey matter volume, fMRI activity in the left putamen, and increased SMA-left putamen functional connectivity. Although we did not directly target the putamen and corticostriatal connectivity during neurofeedback training, our results suggest that training the SMA can lead to regulation of associated networks with beneficial effects in behavior. We conclude that neurofeedback training can induce plasticity in patients with Huntington's disease despite the presence of neurodegeneration, and the effects of training a single region may engage other regions and circuits implicated in disease pathology. © 2017 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Stimulating neural plasticity with real‐time fMRI neurofeedback in Huntington's disease: A proof of concept study

PubMed Central

Papoutsi, Marina; Weiskopf, Nikolaus; Langbehn, Douglas; Reilmann, Ralf; Rees, Geraint

2017-01-01

Abstract Novel methods that stimulate neuroplasticity are increasingly being studied to treat neurological and psychiatric conditions. We sought to determine whether real‐time fMRI neurofeedback training is feasible in Huntington's disease (HD), and assess any factors that contribute to its effectiveness. In this proof‐of‐concept study, we used this technique to train 10 patients with HD to volitionally regulate the activity of their supplementary motor area (SMA). We collected detailed behavioral and neuroimaging data before and after training to examine changes of brain function and structure, and cognitive and motor performance. We found that patients overall learned to increase activity of the target region during training with variable effects on cognitive and motor behavior. Improved cognitive and motor performance after training predicted increases in pre‐SMA grey matter volume, fMRI activity in the left putamen, and increased SMA–left putamen functional connectivity. Although we did not directly target the putamen and corticostriatal connectivity during neurofeedback training, our results suggest that training the SMA can lead to regulation of associated networks with beneficial effects in behavior. We conclude that neurofeedback training can induce plasticity in patients with Huntington's disease despite the presence of neurodegeneration, and the effects of training a single region may engage other regions and circuits implicated in disease pathology. PMID:29239063

The posterior parietal paradox: Why do functional magnetic resonance imaging and lesion studies on episodic memory produce conflicting results?

PubMed

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

2011-03-01

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

Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity.

PubMed

Napadow, Vitaly; LaCount, Lauren; Park, Kyungmo; As-Sanie, Sawsan; Clauw, Daniel J; Harris, Richard E

2010-08-01

Fibromyalgia (FM) is considered to be the prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. Multiple studies have demonstrated altered brain activity in these patients. The objective of this study was to investigate the degree of connectivity between multiple brain networks in patients with FM, as well as how activity in these networks correlates with the level of spontaneous pain. Resting-state functional magnetic resonance imaging (FMRI) data from 18 patients with FM and 18 age-matched healthy control subjects were analyzed using dual-regression independent components analysis, which is a data-driven approach for the identification of independent brain networks. Intrinsic, or resting-state, connectivity was evaluated in multiple brain networks: the default mode network (DMN), the executive attention network (EAN), and the medial visual network (MVN), with the MVN serving as a negative control. Spontaneous pain levels were also analyzed for covariance with intrinsic connectivity. Patients with FM had greater connectivity within the DMN and right EAN (corrected P [P(corr)] < 0.05 versus controls), and greater connectivity between the DMN and the insular cortex, which is a brain region known to process evoked pain. Furthermore, greater intensity of spontaneous pain at the time of the FMRI scan correlated with greater intrinsic connectivity between the insula and both the DMN and right EAN (P(corr) < 0.05). These findings indicate that resting brain activity within multiple networks is associated with spontaneous clinical pain in patients with FM. These findings may also have broader implications for how subjective experiences such as pain arise from a complex interplay among multiple brain networks.

Structural and functional network connectivity breakdown in Alzheimer’s disease studied with magnetic resonance imaging techniques.

PubMed

Filippi, Massimo; Agosta, Federica

2011-01-01

Patients with Alzheimer’s disease (AD) experience a brain network breakdown, reflecting disconnection at both the structural and functional system level. Resting-state (RS) functional MRI (fMRI) studies demonstrated that the regional coherence of the fMRI signal is significantly altered in patients with AD and amnestic mild cognitive impairment. Diffusion tensor (DT) MRI has made it possible to track fiber bundle projections across the brain, revealing a substantially abnormal interplay of “critical” white matter tracts in these conditions. The observed agreement between the results of RS fMRI and DT MRI tractography studies in healthy individuals is encouraging and offers interesting hypotheses to be tested in patients with AD, a MCI, and other dementias in order to improve our understanding of their pathobiology in vivo. In this review,we describe the major findings obtained in AD using RS fMRI and DT MRI tractography, and discuss how the relationship between structure and function of the brain networks in AD may be better understood through the application of MR-based technology. This research endeavor holds a great promise in clarifying the mechanisms of cognitive decline in complex chronic neurodegenerative disorders.

Reduced Functional Connectivity within the Mesocorticolimbic System in Substance Use Disorders: An fMRI Study of Puerto Rican Young Adults

PubMed Central

Posner, Jonathan; Amira, Leora; Algaze, Antonio; Canino, Glorisa; Duarte, Cristiane S.

2016-01-01

Studies of the mesocorticolimbic reward system (MCLS) and its relationship with impulsivity and substance use disorders (SUD) have largely focused on individuals from non-minority backgrounds. This represents a significant gap in the literature particularly for minority populations who are disproportionately affected by the consequences of SUD. Using resting-state functional MRI (fMRI), we examined the coherence of neural activity, or functional connectivity, within the brain’s MCLS in 28 young adult Puerto Ricans (ages 25–27) who were part of a population-based cohort study. Half of the sample lived in San Juan, Puerto Rico; the other half lived in the South Bronx, New York. At each of the two sites, half of the sample had a history of a SUD. Relative to those without SUD, individuals with SUD had decreased connectivity between the nucleus accumbens (NAcc) and several regions within the MCLS. This finding was true irrespective of study site (i.e., San Juan or South Bronx). Reduced connectivity within the MCLS was also associated with higher self-reported levels of impulsivity. Path analysis suggested a potential mechanism linking impulsivity, the MCLS, and SUD: impulsivity, potentially by chronically promoting reward seeking behaviors, may contribute to decreased MCLS connectivity, which in turn, may confer vulnerability for SUD. Expanding upon prior studies suggesting that alterations within the MCLS underlie SUD, our findings suggest that such alterations are also related to impulsivity and are present in a high-risk young minority population. PMID:27252633

The relationship between default mode network connectivity and social functioning in individuals at familial high-risk for schizophrenia.

PubMed

Dodell-Feder, David; Delisi, Lynn E; Hooker, Christine I

2014-06-01

Unaffected first-degree relatives of individuals with schizophrenia (i.e., those at familial high-risk [FHR]), demonstrate social dysfunction qualitatively similar though less severe than that of their affected relatives. These social difficulties may be the consequence of genetically conferred disruption to aspects of the default mode network (DMN), such as the dMPFC subsystem, which overlaps with the network of brain regions recruited during social cognitive processes. In the present study, we investigate this possibility, testing DMN connectivity and its relationship to social functioning in FHR using resting-state fMRI. Twenty FHR individuals and 17 controls underwent fMRI during a resting-state scan. Hypothesis-driven functional connectivity analyses examined ROI-to-ROI correlations between the DMN's hubs, and regions of the dMPFC subsystem and MTL subsystem. Connectivity values were examined in relationship to a measure of social functioning and empathy/perspective-taking. Results demonstrate that FHR exhibit reduced connectivity specifically within the dMPFC subsystem of the DMN. Certain ROI-to-ROI correlations predicted aspects of social functioning and empathy/perspective-taking across all participants. Together, the data indicate that disruption to the dMPFC subsystem of the DMN may be associated with familial risk for schizophrenia, and that these intrinsic connections may carry measurable consequences for social functioning. Copyright © 2014 Elsevier B.V. All rights reserved.

The relationship between default mode network connectivity and social functioning in individuals at familial high-risk for schizophrenia

PubMed Central

Dodell-Feder, David; DeLisi, Lynn E.; Hooker, Christine I.

2014-01-01

Unaffected first-degree relatives of individuals with schizophrenia (i.e., those at familial high-risk [FHR]), demonstrate social dysfunction qualitatively similar though less severe than that of their affected relatives. These social difficulties may be the consequence of genetically conferred disruption to aspects of the default mode network (DMN), such as the dMPFC subsystem, which overlaps with the network of brain regions recruited during social cognitive processes. In the present study, we investigate this possibility, testing DMN connectivity and its relationship to social functioning in FHR using resting-state fMRI. Twenty FHR individuals and 17 controls underwent fMRI during a resting-state scan. Hypothesis-driven functional connectivity analyses examined ROI-to-ROI correlations between the DMN’s hubs, and regions of the dMPFC subsystem and MTL subsystem. Connectivity values were examined in relationship to a measure of social functioning and empathy/perspective-taking. Results demonstrate that FHR exhibit reduced connectivity specifically within the dMPFC subsystem of the DMN. Certain ROI-to-ROI correlations predicted aspects of social functioning and empathy/perspective-taking across all participants. Together, the data indicate that disruption to the dMPFC subsystem of the DMN may be associated with familial risk for schizophrenia, and that these intrinsic connections may carry measurable consequences for social functioning. PMID:24768131

Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting‐state functional connectivity

PubMed Central

Scholtens, Lianne H.; Turk, Elise; Mantini, Dante; Vanduffel, Wim; Feldman Barrett, Lisa

2016-01-01

Abstract The cerebral cortex is well known to display a large variation in excitatory and inhibitory chemoarchitecture, but the effect of this variation on global scale functional neural communication and synchronization patterns remains less well understood. Here, we provide evidence of the chemoarchitecture of cortical regions to be associated with large‐scale region‐to‐region resting‐state functional connectivity. We assessed the excitatory versus inhibitory chemoarchitecture of cortical areas as an ExIn ratio between receptor density mappings of excitatory (AMPA, M1) and inhibitory (GABAA, M2) receptors, computed on the basis of data collated from pioneering studies of autoradiography mappings as present in literature of the human (2 datasets) and macaque (1 dataset) cortex. Cortical variation in ExIn ratio significantly correlated with total level of functional connectivity as derived from resting‐state functional connectivity recordings of cortical areas across all three datasets (human I: P = 0.0004; human II: P = 0.0008; macaque: P = 0.0007), suggesting cortical areas with an overall more excitatory character to show higher levels of intrinsic functional connectivity during resting‐state. Our findings are indicative of the microscale chemoarchitecture of cortical regions to be related to resting‐state fMRI connectivity patterns at the global system's level of connectome organization. Hum Brain Mapp 37:3103–3113, 2016. © 2016 Wiley Periodicals, Inc. PMID:27207489

Investigating changes in resting-state connectivity from functional MRI data in patients with HIV associated neurocognitive disorder using MCA and machine learning

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Wismüller, Axel

2017-03-01

Infection of the brain by the Human Immunodeficiency Virus (HIV) causes irreversible damage to the synaptic connections resulting in cognitive impairment. Patients with HIV infection, showing signs of impairment in multiple cognitive domains, as assessed by neuropsychological testing, are said to exhibit symptoms of HIV Associated Neurocognitive Disorder (HAND). In this study, we use resting-state functional MRI (fMRI) data to distinguish between healthy subjects and subjects with symptoms of HAND. To this end, we first establish a measure of interaction between pairs of regional time-series by quantifying their non-linear functional connectivity using Mutual Connectivity Analysis (MCA). Subsequently, we use a classifier to distinguish patterns of interaction between healthy and diseased individuals. Our results, quantified as the mean Area under the ROC curve (AUC) over 75 iterations, indicate that, using fMRI data, we can discriminate between the two cohorts well (AUC > 0.8). Specifically, we find that MCA (mean AUC = 0.89) based connectivity features perform significantly better (p < 0.05) when compared to cross-correlation (mean AUC = 0.82) at the classification task. A higher AUC using our approach suggests that such a nonlinear approach is better able to capture connectivity changes between brain regions and has potential for the development of novel neuro-imaging biomarkers.

Functional connectivity pattern during rest within the episodic memory network in association with episodic memory performance in bipolar disorder.

PubMed

Oertel-Knöchel, Viola; Reinke, Britta; Matura, Silke; Prvulovic, David; Linden, David E J; van de Ven, Vincent

2015-02-28

In this study, we sought to examine the intrinsic functional organization of the episodic memory network during rest in bipolar disorder (BD). The previous work suggests that deficits in intrinsic functional connectivity may account for impaired memory performance. We hypothesized that regions involved in episodic memory processing would reveal aberrant functional connectivity in patients with bipolar disorder. We examined 21 patients with BD and 21 healthy matched controls who underwent functional magnetic resonance imaging (fMRI) during a resting condition. We did a seed-based functional connectivity analysis (SBA), using the regions of the episodic memory network that showed a significantly different activation pattern during task-related fMRI as seeds. The functional connectivity scores (FC) were further correlated with episodic memory task performance. Our results revealed decreased FC scores within frontal areas and between frontal and temporal/hippocampal/limbic regions in BD patients in comparison with controls. We observed higher FC in BD patients compared with controls between frontal and limbic regions. The decrease in fronto-frontal functional connectivity in BD patients showed a significant positive association with episodic memory performance. The association between task-independent dysfunctional frontal-limbic FC and episodic memory performance may be relevant for current pathophysiological models of the disease. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Correlated gene expression and anatomical communication support synchronized brain activity in the mouse functional connectome.

PubMed

Mills, Brian D; Grayson, David S; Shunmugavel, Anandakumar; Miranda-Dominguez, Oscar; Feczko, Eric; Earl, Eric; Neve, Kim; Fair, Damien A

2018-05-22

Cognition and behavior depend on synchronized intrinsic brain activity that is organized into functional networks across the brain. Research has investigated how anatomical connectivity both shapes and is shaped by these networks, but not how anatomical connectivity interacts with intra-areal molecular properties to drive functional connectivity. Here, we present a novel linear model to explain functional connectivity by integrating systematically obtained measurements of axonal connectivity, gene expression, and resting state functional connectivity MRI in the mouse brain. The model suggests that functional connectivity arises from both anatomical links and inter-areal similarities in gene expression. By estimating these effects, we identify anatomical modules in which correlated gene expression and anatomical connectivity support functional connectivity. Along with providing evidence that not all genes equally contribute to functional connectivity, this research establishes new insights regarding the biological underpinnings of coordinated brain activity measured by BOLD fMRI. SIGNIFICANCE STATEMENT Efforts at characterizing the functional connectome with fMRI have risen exponentially over the last decade. Yet despite this rise, the biological underpinnings of these functional measurements are still largely unknown. The current report begins to fill this void by investigating the molecular underpinnings of the functional connectome through an integration of systematically obtained structural information and gene expression data throughout the rodent brain. We find that both white matter connectivity and similarity in regional gene expression relate to resting state functional connectivity. The current report furthers our understanding of the biological underpinnings of the functional connectome and provides a linear model that can be utilized to streamline preclinical animal studies of disease. Copyright © 2018 the authors.

Information flow between interacting human brains: Identification, validation, and relationship to social expertise.

PubMed

Bilek, Edda; Ruf, Matthias; Schäfer, Axel; Akdeniz, Ceren; Calhoun, Vince D; Schmahl, Christian; Demanuele, Charmaine; Tost, Heike; Kirsch, Peter; Meyer-Lindenberg, Andreas

2015-04-21

Social interactions are fundamental for human behavior, but the quantification of their neural underpinnings remains challenging. Here, we used hyperscanning functional MRI (fMRI) to study information flow between brains of human dyads during real-time social interaction in a joint attention paradigm. In a hardware setup enabling immersive audiovisual interaction of subjects in linked fMRI scanners, we characterize cross-brain connectivity components that are unique to interacting individuals, identifying information flow between the sender's and receiver's temporoparietal junction. We replicate these findings in an independent sample and validate our methods by demonstrating that cross-brain connectivity relates to a key real-world measure of social behavior. Together, our findings support a central role of human-specific cortical areas in the brain dynamics of dyadic interactions and provide an approach for the noninvasive examination of the neural basis of healthy and disturbed human social behavior with minimal a priori assumptions.

Oxytocin, brain physiology, and functional connectivity: a review of intranasal oxytocin fMRI studies.

PubMed

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

2013-07-01

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

Right is not always wrong: DTI and fMRI evidence for the reliance of reading comprehension on language-comprehension networks in the right hemisphere.

PubMed

Horowitz-Kraus, Tzipi; Grainger, Molly; DiFrancesco, Mark; Vannest, Jennifer; Holland, Scott K

2015-03-01

The Simple View theory suggests that reading comprehension relies on automatic recognition of words combined with language comprehension. The goal of the current study was to examine the structural and functional connectivity in networks supporting reading comprehension and their relationship with language comprehension within 7-9 year old children using Diffusion Tensor Imaging (DTI) and fMRI during a Sentence Picture Matching task. Fractional Anisotropy (FA) values in the left and right Inferior Longitudinal Fasciculus (ILF) and Superior Longitudinal Fasciculus (SLF), known language-related tracts, were correlated from DTI data with scores from the Woodcock-Johnson III (WJ-III) Passage Comprehension sub-test. Brodmann areas most proximal to white-matter regions with significant correlation to Passage Comprehension scores were chosen as Regions-of-Interest (ROIs) and used as seeds in a functional connectivity analysis using the Sentence Picture Matching task. The correlation between percentile scores for the WJ-III Passage Comprehension subtest and the FA values in the right and left ILF and SLF indicated positive correlation in language-related ROIs, with greater distribution in the right hemisphere, which in turn showed strong connectivity in the fMRI data from the Sentence Picture Matching task. These results support the participation of the right hemisphere in reading comprehension and may provide physiologic support for a distinction between different types of reading comprehension deficits vs difficulties in technical reading.

Graph-theoretical analysis of resting-state fMRI in pediatric obsessive-compulsive disorder

PubMed Central

Armstrong, Casey C.; Moody, Teena D.; Feusner, Jamie D.; McCracken, James T.; Chang, Susanna; Levitt, Jennifer G.; Piacentini, John C.; O'Neill, Joseph

2018-01-01

Background fMRI graph theory reveals resting-state brain networks, but has never been used in pediatric OCD. Methods Whole-brain resting-state fMRI was acquired at 3 T from 21 children with OCD and 20 age-matched healthy controls. BOLD connectivity was analyzed yielding global and local graph-theory metrics across 100 child-based functional nodes. We also compared local metrics between groups in frontopolar, supplementary motor, and sensorimotor cortices, regions implicated in recent neuroimaging and/or brain stimulation treatment studies in OCD. Results As in adults, the global metric small-worldness was significantly (P<0.05) lower in patients than controls, by 13.5% (%mean difference = 100%×(OCD mean – control mean)/control mean). This suggests less efficient information transfer in patients. In addition, modularity was lower in OCD (15.1%, P<0.01), suggesting less granular-- or differently organized-- functional brain parcellation. Higher clustering coefficients (23.9-32.4%, P<0.05) were observed in patients in frontopolar, supplementary motor, sensorimotor, and cortices with lower betweenness centrality (-63.6%, P<0.01) at one frontopolar site. These findings are consistent with more locally intensive connectivity or less interaction with other brain regions at these sites. Limitations Relatively large node size; relatively small sample size, comorbidities in some patients. Conclusions Pediatric OCD patients demonstrate aberrant global and local resting-state network connectivity topologies compared to healthy children. Local results accord with recent views of OCD as a disorder with sensorimotor component. PMID:26773910

Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective.

PubMed

Mash, Lisa E; Reiter, Maya A; Linke, Annika C; Townsend, Jeanne; Müller, Ralph-Axel

2018-05-01

Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018. © 2017 Wiley Periodicals, Inc.

Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective.

PubMed

Gilson, Matthieu

2018-04-01

Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping-determined by EC-for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data-movie viewing versus resting state-illustrates that changes in local variability and changes in brain coordination go hand in hand.

Multi-modal neuroimaging of adolescents with non-suicidal self-injury: Amygdala functional connectivity.

PubMed

Westlund Schreiner, Melinda; Klimes-Dougan, Bonnie; Mueller, Bryon A; Eberly, Lynn E; Reigstad, Kristina M; Carstedt, Patricia A; Thomas, Kathleen M; Hunt, Ruskin H; Lim, Kelvin O; Cullen, Kathryn R

2017-10-15

Non-suicidal self-injury (NSSI) is a significant mental health problem among adolescents. Research is needed to clarify the neurobiology of NSSI and identify candidate neurobiological targets for interventions. Based on prior research implicating heightened negative affect and amygdala hyperactivity in NSSI, we pursued a systems approach to characterize amygdala functional connectivity networks during rest (resting-state functional connectivity [RSFC)]) and a task (task functional connectivity [TFC]) in adolescents with NSSI. We examined amygdala networks in female adolescents with NSSI and healthy controls (n = 45) using resting-state fMRI and a negative emotion face-matching fMRI task designed to activate the amygdala. Connectivity analyses included amygdala RSFC, amygdala TFC, and psychophysiological interactions (PPI) between amygdala connectivity and task conditions. Compared to healthy controls, adolescents with NSSI showed atypical amygdala-frontal connectivity during rest and task; greater amygdala RSFC in supplementary motor area (SMA) and dorsal anterior cingulate; and differential amygdala-occipital connectivity between rest and task. After correcting for depression symptoms, amygdala-SMA RSFC abnormalities, among others, remained significant. This study's limitations include its cross-sectional design and its absence of a psychiatric control group. Using a multi-modal approach, we identified widespread amygdala circuitry anomalies in adolescents with NSSI. While deficits in amygdala-frontal connectivity (driven by depression symptoms) replicates prior work in depression, hyperconnectivity between amygdala and SMA (independent of depression symptoms) has not been previously reported. This circuit may represent an important mechanism underlying the link between negative affect and habitual behaviors. These abnormalities may represent intervention targets for adolescents with NSSI. Copyright © 2017 Elsevier B.V. All rights reserved.

Altered task-based and resting-state amygdala functional connectivity following real-time fMRI amygdala neurofeedback training in major depressive disorder.

PubMed

Young, Kymberly D; Siegle, Greg J; Misaki, Masaya; Zotev, Vadim; Phillips, Raquel; Drevets, Wayne C; Bodurka, Jerzy

2018-01-01

We have previously shown that in participants with major depressive disorder (MDD) trained to upregulate their amygdala hemodynamic response during positive autobiographical memory (AM) recall with real-time fMRI neurofeedback (rtfMRI-nf) training, depressive symptoms diminish. Here, we assessed the effect of rtfMRI-nf on amygdala functional connectivity during both positive AM recall and rest. The current manuscript consists of a secondary analysis on data from our published clinical trial of neurofeedback. Patients with MDD completed two rtfMRI-nf sessions (18 received amygdala rtfMRI-nf, 16 received control parietal rtfMRI-nf). One-week prior-to and following training participants also completed a resting-state fMRI scan. A GLM-based functional connectivity analysis was applied using a seed ROI in the left amygdala. We compared amygdala functional connectivity changes while recalling positive AMs from the baseline run to the final transfer run during rtfMRI-nf training, as well during rest from the baseline to the one-week follow-up visit. Finally, we assessed the correlation between change in depression scores and change in amygdala connectivity, as well as correlations between amygdala regulation success and connectivity changes. Following training, amygdala connectivity during positive AM recall increased with widespread regions in the frontal and limbic network. During rest, amygdala connectivity increased following training within the fronto-temporal-limbic network. During both task and resting-state analyses, amygdala-temporal pole connectivity decreased. We identified increased amygdala-precuneus and amygdala-inferior frontal gyrus connectivity during positive memory recall and increased amygdala-precuneus and amygdala-thalamus connectivity during rest as functional connectivity changes that explained significant variance in symptom improvement. Amygdala-precuneus connectivity changes also explain a significant amount of variance in neurofeedback regulation success. Neurofeedback training to increase amygdala hemodynamic activity during positive AM recall increased amygdala connectivity with regions involved in self-referential, salience, and reward processing. Results suggest future targets for neurofeedback interventions, particularly interventions involving the precuneus.

The hierarchical brain network for face recognition.

PubMed

Zhen, Zonglei; Fang, Huizhen; Liu, Jia

2013-01-01

Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

Neuroticism modulates brain visuo-vestibular and anxiety systems during a virtual rollercoaster task.

PubMed

Riccelli, Roberta; Indovina, Iole; Staab, Jeffrey P; Nigro, Salvatore; Augimeri, Antonio; Lacquaniti, Francesco; Passamonti, Luca

2017-02-01

Different lines of research suggest that anxiety-related personality traits may influence the visual and vestibular control of balance, although the brain mechanisms underlying this effect remain unclear. To our knowledge, this is the first functional magnetic resonance imaging (fMRI) study that investigates how individual differences in neuroticism and introversion, two key personality traits linked to anxiety, modulate brain regional responses and functional connectivity patterns during a fMRI task simulating self-motion. Twenty-four healthy individuals with variable levels of neuroticism and introversion underwent fMRI while performing a virtual reality rollercoaster task that included two main types of trials: (1) trials simulating downward or upward self-motion (vertical motion), and (2) trials simulating self-motion in horizontal planes (horizontal motion). Regional brain activity and functional connectivity patterns when comparing vertical versus horizontal motion trials were correlated with personality traits of the Five Factor Model (i.e., neuroticism, extraversion-introversion, openness, agreeableness, and conscientiousness). When comparing vertical to horizontal motion trials, we found a positive correlation between neuroticism scores and regional activity in the left parieto-insular vestibular cortex (PIVC). For the same contrast, increased functional connectivity between the left PIVC and right amygdala was also detected as a function of higher neuroticism scores. Together, these findings provide new evidence that individual differences in personality traits linked to anxiety are significantly associated with changes in the activity and functional connectivity patterns within visuo-vestibular and anxiety-related systems during simulated vertical self-motion. Hum Brain Mapp 38:715-726, 2017. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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

Resilience and amygdala function in older healthy and depressed adults.

PubMed

Leaver, Amber M; Yang, Hongyu; Siddarth, Prabha; Vlasova, Roza M; Krause, Beatrix; St Cyr, Natalie; Narr, Katherine L; Lavretsky, Helen

2018-09-01

Previous studies suggest that low emotional resilience may correspond with increased or over-active amygdala function. Complementary studies suggest that emotional resilience increases with age; older adults tend to have decreased attentional bias to negative stimuli compared to younger adults. Amygdala nuclei and related brain circuits have been linked to negative affect, and depressed patients have been demonstrated to have abnormal amygdala function. In the current study, we correlated psychological resilience measures with amygdala function measured with resting-state arterial spin-labelled (ASL) and blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in older adults with and without depression. Specifically, we targeted the basolateral, centromedial, and superficial nuclei groups of the amygdala, which have different functions and brain connections. High levels of psychological resilience correlated with lower basal levels of amygdala activity measured with ASL fMRI. High resilience also correlated with decreased connectivity between amygdala nuclei and the ventral default-mode network independent of depression status. Instead, lower depression symptoms were associated with higher connectivity between the amygdalae and dorsal frontal networks. Future multi-site studies with larger sample size and improved neuroimaging technologies are needed. Longitudinal studies that target resilience to naturalistic stressors will also be a powerful contribution to the field. Our results suggest that resilience in older adults is more closely related to function in ventral amygdala networks, while late-life depression is related to reduced connectivity between the amygdala and dorsal frontal regions. Copyright © 2018 Elsevier B.V. All rights reserved.

Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity.

PubMed

Wang, Danny J J; Jann, Kay; Fan, Chang; Qiao, Yang; Zang, Yu-Feng; Lu, Hanbing; Yang, Yihong

2018-01-01

Recently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of electrophysiology and fMRI signals and their relations to functional connectivity (FC). MSE and FC analyses were performed on simulated data using neural mass model based brain network model with the Brain Dynamics Toolbox, on animal models with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations, and on resting-state fMRI data from the Human Connectome Project. Our results show that the complexity of regional electrophysiology and fMRI signals is positively correlated with network FC. The associations between MSE and FC are dependent on the temporal scales or frequencies, with higher associations between MSE and FC at lower temporal frequencies. Our results from theoretical modeling, animal experiment and human fMRI indicate that (1) Regional neural complexity and network FC may be two related aspects of brain's information processing: the more complex regional neural activity, the higher FC this region has with other brain regions; (2) MSE at high and low frequencies may represent local and distributed information processing across brain regions. Based on literature and our data, we propose that the complexity of regional neural signals may serve as an index of the brain's capacity of information processing-increased complexity may indicate greater transition or exploration between different states of brain networks, thereby a greater propensity for information processing.

The role of the DLPFC in inductive reasoning of MCI patients and normal agings: an fMRI study.

PubMed

Yang, YanHui; Liang, PeiPeng; Lu, ShengFu; Li, KunCheng; Zhong, Ning

2009-08-01

Previous studies of young people have revealed that the left dorsolateral prefrontal cortex (DLPFC) plays an important role in inductive reasoning. An fMRI experiment was performed in this study to examine whether the left DLPFC was involved in inductive reasoning of MCI patients and normal aging, and whether the activation pattern of this region was different between MCI patients and normal aging. The fMRI results indicated that MCI patients had no difference from normal aging in behavior performance (reaction time and accuracy) and the activation pattern of DLPFC. However, the BOLD response of the DLPFC region for MCI patients was weaker than that for normal aging, and the functional connectivity between the bilateral DLPFC regions for MCI patients was significantly higher than for normal aging. Taken together, these results indicated that DLPFC plays an important role in inductive reasoning of aging, and the functional abnormity of DLPFC may be an earlier marker of MCI before structural alterations.

Detecting altered connectivity patterns in HIV associated neurocognitive impairment using mutual connectivity analysis

NASA Astrophysics Data System (ADS)

Abidin, Anas Zainul; D'Souza, Adora M.; Nagarajan, Mahesh B.; Wismüller, Axel

2016-03-01

The use of functional Magnetic Resonance Imaging (fMRI) has provided interesting insights into our understanding of the brain. In clinical setups these scans have been used to detect and study changes in the brain network properties in various neurological disorders. A large percentage of subjects infected with HIV present cognitive deficits, which are known as HIV associated neurocognitive disorder (HAND). In this study we propose to use our novel technique named Mutual Connectivity Analysis (MCA) to detect differences in brain networks in subjects with and without HIV infection. Resting state functional MRI scans acquired from 10 subjects (5 HIV+ and 5 HIV-) were subject to standard preprocessing routines. Subsequently, the average time-series for each brain region of the Automated Anatomic Labeling (AAL) atlas are extracted and used with the MCA framework to obtain a graph characterizing the interactions between them. The network graphs obtained for different subjects are then compared using Network-Based Statistics (NBS), which is an approach to detect differences between graphs edges while controlling for the family-wise error rate when mass univariate testing is performed. Applying this approach on the graphs obtained yields a single network encompassing 42 nodes and 65 edges, which is significantly different between the two subject groups. Specifically connections to the regions in and around the basal ganglia are significantly decreased. Also some nodes corresponding to the posterior cingulate cortex are affected. These results are inline with our current understanding of pathophysiological mechanisms of HIV associated neurocognitive disease (HAND) and other HIV based fMRI connectivity studies. Hence, we illustrate the applicability of our novel approach with network-based statistics in a clinical case-control study to detect differences connectivity patterns.

Local brain connectivity across development in autism spectrum disorder: A cross-sectional investigation.

PubMed

Dajani, Dina R; Uddin, Lucina Q

2016-01-01

There is a general consensus that autism spectrum disorder (ASD) is accompanied by alterations in brain connectivity. Much of the neuroimaging work has focused on assessing long-range connectivity disruptions in ASD. However, evidence from both animal models and postmortem examination of the human brain suggests that local connections may also be disrupted in individuals with the disorder. Here, we investigated how regional homogeneity (ReHo), a measure of similarity of a voxel's timeseries to its nearest neighbors, varies across age in individuals with ASD and typically developing (TD) individuals using a cross-sectional design. Resting-state fMRI data obtained from a publicly available database were analyzed to determine group differences in ReHo between three age cohorts: children, adolescents, and adults. In typical development, ReHo across the entire brain was higher in children than in adolescents and adults. In contrast, children with ASD exhibited marginally lower ReHo than TD children, while adolescents and adults with ASD exhibited similar levels of local connectivity as age-matched neurotypical individuals. During all developmental stages, individuals with ASD exhibited lower local connectivity in sensory processing brain regions and higher local connectivity in complex information processing regions. Further, higher local connectivity in ASD corresponded to more severe ASD symptomatology. These results demonstrate that local connectivity is disrupted in ASD across development, with the most pronounced differences occurring in childhood. Developmental changes in ReHo do not mirror findings from fMRI studies of long-range connectivity in ASD, pointing to a need for more nuanced accounts of brain connectivity alterations in the disorder. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Altered resting-state functional connectivity of the frontal-striatal reward system in social anxiety disorder.

PubMed

Manning, Joshua; Reynolds, Gretchen; Saygin, Zeynep M; Hofmann, Stefan G; Pollack, Mark; Gabrieli, John D E; Whitfield-Gabrieli, Susan

2015-01-01

We investigated differences in the intrinsic functional brain organization (functional connectivity) of the human reward system between healthy control participants and patients with social anxiety disorder. Functional connectivity was measured in the resting-state via functional magnetic resonance imaging (fMRI). 53 patients with social anxiety disorder and 33 healthy control participants underwent a 6-minute resting-state fMRI scan. Functional connectivity of the reward system was analyzed by calculating whole-brain temporal correlations with a bilateral nucleus accumbens seed and a ventromedial prefrontal cortex seed. Patients with social anxiety disorder, relative to the control group, had (1) decreased functional connectivity between the nucleus accumbens seed and other regions associated with reward, including ventromedial prefrontal cortex; (2) decreased functional connectivity between the ventromedial prefrontal cortex seed and lateral prefrontal regions, including the anterior and dorsolateral prefrontal cortices; and (3) increased functional connectivity between both the nucleus accumbens seed and the ventromedial prefrontal cortex seed with more posterior brain regions, including anterior cingulate cortex. Social anxiety disorder appears to be associated with widespread differences in the functional connectivity of the reward system, including markedly decreased functional connectivity between reward regions and between reward regions and lateral prefrontal cortices, and markedly increased functional connectivity between reward regions and posterior brain regions.

Opposing Amygdala and Ventral Striatum Connectivity during Emotion Identification

ERIC Educational Resources Information Center

Satterthwaite, Theodore D.; Wolf, Daniel H.; Pinkham, Amy E.; Ruparel, Kosha; Elliott, Mark A.; Valdez, Jeffrey N.; Overton, Eve; Seubert, Janina; Gur, Raquel E.; Gur, Ruben C.; Loughead, James

2011-01-01

Lesion and electrophysiological studies in animals provide evidence of opposing functions for subcortical nuclei such as the amygdala and ventral striatum, but the implications of these findings for emotion identification in humans remain poorly described. Here we report a high-resolution fMRI study in a sample of 39 healthy subjects who performed…

Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach

PubMed Central

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

2014-01-01

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

Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

PubMed

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

2018-06-02

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

Fetal functional imaging portrays heterogeneous development of emerging human brain networks

PubMed Central

Jakab, András; Schwartz, Ernst; Kasprian, Gregor; Gruber, Gerlinde M.; Prayer, Daniela; Schöpf, Veronika; Langs, Georg

2014-01-01

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26–29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity. PMID:25374531

Fetal functional imaging portrays heterogeneous development of emerging human brain networks.

PubMed

Jakab, András; Schwartz, Ernst; Kasprian, Gregor; Gruber, Gerlinde M; Prayer, Daniela; Schöpf, Veronika; Langs, Georg

2014-01-01

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26-29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity.

Sex differences in effective fronto-limbic connectivity during negative emotion processing.

PubMed

Lungu, Ovidiu; Potvin, Stéphane; Tikàsz, Andràs; Mendrek, Adrianna

2015-12-01

In view of the greater prevalence of depression and anxiety disorders in women than in men, functional magnetic resonance imaging (fMRI) studies have examined sex-differences in brain activations during emotion processing. Comparatively, sex-differences in brain connectivity received little attention, despite evidence for important fronto-limbic connections during emotion processing across sexes. Here, we investigated sex-differences in fronto-limbic connectivity during negative emotion processing. Forty-six healthy individuals (25 women, 21 men) viewed negative, positive and neutral images during an fMRI session. Effective connectivity between significantly activated regions was examined using Granger causality and psychophysical interaction analyses. Sex steroid hormones and feminine-masculine traits were also measured. Subjective ratings of negative emotional images were higher in women than in men. Across sexes, significant activations were observed in the dorso-medial prefrontal cortex (dmPFC) and the right amygdala. Granger connectivity from right amygdala was significantly greater than that from dmPFC during the 'high negative' condition, an effect driven by men. Magnitude of this effect correlated negatively with highly negative image ratings and feminine traits and positively with testosterone levels. These results highlight critical sex differences in brain connectivity during negative emotion processing and point to the fact that both biological (sex steroid hormones) and psychosocial (gender role and identity) variables contribute to them. As the dmPFC is involved in social cognition and action planning, and the amygdala-in threat detection, the connectivity results suggest that compared to women, men have a more evaluative, rather than purely affective, brain response during negative emotion processing. Copyright © 2015 Elsevier Ltd. 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

Increased ventral-striatal activity during monetary decision making is a marker of problem poker gambling severity.

PubMed

Brevers, Damien; Noël, Xavier; He, Qinghua; Melrose, James A; Bechara, Antoine

2016-05-01

The aim of this study was to examine the impact of different neural systems on monetary decision making in frequent poker gamblers, who vary in their degree of problem gambling. Fifteen frequent poker players, ranging from non-problem to high-problem gambling, and 15 non-gambler controls were scanned using functional magnetic resonance imaging (fMRI) while performing the Iowa Gambling Task (IGT). During IGT deck selection, between-group fMRI analyses showed that frequent poker gamblers exhibited higher ventral-striatal but lower dorsolateral prefrontal and orbitofrontal activations as compared with controls. Moreover, using functional connectivity analyses, we observed higher ventral-striatal connectivity in poker players, and in regions involved in attentional/motor control (posterior cingulate), visual (occipital gyrus) and auditory (temporal gyrus) processing. In poker gamblers, scores of problem gambling severity were positively associated with ventral-striatal activations and with the connectivity between the ventral-striatum seed and the occipital fusiform gyrus and the middle temporal gyrus. Present results are consistent with findings from recent brain imaging studies showing that gambling disorder is associated with heightened motivational-reward processes during monetary decision making, which may hamper one's ability to moderate his level of monetary risk taking. © 2015 Society for the Study of Addiction.

A computational study of whole-brain connectivity in resting state and task fMRI

PubMed Central

Goparaju, Balaji; Rana, Kunjan D.; Calabro, Finnegan J.; Vaina, Lucia Maria

2014-01-01

Background We compared the functional brain connectivity produced during resting-state in which subjects were not actively engaged in a task with that produced while they actively performed a visual motion task (task-state). Material/Methods In this paper we employed graph-theoretical measures and network statistics in novel ways to compare, in the same group of human subjects, functional brain connectivity during resting-state fMRI with brain connectivity during performance of a high level visual task. We performed a whole-brain connectivity analysis to compare network statistics in resting and task states among anatomically defined Brodmann areas to investigate how brain networks spanning the cortex changed when subjects were engaged in task performance. Results In the resting state, we found strong connectivity among the posterior cingulate cortex (PCC), precuneus, medial prefrontal cortex (MPFC), lateral parietal cortex, and hippocampal formation, consistent with previous reports of the default mode network (DMN). The connections among these areas were strengthened while subjects actively performed an event-related visual motion task, indicating a continued and strong engagement of the DMN during task processing. Regional measures such as degree (number of connections) and betweenness centrality (number of shortest paths), showed that task performance induces stronger inter-regional connections, leading to a denser processing network, but that this does not imply a more efficient system as shown by the integration measures such as path length and global efficiency, and from global measures such as small-worldness. Conclusions In spite of the maintenance of connectivity and the “hub-like” behavior of areas, our results suggest that the network paths may be rerouted when performing the task condition. PMID:24947491

Brain Functional Connectivity Is Modified by a Hypocaloric Mediterranean Diet and Physical Activity in Obese Women.

PubMed

García-Casares, Natalia; Bernal-López, María R; Roé-Vellvé, Nuria; Gutiérrez-Bedmar, Mario; Fernández-García, Jose C; García-Arnés, Juan A; Ramos-Rodriguez, José R; Alfaro, Francisco; Santamaria-Fernández, Sonia; Steward, Trevor; Jiménez-Murcia, Susana; Garcia-Garcia, Isabel; Valdivielso, Pedro; Fernández-Aranda, Fernando; Tinahones, Francisco J; Gómez-Huelgas, Ricardo

2017-07-01

Functional magnetic resonance imaging (fMRI) in the resting state has shown altered brain connectivity networks in obese individuals. However, the impact of a Mediterranean diet on cerebral connectivity in obese patients when losing weight has not been previously explored. The aim of this study was to examine the connectivity between brain structures before and six months after following a hypocaloric Mediterranean diet and physical activity program in a group of sixteen obese women aged 46.31 ± 4.07 years. Before and after the intervention program, the body mass index (BMI) (kg/m²) was 38.15 ± 4.7 vs. 34.18 ± 4.5 ( p < 0.02), and body weight (kg) was 98.5 ± 13.1 vs. 88.28 ± 12.2 ( p < 0.03). All subjects underwent a pre- and post-intervention fMRI under fasting conditions. Functional connectivity was assessed using seed-based correlations. After the intervention, we found decreased connectivity between the left inferior parietal cortex and the right temporal cortex ( p < 0.001), left posterior cingulate ( p < 0.001), and right posterior cingulate ( p < 0.03); decreased connectivity between the left superior frontal gyrus and the right temporal cortex ( p < 0.01); decreased connectivity between the prefrontal cortex and the somatosensory cortex ( p < 0.025); and decreased connectivity between the left and right posterior cingulate ( p < 0.04). Results were considered significant at a voxel-wise threshold of p ≤ 0.05, and a cluster-level family-wise error correction for multiple comparisons of p ≤ 0.05. In conclusion, functional connectivity between brain structures involved in the pathophysiology of obesity (the inferior parietal lobe, posterior cingulate, temporo-insular cortex, prefrontal cortex) may be modified by a weight loss program including a Mediterranean diet and physical exercise.

Effects of resting state condition on reliability, trait specificity, and network connectivity of brain function measured with arterial spin labeled perfusion MRI.

PubMed

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

2018-06-01

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

Plastic modulation of PTSD resting-state networks and subjective wellbeing by EEG neurofeedback.

PubMed

Kluetsch, R C; Ros, T; Théberge, J; Frewen, P A; Calhoun, V D; Schmahl, C; Jetly, R; Lanius, R A

2014-08-01

Electroencephalographic (EEG) neurofeedback training has been shown to produce plastic modulations in salience network and default mode network functional connectivity in healthy individuals. In this study, we investigated whether a single session of neurofeedback training aimed at the voluntary reduction of alpha rhythm (8-12 Hz) amplitude would be related to differences in EEG network oscillations, functional MRI (fMRI) connectivity, and subjective measures of state anxiety and arousal in a group of individuals with post-traumatic stress disorder (PTSD). Twenty-one individuals with PTSD related to childhood abuse underwent 30 min of EEG neurofeedback training preceded and followed by a resting-state fMRI scan. Alpha desynchronizing neurofeedback was associated with decreased alpha amplitude during training, followed by a significant increase ('rebound') in resting-state alpha synchronization. This rebound was linked to increased calmness, greater salience network connectivity with the right insula, and enhanced default mode network connectivity with bilateral posterior cingulate, right middle frontal gyrus, and left medial prefrontal cortex. Our study represents a first step in elucidating the potential neurobehavioural mechanisms mediating the effects of neurofeedback treatment on regulatory systems in PTSD. Moreover, it documents for the first time a spontaneous EEG 'rebound' after neurofeedback, pointing to homeostatic/compensatory mechanisms operating in the brain. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Disrupted Brain Functional Network Architecture in Chronic Tinnitus Patients

PubMed Central

Chen, Yu-Chen; Feng, Yuan; Xu, Jin-Jing; Mao, Cun-Nan; Xia, Wenqing; Ren, Jun; Yin, Xindao

2016-01-01

Purpose: Resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated the disruptions of multiple brain networks in tinnitus patients. Nonetheless, several studies found no differences in network processing between tinnitus patients and healthy controls (HCs). Its neural bases are poorly understood. To identify aberrant brain network architecture involved in chronic tinnitus, we compared the resting-state fMRI (rs-fMRI) patterns of tinnitus patients and HCs. Materials and Methods: Chronic tinnitus patients (n = 24) with normal hearing thresholds and age-, sex-, education- and hearing threshold-matched HCs (n = 22) participated in the current study and underwent the rs-fMRI scanning. We used degree centrality (DC) to investigate functional connectivity (FC) strength of the whole-brain network and Granger causality to analyze effective connectivity in order to explore directional aspects involved in tinnitus. Results: Compared to HCs, we found significantly increased network centrality in bilateral superior frontal gyrus (SFG). Unidirectionally, the left SFG revealed increased effective connectivity to the left middle orbitofrontal cortex (OFC), left posterior lobe of cerebellum (PLC), left postcentral gyrus, and right middle occipital gyrus (MOG) while the right SFG exhibited enhanced effective connectivity to the right supplementary motor area (SMA). In addition, the effective connectivity from the bilateral SFG to the OFC and SMA showed positive correlations with tinnitus distress. Conclusions: Rs-fMRI provides a new and novel method for identifying aberrant brain network architecture. Chronic tinnitus patients have disrupted FC strength and causal connectivity mostly in non-auditory regions, especially the prefrontal cortex (PFC). The current findings will provide a new perspective for understanding the neuropathophysiological mechanisms in chronic tinnitus. PMID:27458377

Emotional processing and brain activity in youth at high risk for alcoholism.

PubMed

Cservenka, Anita; Fair, Damien A; Nagel, Bonnie J

2014-07-01

Even in the absence of heavy alcohol use, youth with familial alcoholism (family history positive [FHP]) exhibit atypical brain functioning and behavior. Although emotional and cognitive systems are affected in alcohol use disorders (AUDs), little attention has focused on whether brain and behavior phenotypes related to the interplay between affective and executive functioning may be a premorbid risk factor for the development of AUDs in FHP youth. Twenty-four FHP and 22 family history negative (FHN) 12- to 16-year-old adolescents completed study procedures. After exclusion of participants with clinically significant depressive symptoms and those who did not meet performance criteria during an Emotional Go-NoGo task, 19 FHP and 17 FHN youth were included in functional magnetic resonance imaging (fMRI) analyses. Resting state functional connectivity MRI, using amygdalar seed regions, was analyzed in 16 FHP and 18 FHN youth, after exclusion of participants with excessive head movement. fMRI showed that brain activity in FHP youth, compared with FHN peers, was reduced during emotional processing in the superior temporal cortex, as well as during cognitive control within emotional contexts in frontal and striatal regions. Group differences in resting state amygdalar connectivity were seen bilaterally between FHP and FHN youth. In FHP youth, reduced resting state synchrony between the left amygdala and left superior frontal gyrus was related to poorer response inhibition, as measured during the fMRI task. To our knowledge, this is the first study to examine emotion-cognition interactions and resting state functional connectivity in FHP youth. Findings from this research provide insight into neural and behavioral phenotypes associated with emotional processing in familial alcoholism, which may relate to increased risk of developing AUDs. Copyright © 2014 by the Research Society on Alcoholism.

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

Novel MRI methodology to detect human whole-brain connectivity changes after ingestion of fructose or glucose

NASA Astrophysics Data System (ADS)

Tsao, Sinchai; Wilkins, Bryce; Page, Kathleen A.; Singh, Manbir

2012-03-01

A novel MRI protocol has been developed to investigate the differential effects of glucose or fructose consumption on whole-brain functional brain connectivity. A previous study has reported a decrease in the fMRI blood oxygen level dependent (BOLD) signal of the hypothalamus following glucose ingestion, but due to technical limitations, was restricted to a single slice covering the hypothalamus, and thus unable to detect whole-brain connectivity. In another previous study, a protocol was devised to acquire whole-brain fMRI data following food intake, but only after restricting image acquisition to an MR sampling or repetition time (TR) of 20s, making the protocol unsuitable to detect functional connectivity above 0.025Hz. We have successfully implemented a continuous 36-min, 40 contiguous slices, whole-brain BOLD acquisition protocol on a 3T scanner with TR=4.5s to ensure detection of up to 0.1Hz frequencies for whole-brain functional connectivity analysis. Human data were acquired first with ingestion of water only, followed by a glucose or fructose drink within the scanner, without interrupting the scanning. Whole-brain connectivity was analyzed using standard correlation methodology in the 0.01-0.1 Hz range. The correlation coefficient differences between fructose and glucose ingestion among targeted regions were converted to t-scores using the water-only correlation coefficients as a null condition. Results show a dramatic increase in the hypothalamic connectivity to the hippocampus, amygdala, insula, caudate and the nucleus accumben for fructose over glucose. As these regions are known to be key components of the feeding and reward brain circuits, these results suggest a preference for fructose ingestion.

Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal.

PubMed

Wen, Haiguang; Liu, Zhongming

2016-06-01

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

A Simple fMRI Compatible Robotic Stimulator to Study the Neural Mechanisms of Touch and Pain.

PubMed

Riillo, F; Bagnato, C; Allievi, A G; Takagi, A; Fabrizi, L; Saggio, G; Arichi, T; Burdet, E

2016-08-01

This paper presents a simple device for the investigation of the human somatosensory system with functional magnetic imaging (fMRI). PC-controlled pneumatic actuation is employed to produce innocuous or noxious mechanical stimulation of the skin. Stimulation patterns are synchronized with fMRI and other relevant physiological measurements like electroencephalographic activity and vital physiological parameters. The system allows adjustable regulation of stimulation parameters and provides consistent patterns of stimulation. A validation experiment demonstrates that the system safely and reliably identifies clusters of functional activity in brain regions involved in the processing of pain. This new device is inexpensive, portable, easy-to-assemble and customizable to suit different experimental requirements. It provides robust and consistent somatosensory stimulation, which is of crucial importance to investigating the mechanisms of pain and its strong connection with the sense of touch.

Tract-based Spatial Statistics and fMRI Analysis in Patients with Small Cell Lung Cancer before Prophylactic Cranial Irradiation

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

The Association of Macro- and Microstructure of the Corpus Callosum and Language Lateralisation

ERIC Educational Resources Information Center

Westerhausen, Rene; Kreuder, Frank; Sequeira, Sarah Dos Santos; Walter, Christof; Woerner, Wolfgang; Wittling, Ralf Arne; Schweiger, Elisabeth; Wittling, Werner

2006-01-01

The present study aimed to examine how differences in functional lateralisation of language are related to interindividual variations in interhemispheric connectivity. Utilising an fMRI silent word-generation paradigm, 89 left- and right-handed subjects were subdivided into four lateralisation subgroups. Applying morphological and diffusion-tensor…

Enhanced limbic/impaired cortical-loop connection onto the hippocampus of NHE rats: Application of resting-state functional connectivity in a preclinical ADHD model.

PubMed

Zoratto, F; Palombelli, G M; Ruocco, L A; Carboni, E; Laviola, G; Sadile, A G; Adriani, W; Canese, R

2017-08-30

Due to a hyperfunctioning mesocorticolimbic system, Naples-High-Excitability (NHE) rats have been proposed to model for the meso-cortical variant of attention deficit/hyperactivity disorder (ADHD). Compared to Naples Random-Bred (NRB) controls, NHE rats show hyperactivity, impaired non-selective attention (Aspide et al., 1998), and impaired selective spatial attention (Ruocco et al., 2009a, 2014). Alteration in limbic functions has been proposed; however, resulting unbalance among forebrain areas has not been assessed yet. By resting-state functional Magnetic-Resonance Imaging (fMRI) in vivo, we investigated the connectivity of neuronal networks belonging to limbic vs. cortical loops in NHE and NRB rats (n=10 each). Notably, resting-state fMRI was applied using a multi-slice sagittal, gradient-echo sequence. Voxel-wise connectivity maps at rest, based on temporal correlation among fMRI time-series, were computed by seeding the hippocampus (Hip), nucleus accumbens (NAcc), dorsal striatum (dStr), amygdala (Amy) and dorsal/medial prefrontal cortex (PFC), both hemispheres. To summarize patterns of altered connection, clearly directional connectivity was evident within the cortical loop: bilaterally and specularly, from orbital and dorsal PFCs through dStr and hence towards Hip. Such network communication was reduced in NHE rats (also, with less mesencephalic/pontine innervation). Conversely, enhanced network activity emerged within the limbic loop of NHE rats: from left PFC, both through the NAcc and directly, to the Hip (all of which received greater ventral tegmental innervation, likely dopamine). Together with tuned-down cortical loop, this potentiated limbic loop may serve a major role in controlling ADHD-like behavioral symptoms in NHE rats. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Multimodal effective connectivity analysis reveals seizure focus and propagation in musicogenic epilepsy.

PubMed

Klamer, Silke; Rona, Sabine; Elshahabi, Adham; Lerche, Holger; Braun, Christoph; Honegger, Jürgen; Erb, Michael; Focke, Niels K

2015-06-01

Dynamic causal modeling (DCM) is a method to non-invasively assess effective connectivity between brain regions. 'Musicogenic epilepsy' is a rare reflex epilepsy syndrome in which seizures can be elicited by musical stimuli and thus represents a unique possibility to investigate complex human brain networks and test connectivity analysis tools. We investigated effective connectivity in a case of musicogenic epilepsy using DCM for fMRI, high-density (hd-) EEG and MEG and validated results with intracranial EEG recordings. A patient with musicogenic seizures was examined using hd-EEG/fMRI and simultaneous '256-channel hd-EEG'/'whole head MEG' to characterize the epileptogenic focus and propagation effects using source analysis techniques and DCM. Results were validated with invasive EEG recordings. We recorded one seizure with hd-EEG/fMRI and four auras with hd-EEG/MEG. During the seizures, increases of activity could be observed in the right mesial temporal region as well as bilateral mesial frontal regions. Effective connectivity analysis of fMRI and hd-EEG/MEG indicated that right mesial temporal neuronal activity drives changes in the frontal areas consistently in all three modalities, which was confirmed by the results of invasive EEG recordings. Seizures thus seem to originate in the right mesial temporal lobe and propagate to mesial frontal regions. Using DCM for fMRI, hd-EEG and MEG we were able to correctly localize focus and propagation of epileptic activity and thereby characterize the underlying epileptic network in a patient with musicogenic epilepsy. The concordance between all three functional modalities validated by invasive monitoring is noteworthy, both for epileptic activity spread as well as for effective connectivity analysis in general. Copyright © 2015 Elsevier Inc. All rights reserved.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing.

PubMed

Starck, Tuomo; Nikkinen, Juha; Rahko, Jukka; Remes, Jukka; Hurtig, Tuula; Haapsamo, Helena; Jussila, Katja; Kuusikko-Gauffin, Sanna; Mattila, Marja-Leena; Jansson-Verkasalo, Eira; Pauls, David L; Ebeling, Hanna; Moilanen, Irma; Tervonen, Osmo; Kiviniemi, Vesa J

2013-01-01

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

Altered effective connectivity within default mode network in major depression disorder

NASA Astrophysics Data System (ADS)

Li, Liang; Li, Baojuan; Bai, Yuanhan; Wang, Huaning; Zhang, Linchuan; Cui, Longbiao; Lu, Hongbing

2016-03-01

Understanding the neural basis of Major Depressive Disorder (MDD) is important for the diagnosis and treatment of this mental disorder. The default mode network (DMN) is considered to be highly involved in the MDD. To find directed interaction between DMN regions associated with the development of MDD, the effective connectivity within the DMN of the MDD patients and matched healthy controls was estimated by using a recently developed spectral dynamic causal modeling. Sixteen patients with MDD and sixteen matched healthy control subjects were included in this study. While the control group underwent the resting state fMRI scan just once, all patients underwent resting state fMRI scans before and after two months' treatment. The spectral dynamic causal modeling was used to estimate directed connections between four DMN nodes. Statistical analysis on connection strengths indicated that efferent connections from the medial frontal cortex (MFC) to posterior cingulate cortex (PCC) and to right parietal cortex (RPC) were significant higher in pretreatment MDD patients than those of the control group. After two-month treatment, the efferent connections from the MFC decreased significantly, while those from the left parietal cortex (LPC) to MFC, PCC and RPC showed a significant increase. These findings suggest that the MFC may play an important role for inhibitory conditioning of the DMN, which was disrupted in MDD patients. It also indicates that disrupted suppressive function of the MFC could be effectively restored after two-month treatment.

Multimodal frontostriatal connectivity underlies individual differences in self-esteem

PubMed Central

Heatherton, Todd F.

2015-01-01

A heightened sense of self-esteem is associated with a reduced risk for several types of affective and psychiatric disorders, including depression, anxiety and eating disorders. However, little is known about how brain systems integrate self-referential processing and positive evaluation to give rise to these feelings. To address this, we combined diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) to test how frontostriatal connectivity reflects long-term trait and short-term state aspects of self-esteem. Using DTI, we found individual variability in white matter structural integrity between the medial prefrontal cortex and the ventral striatum was related to trait measures of self-esteem, reflecting long-term stability of self-esteem maintenance. Using fMRI, we found that functional connectivity of these regions during positive self-evaluation was related to current feelings of self-esteem, reflecting short-term state self-esteem. These results provide convergent anatomical and functional evidence that self-esteem is related to the connectivity of frontostriatal circuits and suggest that feelings of self-worth may emerge from neural systems integrating information about the self with positive affect and reward. This information could potentially inform the etiology of diminished self-esteem underlying multiple psychiatric conditions and inform future studies of evaluative self-referential processing. PMID:24795440

On nodes and modes in resting state fMRI

PubMed Central

Friston, Karl J.; Kahan, Joshua; Razi, Adeel; Stephan, Klaas Enno; Sporns, Olaf

2014-01-01

This paper examines intrinsic brain networks in light of recent developments in the characterisation of resting state fMRI timeseries — and simulations of neuronal fluctuations based upon the connectome. Its particular focus is on patterns or modes of distributed activity that underlie functional connectivity. We first demonstrate that the eigenmodes of functional connectivity – or covariance among regions or nodes – are the same as the eigenmodes of the underlying effective connectivity, provided we limit ourselves to symmetrical connections. This symmetry constraint is motivated by appealing to proximity graphs based upon multidimensional scaling. Crucially, the principal modes of functional connectivity correspond to the dynamically unstable modes of effective connectivity that decay slowly and show long term memory. Technically, these modes have small negative Lyapunov exponents that approach zero from below. Interestingly, the superposition of modes – whose exponents are sampled from a power law distribution – produces classical 1/f (scale free) spectra. We conjecture that the emergence of dynamical instability – that underlies intrinsic brain networks – is inevitable in any system that is separated from external states by a Markov blanket. This conjecture appeals to a free energy formulation of nonequilibrium steady-state dynamics. The common theme that emerges from these theoretical considerations is that endogenous fluctuations are dominated by a small number of dynamically unstable modes. We use this as the basis of a dynamic causal model (DCM) of resting state fluctuations — as measured in terms of their complex cross spectra. In this model, effective connectivity is parameterised in terms of eigenmodes and their Lyapunov exponents — that can also be interpreted as locations in a multidimensional scaling space. Model inversion provides not only estimates of edges or connectivity but also the topography and dimensionality of the underlying scaling space. Here, we focus on conceptual issues with simulated fMRI data and provide an illustrative application using an empirical multi-region timeseries. PMID:24862075

The functional connectivity of semantic task changes in the recovery from stroke aphasia

NASA Astrophysics Data System (ADS)

Lu, Jie; Wu, Xia; Yao, Li; Li, Kun-Cheng; Shu, Hua; Dong, Qi

2007-03-01

Little is known about the difference of functional connectivity of semantic task between the recovery aphasic patients and normal subject. In this paper, an fMRI experiment was performed in a patient with aphasia following a left-sided ischemic lesion and normal subject. Picture naming was used as semantic activation task in this study. We compared the preliminary functional connectivity results of the recovery aphasic patient with the normal subject. The fMRI data were separated by independent component analysis (ICA) into 90 components. According to our experience and other papers, we chose a region of interest (ROI) of semantic (x=-57, y=15, z=8, r=11mm). From the 90 components, we chose one component as the functional connectivity of the semantic ROI according to one criterion. The criterion is the mean value of the voxels in the ROI. So the component of the highest mean value of the ROI is the functional connectivity of the ROI. The voxel with its value higher than 2.4 was thought as activated (p<0.05). And the functional connectivity networks of the normal subjects were t-tested as group network. From the result, we can know the semantic functional connectivity of stroke aphasic patient and normal subjects are different. The activated areas of the left inferior frontal gyrus and inferior/middle temporal gyrus are larger than the ones of normal. The activated area of the right inferior frontal gyrus is smaller than the ones of normal. The functional connectivity of stroke aphasic patient under semantic condition is different with the normal one. The focus of the stroke aphasic patient can affect the functional connectivity.

Functional magnetic resonance imaging.

PubMed

Buchbinder, Bradley R

2016-01-01

Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks. © 2016 Elsevier B.V. All rights reserved.

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

PubMed Central

Meszlényi, Regina J.; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network. PMID:29089883

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

PubMed

Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

Consolidation in older adults depends upon competition between resting-state networks

PubMed Central

Jacobs, Heidi I. L.; Dillen, Kim N. H.; Risius, Okka; Göreci, Yasemin; Onur, Oezguer A.; Fink, Gereon R.; Kukolja, Juraj

2015-01-01

Memory encoding and retrieval problems are inherent to aging. To date, however, the effect of aging upon the neural correlates of forming memory traces remains poorly understood. Resting-state fMRI connectivity can be used to investigate initial consolidation. We compared within and between network connectivity differences between healthy young and older participants before encoding, after encoding and before retrieval by means of resting-state fMRI. Alterations over time in the between-network connectivity analyses correlated with retrieval performance, whereas within-network connectivity did not: a higher level of negative coupling or competition between the default mode and the executive networks during the after encoding condition was associated with increased retrieval performance in the older adults, but not in the young group. Data suggest that the effective formation of memory traces depends on an age-dependent, dynamic reorganization of the interaction between multiple, large-scale functional networks. Our findings demonstrate that a cross-network based approach can further the understanding of the neural underpinnings of aging-associated memory decline. PMID:25620930

A Single Session of rTMS Enhances Small-Worldness in Writer's Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph.

PubMed

Bharath, Rose D; Panda, Rajanikant; Reddam, Venkateswara Reddy; Bhaskar, M V; Gohel, Suril; Bhardwaj, Sujas; Prajapati, Arvind; Pal, Pramod Kumar

2017-01-01

Background and Purpose : Repetitive transcranial magnetic stimulation (rTMS) induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI. Method : Simultaneous EEG-fMRI was acquired in duplicate before (R1) and after (R2) a single session of rTMS in 14 patients with Writer's Cramp (WC). Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI). Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients. Result : A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI ( p < 0.05). Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe. Conclusion : Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo . Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not "noise".

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

PubMed Central

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

2016-01-01

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

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

PubMed

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

2017-01-15

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

Relationships between Translation and Transcription Processes during fMRI Connectivity Scanning and Coded Translation and Transcription in Writing Products after Scanning in Children with and without Transcription Disabilities

PubMed Central

Wallis, Peter; Richards, Todd; Boord, Peter; Abbott, Robert; Berninger, Virginia

2018-01-01

Students with transcription disabilities (dysgraphia/impaired handwriting, n = 13 or dyslexia/impaired word spelling, n = 16) or without transcription disabilities (controls) completed transcription and translation (idea generating, planning, and creating) writing tasks during fMRI connectivity scanning and compositions after scanning, which were coded for transcription and translation variables. Compositions in both groups showed diversity in genre beyond usual narrative-expository distinction; groups differed in coded transcription but not translation variables. For the control group specific transcription or translation tasks during scanning correlated with corresponding coded transcription or translation skills in composition, but connectivity during scanning was not correlated with coded handwriting during composing in dysgraphia group and connectivity during translating was not correlated with any coded variable during composing in dyslexia group. Results are discussed in reference to the trend in neuroscience to use connectivity from relevant seed points while performing tasks and trends in education to recognize the generativity (creativity) of composing at both the genre and syntax levels. PMID:29600113

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation.

PubMed

Tsvetanov, Kamen A; Henson, Richard N A; Tyler, Lorraine K; Razi, Adeel; Geerligs, Linda; Ham, Timothy E; Rowe, James B

2016-03-16

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18-88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability. Copyright © 2016 Tsvetanov et al.

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation

PubMed Central

Henson, Richard N.A.; Tyler, Lorraine K.; Razi, Adeel; Geerligs, Linda; Ham, Timothy E.; Rowe, James B.

2016-01-01

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. SIGNIFICANCE STATEMENT Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population-based cohort (n = 602, 18–88 years), separating neural connectivity from vascular components of fMRI signals. Cognitive ability was influenced by the strength of connection within and between functional brain networks, and this positive relationship increased with age. In older adults, there was more rapid decay of intrinsic neuronal activity in multiple regions of the brain networks, which related to cognitive performance. Our data demonstrate increased reliance on network flexibility to maintain cognitive function, in the presence of more rapid decay of neural activity. These insights will facilitate the development of new strategies to maintain cognitive ability. PMID:26985024

Attention bias in older women with remitted depression is associated with enhanced amygdala activity and functional connectivity.

PubMed

Albert, Kimberly; Gau, Violet; Taylor, Warren D; Newhouse, Paul A

2017-03-01

Cognitive bias is a common characteristic of major depressive disorder (MDD) and is posited to remain during remission and contribute to recurrence risk. Attention bias may be related to enhanced amygdala activity or altered amygdala functional connectivity in depression. The current study examined attention bias, brain activity for emotional images, and functional connectivity in post-menopausal women with and without a history of major depression. Attention bias for emotionally valenced images was examined in 33 postmenopausal women with (n=12) and without (n=21) a history of major depression using an emotion dot probe task during fMRI. Group differences in amygdala activity and functional connectivity were assessed using fMRI and examined for correlations to attention performance. Women with a history of MDD showed greater attentional bias for negative images and greater activity in brain areas including the amygdala for both positive and negative images (pcorr <0.001) than women without a history of MDD. In all participants, amygdala activity for negative images was correlated with attention facilitation for emotional images. Women with a history of MDD had significantly greater functional connectivity between the amygdala and hippocampal complex. In all participants amygdala-hippocampal connectivity was positively correlated with attention facilitation for negative images. Small sample with unbalanced groups. These findings provide evidence for negative attentional bias in euthymic, remitted depressed individuals. Activity and functional connectivity in limbic and attention networks may provide a neurobiological basis for continued cognitive bias in remitted depression. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Statistical inference of dynamic resting-state functional connectivity using hierarchical observation modeling.

PubMed

Sojoudi, Alireza; Goodyear, Bradley G

2016-12-01

Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Characterization of the Spatial Structure of Local Functional Connectivity Using Multidistance Average Correlation Measures.

PubMed

Macià, Dídac; Pujol, Jesus; Blanco-Hinojo, Laura; Martínez-Vilavella, Gerard; Martín-Santos, Rocío; Deus, Joan

2018-06-01

There is ample evidence from basic research in neuroscience of the importance of local corticocortical networks. Millimetric resolution is achievable with current functional magnetic resonance imaging (fMRI) scanners and sequences, and consequently a number of "local" activity similarity measures have been defined to describe patterns of segregation and integration at this spatial scale. We have introduced the use of IsoDistant Average Correlation (IDAC), easily defined as the average fMRI temporal correlation of a given voxel with other voxels placed at increasingly separated isodistant intervals, to characterize the curve of local fMRI signal similarities. IDAC curves can be statistically compared using parametric multivariate statistics. Furthermore, by using red-green-blue color coding to display jointly IDAC values belonging to three different distance lags, IDAC curves can also be displayed as multidistance IDAC maps. We applied IDAC analysis to a sample of 41 subjects scanned under two different conditions, a resting state and an auditory-visual continuous stimulation. Multidistance IDAC mapping was able to discriminate between gross anatomofunctional cortical areas and, moreover, was sensitive to modulation between the two brain conditions in areas known to activate and deactivate during audiovisual tasks. Unlike previous fMRI local similarity measures already in use, our approach draws special attention to the continuous smooth pattern of local functional connectivity.

How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals.

PubMed

Wang, Xiaoying; Peelen, Marius V; Han, Zaizhu; He, Chenxi; Caramazza, Alfonso; Bi, Yanchao

2015-09-09

Classical animal visual deprivation studies and human neuroimaging studies have shown that visual experience plays a critical role in shaping the functionality and connectivity of the visual cortex. Interestingly, recent studies have additionally reported circumscribed regions in the visual cortex in which functional selectivity was remarkably similar in individuals with and without visual experience. Here, by directly comparing resting-state and task-based fMRI data in congenitally blind and sighted human subjects, we obtained large-scale continuous maps of the degree to which connectional and functional "fingerprints" of ventral visual cortex depend on visual experience. We found a close agreement between connectional and functional maps, pointing to a strong interdependence of connectivity and function. Visual experience (or the absence thereof) had a pronounced effect on the resting-state connectivity and functional response profile of occipital cortex and the posterior lateral fusiform gyrus. By contrast, connectional and functional fingerprints in the anterior medial and posterior lateral parts of the ventral visual cortex were statistically indistinguishable between blind and sighted individuals. These results provide a large-scale mapping of the influence of visual experience on the development of both functional and connectivity properties of visual cortex, which serves as a basis for the formulation of new hypotheses regarding the functionality and plasticity of specific subregions. Significance statement: How is the functionality and connectivity of the visual cortex shaped by visual experience? By directly comparing resting-state and task-based fMRI data in congenitally blind and sighted subjects, we obtained large-scale continuous maps of the degree to which connectional and functional "fingerprints" of ventral visual cortex depend on visual experience. In addition to revealing regions that are strongly dependent on visual experience (early visual cortex and posterior fusiform gyrus), our results showed regions in which connectional and functional patterns are highly similar in blind and sighted individuals (anterior medial and posterior lateral ventral occipital temporal cortex). These results serve as a basis for the formulation of new hypotheses regarding the functionality and plasticity of specific subregions of the visual cortex. Copyright © 2015 the authors 0270-6474/15/3512545-15$15.00/0.

Effective connectivity between superior temporal gyrus and Heschl's gyrus during white noise listening: linear versus non-linear models.

PubMed

Hamid, Ka; Yusoff, An; Rahman, Mza; Mohamad, M; Hamid, Aia

2012-04-01

This fMRI study is about modelling the effective connectivity between Heschl's gyrus (HG) and the superior temporal gyrus (STG) in human primary auditory cortices. MATERIALS #ENTITYSTARTX00026; Ten healthy male participants were required to listen to white noise stimuli during functional magnetic resonance imaging (fMRI) scans. Statistical parametric mapping (SPM) was used to generate individual and group brain activation maps. For input region determination, two intrinsic connectivity models comprising bilateral HG and STG were constructed using dynamic causal modelling (DCM). The models were estimated and inferred using DCM while Bayesian Model Selection (BMS) for group studies was used for model comparison and selection. Based on the winning model, six linear and six non-linear causal models were derived and were again estimated, inferred, and compared to obtain a model that best represents the effective connectivity between HG and the STG, balancing accuracy and complexity. Group results indicated significant asymmetrical activation (p(uncorr) < 0.001) in bilateral HG and STG. Model comparison results showed strong evidence of STG as the input centre. The winning model is preferred by 6 out of 10 participants. The results were supported by BMS results for group studies with the expected posterior probability, r = 0.7830 and exceedance probability, ϕ = 0.9823. One-sample t-tests performed on connection values obtained from the winning model indicated that the valid connections for the winning model are the unidirectional parallel connections from STG to bilateral HG (p < 0.05). Subsequent model comparison between linear and non-linear models using BMS prefers non-linear connection (r = 0.9160, ϕ = 1.000) from which the connectivity between STG and the ipsi- and contralateral HG is gated by the activity in STG itself. We are able to demonstrate that the effective connectivity between HG and STG while listening to white noise for the respective participants can be explained by a non-linear dynamic causal model with the activity in STG influencing the STG-HG connectivity non-linearly.

The Hierarchical Brain Network for Face Recognition

PubMed Central

Zhen, Zonglei; Fang, Huizhen; Liu, Jia

2013-01-01

Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level. PMID:23527282

219. Changes in Functional Networks Underlying Social Cognition Following Cognitive Training in Individuals at Risk for Psychosis

PubMed Central

Haut, Kristen; Saxena, Abhishek; Yin, Hong; Carol, Emily; Dodell-Feder, David; Lincoln, Sarah Hope; Tully, Laura; Keshavan, Matcheri; Seidman, Larry J.; Nahum, Mor; Hooker, Christine

2017-01-01

Abstract Background: Deficits in social cognition are prominent features of schizophrenia that play a large role in functional impairments and disability. Performance deficits in these domains are associated with altered activity in functional networks, including those that support social cognitive abilities such as emotion recognition. These social cognitive deficits and alterations in neural networks are present prior to the onset of frank psychotic symptoms and thus present a potential target for intervention in early phases of the illness, including in individuals at clinical high risk (CHR) for psychosis. This study assessed changes in social cognitive functional networks following targeted cognitive training (TCT) in CHR individuals. Methods: 14 CHR subjects (7 male, mean age = 21.9) showing attenuated psychotic symptoms as assessed by the SIPS were included in the study. Subjects underwent a clinical evaluation and a functional MRI session prior to and subsequent to completing 40 hours (8 weeks) of targeted cognitive and social cognitive training using Lumosity and SocialVille. 14 matched healthy control (HC) subjects also underwent a single fMRI session as a comparison group for functional activity. Resting state fMRI was acquired as well as fMRI during performance of an emotion recognition task. Group level differences in BOLD activity between HC and CHR group before TCT, and CHR group before and after TCT were computed. Changes in social cognitive network functional connectivity at rest and during task performance was evaluated using seed-based connectivity analyses and psychophysiological interaction (PPI). Results: Prior to training, CHR individuals demonstrated hyperactivity in the amygdala, posterior cingulate, and superior temporal sulcus (STS) during emotion recognition, suggesting inefficient processing. This hyperactivity normalized somewhat after training, with CHR individuals showing less hyperactivity in the amygdala in response to emotional faces. In addition, training was associated with increased connectivity in emotion processing networks, including greater STS-medial prefrontal connectivity and normalization of amygdala connectivity patterns. Conclusion: These results suggest that targeted cognitive training produced improvements in emotion recognition and may be effective in altering functional network connectivity in networks associated with psychosis risk. TCT may be a useful tool for early intervention in individuals at risk for psychotic disorders to address behaviors that impact functional outcome.

Connectome Signatures of Neurocognitive Abnormalities in Euthymic Bipolar I Disorder

PubMed Central

Ajilore, Olusola; Vizueta, Nathalie; Walshaw, Patricia; Zhan, Liang; Leow, Alex; Altshuler, Lori L.

2015-01-01

Objectives Connectomics have allowed researchers to study integrative patterns of neural connectivity in humans. Yet, it is unclear how connectomics may elucidate structure-function relationships in bipolar I disorder (BPI). Expanding on our previous structural connectome study, here we used an overlapping sample with additional psychometric and fMRI data to relate structural connectome properties to both fMRI signals and cognitive performance. Methods 42 subjects completed a neuropsychological (NP) battery covering domains of processing speed, verbal memory, working memory, and cognitive flexibility. 32 subjects also had fMRI data performing a Go/NoGo task. Results Bipolar participants had lower NP performance across all domains, but only working memory reached statistical significance. In BPI participants, processing speed was significantly associated with both white matter integrity (WMI) in the corpus callosum and interhemispheric network integration. Mediation models further revealed that the relationship between interhemispheric integration and processing speed was mediated by WMI, and processing speed mediated the relationship between WMI and working memory. Bipolar subjects had significantly decreased BA47 activation during NoGo vs. Go. Significant predictors of BA47 fMRI activations during the Go/NoGo task were its nodal path length (left hemisphere) and its nodal clustering coefficient (right hemisphere). Conclusions This study suggests that structural connectome changes underlie abnormalities in fMRI activation and cognitive performance in euthymic BPI subjects. Results support that BA47 structural connectome changes may be a trait marker for BPI. Future studies are needed to determine if these “connectome signatures” may also confer a biological risk and/or serve as predictors of relapse. PMID:26228398

Connectopic mapping with resting-state fMRI.

PubMed

Haak, Koen V; Marquand, Andre F; Beckmann, Christian F

2018-04-15

Brain regions are often topographically connected: nearby locations within one brain area connect with nearby locations in another area. Mapping these connection topographies, or 'connectopies' in short, is crucial for understanding how information is processed in the brain. Here, we propose principled, fully data-driven methods for mapping connectopies using functional magnetic resonance imaging (fMRI) data acquired at rest by combining spectral embedding of voxel-wise connectivity 'fingerprints' with a novel approach to spatial statistical inference. We apply the approach in human primary motor and visual cortex, and show that it can trace biologically plausible, overlapping connectopies in individual subjects that follow these regions' somatotopic and retinotopic maps. As a generic mechanism to perform inference over connectopies, the new spatial statistics approach enables rigorous statistical testing of hypotheses regarding the fine-grained spatial profile of functional connectivity and whether that profile is different between subjects or between experimental conditions. The combined framework offers a fundamental alternative to existing approaches to investigating functional connectivity in the brain, from voxel- or seed-pair wise characterizations of functional association, towards a full, multivariate characterization of spatial topography. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Rapid geodesic mapping of brain functional connectivity: implementation of a dedicated co-processor in a field-programmable gate array (FPGA) and application to resting state functional MRI.

PubMed

Minati, Ludovico; Cercignani, Mara; Chan, Dennis

2013-10-01

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

Functional connectivity studies of patients with auditory verbal hallucinations.

PubMed

Hoffman, Ralph E; Hampson, Michelle

2011-12-02

Functional connectivity (FC) studies of brain mechanisms leading to auditory verbal hallucinations (AVHs) utilizing functional magnetic resonance imaging (fMRI) data are reviewed. Initial FC studies utilized fMRI data collected during performance of various tasks, which suggested frontotemporal disconnection and/or source-monitoring disturbances. Later FC studies have utilized resting (no-task) fMRI data. These studies have produced a mixed picture of disconnection and hyperconnectivity involving different pathways associated with AVHs. Results of our most recent FC study of AVHs are reviewed in detail. This study suggests that the core mechanism producing AVHs involves not a single pathway, but a more complex functional loop. Components of this loop include Wernicke's area and its right homologue, the left inferior frontal cortex, and the putamen. It is noteworthy that the putamen appears to play a critical role in the generation of spontaneous language, and in determining whether auditory stimuli are registered consciously as percepts. Excessive functional coordination linking this region with the Wernicke's seed region in patients with schizophrenia could, therefore, generate an overabundance of potentially conscious language representations. In our model, intact FC in the other two legs of corticostriatal loop (Wernicke's with left IFG, and left IFG with putamen) appeared to allow hyperconnectivity linking the putamen and Wernicke's area (common to schizophrenia overall) to be expressed as conscious hallucinations of speech. Recommendations for future studies are discussed, including inclusion of multiple methodologies applied to the same subjects in order to compare and contrast different mechanistic hypotheses, utilizing EEG to better parse time-course of neural synchronization leading to AVHs, and ascertaining experiential subtypes of AVHs that may reflect distinct mechanisms.

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

Multisensory integration processing during olfactory-visual stimulation-An fMRI graph theoretical network analysis.

PubMed

Ripp, Isabelle; Zur Nieden, Anna-Nora; Blankenagel, Sonja; Franzmeier, Nicolai; Lundström, Johan N; Freiherr, Jessica

2018-05-07

In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealed-not significant, but very consistent-that MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing. © 2018 Wiley Periodicals, Inc.

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.

Reduced connectivity of the auditory cortex in patients with auditory hallucinations: a resting state functional magnetic resonance imaging study.

PubMed

Gavrilescu, M; Rossell, S; Stuart, G W; Shea, T L; Innes-Brown, H; Henshall, K; McKay, C; Sergejew, A A; Copolov, D; Egan, G F

2010-07-01

Previous research has reported auditory processing deficits that are specific to schizophrenia patients with a history of auditory hallucinations (AH). One explanation for these findings is that there are abnormalities in the interhemispheric connectivity of auditory cortex pathways in AH patients; as yet this explanation has not been experimentally investigated. We assessed the interhemispheric connectivity of both primary (A1) and secondary (A2) auditory cortices in n=13 AH patients, n=13 schizophrenia patients without auditory hallucinations (non-AH) and n=16 healthy controls using functional connectivity measures from functional magnetic resonance imaging (fMRI) data. Functional connectivity was estimated from resting state fMRI data using regions of interest defined for each participant based on functional activation maps in response to passive listening to words. Additionally, stimulus-induced responses were regressed out of the stimulus data and the functional connectivity was estimated for the same regions to investigate the reliability of the estimates. AH patients had significantly reduced interhemispheric connectivity in both A1 and A2 when compared with non-AH patients and healthy controls. The latter two groups did not show any differences in functional connectivity. Further, this pattern of findings was similar across the two datasets, indicating the reliability of our estimates. These data have identified a trait deficit specific to AH patients. Since this deficit was characterized within both A1 and A2 it is expected to result in the disruption of multiple auditory functions, for example, the integration of basic auditory information between hemispheres (via A1) and higher-order language processing abilities (via A2).

The reorganization of functional architecture in the early-stages of Parkinson's disease.

PubMed

Tuovinen, Noora; Seppi, Klaus; de Pasquale, Francesco; Müller, Christoph; Nocker, Michael; Schocke, Michael; Gizewski, Elke R; Kremser, Christian; Wenning, Gregor K; Poewe, Werner; Djamshidian, Atbin; Scherfler, Christoph; Seki, Morinobu

2018-05-01

The study aim was to identify longitudinal abnormalities of functional connectivity and its relation with motor disability in early to moderately advanced stages of Parkinson's disease patients. 3.0T structural and resting-state functional MRI was performed in healthy subjects (n = 16) and Parkinson's disease patients (n = 16) with mean disease duration of 2.2 ± 1.2 years at baseline with a clinical follow-up of 1.5 ± 0.3 years. Resting-state fMRI analysis included region-to-region connectivity in correlation with UPDRS-III scores and computation of Global Efficiency and Degree Centrality. At baseline, patients' connectivity increased between the cerebellum and somatomotor network, and decreased between motor regions (Rolandic operculum, precentral gyrus, supplementary motor area, postcentral gyrus) and cingulate connectivity. At 1.5 years follow-up, connectivity remained altered in the same regions identified at baseline. The cerebellum showed additional hyperconnectivity within itself and to the caudate nucleus, thalamus and amygdala compared to controls. These differences correlated with UPDRS-III scores. Seed-based connectivity revealed increased involvement of the default mode network with precentral gyrus in patients at follow-up investigation. Resting-state fMRI identified marked disturbances of the overall architecture of connectivity in Parkinson's disease. The noted alterations in cortical motor areas were associated with cerebellar hyperconnectivity in early to moderately advanced stages of Parkinson's disease suggesting ongoing attempts of recovery and compensatory mechanism for affected functions. The potential to identify connectivity alterations in regions related to both motor and attentional functions requires further evaluation as an objective marker to monitor disease progression, and medical, as well as surgical interventions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Top-Down Network Effective Connectivity in Abstinent Substance Dependent Individuals

PubMed Central

Regner, Michael F.; Saenz, Naomi; Maharajh, Keeran; Yamamoto, Dorothy J.; Mohl, Brianne; Wylie, Korey; Tregellas, Jason; Tanabe, Jody

2016-01-01

Objective We hypothesized that compared to healthy controls, long-term abstinent substance dependent individuals (SDI) will differ in their effective connectivity between large-scale brain networks and demonstrate increased directional information from executive control to interoception-, reward-, and habit-related networks. In addition, using graph theory to compare network efficiencies we predicted decreased small-worldness in SDI compared to controls. Methods 50 SDI and 50 controls of similar sex and age completed psychological surveys and resting state fMRI. fMRI results were analyzed using group independent component analysis; 14 networks-of-interest (NOI) were selected using template matching to a canonical set of resting state networks. The number, direction, and strength of connections between NOI were analyzed with Granger Causality. Within-group thresholds were p<0.005 using a bootstrap permutation. Between group thresholds were p<0.05, FDR-corrected for multiple comparisons. NOI were correlated with behavioral measures, and group-level graph theory measures were compared. Results Compared to controls, SDI showed significantly greater Granger causal connectivity from right executive control network (RECN) to dorsal default mode network (dDMN) and from dDMN to basal ganglia network (BGN). RECN was negatively correlated with impulsivity, behavioral approach, and negative affect; dDMN was positively correlated with impulsivity. Among the 14 NOI, SDI showed greater bidirectional connectivity; controls showed more unidirectional connectivity. SDI demonstrated greater global efficiency and lower local efficiency. Conclusions Increased effective connectivity in long-term abstinent drug users may reflect improved cognitive control over habit and reward processes. Higher global and lower local efficiency across all networks in SDI compared to controls may reflect connectivity changes associated with drug dependence or remission and requires future, longitudinal studies to confirm. PMID:27776135

Tractography-Based Score for Learning Effective Connectivity From Multimodal Imaging Data Using Dynamic Bayesian Networks.

PubMed

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

2018-05-01

Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition EC is "the causal influence exerted by one neuronal group on another" which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs). DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data. Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments. EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality. Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the single modality analysis.

Aberrant functional network connectivity in psychopathy from a large (N = 985) forensic sample.

PubMed

Espinoza, Flor A; Vergara, Victor M; Reyes, Daisy; Anderson, Nathaniel E; Harenski, Carla L; Decety, Jean; Rachakonda, Srinivas; Damaraju, Eswar; Rashid, Barnaly; Miller, Robyn L; Koenigs, Michael; Kosson, David S; Harenski, Keith; Kiehl, Kent A; Calhoun, Vince D

2018-06-01

Psychopathy is a personality disorder characterized by antisocial behavior, lack of remorse and empathy, and impaired decision making. The disproportionate amount of crime committed by psychopaths has severe emotional and economic impacts on society. Here we examine the neural correlates associated with psychopathy to improve early assessment and perhaps inform treatments for this condition. Previous resting-state functional magnetic resonance imaging (fMRI) studies in psychopathy have primarily focused on regions of interest. This study examines whole-brain functional connectivity and its association to psychopathic traits. Psychopathy was hypothesized to be characterized by aberrant functional network connectivity (FNC) in several limbic/paralimbic networks. Group-independent component and regression analyses were applied to a data set of resting-state fMRI from 985 incarcerated adult males. We identified resting-state networks (RSNs), estimated FNC between RSNs, and tested their association to psychopathy factors and total summary scores (Factor 1, interpersonal/affective; Factor 2, lifestyle/antisocial). Factor 1 scores showed both increased and reduced functional connectivity between RSNs from seven brain domains (sensorimotor, cerebellar, visual, salience, default mode, executive control, and attentional). Consistent with hypotheses, RSNs from the paralimbic system-insula, anterior and posterior cingulate cortex, amygdala, orbital frontal cortex, and superior temporal gyrus-were related to Factor 1 scores. No significant FNC associations were found with Factor 2 and total PCL-R scores. In summary, results suggest that the affective and interpersonal symptoms of psychopathy (Factor 1) are associated with aberrant connectivity in multiple brain networks, including paralimbic regions. © 2018 Wiley Periodicals, Inc.

Personality traits modulate subcortical and cortical vestibular and anxiety responses to sound-evoked otolithic receptor stimulation.

PubMed

Indovina, Iole; Riccelli, Roberta; Staab, Jeffrey P; Lacquaniti, Francesco; Passamonti, Luca

2014-11-01

Strong links between anxiety, space-motion perception, and vestibular symptoms have been recognized for decades. These connections may extend to anxiety-related personality traits. Psychophysical studies showed that high trait anxiety affected postural control and visual scanning strategies under stress. Neuroticism and introversion were identified as risk factors for chronic subjective dizziness (CSD), a common psychosomatic syndrome. This study examined possible relationships between personality traits and activity in brain vestibular networks for the first time using functional magnetic resonance imaging (fMRI). Twenty-six right-handed healthy individuals underwent fMRI during sound-evoked vestibular stimulation. Regional brain activity and functional connectivity measures were correlated with personality traits of the Five Factor Model (neuroticism, extraversion-introversion, openness, agreeableness, consciousness). Neuroticism correlated positively with activity in the pons, vestibulo-cerebellum, and para-striate cortex, and negatively with activity in the supra-marginal gyrus. Neuroticism also correlated positively with connectivity between pons and amygdala, vestibulo-cerebellum and amygdala, inferior frontal gyrus and supra-marginal gyrus, and inferior frontal gyrus and para-striate cortex. Introversion correlated positively with amygdala activity and negatively with connectivity between amygdala and inferior frontal gyrus. Neuroticism and introversion correlated with activity and connectivity in cortical and subcortical vestibular, visual, and anxiety systems during vestibular stimulation. These personality-related changes in brain activity may represent neural correlates of threat sensitivity in posture and gaze control mechanisms in normal individuals. They also may reflect risk factors for anxiety-related morbidity in patients with vestibular disorders, including previously observed associations of neuroticism and introversion with CSD. Copyright © 2014 Elsevier Inc. All rights reserved.

Resting-state fMRI study of patients with fragile X syndrome

NASA Astrophysics Data System (ADS)

Isanova, E.; Petrovskiy, E.; Savelov, A.; Yudkin, D.; Tulupov, A.

2017-08-01

The study aimed to assess the neural activity of different brain regions in patients with fragile X syndrome (FXS) and the healthy volunteers by resting-state functional magnetic resonance imaging (fMRI) on a 1.5 T MRI Achieva scanner (Philips). Results: The fMRI study showed a DMN of brain function in patients with FXS, as well as in the healthy volunteers. Furthermore, it was found that a default mode network of the brain in patients with FXS and healthy volunteers does not have statistically significant differences (p>0.05), which may indicate that the basal activity of neurons in patients with FXS is not reduced. In addition, we have found a significant (p<0.001) increase in the FC within the right inferior parietal and right angular gyrus in the resting state in patients with FXS. Conclusion: New data of functional status of the brain in patients with FXS were received. The significant increase in the resting state functional connectivity within the right inferior parietal and right angular gyrus (p<0.001) in patients with FXS was found.

Beef assessments using functional magnetic resonance imaging and sensory evaluation.

PubMed

Tapp, W N; Davis, T H; Paniukov, D; Brooks, J C; Brashears, M M; Miller, M F

2017-04-01

Functional magnetic resonance imaging (fMRI) has been used to unveil how some foods and basic rewards are processed in the human brain. This study evaluated how resting state functional connectivity in regions of the human brain changed after differing qualities of beef steaks were consumed. Functional images of participants (n=8) were collected after eating high or low quality beef steaks on separate days, after consumption a sensory ballot was administered to evaluate consumers' perceptions of tenderness, juiciness, flavor, and overall liking. Imaging data showed that high quality steak samples resulted in greater functional connectivity to the striatum, medial orbitofrontal cortex, and insular cortex at various stages after consumption (P≤0.05). Furthermore, high quality steaks elicited higher sensory ballot scores for each palatability trait (P≤0.01). Together, these results suggest that resting state fMRI may be a useful tool for evaluating the neural process that follows positive sensory experiences such as the enjoyment of high quality beef steaks. Published by Elsevier Ltd.

Information flow between interacting human brains: Identification, validation, and relationship to social expertise

PubMed Central

Bilek, Edda; Ruf, Matthias; Schäfer, Axel; Akdeniz, Ceren; Calhoun, Vince D.; Schmahl, Christian; Demanuele, Charmaine; Tost, Heike; Kirsch, Peter; Meyer-Lindenberg, Andreas

2015-01-01

Social interactions are fundamental for human behavior, but the quantification of their neural underpinnings remains challenging. Here, we used hyperscanning functional MRI (fMRI) to study information flow between brains of human dyads during real-time social interaction in a joint attention paradigm. In a hardware setup enabling immersive audiovisual interaction of subjects in linked fMRI scanners, we characterize cross-brain connectivity components that are unique to interacting individuals, identifying information flow between the sender’s and receiver’s temporoparietal junction. We replicate these findings in an independent sample and validate our methods by demonstrating that cross-brain connectivity relates to a key real-world measure of social behavior. Together, our findings support a central role of human-specific cortical areas in the brain dynamics of dyadic interactions and provide an approach for the noninvasive examination of the neural basis of healthy and disturbed human social behavior with minimal a priori assumptions. PMID:25848050

Differential Hemodynamic Response in Affective Circuitry with Aging: An fMRI Study of Novelty, Valence, and Arousal

PubMed Central

Moriguchi, Yoshiya; Negreira, Alyson; Weierich, Mariann; Dautoff, Rebecca; Dickerson, Bradford C.; Wright, Christopher I.; Barrett, Lisa Feldman

2011-01-01

Emerging evidence indicates that stimulus novelty is affectively potent and reliably engages the amygdala and other portions of the affective workspace in the brain. Using fMRI, we examined whether novel stimuli remain affectively salient across the lifespan, and therefore, whether novelty processing—a potentially survival-relevant function—is preserved with aging. Nineteen young and 22 older healthy adults were scanned during observing novel and familiar affective pictures while estimating their own subjectively experienced aroused levels. We investigated age-related difference of magnitude of activation, hemodynamic time course, and functional connectivity of BOLD responses in the amygdala. Although there were no age-related differences in the peak response of the amygdala to novelty, older individuals showed a narrower, sharper (i.e., “peakier”) hemodynamic time course in response to novel stimuli, as well as decreased connectivity between the left amygdala and the affective areas including orbito-frontal regions. These findings have relevance for understanding age-related differences in memory and affect regulation. PMID:20521849

Mechanisms of Working Memory Disruption by External Interference

PubMed Central

Rubens, Michael T.; Gazzaley, Adam

2010-01-01

The negative impact of external interference on working memory (WM) performance is well documented; yet, the mechanisms underlying this disruption are not sufficiently understood. In this study, electroencephalogram and functional magnetic resonance imaging (fMRI) data were recorded in separate experiments that each introduced different types of visual interference during a period of WM maintenance: distraction (irrelevant stimuli) and interruption (stimuli that required attention). The data converged to reveal that regardless of the type of interference, the magnitude of processing interfering stimuli in the visual cortex (as rapidly as 100 ms) predicted subsequent WM recognition accuracy for stored items. fMRI connectivity analyses suggested that in the presence of distraction, encoded items were maintained throughout the delay period via connectivity between the middle frontal gyrus and visual association cortex, whereas memoranda were not maintained when subjects were interrupted but rather reactivated in the postinterruption period. These results elucidate the mechanisms of external interference on WM performance and highlight similarities and differences of distraction and multitasking. PMID:19648173

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.

Intrinsic functional connectivity underlying successful emotion regulation of angry faces

PubMed Central

Morawetz, Carmen; Kellermann, Tanja; Kogler, Lydia; Radke, Sina; Blechert, Jens; Derntl, Birgit

2016-01-01

Most of our social interaction is naturally based on emotional information derived from the perception of faces of other people. Negative facial expressions of a counterpart might trigger negative emotions and initiate emotion regulatory efforts to reduce the impact of the received emotional message in a perceiver. Despite the high adaptive value of emotion regulation in social interaction, the neural underpinnings of it are largely unknown. To remedy this, this study investigated individual differences in emotion regulation effectiveness during the reappraisal of angry faces on the underlying functional activity using functional magnetic resonance imaging (fMRI) as well as the underlying functional connectivity using resting-state fMRI. Greater emotion regulation ability was associated with greater functional activity in the ventromedial prefrontal cortex. Furthermore, greater functional coupling between activity in the ventrolateral prefrontal cortex and the amygdala was associated with emotion regulation success. Our findings provide a first link between prefrontal cognitive control and subcortical emotion processing systems during successful emotion regulation in an explicitly social context. PMID:27510495

BRAPH: A graph theory software for the analysis of brain connectivity

PubMed Central

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B.; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. PMID:28763447

BRAPH: A graph theory software for the analysis of brain connectivity.

PubMed

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH-BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.

The Brain Goes to School: Strengthening the Education-Neuroscience Connection

ERIC Educational Resources Information Center

Ansari, Daniel

2008-01-01

Investigations on the brain processes using a technology such as functional magnetic resonance imaging (fMRI) have led to the creation of a new field of research that bridges the gap between cognitive psychology and neuroscience: "cognitive neuroscience." Within this new field, studies examining the processes of learning and developing are…

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.

Resting-state functional magnetic resonance imaging in hepatic encephalopathy: current status and perspectives.

PubMed

Zhang, Long Jiang; Wu, Shengyong; Ren, Jiaqian; Lu, Guang Ming

2014-09-01

Hepatic encephalopathy (HE) is a neuropsychiatric syndrome which develops in patients with severe liver diseases and/or portal-systemic shunting. Minimal HE, the earliest manifestation of HE, has drawn increasing attention in the last decade. Minimal HE is associated with a series of brain functional changes, such as attention, working memory, and so on. Blood oxygen level dependent (BOLD) functional MRI (fMRI), especially resting-state fMRI has been used to explore the brain functional changes of HE, yielding important insights for understanding pathophysiological mechanisms and functional reorganization of HE. This paper briefly reviews the principles of BOLD fMRI, potential applications of resting-state fMRI with advanced post-processing algorithms such as regional homogeneity, amplitude of low frequency fluctuation, functional connectivity and future research perspective in this field.

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.

Effortful control and resting state networks: A longitudinal EEG study.

PubMed

Knyazev, Gennady G; Savostyanov, Alexander N; Bocharov, Andrey V; Slobodskaya, Helena R; Bairova, Nadezhda B; Tamozhnikov, Sergey S; Stepanova, Valentina V

2017-03-27

Resting state networks' (RSNs) architecture is well delineated in mature brain, but our understanding of their development remains limited. Particularly, there are few longitudinal studies. Besides, all existing evidence is obtained using functional magnetic resonance imaging (fMRI) and there are no data on electrophysiological correlates of RSN maturation. We acquired three yearly waves of resting state EEG data in 80 children between 7 and 9years and in 55 adults. Children's parents filled in the Effortful Control (EC) scale. Seed-based oscillatory power envelope correlation in conjunction with beamformer spatial filtering was used to obtain electrophysiological signatures of the default mode network (DMN) and two task-positive networks (TPN). In line with existing fMRI evidence, both cross-sectional comparison with adults and longitudinal analysis showed that the general pattern of maturation consisted in an increase in long-distance connections with posterior cortical regions and a decrease in short connections within prefrontal cortical areas. Latent growth curve analysis showed that EC scores were predicted by a linear increase over time in DMN integrity in alpha band and an increase in the segregation between DMN and TPN in beta band. These data confirm the neural basis of observed in fMRI research maturation-related changes and show that integrity of the DMN and sufficient level of segregation between DMN and TPN is a prerequisite for appropriate attentional and behavioral control. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

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.

The effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment.

PubMed

Papma, Janne M; Smits, Marion; de Groot, Marius; Mattace Raso, Francesco U; van der Lugt, Aad; Vrooman, Henri A; Niessen, Wiro J; Koudstaal, Peter J; van Swieten, John C; van der Veen, Frederik M; Prins, Niels D

2017-09-01

Diminished function of the posterior cingulate cortex (PCC) is a typical finding in early Alzheimer's disease (AD). It is hypothesized that in early stage AD, PCC functioning relates to or reflects hippocampal dysfunction or atrophy. The aim of this study was to examine the relationship between hippocampus function, volume and structural connectivity, and PCC activation during an episodic memory task-related fMRI study in mild cognitive impairment (MCI). MCI patients (n = 27) underwent episodic memory task-related fMRI, 3D-T1w MRI, 2D T2-FLAIR MRI and diffusion tensor imaging. Stepwise linear regression analysis was performed to examine the relationship between PCC activation and hippocampal activation, hippocampal volume and diffusion measures within the cingulum along the hippocampus. We found a significant relationship between PCC and hippocampus activation during successful episodic memory encoding and correct recognition in MCI patients. We found no relationship between the PCC and structural hippocampal predictors. Our results indicate a relationship between PCC and hippocampus activation during episodic memory engagement in MCI. This may suggest that during episodic memory, functional network deterioration is the most important predictor of PCC functioning in MCI. • PCC functioning during episodic memory relates to hippocampal functioning in MCI. • PCC functioning during episodic memory does not relate to hippocampal structure in MCI. • Functional network changes are an important predictor of PCC functioning in MCI.

Dominance of the Unaffected Hemisphere Motor Network and Its Role in the Behavior of Chronic Stroke Survivors

PubMed Central

Bajaj, Sahil; Housley, Stephen N.; Wu, David; Dhamala, Mukesh; James, G. A.; Butler, Andrew J.

2016-01-01

Balance of motor network activity between the two brain hemispheres after stroke is crucial for functional recovery. Several studies have extensively studied the role of the affected brain hemisphere to better understand changes in motor network activity following stroke. Very few studies have examined the role of the unaffected brain hemisphere and confirmed the test–retest reliability of connectivity measures on unaffected hemisphere. We recorded blood oxygenation level dependent functional magnetic resonance imaging (fMRI) signals from nine stroke survivors with hemiparesis of the left or right hand. Participants performed a motor execution task with affected hand, unaffected hand, and both hands simultaneously. Participants returned for a repeat fMRI scan 1 week later. Using dynamic causal modeling (DCM), we evaluated effective connectivity among three motor areas: the primary motor area (M1), the premotor cortex (PMC) and the supplementary motor area for the affected and unaffected hemispheres separately. Five participants’ manual motor ability was assessed by Fugl-Meyer Motor Assessment scores and root-mean square error of participants’ tracking ability during a robot-assisted game. We found (i) that the task performance with the affected hand resulted in strengthening of the connectivity pattern for unaffected hemisphere, (ii) an identical network of the unaffected hemisphere when participants performed the task with their unaffected hand, and (iii) the pattern of directional connectivity observed in the affected hemisphere was identical for tasks using the affected hand only or both hands. Furthermore, paired t-test comparison found no significant differences in connectivity strength for any path when compared with one-week follow-up. Brain-behavior linear correlation analysis showed that the connectivity patterns in the unaffected hemisphere more accurately reflected the behavioral conditions than the connectivity patterns in the affected hemisphere. Above findings enrich our knowledge of unaffected brain hemisphere following stroke, which further strengthens our neurobiological understanding of stroke-affected brain and can help to effectively identify and apply stroke-treatments. PMID:28082882

Identifying the Neural Substrates of Procrastination: a Resting-State fMRI Study.

PubMed

Zhang, Wenwen; Wang, Xiangpeng; Feng, Tingyong

2016-09-12

Procrastination is a prevalent problematic behavior that brings serious consequences to individuals who suffer from it. Although this phenomenon has received increasing attention from researchers, the underpinning neural substrates of it is poorly studied. To examine the neural bases subserving procrastination, the present study employed resting-state fMRI. The main results were as follows: (1) the behavioral procrastination was positively correlated with the regional activity of the ventromedial prefrontal cortex (vmPFC) and the parahippocampal cortex (PHC), while negatively correlated with that of the anterior prefrontal cortex (aPFC). (2) The aPFC-seed connectivity with the anterior medial prefrontal cortex and the posterior cingulate cortex was positively associated with procrastination. (3) The connectivity between vmPFC and several other regions, such as the dorsomedial prefrontal cortex, the bilateral inferior prefrontal cortex showed a negative association with procrastination. These results suggested that procrastination could be attributed to, on the one hand, hyper-activity of the default mode network (DMN) that overrides the prefrontal control signal; while on the other hand, the failure of top-down control exerted by the aPFC on the DMN. Therefore, the present study unravels the biomarkers of procrastination and provides treatment targets for procrastination prevention.

Identifying the Neural Substrates of Procrastination: a Resting-State fMRI Study

PubMed Central

Zhang, Wenwen; Wang, Xiangpeng; Feng, Tingyong

2016-01-01

Procrastination is a prevalent problematic behavior that brings serious consequences to individuals who suffer from it. Although this phenomenon has received increasing attention from researchers, the underpinning neural substrates of it is poorly studied. To examine the neural bases subserving procrastination, the present study employed resting-state fMRI. The main results were as follows: (1) the behavioral procrastination was positively correlated with the regional activity of the ventromedial prefrontal cortex (vmPFC) and the parahippocampal cortex (PHC), while negatively correlated with that of the anterior prefrontal cortex (aPFC). (2) The aPFC-seed connectivity with the anterior medial prefrontal cortex and the posterior cingulate cortex was positively associated with procrastination. (3) The connectivity between vmPFC and several other regions, such as the dorsomedial prefrontal cortex, the bilateral inferior prefrontal cortex showed a negative association with procrastination. These results suggested that procrastination could be attributed to, on the one hand, hyper-activity of the default mode network (DMN) that overrides the prefrontal control signal; while on the other hand, the failure of top-down control exerted by the aPFC on the DMN. Therefore, the present study unravels the biomarkers of procrastination and provides treatment targets for procrastination prevention. PMID:27616687

(S)-citalopram influences amygdala modulation in healthy subjects: a randomized placebo-controlled double-blind fMRI study using dynamic causal modeling.

PubMed

Sladky, Ronald; Spies, Marie; Hoffmann, Andre; Kranz, Georg; Hummer, Allan; Gryglewski, Gregor; Lanzenberger, Rupert; Windischberger, Christian; Kasper, Siegfried

2015-03-01

Citalopram and Escitalopram are gold standard pharmaceutical treatment options for affective, anxiety, and other psychiatric disorders. However, their neurophysiologic function on cortico-limbic circuits is incompletely characterized. Here we studied the neuropharmacological influence of Citalopram and Escitalopram on cortico-limbic regulatory processes by assessing the effective connectivity between orbitofrontal cortex (OFC) and amygdala using dynamic causal modeling (DCM) applied to functional MRI data. We investigated a cohort of 15 healthy subjects in a randomized, crossover, double-blind design after 10days of Escitalopram (10mg/d (S)-citalopram), Citalopram (10mg/d (S)-citalopram and 10mg/d (R)-citalopram), or placebo. Subjects performed an emotional face discrimination task, while undergoing functional magnetic resonance imaging (fMRI) scanning at 3 Tesla. As hypothesized, the OFC, in the context of the emotional face discrimination task, exhibited a down-regulatory effect on amygdala activation. This modulatory effect was significantly increased by (S)-citalopram, but not (R)-citalopram. For the first time, this study shows that (1) the differential effects of the two enantiomers (S)- and (R)-citalopram on cortico-limbic connections can be demonstrated by modeling effective connectivity methods, and (2) one of their mechanisms can be linked to an increased inhibition of amygdala activation by the orbitofrontal cortex. Copyright © 2014 Elsevier Inc. All rights reserved.

Cortical underconnectivity coupled with preserved visuospatial cognition in autism: Evidence from an fMRI study of an embedded figures task

PubMed Central

Damarla, Saudamini Roy; Keller, Timothy A.; Kana, Rajesh K.; Cherkassky, Vladimir L.; Williams, Diane L.; Minshew, Nancy J.; Just, Marcel Adam

2010-01-01

Individuals with high-functioning autism sometimes exhibit intact or superior performance on visuospatial tasks, in contrast to impaired functioning in other domains such as language comprehension, executive tasks, and social functions. The goal of the current study was to investigate the neural bases of preserved visuospatial processing in high-functioning autism from the perspective of the cortical underconnectivity theory. We used a combination of behavioral, functional magnetic resonance imaging (fMRI), functional connectivity, and corpus callosum morphometric methodological tools. Thirteen participants with high-functioning autism and thirteen controls (age-, IQ-, and gender-matched) were scanned while performing an Embedded Figures Task (EFT). Despite the ability of the autism group to attain behavioral performance comparable to the control group, the brain imaging results revealed several group differences consistent with the cortical underconnectivity account of autism. First, relative to controls, the autism group showed less activation in left dorsolateral prefrontal and inferior parietal areas and more activation in visuospatial (bilateral superior parietal extending to inferior parietal and right occipital) areas. Second, the autism group demonstrated lower functional connectivity between higher-order working memory/executive areas and visuospatial regions (between frontal and parietal-occipital). Third, the size of the corpus callosum (an index of anatomical connectivity) was positively correlated with frontal-posterior (parietal and occipital) functional connectivity in the autism group. Thus, even in the visuospatial domain, where preserved performance among people with autism is observed, the neuroimaging signatures of cortical underconnectivity persist. PMID:20740492

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

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.

Neural correlates and network connectivity underlying narrative production and comprehension: a combined fMRI and PET study.

PubMed

AbdulSabur, Nuria Y; Xu, Yisheng; Liu, Siyuan; Chow, Ho Ming; Baxter, Miranda; Carson, Jessica; Braun, Allen R

2014-08-01

The neural correlates of narrative production and comprehension remain poorly understood. Here, using positron emission tomography (PET), functional magnetic resonance imaging (fMRI), contrast and functional network connectivity analyses we comprehensively characterize the neural mechanisms underlying these complex behaviors. Eighteen healthy subjects told and listened to fictional stories during scanning. In addition to traditional language areas (e.g., left inferior frontal and posterior middle temporal gyri), both narrative production and comprehension engaged regions associated with mentalizing and situation model construction (e.g., dorsomedial prefrontal cortex, precuneus and inferior parietal lobules) as well as neocortical premotor areas, such as the pre-supplementary motor area and left dorsal premotor cortex. Narrative comprehension alone showed marked bilaterality, activating right hemisphere homologs of perisylvian language areas. Narrative production remained predominantly left lateralized, uniquely activating executive and motor-related regions essential to language formulation and articulation. Connectivity analyses revealed strong associations between language areas and the superior and middle temporal gyri during both tasks. However, only during storytelling were these same language-related regions connected to cortical and subcortical motor regions. In contrast, during story comprehension alone, they were strongly linked to regions supporting mentalizing. Thus, when employed in a more complex, ecologically-valid context, language production and comprehension show both overlapping and idiosyncratic patterns of activation and functional connectivity. Importantly, in each case the language system is integrated with regions that support other cognitive and sensorimotor domains. Copyright © 2014. Published by Elsevier Ltd.

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

2012-01-01

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

Large-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder.

PubMed

Sacchet, Matthew D; Ho, Tiffany C; Connolly, Colm G; Tymofiyeva, Olga; Lewinn, Kaja Z; Han, Laura Km; Blom, Eva H; Tapert, Susan F; Max, Jeffrey E; Frank, Guido Kw; Paulus, Martin P; Simmons, Alan N; Gotlib, Ian H; Yang, Tony T

2016-11-01

Major depressive disorder (MDD) often emerges during adolescence, a critical period of brain development. Recent resting-state fMRI studies of adults suggest that MDD is associated with abnormalities within and between resting-state networks (RSNs). Here we tested whether adolescent MDD is characterized by abnormalities in interactions among RSNs. Participants were 55 unmedicated adolescents diagnosed with MDD and 56 matched healthy controls. Functional connectivity was mapped using resting-state fMRI. We used the network-based statistic (NBS) to compare large-scale connectivity between groups and also compared the groups on graph metrics. We further assessed whether group differences identified using nodes defined from functionally defined RSNs were also evident when using anatomically defined nodes. In addition, we examined relations between network abnormalities and depression severity and duration. Finally, we compared intranetwork connectivity between groups and assessed the replication of previously reported MDD-related abnormalities in connectivity. The NBS indicated that, compared with controls, depressed adolescents exhibited reduced connectivity (p<0.024, corrected) between a specific set of RSNs, including components of the attention, central executive, salience, and default mode networks. The NBS did not identify group differences in network connectivity when using anatomically defined nodes. Longer duration of depression was significantly correlated with reduced connectivity in this set of network interactions (p=0.020, corrected), specifically with reduced connectivity between components of the dorsal attention network. The dorsal attention network was also characterized by reduced intranetwork connectivity in the MDD group. Finally, we replicated previously reported abnormal connectivity in individuals with MDD. In summary, adolescents with MDD show hypoconnectivity between large-scale brain networks compared with healthy controls. Given that connectivity among these networks typically increases during adolescent neurodevelopment, these results suggest that adolescent depression is associated with abnormalities in neural systems that are still developing during this critical period.

Large-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder

PubMed Central

Sacchet, Matthew D; Ho, Tiffany C; Connolly, Colm G; Tymofiyeva, Olga; Lewinn, Kaja Z; Han, Laura KM; Blom, Eva H; Tapert, Susan F; Max, Jeffrey E; Frank, Guido KW; Paulus, Martin P; Simmons, Alan N; Gotlib, Ian H; Yang, Tony T

2016-01-01

Major depressive disorder (MDD) often emerges during adolescence, a critical period of brain development. Recent resting-state fMRI studies of adults suggest that MDD is associated with abnormalities within and between resting-state networks (RSNs). Here we tested whether adolescent MDD is characterized by abnormalities in interactions among RSNs. Participants were 55 unmedicated adolescents diagnosed with MDD and 56 matched healthy controls. Functional connectivity was mapped using resting-state fMRI. We used the network-based statistic (NBS) to compare large-scale connectivity between groups and also compared the groups on graph metrics. We further assessed whether group differences identified using nodes defined from functionally defined RSNs were also evident when using anatomically defined nodes. In addition, we examined relations between network abnormalities and depression severity and duration. Finally, we compared intranetwork connectivity between groups and assessed the replication of previously reported MDD-related abnormalities in connectivity. The NBS indicated that, compared with controls, depressed adolescents exhibited reduced connectivity (p<0.024, corrected) between a specific set of RSNs, including components of the attention, central executive, salience, and default mode networks. The NBS did not identify group differences in network connectivity when using anatomically defined nodes. Longer duration of depression was significantly correlated with reduced connectivity in this set of network interactions (p=0.020, corrected), specifically with reduced connectivity between components of the dorsal attention network. The dorsal attention network was also characterized by reduced intranetwork connectivity in the MDD group. Finally, we replicated previously reported abnormal connectivity in individuals with MDD. In summary, adolescents with MDD show hypoconnectivity between large-scale brain networks compared with healthy controls. Given that connectivity among these networks typically increases during adolescent neurodevelopment, these results suggest that adolescent depression is associated with abnormalities in neural systems that are still developing during this critical period. PMID:27238621

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.

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.

Working Memory-Related Effective Connectivity in Huntington's Disease Patients.

PubMed

Lahr, Jacob; Minkova, Lora; Tabrizi, Sarah J; Stout, Julie C; Klöppel, Stefan; Scheller, Elisa

2018-01-01

Huntington's disease (HD) is a genetically caused neurodegenerative disorder characterized by heterogeneous motor, psychiatric, and cognitive symptoms. Although motor symptoms may be the most prominent presentation, cognitive symptoms such as memory deficits and executive dysfunction typically co-occur. We used functional magnetic resonance imaging (fMRI) and task fMRI-based dynamic causal modeling (DCM) to evaluate HD-related changes in the neural network underlying working memory (WM). Sixty-four pre-symptomatic HD mutation carriers (preHD), 20 patients with early manifest HD symptoms (earlyHD), and 83 healthy control subjects performed an n -back fMRI task with two levels of WM load. Effective connectivity was assessed in five predefined regions of interest, comprising bilateral inferior parietal cortex, left anterior cingulate cortex, and bilateral dorsolateral prefrontal cortex. HD mutation carriers performed less accurately and more slowly at high WM load compared with the control group. While between-group comparisons of brain activation did not reveal differential recruitment of the cortical WM network in mutation carriers, comparisons of brain connectivity as identified with DCM revealed a number of group differences across the whole WM network. Most strikingly, we observed decreasing connectivity from several regions toward right dorsolateral prefrontal cortex (rDLPFC) in preHD and even more so in earlyHD. The deterioration in rDLPFC connectivity complements results from previous studies and might mirror beginning cortical neural decline at premanifest and early manifest stages of HD. We were able to characterize effective connectivity in a WM network of HD mutation carriers yielding further insight into patterns of cognitive decline and accompanying neural deterioration.

Disruption of posteromedial large-scale neural communication predicts recovery from coma

PubMed Central

de Pasquale, Francesco; Vuillaume, Corine; Riu, Beatrice; Loubinoux, Isabelle; Geeraerts, Thomas; Seguin, Thierry; Bounes, Vincent; Fourcade, Olivier; Demonet, Jean-Francois; Péran, Patrice

2015-01-01

Objective: We hypothesize that the major consciousness deficit observed in coma is due to the breakdown of long-range neuronal communication supported by precuneus and posterior cingulate cortex (PCC), and that prognosis depends on a specific connectivity pattern in these networks. Methods: We compared 27 prospectively recruited comatose patients who had severe brain injury (Glasgow Coma Scale score <8; 14 traumatic and 13 anoxic cases) with 14 age-matched healthy participants. Standardized clinical assessment and fMRI were performed on average 4 ± 2 days after withdrawal of sedation. Analysis of resting-state fMRI connectivity involved a hypothesis-driven, region of interest–based strategy. We assessed patient outcome after 3 months using the Coma Recovery Scale–Revised (CRS-R). Results: Patients who were comatose showed a significant disruption of functional connectivity of brain areas spontaneously synchronized with PCC, globally notwithstanding etiology. The functional connectivity strength between PCC and medial prefrontal cortex (mPFC) was significantly different between comatose patients who went on to recover and those who eventually scored an unfavorable outcome 3 months after brain injury (Kruskal-Wallis test, p < 0.001; linear regression between CRS-R and PCC-mPFC activity coupling at rest, Spearman ρ = 0.93, p < 0.003). Conclusion: In both etiology groups (traumatic and anoxic), changes in the connectivity of PCC-centered, spontaneously synchronized, large-scale networks account for the loss of external and internal self-centered awareness observed during coma. Sparing of functional connectivity between PCC and mPFC may predict patient outcome, and further studies are needed to substantiate this potential prognosis biomarker. PMID:26561296

Disruption of posteromedial large-scale neural communication predicts recovery from coma.

PubMed

Silva, Stein; de Pasquale, Francesco; Vuillaume, Corine; Riu, Beatrice; Loubinoux, Isabelle; Geeraerts, Thomas; Seguin, Thierry; Bounes, Vincent; Fourcade, Olivier; Demonet, Jean-Francois; Péran, Patrice

2015-12-08

We hypothesize that the major consciousness deficit observed in coma is due to the breakdown of long-range neuronal communication supported by precuneus and posterior cingulate cortex (PCC), and that prognosis depends on a specific connectivity pattern in these networks. We compared 27 prospectively recruited comatose patients who had severe brain injury (Glasgow Coma Scale score <8; 14 traumatic and 13 anoxic cases) with 14 age-matched healthy participants. Standardized clinical assessment and fMRI were performed on average 4 ± 2 days after withdrawal of sedation. Analysis of resting-state fMRI connectivity involved a hypothesis-driven, region of interest-based strategy. We assessed patient outcome after 3 months using the Coma Recovery Scale-Revised (CRS-R). Patients who were comatose showed a significant disruption of functional connectivity of brain areas spontaneously synchronized with PCC, globally notwithstanding etiology. The functional connectivity strength between PCC and medial prefrontal cortex (mPFC) was significantly different between comatose patients who went on to recover and those who eventually scored an unfavorable outcome 3 months after brain injury (Kruskal-Wallis test, p < 0.001; linear regression between CRS-R and PCC-mPFC activity coupling at rest, Spearman ρ = 0.93, p < 0.003). In both etiology groups (traumatic and anoxic), changes in the connectivity of PCC-centered, spontaneously synchronized, large-scale networks account for the loss of external and internal self-centered awareness observed during coma. Sparing of functional connectivity between PCC and mPFC may predict patient outcome, and further studies are needed to substantiate this potential prognosis biomarker. © 2015 American Academy of Neurology.

The hypnotic zolpidem increases the synchrony of BOLD signal fluctuations in widespread brain networks during a resting paradigm

PubMed Central

Licata, Stephanie C.; Nickerson, Lisa D.; Lowen, Steven B.; Trksak, George H.; MacLean, Robert R.; Lukas, Scott E.

2013-01-01

Networks of brain regions having synchronized fluctuations of the blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) time-series at rest, or “resting state networks” (RSNs), are emerging as a basis for understanding intrinsic brain activity. RSNs are topographically consistent with activity-related networks subserving sensory, motor, and cognitive processes, and studying their spontaneous fluctuations following acute drug challenge may provide a way to understand better the neuroanatomical substrates of drug action. The present within-subject double-blind study used BOLD fMRI at 3T to investigate the functional networks influenced by the non-benzodiazepine hypnotic zolpidem (Ambien®). Zolpidem is a positive modulator of γ-aminobutyric acidA (GABAA) receptors, and engenders sedative effects that may be explained in part by how it modulates intrinsic brain activity. Healthy participants (n= 12) underwent fMRI scanning 45 min after acute oral administration of zolpidem (0, 5, 10, or 20 mg), and changes in BOLD signal were measured while participants gazed at a static fixation point (i.e., at rest). Data were analyzed using group independent component analysis (ICA) with dual regression and results indicated that compared to placebo, the highest dose of zolpidem increased functional connectivity within a number of sensory, motor, and limbic networks. These results are consistent with previous studies showing an increase in functional connectivity at rest following administration of the positive GABAA receptor modulators midazolam and alcohol, and suggest that investigating how zolpidem modulates intrinsic brain activity may have implications for understanding the etiology of its powerful sedative effects. PMID:23296183

Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders.

PubMed

Takamura, T; Hanakawa, T

2017-07-01

Although functional magnetic resonance imaging (fMRI) has long been used to assess task-related brain activity in neuropsychiatric disorders, it has not yet become a widely available clinical tool. Resting-state fMRI (rs-fMRI) has been the subject of recent attention in the fields of basic and clinical neuroimaging research. This method enables investigation of the functional organization of the brain and alterations of resting-state networks (RSNs) in patients with neuropsychiatric disorders. Rs-fMRI does not require participants to perform a demanding task, in contrast to task fMRI, which often requires participants to follow complex instructions. Rs-fMRI has a number of advantages over task fMRI for application with neuropsychiatric patients, for example, although applications of task fMR to participants for healthy are easy. However, it is difficult to apply these applications to patients with psychiatric and neurological disorders, because they may have difficulty in performing demanding cognitive task. Here, we review the basic methodology and analysis techniques relevant to clinical studies, and the clinical applications of the technique for examining neuropsychiatric disorders, focusing on mood disorders (major depressive disorder and bipolar disorder) and dementia (Alzheimer's disease and mild cognitive impairment).

Cerebro-cerebellar connectivity is increased in primary lateral sclerosis.

PubMed

Meoded, Avner; Morrissette, Arthur E; Katipally, Rohan; Schanz, Olivia; Gotts, Stephen J; Floeter, Mary Kay

2015-01-01

Increased functional connectivity in resting state networks was found in several studies of patients with motor neuron disorders, although diffusion tensor imaging studies consistently show loss of white matter integrity. To understand the relationship between structural connectivity and functional connectivity, we examined the structural connections between regions with altered functional connectivity in patients with primary lateral sclerosis (PLS), a long-lived motor neuron disease. Connectivity matrices were constructed from resting state fMRI in 16 PLS patients to identify areas of differing connectivity between patients and healthy controls. Probabilistic fiber tracking was used to examine structural connections between regions of differing connectivity. PLS patients had 12 regions with increased functional connectivity compared to controls, with a predominance of cerebro-cerebellar connections. Increased functional connectivity was strongest between the cerebellum and cortical motor areas and between the cerebellum and frontal and temporal cortex. Fiber tracking detected no difference in connections between regions with increased functional connectivity. We conclude that functional connectivity changes are not strongly based in structural connectivity. Increased functional connectivity may be caused by common inputs, or by reduced selectivity of cortical activation, which could result from loss of intracortical inhibition when cortical afferents are intact.

Directional patterns of cross frequency phase and amplitude coupling within the resting state mimic patterns of fMRI functional connectivity

PubMed Central

Weaver, Kurt E.; Wander, Jeremiah D.; Ko, Andrew L.; Casimo, Kaitlyn; Grabowski, Thomas J.; Ojemann, Jeffrey G.; Darvas, Felix

2016-01-01

Functional imaging investigations into the brain's resting state interactions have yielded a wealth of insight into the intrinsic and dynamic neural architecture supporting cognition and behavior. Electrophysiological studies however have highlighted the fact that synchrony across large-scale cortical systems is composed of spontaneous interactions occurring at timescales beyond the traditional resolution of fMRI, a feature that limits the capacity of fMRI to draw inference on the true directional relationship between network nodes. To approach the question of directionality in resting state signals, we recorded resting state functional MRI (rsfMRI) and electrocorticography (ECoG) from four human subjects undergoing invasive epilepsy monitoring. Using a seed-point based approach, we employed phase-amplitude coupling (PAC) and biPhase Locking Values (bPLV), two measures of cross-frequency coupling (CFC) to explore both outgoing and incoming connections between the seed and all non-seed, site electrodes. We observed robust PAC between a wide range of low-frequency phase and high frequency amplitude estimates. However, significant bPLV, a CFC measure of phase-phase synchrony, was only observed at specific narrow low and high frequency bandwidths. Furthermore, the spatial patterns of outgoing PAC connectivity were most closely associated with the rsfMRI connectivity maps. Our results support the hypothesis that PAC is relatively ubiquitous phenomenon serving as a mechanism for coordinating high-frequency amplitudes across distant neuronal assemblies even in absence of overt task structure. Additionally, we demonstrate that the spatial distribution of a seed-point rsfMRI sensorimotor network is strikingly similar to specific patterns of directional PAC. Specifically, the high frequency activities of distal patches of cortex owning membership in a rsfMRI sensorimotor network were most likely to be entrained to the phase of a low frequency rhythm engendered from the neural populations at the seed-point, suggestive of greater directional coupling from the seed out to the site electrodes. PMID:26747745

Inhibitory Behavioral Control: A Stochastic Dynamic Causal Modeling Study Using Network Discovery Analysis

PubMed Central

Steinberg, Joel L.; Cunningham, Kathryn A.; Lane, Scott D.; Kramer, Larry A.; Narayana, Ponnada A.; Kosten, Thomas R.; Bechara, Antoine; Moeller, F. Gerard

2015-01-01

Abstract This study employed functional magnetic resonance imaging (fMRI)-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity underlying inhibitory behavioral control. fMRI data were acquired from 15 healthy subjects while they performed a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard NoGo conditions) in distinguishing spatial patterns of lines. Based on the previous inhibitory control literature and the present fMRI activation results, 10 brain regions were postulated as nodes in the effective connectivity model. Due to the large number of potential interconnections among these nodes, the number of models for final analysis was reduced to a manageable level for the whole group by conducting DCM Network Discovery, which is a recently developed option within the Statistical Parametric Mapping software package. Given the optimum network model, the DCM Network Discovery analysis found that the locations of the driving input into the model from all the experimental stimuli in the Go/NoGo task were the amygdala and the hippocampus. The strengths of several cortico-subcortical connections were modulated (influenced) by the two NoGo conditions. Specifically, connectivity from the middle frontal gyrus (MFG) to hippocampus was enhanced by the Easy condition and further enhanced by the Hard NoGo condition, possibly suggesting that compared with the Easy NoGo condition, stronger control from MFG was needed for the hippocampus to discriminate/learn the spatial pattern in order to respond correctly (inhibit), during the Hard NoGo condition. PMID:25336321

Flexible modulation of network connectivity related to cognition in Alzheimer's disease.

PubMed

McLaren, Donald G; Sperling, Reisa A; Atri, Alireza

2014-10-15

Functional neuroimaging tools, such as fMRI methods, may elucidate the neural correlates of clinical, behavioral, and cognitive performance. Most functional imaging studies focus on regional task-related activity or resting state connectivity rather than how changes in functional connectivity across conditions and tasks are related to cognitive and behavioral performance. To investigate the promise of characterizing context-dependent connectivity-behavior relationships, this study applies the method of generalized psychophysiological interactions (gPPI) to assess the patterns of associative-memory-related fMRI hippocampal functional connectivity in Alzheimer's disease (AD) associated with performance on memory and other cognitively demanding neuropsychological tests and clinical measures. Twenty-four subjects with mild AD dementia (ages 54-82, nine females) participated in a face-name paired-associate encoding memory study. Generalized PPI analysis was used to estimate the connectivity between the hippocampus and the whole brain during encoding. The difference in hippocampal-whole brain connectivity between encoding novel and encoding repeated face-name pairs was used in multiple-regression analyses as an independent predictor for 10 behavioral, neuropsychological and clinical tests. The analysis revealed connectivity-behavior relationships that were distributed, dynamically overlapping, and task-specific within and across intrinsic networks; hippocampal-whole brain connectivity-behavior relationships were not isolated to single networks, but spanned multiple brain networks. Importantly, these spatially distributed performance patterns were unique for each measure. In general, out-of-network behavioral associations with encoding novel greater than repeated face-name pairs hippocampal-connectivity were observed in the default-mode network, while correlations with encoding repeated greater than novel face-name pairs hippocampal-connectivity were observed in the executive control network (p<0.05, cluster corrected). Psychophysiological interactions revealed significantly more extensive and robust associations between paired-associate encoding task-dependent hippocampal-whole brain connectivity and performance on memory and behavioral/clinical measures than previously revealed by standard activity-behavior analysis. Compared to resting state and task-activation methods, gPPI analyses may be more sensitive to reveal additional complementary information regarding subtle within- and between-network relations. The patterns of robust correlations between hippocampal-whole brain connectivity and behavioral measures identified here suggest that there are 'coordinated states' in the brain; that the dynamic range of these states is related to behavior and cognition; and that these states can be observed and quantified, even in individuals with mild AD. Copyright © 2014 Elsevier Inc. All rights reserved.

Resting-State fMRI Activity Predicts Unsupervised Learning and Memory in an Immersive Virtual Reality Environment

PubMed Central

Wong, Chi Wah; Olafsson, Valur; Plank, Markus; Snider, Joseph; Halgren, Eric; Poizner, Howard; Liu, Thomas T.

2014-01-01

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment. PMID:25286145

Multimodal frontostriatal connectivity underlies individual differences in self-esteem.

PubMed

Chavez, Robert S; Heatherton, Todd F

2015-03-01

A heightened sense of self-esteem is associated with a reduced risk for several types of affective and psychiatric disorders, including depression, anxiety and eating disorders. However, little is known about how brain systems integrate self-referential processing and positive evaluation to give rise to these feelings. To address this, we combined diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) to test how frontostriatal connectivity reflects long-term trait and short-term state aspects of self-esteem. Using DTI, we found individual variability in white matter structural integrity between the medial prefrontal cortex and the ventral striatum was related to trait measures of self-esteem, reflecting long-term stability of self-esteem maintenance. Using fMRI, we found that functional connectivity of these regions during positive self-evaluation was related to current feelings of self-esteem, reflecting short-term state self-esteem. These results provide convergent anatomical and functional evidence that self-esteem is related to the connectivity of frontostriatal circuits and suggest that feelings of self-worth may emerge from neural systems integrating information about the self with positive affect and reward. This information could potentially inform the etiology of diminished self-esteem underlying multiple psychiatric conditions and inform future studies of evaluative self-referential processing. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

The Effects of Music Intervention on Functional Connectivity Strength of the Brain in Schizophrenia.

PubMed

Yang, Mi; He, Hui; Duan, Mingjun; Chen, Xi; Chang, Xin; Lai, Yongxiu; Li, Jianfu; Liu, Tiejun; Luo, Cheng; Yao, Dezhong

2018-01-01

Schizophrenia is often associated with behavior abnormality in the cognitive and affective domain. Music intervention is used as a complementary treatment for improving symptoms in patients with schizophrenia. However, the neurophysiological correlates of these remissions remain poorly understood. Here, we investigated the effects of music intervention in neural circuits through functional magnetic resonance imaging (fMRI) study in schizophrenic subjects. Under the standard care, patients were randomly assigned to music and non-music interventions (MTSZ, UMTSZ) for 1 month. Resting-state fMRI were acquired over three time points (baseline, 1 month, and 6 months later) in patients and analyzed using functional connectivity strength (FCS) and seed-based functional connection (FC) approaches. At baseline, compared with healthy controls, decreased FCS in the right middle temporal gyrus (MTG) was observed in patients. However, after music intervention, the functional circuitry of the right MTG, which was related with the function of emotion and sensorimotor, was improved in MTSZ. Furthermore, the FC increments were significantly correlated with the improvement of symptoms, while vanishing 6 months later. Together, these findings provided evidence that music intervention might positively modulate the functional connectivity of MTG in patients with schizophrenia; such changes might be associated with the observed therapeutic effects of music intervention on neurocognitive function. This trial is registered with ChiCTR-OPC-14005339.

Multimodal description of whole brain connectivity: A comparison of resting state MEG, fMRI, and DWI.

PubMed

Garcés, Pilar; Pereda, Ernesto; Hernández-Tamames, Juan A; Del-Pozo, Francisco; Maestú, Fernando; Pineda-Pardo, José Ángel

2016-01-01

Structural and functional connectivity (SC and FC) have received much attention over the last decade, as they offer unique insight into the coordination of brain functioning. They are often assessed independently with three imaging modalities: SC using diffusion-weighted imaging (DWI), FC using functional magnetic resonance imaging (fMRI), and magnetoencephalography/electroencephalography (MEG/EEG). DWI provides information about white matter organization, allowing the reconstruction of fiber bundles. fMRI uses blood-oxygenation level-dependent (BOLD) contrast to indirectly map neuronal activation. MEG and EEG are direct measures of neuronal activity, as they are sensitive to the synchronous inputs in pyramidal neurons. Seminal studies have targeted either the electrophysiological substrate of BOLD or the anatomical basis of FC. However, multimodal comparisons have been scarcely performed, and the relation between SC, fMRI-FC, and MEG-FC is still unclear. Here we present a systematic comparison of SC, resting state fMRI-FC, and MEG-FC between cortical regions, by evaluating their similarities at three different scales: global network, node, and hub distribution. We obtained strong similarities between the three modalities, especially for the following pairwise combinations: SC and fMRI-FC; SC and MEG-FC at theta, alpha, beta and gamma bands; and fMRI-FC and MEG-FC in alpha and beta. Furthermore, highest node similarity was found for regions of the default mode network and primary motor cortex, which also presented the highest hubness score. Distance was partially responsible for these similarities since it biased all three connectivity estimates, but not the unique contributor, since similarities remained after controlling for distance. © 2015 Wiley Periodicals, Inc.

Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons.

PubMed

Ma, Ying; Shaik, Mohammed A; Kozberg, Mariel G; Kim, Sharon H; Portes, Jacob P; Timerman, Dmitriy; Hillman, Elizabeth M C

2016-12-27

Brain hemodynamics serve as a proxy for neural activity in a range of noninvasive neuroimaging techniques including functional magnetic resonance imaging (fMRI). In resting-state fMRI, hemodynamic fluctuations have been found to exhibit patterns of bilateral synchrony, with correlated regions inferred to have functional connectivity. However, the relationship between resting-state hemodynamics and underlying neural activity has not been well established, making the neural underpinnings of functional connectivity networks unclear. In this study, neural activity and hemodynamics were recorded simultaneously over the bilateral cortex of awake and anesthetized Thy1-GCaMP mice using wide-field optical mapping. Neural activity was visualized via selective expression of the calcium-sensitive fluorophore GCaMP in layer 2/3 and 5 excitatory neurons. Characteristic patterns of resting-state hemodynamics were accompanied by more rapidly changing bilateral patterns of resting-state neural activity. Spatiotemporal hemodynamics could be modeled by convolving this neural activity with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Simultaneous imaging and electrophysiology confirmed that Thy1-GCaMP signals are well-predicted by multiunit activity. Neurovascular coupling between resting-state neural activity and hemodynamics was robust and fast in awake animals, whereas coupling in urethane-anesthetized animals was slower, and in some cases included lower-frequency (<0.04 Hz) hemodynamic fluctuations that were not well-predicted by local Thy1-GCaMP recordings. These results support that resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying patterns of excitatory neural activity. The patterns of bilaterally-symmetric spontaneous neural activity revealed by wide-field Thy1-GCaMP imaging may depict the neural foundation of functional connectivity networks detected in resting-state fMRI.

Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons

PubMed Central

Ma, Ying; Shaik, Mohammed A.; Kozberg, Mariel G.; Portes, Jacob P.; Timerman, Dmitriy

2016-01-01

Brain hemodynamics serve as a proxy for neural activity in a range of noninvasive neuroimaging techniques including functional magnetic resonance imaging (fMRI). In resting-state fMRI, hemodynamic fluctuations have been found to exhibit patterns of bilateral synchrony, with correlated regions inferred to have functional connectivity. However, the relationship between resting-state hemodynamics and underlying neural activity has not been well established, making the neural underpinnings of functional connectivity networks unclear. In this study, neural activity and hemodynamics were recorded simultaneously over the bilateral cortex of awake and anesthetized Thy1-GCaMP mice using wide-field optical mapping. Neural activity was visualized via selective expression of the calcium-sensitive fluorophore GCaMP in layer 2/3 and 5 excitatory neurons. Characteristic patterns of resting-state hemodynamics were accompanied by more rapidly changing bilateral patterns of resting-state neural activity. Spatiotemporal hemodynamics could be modeled by convolving this neural activity with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Simultaneous imaging and electrophysiology confirmed that Thy1-GCaMP signals are well-predicted by multiunit activity. Neurovascular coupling between resting-state neural activity and hemodynamics was robust and fast in awake animals, whereas coupling in urethane-anesthetized animals was slower, and in some cases included lower-frequency (<0.04 Hz) hemodynamic fluctuations that were not well-predicted by local Thy1-GCaMP recordings. These results support that resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying patterns of excitatory neural activity. The patterns of bilaterally-symmetric spontaneous neural activity revealed by wide-field Thy1-GCaMP imaging may depict the neural foundation of functional connectivity networks detected in resting-state fMRI. PMID:27974609

Reductions in Cortico-Striatal Hyperconnectivity Accompany Successful Treatment of Obsessive-Compulsive Disorder with Dorsomedial Prefrontal rTMS

PubMed Central

Dunlop, Katharine; Woodside, Blake; Olmsted, Marion; Colton, Patricia; Giacobbe, Peter; Downar, Jonathan

2016-01-01

Obsessive-compulsive disorder (OCD) is a disabling illness with high rates of nonresponse to conventional treatments. OCD pathophysiology is believed to involve abnormalities in cortico-striatal-thalamic-cortical circuits through regions such as dorsomedial prefrontal cortex (dmPFC) and ventral striatum. These regions may constitute therapeutic targets for neuromodulation treatments, such as repetitive transcranial magnetic stimulation (rTMS). However, the neurobiological predictors and correlates of successful rTMS treatment for OCD are unclear. Here, we used resting-state functional magnetic resonance imaging (fMRI) to identify neural predictors and correlates of response to 20–30 sessions of bilateral 10 Hz dmPFC-rTMS in 20 treatment-resistant OCD patients, with 40 healthy controls as baseline comparators. A region of interest in the dmPFC was used to generate whole-brain functional connectivity maps pre-treatment and post treatment. Ten of 20 patients met the response criteria (⩾50% improvement on Yale-Brown Obsessive-Compulsive Scale, YBOCS); response to dmPFC-rTMS was sharply bimodal. dmPFC-rTMS responders had higher dmPFC-ventral striatal connectivity at baseline. The degree of reduction in this connectivity, from pre- to post-treatment, correlated to the degree of YBOCS symptomatic improvement. Baseline clinical and psychometric data did not predict treatment response. In summary, reductions in fronto-striatal hyperconnectivity were associated with treatment response to dmPFC-rTMS in OCD. This finding is consistent with previous fMRI studies of deep brain stimulation in OCD, but opposite to previous reports on mechanisms of dmPFC-rTMS in major depression. fMRI could prove useful in predicting the response to dmPFC-rTMS in OCD. PMID:26440813

Reductions in Cortico-Striatal Hyperconnectivity Accompany Successful Treatment of Obsessive-Compulsive Disorder with Dorsomedial Prefrontal rTMS.

PubMed

Dunlop, Katharine; Woodside, Blake; Olmsted, Marion; Colton, Patricia; Giacobbe, Peter; Downar, Jonathan

2016-04-01

Obsessive-compulsive disorder (OCD) is a disabling illness with high rates of nonresponse to conventional treatments. OCD pathophysiology is believed to involve abnormalities in cortico-striatal-thalamic-cortical circuits through regions such as dorsomedial prefrontal cortex (dmPFC) and ventral striatum. These regions may constitute therapeutic targets for neuromodulation treatments, such as repetitive transcranial magnetic stimulation (rTMS). However, the neurobiological predictors and correlates of successful rTMS treatment for OCD are unclear. Here, we used resting-state functional magnetic resonance imaging (fMRI) to identify neural predictors and correlates of response to 20-30 sessions of bilateral 10 Hz dmPFC-rTMS in 20 treatment-resistant OCD patients, with 40 healthy controls as baseline comparators. A region of interest in the dmPFC was used to generate whole-brain functional connectivity maps pre-treatment and post treatment. Ten of 20 patients met the response criteria (⩾50% improvement on Yale-Brown Obsessive-Compulsive Scale, YBOCS); response to dmPFC-rTMS was sharply bimodal. dmPFC-rTMS responders had higher dmPFC-ventral striatal connectivity at baseline. The degree of reduction in this connectivity, from pre- to post-treatment, correlated to the degree of YBOCS symptomatic improvement. Baseline clinical and psychometric data did not predict treatment response. In summary, reductions in fronto-striatal hyperconnectivity were associated with treatment response to dmPFC-rTMS in OCD. This finding is consistent with previous fMRI studies of deep brain stimulation in OCD, but opposite to previous reports on mechanisms of dmPFC-rTMS in major depression. fMRI could prove useful in predicting the response to dmPFC-rTMS in OCD.

Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury.

PubMed

Yan, Yan; Song, Jian; Xu, Guozheng; Yao, Shun; Cao, Chenglong; Li, Chang; Peng, Guibao; Du, Hao

2017-10-01

This study investigated the characteristics of the small-world brain network architecture of patients with mild traumatic brain injury (MTBI), and a correlation between brain functional connectivity network properties in the resting-state fMRI and Standardized Assessment of Concussion (SAC) parameters. The neurological conditions of 22 MTBI patients and 17 normal control individuals were evaluated according to the SAC. Resting-state fMRI was performed in all subjects 3 and 7days after injury respectively. After preprocessing the fMRI data, cortex functional regions were marked using AAL90 and Dosenbach160 templates. The small-world network parameters and areas under the integral curves were computed in the range of sparsity from 0.01 to 0.5. Independent-sample t-tests were used to compare these parameters between the MTBI and control group. Significantly different parameters were investigated for correlations with SAC scores; those that correlated were chosen for further curve fitting. The clustering coefficient, the communication efficiency across in local networks, and the strength of connectivity were all higher in MTBI patients relative to control individuals. Parameters in 160 brain regions of the MTBI group significantly correlated with total SAC score and score for attention; the network parameters may be a quadratic function of attention scores of SAC and a cubic function of SAC scores. MTBI patients were characterized by elevated communication efficiency across global brain regions, and in local networks, and strength of mean connectivity. These features may be associated with brain function compensation. The network parameters significantly correlated with SAC total and attention scores. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transport on Riemannian manifold for functional connectivity-based classification.

PubMed

Ng, Bernard; Dressler, Martin; Varoquaux, Gaël; Poline, Jean Baptiste; Greicius, Michael; Thirion, Bertrand

2014-01-01

We present a Riemannian approach for classifying fMRI connectivity patterns before and after intervention in longitudinal studies. A fundamental difficulty with using connectivity as features is that covariance matrices live on the positive semi-definite cone, which renders their elements inter-related. The implicit independent feature assumption in most classifier learning algorithms is thus violated. In this paper, we propose a matrix whitening transport for projecting the covariance estimates onto a common tangent space to reduce the statistical dependencies between their elements. We show on real data that our approach provides significantly higher classification accuracy than directly using Pearson's correlation. We further propose a non-parametric scheme for identifying significantly discriminative connections from classifier weights. Using this scheme, a number of neuroanatomically meaningful connections are found, whereas no significant connections are detected with pure permutation testing.

Resting State Functional Connectivity in Mild Traumatic Brain Injury at the Acute Stage: Independent Component and Seed-Based Analyses

PubMed Central

Iraji, Armin; Benson, Randall R.; Welch, Robert D.; O'Neil, Brian J.; Woodard, John L.; Imran Ayaz, Syed; Kulek, Andrew; Mika, Valerie; Medado, Patrick; Soltanian-Zadeh, Hamid; Liu, Tianming; Haacke, E. Mark

2015-01-01

Abstract Mild traumatic brain injury (mTBI) accounts for more than 1 million emergency visits each year. Most of the injured stay in the emergency department for a few hours and are discharged home without a specific follow-up plan because of their negative clinical structural imaging. Advanced magnetic resonance imaging (MRI), particularly functional MRI (fMRI), has been reported as being sensitive to functional disturbances after brain injury. In this study, a cohort of 12 patients with mTBI were prospectively recruited from the emergency department of our local Level-1 trauma center for an advanced MRI scan at the acute stage. Sixteen age- and sex-matched controls were also recruited for comparison. Both group-based and individual-based independent component analysis of resting-state fMRI (rsfMRI) demonstrated reduced functional connectivity in both posterior cingulate cortex (PCC) and precuneus regions in comparison with controls, which is part of the default mode network (DMN). Further seed-based analysis confirmed reduced functional connectivity in these two regions and also demonstrated increased connectivity between these regions and other regions of the brain in mTBI. Seed-based analysis using the thalamus, hippocampus, and amygdala regions further demonstrated increased functional connectivity between these regions and other regions of the brain, particularly in the frontal lobe, in mTBI. Our data demonstrate alterations of multiple brain networks at the resting state, particularly increased functional connectivity in the frontal lobe, in response to brain concussion at the acute stage. Resting-state functional connectivity of the DMN could serve as a potential biomarker for improved detection of mTBI in the acute setting. PMID:25285363

Independent functional connectivity networks underpin food and monetary reward sensitivity in excess weight.

PubMed

Verdejo-Román, Juan; Fornito, Alex; Soriano-Mas, Carles; Vilar-López, Raquel; Verdejo-García, Antonio

2017-02-01

Overvaluation of palatable food is a primary driver of obesity, and is associated with brain regions of the reward system. However, it remains unclear if this network is specialized in food reward, or generally involved in reward processing. We used functional magnetic resonance imaging (fMRI) to characterize functional connectivity during processing of food and monetary rewards. Thirty-nine adults with excess weight and 37 adults with normal weight performed the Willingness to Pay for Food task and the Monetary Incentive Delay task in the fMRI scanner. A data-driven graph approach was applied to compare whole-brain, task-related functional connectivity between groups. Excess weight was associated with decreased functional connectivity during the processing of food rewards in a network involving primarily frontal and striatal areas, and increased functional connectivity during the processing of monetary rewards in a network involving principally frontal and parietal areas. These two networks were topologically and anatomically distinct, and were independently associated with BMI. The processing of food and monetary rewards involve segregated neural networks, and both are altered in individuals with excess weight. Copyright © 2016 Elsevier Inc. All rights reserved.

Aberrant functioning of the theory-of-mind network in children and adolescents with autism.

PubMed

Kana, Rajesh K; Maximo, Jose O; Williams, Diane L; Keller, Timothy A; Schipul, Sarah E; Cherkassky, Vladimir L; Minshew, Nancy J; Just, Marcel Adam

2015-01-01

Theory-of-mind (ToM), the ability to infer people's thoughts and feelings, is a pivotal skill in effective social interactions. Individuals with autism spectrum disorders (ASD) have been found to have altered ToM skills, which significantly impacts the quality of their social interactions. Neuroimaging studies have reported altered activation of the ToM cortical network, especially in adults with autism, yet little is known about the brain responses underlying ToM in younger individuals with ASD. This functional magnetic resonance imaging (fMRI) study investigated the neural mechanisms underlying ToM in high-functioning children and adolescents with ASD and matched typically developing (TD) peers. fMRI data were acquired from 13 participants with ASD and 13 TD control participants while they watched animations involving two "interacting" geometrical shapes. Participants with ASD showed significantly reduced activation, relative to TD controls, in regions considered part of the ToM network, the mirror network, and the cerebellum. Functional connectivity analyses revealed underconnectivity between frontal and posterior regions during task performance in the ASD participants. Overall, the findings of this study reveal disruptions in the brain circuitry underlying ToM in ASD at multiple levels, including decreased activation and decreased functional connectivity.

Improving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI Connectivity-Based Neurofeedback.

PubMed

Liew, Sook-Lei; Rana, Mohit; Cornelsen, Sonja; Fortunato de Barros Filho, Marcos; Birbaumer, Niels; Sitaram, Ranganatha; Cohen, Leonardo G; Soekadar, Surjo R

2016-08-01

Two thirds of stroke survivors experience motor impairment resulting in long-term disability. The anatomical substrate is often the disruption of cortico-subcortical pathways. It has been proposed that reestablishment of cortico-subcortical communication relates to functional recovery. In this study, we applied a novel training protocol to augment ipsilesional cortico-subcortical connectivity after stroke. Chronic stroke patients with severe motor impairment were provided online feedback of blood-oxygenation level dependent signal connectivity between cortical and subcortical regions critical for motor function using real-time functional magnetic resonance imaging neurofeedback. In this proof of principle study, 3 out of 4 patients learned to voluntarily modulate cortico-subcortical connectivity as intended. Our results document for the first time the feasibility and safety for patients with chronic stroke and severe motor impairment to self-regulate and augment ipsilesional cortico-subcortical connectivity through neurofeedback using real-time functional magnetic resonance imaging. © The Author(s) 2015.

Mapping Resting-State Brain Networks in Conscious Animals

PubMed Central

Zhang, Nanyin; Rane, Pallavi; Huang, Wei; Liang, Zhifeng; Kennedy, David; Frazier, Jean A.; King, Jean

2010-01-01

In the present study we mapped brain functional connectivity in the conscious rat at the “resting state” based on intrinsic blood-oxygenation-level dependent (BOLD) fluctuations. The conscious condition eliminated potential confounding effects of anesthetic agents on the connectivity between brain regions. Indeed, using correlational analysis we identified multiple cortical and subcortical regions that demonstrated temporally synchronous variation with anatomically well-defined regions that are crucial to cognitive and emotional information processing including the prefrontal cortex (PFC), thalamus and retrosplenial cortex. The functional connectivity maps created were stringently validated by controlling for false positive detection of correlation, the physiologic basis of the signal source, as well as quantitatively evaluating the reproducibility of maps. Taken together, the present study has demonstrated the feasibility of assessing functional connectivity in conscious animals using fMRI and thus provided a convenient and non-invasive tool to systematically investigate the connectional architecture of selected brain networks in multiple animal models. PMID:20382183

Developmental Increase in Top-Down and Bottom-Up Processing in a Phonological Task: An Effective Connectivity, fMRI Study

ERIC Educational Resources Information Center

Bitan, Tali; Cheon, Jimmy; Lu, Dong; Burman, Douglas D.; Booth, James R.

2009-01-01

We examined age-related changes in the interactions among brain regions in children performing rhyming judgments on visually presented words. The difficulty of the task was manipulated by including a conflict between task-relevant (phonological) information and task-irrelevant (orthographic) information. The conflicting conditions included pairs…

Lateral habenula perturbation reduces default-mode network connectivity in a rat model of depression.

PubMed

Clemm von Hohenberg, Christian; Weber-Fahr, Wolfgang; Lebhardt, Philipp; Ravi, Namasivayam; Braun, Urs; Gass, Natalia; Becker, Robert; Sack, Markus; Cosa Linan, Alejandro; Gerchen, Martin Fungisai; Reinwald, Jonathan Rochus; Oettl, Lars-Lennart; Meyer-Lindenberg, Andreas; Vollmayr, Barbara; Kelsch, Wolfgang; Sartorius, Alexander

2018-03-27

Hyperconnectivity of the default-mode network (DMN) is one of the most widely replicated neuroimaging findings in major depressive disorder (MDD). Further, there is growing evidence for a central role of the lateral habenula (LHb) in the pathophysiology of MDD. There is preliminary neuroimaging evidence linking LHb and the DMN, but no causal relationship has been shown to date. We combined optogenetics and functional magnetic resonance imaging (fMRI), to establish a causal relationship, using an animal model of treatment-resistant depression, namely Negative Cognitive State rats. First, an inhibitory light-sensitive ion channel was introduced into the LHb by viral transduction. Subsequently, laser stimulation was performed during fMRI acquisition on a 9.4 Tesla animal scanner. Neural activity and connectivity were assessed, before, during and after laser stimulation. We observed a connectivity decrease in the DMN following laser-induced LHb perturbation. Our data indicate a causal link between LHb downregulation and reduction in DMN connectivity. These findings may advance our mechanistic understanding of LHb inhibition, which had previously been identified as a promising therapeutic principle, especially for treatment-resistant depression.

Resting state brain networks and their implications in neurodegenerative disease

NASA Astrophysics Data System (ADS)

Sohn, William S.; Yoo, Kwangsun; Kim, Jinho; Jeong, Yong

2012-10-01

Neurons are the basic units of the brain, and form network by connecting via synapses. So far, there have been limited ways to measure the brain networks. Recently, various imaging modalities are widely used for this purpose. In this paper, brain network mapping using resting state fMRI will be introduced with several applications including neurodegenerative disease such as Alzheimer's disease, frontotemporal lobar degeneration and Parkinson's disease. The resting functional connectivity using intrinsic functional connectivity in mouse is useful since we can take advantage of perturbation or stimulation of certain nodes of the network. The study of brain connectivity will open a new era in understanding of brain and diseases thus will be an essential foundation for future research.

Brain Functional Connectivity Is Modified by a Hypocaloric Mediterranean Diet and Physical Activity in Obese Women

PubMed Central

García-Casares, Natalia; Bernal-López, María R.; Roé-Vellvé, Nuria; Gutiérrez-Bedmar, Mario; García-Arnés, Juan A.; Ramos-Rodriguez, José R.; Alfaro, Francisco; Santamaria-Fernández, Sonia; Jiménez-Murcia, Susana; Garcia-Garcia, Isabel; Valdivielso, Pedro; Fernández-Aranda, Fernando; Tinahones, Francisco J.; Gómez-Huelgas, Ricardo

2017-01-01

Functional magnetic resonance imaging (fMRI) in the resting state has shown altered brain connectivity networks in obese individuals. However, the impact of a Mediterranean diet on cerebral connectivity in obese patients when losing weight has not been previously explored. The aim of this study was to examine the connectivity between brain structures before and six months after following a hypocaloric Mediterranean diet and physical activity program in a group of sixteen obese women aged 46.31 ± 4.07 years. Before and after the intervention program, the body mass index (BMI) (kg/m2) was 38.15 ± 4.7 vs. 34.18 ± 4.5 (p < 0.02), and body weight (kg) was 98.5 ± 13.1 vs. 88.28 ± 12.2 (p < 0.03). All subjects underwent a pre- and post-intervention fMRI under fasting conditions. Functional connectivity was assessed using seed-based correlations. After the intervention, we found decreased connectivity between the left inferior parietal cortex and the right temporal cortex (p < 0.001), left posterior cingulate (p < 0.001), and right posterior cingulate (p < 0.03); decreased connectivity between the left superior frontal gyrus and the right temporal cortex (p < 0.01); decreased connectivity between the prefrontal cortex and the somatosensory cortex (p < 0.025); and decreased connectivity between the left and right posterior cingulate (p < 0.04). Results were considered significant at a voxel-wise threshold of p ≤ 0.05, and a cluster-level family-wise error correction for multiple comparisons of p ≤ 0.05. In conclusion, functional connectivity between brain structures involved in the pathophysiology of obesity (the inferior parietal lobe, posterior cingulate, temporo-insular cortex, prefrontal cortex) may be modified by a weight loss program including a Mediterranean diet and physical exercise. PMID:28671558

Patient-specific connectivity pattern of epileptic network in frontal lobe epilepsy

PubMed Central

Luo, Cheng; An, Dongmei; Yao, Dezhong; Gotman, Jean

2014-01-01

There is evidence that focal epilepsy may involve the dysfunction of a brain network in addition to the focal region. To delineate the characteristics of this epileptic network, we collected EEG/fMRI data from 23 patients with frontal lobe epilepsy. For each patient, EEG/fMRI analysis was first performed to determine the BOLD response to epileptic spikes. The maximum activation cluster in the frontal lobe was then chosen as the seed to identify the epileptic network in fMRI data. Functional connectivity analysis seeded at the same region was also performed in 63 healthy control subjects. Nine features were used to evaluate the differences of epileptic network patterns in three connection levels between patients and controls. Compared with control subjects, patients showed overall more functional connections between the epileptogenic region and the rest of the brain and higher laterality. However, the significantly increased connections were located in the neighborhood of the seed, but the connections between the seed and remote regions actually decreased. Comparing fMRI runs with interictal epileptic discharges (IEDs) and without IEDs, the patient-specific connectivity pattern was not changed significantly. These findings regarding patient-specific connectivity patterns of epileptic networks in FLE reflect local high connectivity and connections with distant regions differing from those of healthy controls. Moreover, the difference between the two groups in most features was observed in the strictest of the three connection levels. The abnormally high connectivity might reflect a predominant attribute of the epileptic network, which may facilitate propagation of epileptic activity among regions in the network. PMID:24936418

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

PubMed Central

Wang, Chenhao; Ong, Ju Lynn; Patanaik, Amiya; Chee, Michael W. L.

2016-01-01

Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced within-network functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants’ eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal. PMID:27512040

Imaging brain development: the adolescent brain.

PubMed

Blakemore, Sarah-Jayne

2012-06-01

The past 15 years have seen a rapid expansion in the number of studies using neuroimaging techniques to investigate maturational changes in the human brain. In this paper, I review MRI studies on structural changes in the developing brain, and fMRI studies on functional changes in the social brain during adolescence. Both MRI and fMRI studies point to adolescence as a period of continued neural development. In the final section, I discuss a number of areas of research that are just beginning and may be the subject of developmental neuroimaging in the next twenty years. Future studies might focus on complex questions including the development of functional connectivity; how gender and puberty influence adolescent brain development; the effects of genes, environment and culture on the adolescent brain; development of the atypical adolescent brain; and implications for policy of the study of the adolescent brain. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

Psychophysiological interaction between superior temporal gyrus (STG) and cerebellum: An fMRI study

NASA Astrophysics Data System (ADS)

Yusoff, A. N.; Teng, X. L.; Ng, S. B.; Hamid, A. I. A.; Mukari, S. Z. M.

2016-03-01

This study aimed to model the psychophysiological interaction (PPI) between the bilateral STG and cerebellum (lobule VI and lobule VII) during an arithmetic addition task. Eighteen young adults participated in this study. They were instructed to solve single-digit addition tasks in quiet and noisy backgrounds during an fMRI scan. Results showed that in both hemispheres, the response in the cerebellum was found to be linearly influenced by the activity in STG (vice-versa) for both in-quiet and in-noise conditions. However, the influence of the cerebellum on STG seemed to be modulated by noise. A two-way PPI model between STG and cerebellum is suggested. The connectivity between the two regions during a simple addition task in a noisy condition is modulated by the participants’ higher attention to perceive.

Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Time Series Data-fMRI Study.

PubMed

Eslami, Taban; Saeed, Fahad

2018-04-20

Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associations in brain is Pearson’s correlation coefficient. Pearson’s correlation is widely used for constructing functional network and studying dynamic functional connectivity of the brain. These are useful measures for understanding the effects of brain disorders on connectivities among brain regions. The fMRI scanners produce huge number of voxels and using traditional central processing unit (CPU)-based techniques for computing pairwise correlations is very time consuming especially when large number of subjects are being studied. In this paper, we propose a graphics processing unit (GPU)-based algorithm called Fast-GPU-PCC for computing pairwise Pearson’s correlation coefficient. Based on the symmetric property of Pearson’s correlation, this approach returns N ( N − 1 ) / 2 correlation coefficients located at strictly upper triangle part of the correlation matrix. Storing correlations in a one-dimensional array with the order as proposed in this paper is useful for further usage. Our experiments on real and synthetic fMRI data for different number of voxels and varying length of time series show that the proposed approach outperformed state of the art GPU-based techniques as well as the sequential CPU-based versions. We show that Fast-GPU-PCC runs 62 times faster than CPU-based version and about 2 to 3 times faster than two other state of the art GPU-based methods.

Dynamic Changes in Amygdala Activation and Functional Connectivity in Children and Adolescents with Anxiety Disorders

PubMed Central

Swartz, Johnna R.; Phan, K. Luan; Angstadt, Mike; Fitzgerald, Kate D.; Monk, Christopher S.

2015-01-01

Anxiety disorders are associated with abnormalities in amygdala function and prefrontal cortex-amygdala connectivity. The majority of fMRI studies have examined mean group differences in amygdala activation or connectivity in children and adolescents with anxiety disorders relative to controls, but emerging evidence suggests that abnormalities in amygdala function are dependent on the timing of the task and may vary across the course of a scanning session. The goal of the present study was to extend our knowledge of the dynamics of amygdala dysfunction by examining whether changes in amygdala activation and connectivity over scanning differ in pediatric anxiety disorder patients relative to typically developing controls during an emotion processing task. Examining changes in activation over time allows for a comparison of how brain function differs during initial exposure to novel stimuli versus more prolonged exposure. Participants included 34 anxiety disorder patients and 19 controls 7 to 19 years old. Participants performed an emotional face matching task during fMRI scanning and the task was divided into thirds in order to examine change in activation over time. Results demonstrated that patients exhibited an abnormal pattern of amygdala activation characterized by an initially heightened amygdala response relative to controls at the beginning of scanning, followed by significant decreases in activation over time. In addition, controls evidenced greater prefrontal cortex-amygdala connectivity during the beginning of scanning relative to patients. These results indicate that differences in emotion processing between the groups vary from initial exposure to novel stimuli relative to more prolonged exposure. Implications are discussed regarding how this pattern of neural activation may relate to altered early-occurring or anticipatory emotion-regulation strategies and maladaptive later-occurring strategies in children and adolescents with anxiety disorders. PMID:25422963

Serum BDNF correlates with connectivity in the (pre)motor hub in the aging human brain--a resting-state fMRI pilot study.

PubMed

Mueller, Karsten; Arelin, Katrin; Möller, Harald E; Sacher, Julia; Kratzsch, Jürgen; Luck, Tobias; Riedel-Heller, Steffi; Villringer, Arno; Schroeter, Matthias L

2016-02-01

Brain-derived neurotrophic factor (BDNF) has been discussed to be involved in plasticity processes in the human brain, in particular during aging. Recently, aging and its (neurodegenerative) diseases have increasingly been conceptualized as disconnection syndromes. Here, connectivity changes in neural networks (the connectome) are suggested to be the most relevant and characteristic features for such processes or diseases. To further elucidate the impact of aging on neural networks, we investigated the interaction between plasticity processes, brain connectivity, and healthy aging by measuring levels of serum BDNF and resting-state fMRI data in 25 young (mean age 24.8 ± 2.7 (SD) years) and 23 old healthy participants (mean age, 68.6 ± 4.1 years). To identify neural hubs most essentially related to serum BDNF, we applied graph theory approaches, namely the new data-driven and parameter-free approach eigenvector centrality (EC) mapping. The analysis revealed a positive correlation between serum BDNF and EC in the premotor and motor cortex in older participants in contrast to young volunteers, where we did not detect any association. This positive relationship between serum BDNF and EC appears to be specific for older adults. Our results might indicate that the amount of physical activity and learning capacities, leading to higher BDNF levels, increases brain connectivity in (pre)motor areas in healthy aging in agreement with rodent animal studies. Pilot results have to be replicated in a larger sample including behavioral data to disentangle the cause for the relationship between BDNF levels and connectivity. Copyright © 2016 Elsevier Inc. All rights reserved.

Functional brain networks in schizophrenia: a review.

PubMed

Calhoun, Vince D; Eichele, Tom; Pearlson, Godfrey

2009-01-01

Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia.

Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals.

PubMed

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

2016-01-01

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

The Neural Signature of Subliminal Visuomotor Priming: Brain Activity and Functional Connectivity Profiles.

PubMed

Ulrich, Martin; Kiefer, Markus

2016-06-01

Unconscious visuomotor priming defined as the advantage in reaction time (RT) or accuracy for target shapes mapped to the same (congruent condition) when compared with a different (incongruent condition) motor response as a preceding subliminally presented prime shape has been shown to modulate activity within a visuomotor network comprised of parietal and frontal motor areas in previous functional magnetic resonance imaging (fMRI) studies. The present fMRI study investigated whether, in addition to changes in brain activity, unconscious visuomotor priming results in a modulation of functional connectivity profiles. Activity associated with congruent compared with incongruent trials was lower in the bilateral inferior and medial superior frontal gyri, in the inferior parietal lobules, and in the right caudate nucleus and adjacent portions of the thalamus. Functional connectivity increased under congruent relative to incongruent conditions between ventral visual stream areas (e.g., calcarine, fusiform, and lingual gyri), the precentral gyrus, the supplementary motor area, posterior parietal areas, the inferior frontal gyrus, and the caudate nucleus. Our findings suggest that an increase in coupling between visuomotor regions, reflecting higher efficiency of processing, is an important neural mechanism underlying unconscious visuomotor priming, in addition to changes in the magnitude of activation. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing.

PubMed

Liu, Chao; Abu-Jamous, Basel; Brattico, Elvira; Nandi, Asoke K

2017-03-01

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package "UNCLES" available on http://cran.r-project.org/web/packages/UNCLES/index.html .

An electrophysiological validation of stochastic DCM for fMRI

PubMed Central

Daunizeau, J.; Lemieux, L.; Vaudano, A. E.; Friston, K. J.; Stephan, K. E.

2013-01-01

In this note, we assess the predictive validity of stochastic dynamic causal modeling (sDCM) of functional magnetic resonance imaging (fMRI) data, in terms of its ability to explain changes in the frequency spectrum of concurrently acquired electroencephalography (EEG) signal. We first revisit the heuristic model proposed in Kilner et al. (2005), which suggests that fMRI activation is associated with a frequency modulation of the EEG signal (rather than an amplitude modulation within frequency bands). We propose a quantitative derivation of the underlying idea, based upon a neural field formulation of cortical activity. In brief, dense lateral connections induce a separation of time scales, whereby fast (and high spatial frequency) modes are enslaved by slow (low spatial frequency) modes. This slaving effect is such that the frequency spectrum of fast modes (which dominate EEG signals) is controlled by the amplitude of slow modes (which dominate fMRI signals). We then use conjoint empirical EEG-fMRI data—acquired in epilepsy patients—to demonstrate the electrophysiological underpinning of neural fluctuations inferred from sDCM for fMRI. PMID:23346055

The Longitudinal Trajectory of Default Mode Network Connectivity in Healthy Older Adults Varies As a Function of Age and Is Associated with Changes in Episodic Memory and Processing Speed.

PubMed

Staffaroni, Adam M; Brown, Jesse A; Casaletto, Kaitlin B; Elahi, Fanny M; Deng, Jersey; Neuhaus, John; Cobigo, Yann; Mumford, Paige S; Walters, Samantha; Saloner, Rowan; Karydas, Anna; Coppola, Giovanni; Rosen, Howie J; Miller, Bruce L; Seeley, William W; Kramer, Joel H

2018-03-14

The default mode network (DMN) supports memory functioning and may be sensitive to preclinical Alzheimer's pathology. Little is known, however, about the longitudinal trajectory of this network's intrinsic functional connectivity (FC). In this study, we evaluated longitudinal FC in 111 cognitively normal older human adults (ages 49-87, 46 women/65 men), 92 of whom had at least three task-free fMRI scans ( n = 353 total scans). Whole-brain FC and three DMN subnetworks were assessed: (1) within-DMN, (2) between anterior and posterior DMN, and (3) between medial temporal lobe network and posterior DMN. Linear mixed-effects models demonstrated significant baseline age × time interactions, indicating a nonlinear trajectory. There was a trend toward increasing FC between ages 50-66 and significantly accelerating declines after age 74. A similar interaction was observed for whole-brain FC. APOE status did not predict baseline connectivity or change in connectivity. After adjusting for network volume, changes in within-DMN connectivity were specifically associated with changes in episodic memory and processing speed but not working memory or executive functions. The relationship with processing speed was attenuated after covarying for white matter hyperintensities (WMH) and whole-brain FC, whereas within-DMN connectivity remained associated with memory above and beyond WMH and whole-brain FC. Whole-brain and DMN FC exhibit a nonlinear trajectory, with more rapid declines in older age and possibly increases in connectivity early in the aging process. Within-DMN connectivity is a marker of episodic memory performance even among cognitively healthy older adults. SIGNIFICANCE STATEMENT Default mode network and whole-brain connectivity, measured using task-free fMRI, changed nonlinearly as a function of age, with some suggestion of early increases in connectivity. For the first time, longitudinal changes in DMN connectivity were shown to correlate with changes in episodic memory, whereas volume changes in relevant brain regions did not. This relationship was not accounted for by white matter hyperintensities or mean whole-brain connectivity. Functional connectivity may be an early biomarker of changes in aging but should be used with caution given its nonmonotonic nature, which could complicate interpretation. Future studies investigating longitudinal network changes should consider whole-brain changes in connectivity. Copyright © 2018 the authors 0270-6474/18/382810-09$15.00/0.

Clinical Applications of Resting State Functional Connectivity

PubMed Central

Fox, Michael D.; Greicius, Michael

2010-01-01

During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm. PMID:20592951

Changes in Gray Matter Density, Regional Homogeneity, and Functional Connectivity in Methamphetamine-Associated Psychosis: A Resting-State Functional Magnetic Resonance Imaging (fMRI) Study.

PubMed

Zhang, Shengyu; Hu, Qiang; Tang, Tao; Liu, Chao; Li, Chengchong; Zang, Yin-Yin; Cai, Wei-Xiong

2018-06-13

BACKGROUND Using regional homogeneity (ReHo) blood oxygen level-dependent functional MR (BOLD-fMRI), we investigated the structural and functional alterations of brain regions among patients with methamphetamine-associated psychosis (MAP). MATERIAL AND METHODS This retrospective study included 17 MAP patients, 16 schizophrenia (SCZ) patients, and 18 healthy controls. Informed consent was obtained from all patients before the clinical assessment, the severity of clinical symptoms was evaluated prior to the fMRI scanning, and then images were acquired and preprocessed after each participant received 6-min fRMI scanning. The participants all underwent BOLD-fMRI scanning. Voxel-based morphometry was used to measure gray matter density (GMD). Resting-state fMRI (rs-fMRI) was conducted to analyze functional MR, ReHo, and functional connectivity (FC). RESULTS GMD analysis results suggest that MAP patients, SCZ patients, and healthy volunteers show different GMDs within different brain regions. Similarly, the ReHo analysis results suggest that MAP patients, SCZ patients, and healthy volunteers have different GMDs within different brain regions. Negative correlations were found between ReHo- and the PANSS-positive scores within the left orbital interior frontal gyrus (L-orb-IFG) of MAP patients. ReHo- and PANSS-negative scores of R-SFG were negatively correlated among SCZ patients. The abnormal FC of R-MFG showed a negative correlation with the PANSS score among MAP patients. CONCLUSIONS The abnormalities in brain structure and FC were associated with the development of MAP.

Testing assumptions on prefrontal transcranial direct current stimulation: Comparison of electrode montages using multimodal fMRI.

PubMed

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

2018-05-04

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

Functional effects of chronic paroxetine versus placebo on the fear, stress and anxiety brain circuit in Social Anxiety Disorder: initial validation of an imaging protocol for drug discovery.

PubMed

Giménez, Mónica; Ortiz, Hector; Soriano-Mas, Carles; López-Solà, Marina; Farré, Magí; Deus, Joan; Martín-Santos, Rocio; Fernandes, Sofia; Fina, Paolo; Bani, Massimo; Zancan, Stefano; Pujol, Jesús; Merlo-Pich, Emilio

2014-01-01

Recent studies suggest that pharmacologic effects of anxiolytic agents can be mapped as functional changes in the fear, stress and anxiety brain circuit. In this work we investigated the effects of a standard treatment, paroxetine (20mg/day), in subjects with Social Anxiety Disorder (SAD) versus placebo using different fMRI paradigms. The fMRI sessions, performed before and after the treatment, consisted of a public exposition of recorded performance task (PERPT), an emotional face processing task (EFPT) and a 6-min resting state followed by an off-scanner public speaking test. Paroxetine significantly improved the clinical conditions of SAD patients (n=17) vs. placebo (n=16) as measured with Clinical Global Inventory - Improvement (CGI-I) while no change was seen when using Liebowitz Social Anxiety Scale, as expected given the small size of the study population. Paroxetine reduced the activation of insula, thalamus and subgenual/anterior cingulate cortex (ACC) in PERPT. Resting-state fMRI assessment using Independent Component Analysis indicated that paroxetine reduced functional connectivity in insula, thalamus and ACC when compared with placebo. Both paradigms showed significant correlation with CGI-I in rostral prefrontal cortex. Conversely, paroxetine compared to placebo produced activation of right amygdala and bilateral insula and no effects in ACC when tested with EFPT. No treatment effects on distress scores were observed in the off-scanner Public Speaking Test. Overall this study supports the use of fMRI as sensitive approach to explore the neurobiological substrate of the effects of pharmacologic treatments and, in particular, of resting state fMRI given its simplicity and task independence. © 2013 Elsevier B.V. and ECNP. All rights reserved.

Effective connectivity of ascending and descending frontal-thalamic pathways during sustained attention: Complex brain network interactions in adolescence

PubMed Central

Jagtap, Pranav; Diwadkar, Vaibhav A.

2016-01-01

Frontal-thalamic interactions are crucial for bottom-up gating and top-down control, yet have not been well studied from brain network perspectives. We applied network modeling of fMRI signals (Dynamic Causal Modeling; DCM) to investigate frontal-thalamic interactions during an attention task with parametrically varying levels of demand. fMRI was collected while subjects participated in a sustained continuous performance task with low and high attention demands. 162 competing model architectures were employed in DCM to evaluate hypotheses on bilateral frontal-thalamic connections and their modulation by attention demand, selected at a second level using Bayesian Model Selection. The model architecture evinced significant contextual modulation by attention of ascending (thalamus → dPFC) and descending (dPFC → thalamus) pathways. However, modulation of these pathways was asymmetric: While positive modulation of the ascending pathway was comparable across attention demand, modulation of the descending pathway was significantly greater when attention demands were increased. Increased modulation of the (dPFC → thalamus) pathway in response to increased attention demand constitutes novel evidence of attention-related gain in the connectivity of the descending attention pathway. By comparison demand-independent modulation of the ascending (thalamus → dPFC) pathway suggests unbiased thalamic inputs to the cortex in the context of the paradigm. PMID:27145923

Adaptation of brain functional and structural networks in aging.

PubMed

Lee, Annie; Ratnarajah, Nagulan; Tuan, Ta Anh; Chen, Shen-Hsing Annabel; Qiu, Anqi

2015-01-01

The human brain, especially the prefrontal cortex (PFC), is functionally and anatomically reorganized in order to adapt to neuronal challenges in aging. This study employed structural MRI, resting-state fMRI (rs-fMRI), and high angular resolution diffusion imaging (HARDI), and examined the functional and structural reorganization of the PFC in aging using a Chinese sample of 173 subjects aged from 21 years and above. We found age-related increases in the structural connectivity between the PFC and posterior brain regions. Such findings were partially mediated by age-related increases in the structural connectivity of the occipital lobe within the posterior brain. Based on our findings, it is thought that the PFC reorganization in aging could be partly due to the adaptation to age-related changes in the structural reorganization of the posterior brain. This thus supports the idea derived from task-based fMRI that the PFC reorganization in aging may be adapted to the need of compensation for resolving less distinctive stimulus information from the posterior brain regions. In addition, we found that the structural connectivity of the PFC with the temporal lobe was fully mediated by the temporal cortical thickness, suggesting that the brain morphology plays an important role in the functional and structural reorganization with aging.

Tracking the development of brain connectivity in adolescence through a fast Bayesian integrative method

NASA Astrophysics Data System (ADS)

Zhang, Aiying; Jia, Bochao; Wang, Yu-Ping

2018-03-01

Adolescence is a transitional period between childhood and adulthood with physical changes, as well as increasing emotional activity. Studies have shown that the emotional sensitivity is related to a second dramatical brain growth. However, there is little focus on the trend of brain development during this period. In this paper, we aim to track the functional brain connectivity development in adolescence using resting state fMRI (rs-fMRI), which amounts to a time-series analysis problem. Most existing methods either require the time point to be fairly long or are only applicable to small graphs. To this end, we adapted a fast Bayesian integrative analysis (FBIA) to address the short time-series difficulty, and combined with adaptive sum of powered score (aSPU) test for group difference. The data we used are the resting state fMRI (rs-fMRI) obtained from the publicly available Philadelphia Neurodevelopmental Cohort (PNC). They include 861 individuals aged 8-22 years who were divided into five different adolescent stages. We summarized the networks with global measurements: segregation and integration, and provided full brain functional connectivity pattern in various stages of adolescence. Moreover, our research revealed several brain functional modules development trends. Our results are shown to be both statistically and biologically significant.

Electro-acupuncture at different acupoints modulating the relative specific brain functional network

NASA Astrophysics Data System (ADS)

Fang, Jiliang; Wang, Xiaoling; Wang, Yin; Liu, Hesheng; Hong, Yang; Liu, Jun; Zhou, Kehua; Wang, Lei; Xue, Chao; Song, Ming; Liu, Baoyan; Zhu, Bing

2010-11-01

Objective: The specific brain effects of acupoint are important scientific concern in acupuncture. However, previous acupuncture fMRI studies focused on acupoints in muscle layer on the limb. Therefore, researches on acupoints within connective tissue at trunk are warranted. Material and Methods: Brain effects of acupuncture on abdomen at acupoints Guanyuan (CV4) and Zhongwan (CV12) were tested using fMRI on 21 healthy volunteers. The data acquisition was performed at resting state, during needle retention, electroacupuncture (EA) and post-EA resting state. Needling sensations were rated after every electroacupuncture (EA) procedure. The needling sensations and the brain functional activity and connectivity were compared between CV4 and CV12 using SPSS, SPM2 and the local and remote connectivity maps. Results and conclusion: EA at CV4 and CV12 induced apparent deactivation effects in the limbic-paralimbic-neocortical network. The default mode of the brain was modified by needle retention and EA, respectively. The functional brain network was significantly changed post EA. However, the minor differences existed between these two acupoints. The results demonstrated similarity between functional brain network mode of acupuncture modulation and functional circuits of emotional and cognitive regulation. Acupuncture may produce analgesia, anti-anxiety and anti-depression via the limbic-paralimbic-neocortical network (LPNN).

Global brain dynamics during social exclusion predict subsequent behavioral conformity

PubMed Central

Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O’Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

2018-01-01

Abstract Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences. PMID:29529310

Tracking the Re-organization of Motor Functions After Disconnective Surgery: A Longitudinal fMRI and DTI Study

PubMed Central

Rosazza, Cristina; Deleo, Francesco; D'Incerti, Ludovico; Antelmi, Luigi; Tringali, Giovanni; Didato, Giuseppe; Bruzzone, Maria G.; Villani, Flavio; Ghielmetti, Francesco

2018-01-01

Objective: Mechanisms of motor plasticity are critical to maintain motor functions after cerebral damage. This study explores the mechanisms of motor reorganization occurring before and after surgery in four patients with drug-refractory epilepsy candidate to disconnective surgery. Methods: We studied four patients with early damage, who underwent tailored hemispheric surgery in adulthood, removing the cortical motor areas and disconnecting the corticospinal tract (CST) from the affected hemisphere. Motor functions were assessed clinically, with functional MRI (fMRI) tasks of arm and leg movement and Diffusion Tensor Imaging (DTI) before and after surgery with assessments of up to 3 years. Quantifications of fMRI motor activations and DTI fractional anisotropy (FA) color maps were performed to assess the lateralization of motor network. We hypothesized that lateralization of motor circuits assessed preoperatively with fMRI and DTI was useful to evaluate the motor outcome in these patients. Results: In two cases preoperative DTI-tractography did not reconstruct the CST, and FA-maps were strongly asymmetric. In the other two cases, the affected CST appeared reduced compared to the contralateral one, with modest asymmetry in the FA-maps. fMRI showed different degrees of lateralization of the motor network and the SMA of the intact hemisphere was mostly engaged in all cases. After surgery, patients with a strongly lateralized motor network showed a stable performance. By contrast, a patient with a more bilateral pattern showed worsening of the upper limb function. For all cases, fMRI activations shifted to the intact hemisphere. Structural alterations of motor circuits, observed with FA values, continued beyond 1 year after surgery. Conclusion: In our case series fMRI and DTI could track the longitudinal reorganization of motor functions. In these four patients the more the paretic limbs recruited the intact hemisphere in primary motor and associative areas, the greater the chances were of maintaining elementary motor functions after adult surgery. In particular, DTI-tractography and quantification of FA-maps were useful to assess the lateralization of motor network. In these cases reorganization of motor connectivity continued for long time periods after surgery. PMID:29922216

Tracking the Re-organization of Motor Functions After Disconnective Surgery: A Longitudinal fMRI and DTI Study.

PubMed

Rosazza, Cristina; Deleo, Francesco; D'Incerti, Ludovico; Antelmi, Luigi; Tringali, Giovanni; Didato, Giuseppe; Bruzzone, Maria G; Villani, Flavio; Ghielmetti, Francesco

2018-01-01

Objective: Mechanisms of motor plasticity are critical to maintain motor functions after cerebral damage. This study explores the mechanisms of motor reorganization occurring before and after surgery in four patients with drug-refractory epilepsy candidate to disconnective surgery. Methods: We studied four patients with early damage, who underwent tailored hemispheric surgery in adulthood, removing the cortical motor areas and disconnecting the corticospinal tract (CST) from the affected hemisphere. Motor functions were assessed clinically, with functional MRI (fMRI) tasks of arm and leg movement and Diffusion Tensor Imaging (DTI) before and after surgery with assessments of up to 3 years. Quantifications of fMRI motor activations and DTI fractional anisotropy (FA) color maps were performed to assess the lateralization of motor network. We hypothesized that lateralization of motor circuits assessed preoperatively with fMRI and DTI was useful to evaluate the motor outcome in these patients. Results: In two cases preoperative DTI-tractography did not reconstruct the CST, and FA-maps were strongly asymmetric. In the other two cases, the affected CST appeared reduced compared to the contralateral one, with modest asymmetry in the FA-maps. fMRI showed different degrees of lateralization of the motor network and the SMA of the intact hemisphere was mostly engaged in all cases. After surgery, patients with a strongly lateralized motor network showed a stable performance. By contrast, a patient with a more bilateral pattern showed worsening of the upper limb function. For all cases, fMRI activations shifted to the intact hemisphere. Structural alterations of motor circuits, observed with FA values, continued beyond 1 year after surgery. Conclusion: In our case series fMRI and DTI could track the longitudinal reorganization of motor functions. In these four patients the more the paretic limbs recruited the intact hemisphere in primary motor and associative areas, the greater the chances were of maintaining elementary motor functions after adult surgery. In particular, DTI-tractography and quantification of FA-maps were useful to assess the lateralization of motor network. In these cases reorganization of motor connectivity continued for long time periods after surgery.

What is feasible with imaging human brain function and connectivity using functional magnetic resonance imaging

PubMed Central

2016-01-01

When we consider all of the methods we employ to detect brain function, from electrophysiology to optical techniques to functional magnetic resonance imaging (fMRI), we do not really have a ‘golden technique’ that meets all of the needs for studying the brain. We have methods, each of which has significant limitations but provide often complimentary information. Clearly, there are many questions that need to be answered about fMRI, which unlike other methods, allows us to study the human brain. However, there are also extraordinary accomplishments or demonstration of the feasibility of reaching new and previously unexpected scales of function in the human brain. This article reviews some of the work we have pursued, often with extensive collaborations with other co-workers, towards understanding the underlying mechanisms of the methodology, defining its limitations, and developing solutions to advance it. No doubt, our knowledge of human brain function has vastly expanded since the introduction of fMRI. However, methods and instrumentation in this dynamic field have evolved to a state that discoveries about the human brain based on fMRI principles, together with information garnered at a much finer spatial and temporal scale through other methods, are poised to significantly accelerate in the next decade. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574313

What is feasible with imaging human brain function and connectivity using functional magnetic resonance imaging.

PubMed

Ugurbil, Kamil

2016-10-05

When we consider all of the methods we employ to detect brain function, from electrophysiology to optical techniques to functional magnetic resonance imaging (fMRI), we do not really have a 'golden technique' that meets all of the needs for studying the brain. We have methods, each of which has significant limitations but provide often complimentary information. Clearly, there are many questions that need to be answered about fMRI, which unlike other methods, allows us to study the human brain. However, there are also extraordinary accomplishments or demonstration of the feasibility of reaching new and previously unexpected scales of function in the human brain. This article reviews some of the work we have pursued, often with extensive collaborations with other co-workers, towards understanding the underlying mechanisms of the methodology, defining its limitations, and developing solutions to advance it. No doubt, our knowledge of human brain function has vastly expanded since the introduction of fMRI. However, methods and instrumentation in this dynamic field have evolved to a state that discoveries about the human brain based on fMRI principles, together with information garnered at a much finer spatial and temporal scale through other methods, are poised to significantly accelerate in the next decade.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. © 2016 The Author(s).

Effective Brain Connectivity in Children with Reading Difficulties during Phonological Processing

ERIC Educational Resources Information Center

Cao, Fan; Bitan, Tali; Booth, James R.

2008-01-01

Using Dynamic Causal Modeling (DCM) and functional magnetic resonance imaging (fMRI), we examined effective connectivity between three left hemisphere brain regions (inferior frontal gyrus, inferior parietal lobule, fusiform gyrus) and bilateral medial frontal gyrus in 12 children with reading difficulties (M age = 12.4, range: 8.11-14.10) and 12…

Analyzing the association between functional connectivity of the brain and intellectual performance

PubMed Central

Pamplona, Gustavo S. P.; Santos Neto, Gérson S.; Rosset, Sara R. E.; Rogers, Baxter P.; Salmon, Carlos E. G.

2015-01-01

Measurements of functional connectivity support the hypothesis that the brain is composed of distinct networks with anatomically separated nodes but common functionality. A few studies have suggested that intellectual performance may be associated with greater functional connectivity in the fronto-parietal network and enhanced global efficiency. In this fMRI study, we performed an exploratory analysis of the relationship between the brain's functional connectivity and intelligence scores derived from the Portuguese language version of the Wechsler Adult Intelligence Scale (WAIS-III) in a sample of 29 people, born and raised in Brazil. We examined functional connectivity between 82 regions, including graph theoretic properties of the overall network. Some previous findings were extended to the Portuguese-speaking population, specifically the presence of small-world organization of the brain and relationships of intelligence with connectivity of frontal, pre-central, parietal, occipital, fusiform and supramarginal gyrus, and caudate nucleus. Verbal comprehension was associated with global network efficiency, a new finding. PMID:25713528

Alterations of parenchymal microstructure, neuronal connectivity and cerebrovascular resistance at adolescence following mild to moderate traumatic brain injury in early development.

PubMed

Parent, Maxime; Li, Ying; Santhakumar, Vijayalakshmi; Hyder, Fahmeed; Sanganahalli, Basavaraju G; Kannurpatti, Sridhar

2018-06-01

TBI is a leading cause of morbidity in children. To investigate outcome of early developmental TBI during adolescence, a rat model of fluid percussion injury was developed, where previous work reported deficits in sensorimotor behavior and cortical blood flow at adolescence. 1 Based on the non-localized outcome, we hypothesized that multiple neurophysiological components of brain function, namely neuronal connectivity, synapse/axonal microstructural integrity and neurovascular function are altered and magnetic resonance imaging (MRI) methods could be used to determine regional alterations. Adolescent outcomes of developmental TBI were studied 2-months after injury, using functional MRI (fMRI) and Diffusion Tensor Imaging (DTI). fMRI based resting state functional connectivity (RSFC), representing neural connectivity, was significantly altered between sham and TBI. RSFC strength decreased in the cortex, hippocampus and thalamus accompanied by decrease in the spatial extent of their corresponding RSFC networks and inter-hemispheric asymmetry. Cerebrovascular reactivity to arterial CO2 changes diminished after TBI across both hemispheres, with a more pronounced decrease in the ipsilateral hippocampus, thalamus and motor cortex. DTI measures of fractional anisotropy (FA) and apparent diffusion coefficient (ADC), reporting on axonal and microstructural integrity of the brain, indicated similar inter-hemispheric asymmetry, with highest change in the ipsilateral hippocampus and regions adjoining the ipsilateral thalamus, hypothalamus and amygdala. TBI-induced corpus callosal microstructural alterations indicated measurable changes in inter-hemispheric structural connectivity. Hippocampus, thalamus and select cortical regions were most consistently affected in multiple imaging markers. The multi-modal MRI results demonstrate cortical and subcortical alterations in neural connectivity, cerebrovascular resistance and parenchymal microstructure in the adolescent brain, indicating the highly diffuse and persistent nature of the lateral fluid percussion TBI early in development.

Perspectives on How Human Simultaneous Multi-Modal Imaging Adds Directionality to Spread Models of Alzheimer’s Disease

PubMed Central

Neitzel, Julia; Nuttall, Rachel; Sorg, Christian

2018-01-01

Previous animal research suggests that the spread of pathological agents in Alzheimer’s disease (AD) follows the direction of signaling pathways. Specifically, tau pathology has been suggested to propagate in an infection-like mode along axons, from transentorhinal cortices to medial temporal lobe cortices and consequently to other cortical regions, while amyloid-beta (Aβ) pathology seems to spread in an activity-dependent manner among and from isocortical regions into limbic and then subcortical regions. These directed connectivity-based spread models, however, have not been tested directly in AD patients due to the lack of an in vivo method to identify directed connectivity in humans. Recently, a new method—metabolic connectivity mapping (MCM)—has been developed and validated in healthy participants that uses simultaneous FDG-PET and resting-state fMRI data acquisition to identify directed intrinsic effective connectivity (EC). To this end, postsynaptic energy consumption (FDG-PET) is used to identify regions with afferent input from other functionally connected brain regions (resting-state fMRI). Here, we discuss how this multi-modal imaging approach allows quantitative, whole-brain mapping of signaling direction in AD patients, thereby pointing out some of the advantages it offers compared to other EC methods (i.e., Granger causality, dynamic causal modeling, Bayesian networks). Most importantly, MCM provides the basis on which models of pathology spread, derived from animal studies, can be tested in AD patients. In particular, future work should investigate whether tau and Aβ in humans propagate along the trajectories of directed connectivity in order to advance our understanding of the neuropathological mechanisms causing disease progression. PMID:29434570

Introducing graph theory to track for neuroplastic alterations in the resting human brain: a transcranial direct current stimulation study.

PubMed

Polanía, Rafael; Paulus, Walter; Antal, Andrea; Nitsche, Michael A

2011-02-01

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that alters cortical excitability and activity in a polarity-dependent way. Stimulation for a few minutes has been shown to induce plastic alterations of cortical excitability and to improve cognitive performance. These effects might be related to stimulation-induced alterations of functional cortical network connectivity. We aimed to investigate the impact of tDCS on cortical network function by functional connectivity and graph theoretical analysis of the BOLD fMRI spontaneous activity. fMRI resting-state datasets were acquired immediately before and after 10-min bipolar tDCS during rest, with the anode placed over the left primary motor cortex (M1) and the cathode over the contralateral frontopolar cortex. For each dataset, grey matter voxel-based synchronization matrices were calculated and thresholded to construct undirected graphs. Nodal connectivity degree and minimum path length maps were calculated and compared before and after tDCS. Nodal minimum path lengths significantly increased in the left somatomotor (SM1) cortex after anodal tDCS, which means that the number of direct functional connections from the left SM1 to topologically distant grey matter voxels significantly decreased. In contrast, functional coupling between premotor and superior parietal areas with the left SM1 significantly increased. Additionally, the nodal connectivity degree in the left posterior cingulate cortex (PCC) area as well as in the right dorsolateral prefrontal cortex (right DLPFC) significantly increased. In summary, we provide initial support that tDCS-induced neuroplastic alterations might be related to functional connectivity changes in the human brain. Additionally, we propose our approach as a powerful method to track for neuroplastic changes in the human brain. Copyright © 2010 Elsevier Inc. All rights reserved.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

PubMed

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity

PubMed Central

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition. PMID:29615882

Evidence for an anterior-posterior differentiation in the human hippocampal formation revealed by meta-analytic parcellation of fMRI coordinate maps: focus on the subiculum.

PubMed

Chase, Henry W; Clos, Mareike; Dibble, Sofia; Fox, Peter; Grace, Anthony A; Phillips, Mary L; Eickhoff, Simon B

2015-06-01

Previous studies, predominantly in experimental animals, have suggested the presence of a differentiation of function across the hippocampal formation. In rodents, ventral regions are thought to be involved in emotional behavior while dorsal regions mediate cognitive or spatial processes. Using a combination of modeling the co-occurrence of significant activations across thousands of neuroimaging experiments and subsequent data-driven clustering of these data we were able to provide evidence of distinct subregions within a region corresponding to the human subiculum, a critical hub within the hippocampal formation. This connectivity-based model consists of a bilateral anterior region, as well as separate posterior and intermediate regions on each hemisphere. Functional connectivity assessed both by meta-analytic and resting fMRI approaches revealed that more anterior regions were more strongly connected to the default mode network, and more posterior regions were more strongly connected to 'task positive' regions. In addition, our analysis revealed that the anterior subregion was functionally connected to the ventral striatum, midbrain and amygdala, a circuit that is central to models of stress and motivated behavior. Analysis of a behavioral taxonomy provided evidence for a role for each subregion in mnemonic processing, as well as implication of the anterior subregion in emotional and visual processing and the right posterior subregion in reward processing. These findings lend support to models which posit anterior-posterior differentiation of function within the human hippocampal formation and complement other early steps toward a comparative (cross-species) model of the region. Copyright © 2015 Elsevier Inc. All rights reserved.

Bimanual Motor Coordination in Older Adults Is Associated with Increased Functional Brain Connectivity – A Graph-Theoretical Analysis

PubMed Central

Heitger, Marcus H.; Goble, Daniel J.; Dhollander, Thijs; Dupont, Patrick; Caeyenberghs, Karen; Leemans, Alexander; Sunaert, Stefan; Swinnen, Stephan P.

2013-01-01

In bimanual coordination, older and younger adults activate a common cerebral network but the elderly also have additional activation in a secondary network of brain areas to master task performance. It remains unclear whether the functional connectivity within these primary and secondary motor networks differs between the old and the young and whether task difficulty modulates connectivity. We applied graph-theoretical network analysis (GTNA) to task-driven fMRI data in 16 elderly and 16 young participants using a bimanual coordination task including in-phase and anti-phase flexion/extension wrist movements. Network nodes for the GTNA comprised task-relevant brain areas as defined by fMRI activation foci. The elderly matched the motor performance of the young but showed an increased functional connectivity in both networks across a wide range of connectivity metrics, i.e., higher mean connectivity degree, connection strength, network density and efficiency, together with shorter mean communication path length between the network nodes and also a lower betweenness centrality. More difficult movements showed an increased connectivity in both groups. The network connectivity of both groups had “small world” character. The present findings indicate (a) that bimanual coordination in the aging brain is associated with a higher functional connectivity even between areas also activated in young adults, independently from task difficulty, and (b) that adequate motor coordination in the context of task-driven bimanual control in older adults may not be solely due to additional neural recruitment but also to aging-related changes of functional relationships between brain regions. PMID:23637982

Structural connectivity allows for multi-threading during rest: the structure of the cortex leads to efficient alternation between resting state exploratory behavior and default mode processing.

PubMed

Senden, Mario; Goebel, Rainer; Deco, Gustavo

2012-05-01

Despite the absence of stimulation or task conditions the cortex exhibits highly structured spatio-temporal activity patterns. These patterns are known as resting state networks (RSNs) and emerge as low-frequency fluctuations (<0.1 Hz) observed in the fMRI signal of human subjects during rest. We are interested in the relationship between structural connectivity of the cortex and the fluctuations exhibited during resting conditions. We are especially interested in the effect of degree of connectivity on resting state dynamics as the default mode network (DMN) is highly connected. We find in experimental resting fMRI data that the DMN is the functional network that is most frequently active and for the longest time. In large-scale computational simulations of the cortex based on the corresponding underlying DTI/DSI based neuroanatomical connectivity matrix, we additionally find a strong correlation between the mean degree of functional networks and the proportion of time they are active. By artificially modifying different types of neuroanatomical connectivity matrices in the model, we were able to demonstrate that only models based on structural connectivity containing hubs give rise to this relationship. We conclude that, during rest, the cortex alternates efficiently between explorations of its externally oriented functional repertoire and internally oriented processing as a consequence of the DMN's high degree of connectivity. Copyright © 2012 Elsevier Inc. All rights reserved.

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.

Co-activation patterns in resting-state fMRI signals.

PubMed

Liu, Xiao; Zhang, Nanyin; Chang, Catie; Duyn, Jeff H

2018-02-08

The brain is a complex system that integrates and processes information across multiple time scales by dynamically coordinating activities over brain regions and circuits. Correlations in resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used to infer functional connectivity of the brain, providing a metric of functional associations that reflects a temporal average over an entire scan (typically several minutes or longer). Not until recently was the study of dynamic brain interactions at much shorter time scales (seconds to minutes) considered for inference of functional connectivity. One method proposed for this objective seeks to identify and extract recurring co-activation patterns (CAPs) that represent instantaneous brain configurations at single time points. Here, we review the development and recent advancement of CAP methodology and other closely related approaches, as well as their applications and associated findings. We also discuss the potential neural origins and behavioral relevance of CAPs, along with methodological issues and future research directions in the analysis of fMRI co-activation patterns. Copyright © 2018 Elsevier Inc. All rights reserved.

Lateral OFC activity predicts decision bias due to first impressions during ultimatum games.

PubMed

Kim, Hackjin; Choi, Min-Jo; Jang, In-Ji

2012-02-01

Despite the prevalence and potentially harmful consequences of first impression bias during social decision-making, its precise neural underpinnings remain unclear. Here, on the basis of the fMRI study using ultimatum games, the authors show that the responders' decisions to accept or reject offers were significantly affected by facial trustworthiness of proposers. Analysis using a model-based fMRI method revealed that activity in the right lateral OFC (lOFC) of responders increased as a function of negative decision bias, indicating a greater likelihood of rejecting otherwise fair offers, possibly because of the facial trustworthiness of proposers. In addition, lOFC showed changes in functional connectivity strength with amygdala and insula as a function of decision bias, and individual differences in the strengths of connectivities between lOFC and bilateral insula were also found to predict the likelihood of responders to reject offers from untrustworthy-looking proposers. The present findings emphasize that the lOFC plays a pivotal role in integrating signals related to facial impression and creating signal biasing decisions during social interactions.

Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin.

PubMed

Carhart-Harris, Robin L; Erritzoe, David; Williams, Tim; Stone, James M; Reed, Laurence J; Colasanti, Alessandro; Tyacke, Robin J; Leech, Robert; Malizia, Andrea L; Murphy, Kevin; Hobden, Peter; Evans, John; Feilding, Amanda; Wise, Richard G; Nutt, David J

2012-02-07

Psychedelic drugs have a long history of use in healing ceremonies, but despite renewed interest in their therapeutic potential, we continue to know very little about how they work in the brain. Here we used psilocybin, a classic psychedelic found in magic mushrooms, and a task-free functional MRI (fMRI) protocol designed to capture the transition from normal waking consciousness to the psychedelic state. Arterial spin labeling perfusion and blood-oxygen level-dependent (BOLD) fMRI were used to map cerebral blood flow and changes in venous oxygenation before and after intravenous infusions of placebo and psilocybin. Fifteen healthy volunteers were scanned with arterial spin labeling and a separate 15 with BOLD. As predicted, profound changes in consciousness were observed after psilocybin, but surprisingly, only decreases in cerebral blood flow and BOLD signal were seen, and these were maximal in hub regions, such as the thalamus and anterior and posterior cingulate cortex (ACC and PCC). Decreased activity in the ACC/medial prefrontal cortex (mPFC) was a consistent finding and the magnitude of this decrease predicted the intensity of the subjective effects. Based on these results, a seed-based pharmaco-physiological interaction/functional connectivity analysis was performed using a medial prefrontal seed. Psilocybin caused a significant decrease in the positive coupling between the mPFC and PCC. These results strongly imply that the subjective effects of psychedelic drugs are caused by decreased activity and connectivity in the brain's key connector hubs, enabling a state of unconstrained cognition.

Large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

2016-03-01

We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

Altered cortical and subcortical connectivity due to infrasound administered near the hearing threshold - Evidence from fMRI.

PubMed

Weichenberger, Markus; Bauer, Martin; Kühler, Robert; Hensel, Johannes; Forlim, Caroline Garcia; Ihlenfeld, Albrecht; Ittermann, Bernd; Gallinat, Jürgen; Koch, Christian; Kühn, Simone

2017-01-01

In the present study, the brain's response towards near- and supra-threshold infrasound (IS) stimulation (sound frequency < 20 Hz) was investigated under resting-state fMRI conditions. The study involved two consecutive sessions. In the first session, 14 healthy participants underwent a hearing threshold-as well as a categorical loudness scaling measurement in which the individual loudness perception for IS was assessed across different sound pressure levels (SPL). In the second session, these participants underwent three resting-state acquisitions, one without auditory stimulation (no-tone), one with a monaurally presented 12-Hz IS tone (near-threshold) and one with a similar tone above the individual hearing threshold corresponding to a 'medium loud' hearing sensation (supra-threshold). Data analysis mainly focused on local connectivity measures by means of regional homogeneity (ReHo), but also involved independent component analysis (ICA) to investigate inter-regional connectivity. ReHo analysis revealed significantly higher local connectivity in right superior temporal gyrus (STG) adjacent to primary auditory cortex, in anterior cingulate cortex (ACC) and, when allowing smaller cluster sizes, also in the right amygdala (rAmyg) during the near-threshold, compared to both the supra-threshold and the no-tone condition. Additional independent component analysis (ICA) revealed large-scale changes of functional connectivity, reflected in a stronger activation of the right amygdala (rAmyg) in the opposite contrast (no-tone > near-threshold) as well as the right superior frontal gyrus (rSFG) during the near-threshold condition. In summary, this study is the first to demonstrate that infrasound near the hearing threshold may induce changes of neural activity across several brain regions, some of which are known to be involved in auditory processing, while others are regarded as keyplayers in emotional and autonomic control. These findings thus allow us to speculate on how continuous exposure to (sub-)liminal IS could exert a pathogenic influence on the organism, yet further (especially longitudinal) studies are required in order to substantialize these findings.

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.

Volitional control of the anterior insula in criminal psychopaths using real-time fMRI neurofeedback: a pilot study

PubMed Central

Sitaram, Ranganatha; Caria, Andrea; Veit, Ralf; Gaber, Tilman; Ruiz, Sergio; Birbaumer, Niels

2014-01-01

This pilot study aimed to explore whether criminal psychopaths can learn volitional regulation of the left anterior insula with real-time fMRI neurofeedback. Our previous studies with healthy volunteers showed that learned control of the blood oxygenation-level dependent (BOLD) signal was specific to the target region, and not a result of general arousal and global unspecific brain activation, and also that successful regulation modulates emotional responses, specifically to aversive picture stimuli but not neutral stimuli. In this pilot study, four criminal psychopaths were trained to regulate the anterior insula by employing negative emotional imageries taken from previous episodes in their lives, in conjunction with contingent feedback. Only one out of the four participants learned to increase the percent differential BOLD in the up-regulation condition across training runs. Subjects with higher Psychopathic Checklist-Revised (PCL:SV) scores were less able to increase the BOLD signal in the anterior insula than their lower PCL:SV counterparts. We investigated functional connectivity changes in the emotional network due to learned regulation of the successful participant, by employing multivariate Granger Causality Modeling (GCM). Learning to up-regulate the left anterior insula not only increased the number of connections (causal density) in the emotional network in the single successful participant but also increased the difference between the number of outgoing and incoming connections (causal flow) of the left insula. This pilot study shows modest potential for training psychopathic individuals to learn to control brain activity in the anterior insula. PMID:25352793

Cognitive behavioral therapy changes functional connectivity between medial prefrontal and anterior cingulate cortices.

PubMed

Yoshimura, Shinpei; Okamoto, Yasumasa; Matsunaga, Miki; Onoda, Keiichi; Okada, Go; Kunisato, Yoshihiko; Yoshino, Atsuo; Ueda, Kazutaka; Suzuki, Shin-Ichi; Yamawaki, Shigeto

2017-01-15

Depression is characterized by negative self-cognition. Our previous study (Yoshimura et al. 2014) revealed changes in brain activity after cognitive behavioral therapy (CBT) for depression, but changes in functional connectivity were not assessed. This study included 29 depressive patients and 15 healthy control participants. Functional Magnetic Resonance Imaging was used to investigate possible CBT-related functional connectivity changes associated with negative emotional self-referential processing. Depressed and healthy participants (overlapping with our previous study, Yoshimura et al. 2014) were included. We defined a seed region (medial prefrontal cortex) and coupled region (ACC) based on our previous study, and we examined changes in MPFC-ACC functional connectivity from pretreatment to posttreatment. CBT was associated with reduced functional connectivity between the MPFC and ACC. Symptom change with CBT was positively correlated with change in MPFC-ACC functional connectivity. Patients received pharmacotherapy including antidepressant. The present sample size was quite small and more study is needed. Statistical threshold in fMRI analysis was relatively liberal. CBT for depression may disrupt MPFC-ACC connectivity, with associated improvements in depressive symptoms and dysfunctional cognition. Copyright © 2016 Elsevier B.V. All rights reserved.

Abnormal brain synchrony in Down Syndrome☆

PubMed Central

Anderson, Jeffrey S.; Nielsen, Jared A.; Ferguson, Michael A.; Burback, Melissa C.; Cox, Elizabeth T.; Dai, Li; Gerig, Guido; Edgin, Jamie O.; Korenberg, Julie R.

2013-01-01

Down Syndrome is the most common genetic cause for intellectual disability, yet the pathophysiology of cognitive impairment in Down Syndrome is unknown. We compared fMRI scans of 15 individuals with Down Syndrome to 14 typically developing control subjects while they viewed 50 min of cartoon video clips. There was widespread increased synchrony between brain regions, with only a small subset of strong, distant connections showing underconnectivity in Down Syndrome. Brain regions showing negative correlations were less anticorrelated and were among the most strongly affected connections in the brain. Increased correlation was observed between all of the distributed brain networks studied, with the strongest internetwork correlation in subjects with the lowest performance IQ. A functional parcellation of the brain showed simplified network structure in Down Syndrome organized by local connectivity. Despite increased interregional synchrony, intersubject correlation to the cartoon stimuli was lower in Down Syndrome, indicating that increased synchrony had a temporal pattern that was not in response to environmental stimuli, but idiosyncratic to each Down Syndrome subject. Short-range, increased synchrony was not observed in a comparison sample of 447 autism vs. 517 control subjects from the Autism Brain Imaging Exchange (ABIDE) collection of resting state fMRI data, and increased internetwork synchrony was only observed between the default mode and attentional networks in autism. These findings suggest immature development of connectivity in Down Syndrome with impaired ability to integrate information from distant brain regions into coherent distributed networks. PMID:24179822

Sparse network-based models for patient classification using fMRI

PubMed Central

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

2015-01-01

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

Functional Magnetic Resonance Imaging Networks Induced by Intracranial Stimulation May Help Defining the Epileptogenic Zone

PubMed Central

Zhang, Myron; Avitsian, Rafi; Bhattacharyya, Pallab; Bulacio, Juan; Cendes, Fernando; Enatsu, Rei; Lowe, Mark; Najm, Imad; Nair, Dileep; Phillips, Michael; Gonzalez-Martinez, Jorge

2014-01-01

Abstract Patients with medically intractable epilepsy often undergo invasive evaluation and surgery, with a 50% success rate. The low success rate is likely due to poor identification of the epileptogenic zone (EZ), the brain area causing seizures. This work introduces a new method using functional magnetic resonance imaging (fMRI) with simultaneous direct electrical stimulation of the brain that could help localize the EZ, performed in five patients with medically intractable epilepsy undergoing invasive evaluation with intracranial depth electrodes. Stimulation occurred in a location near the hypothesized EZ and a location away. Electrical recordings in response to stimulation were recorded and compared to fMRI. Multiple stimulation parameters were varied, like current and frequency. The brain areas showing fMRI response were compared with the areas resected and the success of surgery. Robust fMRI maps of activation networks were easily produced, which also showed a significant but weak positive correlation between quantitative measures of blood-oxygen-level-dependent (BOLD) activity and measures of electrical activity in response to direct electrical stimulation (mean correlation coefficient of 0.38 for all acquisitions that produced a strong BOLD response). For four patients with outcome data at 6 months, successful surgical outcome is consistent with the resection of brain areas containing high local fMRI activity. In conclusion, this method demonstrates the feasibility of simultaneous direct electrical stimulation and fMRI in humans, which allows the study of brain connectivity with high resolution and full spatial coverage. This innovative technique could be used to better define the localization and extension of the EZ in intractable epilepsies, as well as for other functional neurosurgical procedures. PMID:24735069

Emotional Intent Modulates The Neural Substrates Of Creativity: An fMRI Study of Emotionally Targeted Improvisation in Jazz Musicians.

PubMed

McPherson, Malinda J; Barrett, Frederick S; Lopez-Gonzalez, Monica; Jiradejvong, Patpong; Limb, Charles J

2016-01-04

Emotion is a primary motivator for creative behaviors, yet the interaction between the neural systems involved in creativity and those involved in emotion has not been studied. In the current study, we addressed this gap by using fMRI to examine piano improvisation in response to emotional cues. We showed twelve professional jazz pianists photographs of an actress representing a positive, negative or ambiguous emotion. Using a non-ferromagnetic thirty-five key keyboard, the pianists improvised music that they felt represented the emotion expressed in the photographs. Here we show that activity in prefrontal and other brain networks involved in creativity is highly modulated by emotional context. Furthermore, emotional intent directly modulated functional connectivity of limbic and paralimbic areas such as the amygdala and insula. These findings suggest that emotion and creativity are tightly linked, and that the neural mechanisms underlying creativity may depend on emotional state.

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions.

PubMed

Gilson, Matthieu; Deco, Gustavo; Friston, Karl J; Hagmann, Patric; Mantini, Dante; Betti, Viviana; Romani, Gian Luca; Corbetta, Maurizio

2017-10-09

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain. Copyright © 2017. Published by Elsevier Inc.

Functional hyperconnectivity vanishes in children with developmental dyscalculia after numerical intervention.

PubMed

Michels, Lars; O'Gorman, Ruth; Kucian, Karin

2018-04-01

Developmental dyscalculia (DD) is a developmental learning disability associated with deficits in processing numerical and mathematical information. Although behavioural training can reduce these deficits, it is unclear which neuronal resources show a functional reorganization due to training. We examined typically developing (TD) children (N=16, mean age: 9.5 years) and age-, gender-, and handedness-matched children with DD (N=15, mean age: 9.5 years) during the performance of a numerical order task with fMRI and functional connectivity before and after 5-weeks of number line training. Using the intraparietal sulcus (IPS) as seed region, DD showed hyperconnectivity in parietal, frontal, visual, and temporal regions before the training controlling for age and IQ. Hyperconnectivity disappeared after training, whereas math abilities improved. Multivariate classification analysis of task-related fMRI data corroborated the connectivity results as the same group of TD could be discriminated from DD before but not after number line training (86.4 vs. 38.9%, respectively). Our results indicate that abnormally high functional connectivity in DD can be normalized on the neuronal level by intensive number line training. As functional connectivity in DD was indistinguishable to TD's connectivity after training, we conclude that training lead to a re-organization of inter-regional task engagement. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Concurrent tACS-fMRI Reveals Causal Influence of Power Synchronized Neural Activity on Resting State fMRI Connectivity.

PubMed

Bächinger, Marc; Zerbi, Valerio; Moisa, Marius; Polania, Rafael; Liu, Quanying; Mantini, Dante; Ruff, Christian; Wenderoth, Nicole

2017-05-03

Resting state fMRI (rs-fMRI) is commonly used to study the brain's intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSNs). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices. The two driving tACS signals were tailored to the individual's α rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either α oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS). Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared with phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brain's intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity. SIGNIFICANCE STATEMENT Resting state fMRI (rs-fMRI) has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes. It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power synchronization mechanisms. These novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology, and Psychology. Copyright © 2017 the authors 0270-6474/17/374766-12$15.00/0.

Interhemispheric Connectivity Influences the Degree of Modulation of TMS-Induced Effects during Auditory Processing

PubMed Central

Andoh, Jamila; Zatorre, Robert J.

2011-01-01

Repetitive transcranial magnetic stimulation (rTMS) has been shown to interfere with many components of language processing, including semantic, syntactic, and phonologic. However, not much is known about its effects on nonlinguistic auditory processing, especially its action on Heschl's gyrus (HG). We aimed to investigate the behavioral and neural basis of rTMS during a melody processing task, while targeting the left HG, the right HG, and the Vertex as a control site. Response times (RT) were normalized relative to the baseline-rTMS (Vertex) and expressed as percentage change from baseline (%RT change). We also looked at sex differences in rTMS-induced response as well as in functional connectivity during melody processing using rTMS and functional magnetic resonance imaging (fMRI). fMRI results showed an increase in the right HG compared with the left HG during the melody task, as well as sex differences in functional connectivity indicating a greater interhemispheric connectivity between left and right HG in females compared with males. TMS results showed that 10 Hz-rTMS targeting the right HG induced differential effects according to sex, with a facilitation of performance in females and an impairment of performance in males. We also found a differential correlation between the %RT change after 10 Hz-rTMS targeting the right HG and the interhemispheric functional connectivity between right and left HG, indicating that an increase in interhemispheric functional connectivity was associated with a facilitation of performance. This is the first study to report a differential rTMS-induced interference with melody processing depending on sex. In addition, we showed a relationship between the interference induced by rTMS on behavioral performance and the neural activity in the network connecting left and right HG, suggesting that the interhemispheric functional connectivity could determine the degree of modulation of behavioral performance. PMID:21811478

Glucose administration enhances fMRI brain activation and connectivity related to episodic memory encoding for neutral and emotional stimuli.

PubMed

Parent, Marise B; Krebs-Kraft, Desiree L; Ryan, John P; Wilson, Jennifer S; Harenski, Carla; Hamann, Stephan

2011-04-01

Glucose enhances memory in a variety of species. In humans, glucose administration enhances episodic memory encoding, although little is known regarding the neural mechanisms underlying these effects. Here we examined whether elevating blood glucose would enhance functional MRI (fMRI) activation and connectivity in brain regions associated with episodic memory encoding and whether these effects would differ depending on the emotional valence of the material. We used a double-blind, within-participants, crossover design in which either glucose (50g) or a saccharin placebo were administered before scanning, on days approximately 1 week apart. We scanned healthy young male participants with fMRI as they viewed emotionally arousing negative pictures and emotionally neutral pictures, intermixed with baseline fixation. Free recall was tested at 5 min after scanning and again after 1 day. Glucose administration increased activation in brain regions associated with successful episodic memory encoding. Glucose also enhanced activation in regions whose activity was correlated with subsequent successful recall, including the hippocampus, prefrontal cortex, and other regions, and these effects differed for negative vs. neutral stimuli. Finally, glucose substantially increased functional connectivity between the hippocampus and amygdala and a network of regions previously implicated in successful episodic memory encoding. These findings fit with evidence from nonhuman animals indicating glucose modulates memory by selectively enhancing neural activity in brain regions engaged during memory tasks. Our results highlight the modulatory effects of glucose and the importance of examining both regional changes in activity and functional connectivity to fully characterize the effects of glucose on brain function and memory. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm

PubMed Central

Ji, Junzhong; Liu, Jinduo; Liang, Peipeng; Zhang, Aidong

2016-01-01

Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith’s simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity. PMID:27045295

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

PubMed

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

Cluster Size Statistic and Cluster Mass Statistic: Two Novel Methods for Identifying Changes in Functional Connectivity Between Groups or Conditions

PubMed Central

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods – the cluster size statistic (CSS) and cluster mass statistic (CMS) – are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity. PMID:24906136

Network diffusion accurately models the relationship between structural and functional brain connectivity networks

PubMed Central

Abdelnour, Farras; Voss, Henning U.; Raj, Ashish

2014-01-01

The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain’s long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways. PMID:24384152

An fMRI Investigation of a Novel Analogue to the Trail-Making Test

ERIC Educational Resources Information Center

Jacobson, Sarah C.; Blanchard, Mathieu; Connolly, Colm C.; Cannon, Mary; Garavan, Hugh

2011-01-01

The Trail-Making Test (TMT) is a widely used neuropsychological measure that assesses visuomotor abilities and cognitive flexibility. For the TMT-A condition participants are required to locate and connect numbers (i.e. 1-2-3...) while in the TMT-B condition participants perform the set-shifting task of locating and connecting numbers and letters…

Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism

PubMed Central

Woolley, Daniel G.; Steyaert, Jean; Di Martino, Adriana; Swinnen, Stephan P.; Wenderoth, Nicole

2014-01-01

Neurodevelopmental disconnections have been assumed to cause behavioral alterations in autism spectrum disorders (ASDs). Here, we combined measurements of intrinsic functional connectivity (iFC) from resting-state functional magnetic resonance imaging (fMRI) with task-based fMRI to explore whether altered activity and/or iFC of the right posterior superior temporal sulcus (pSTS) mediates deficits in emotion recognition in ASD. Fifteen adults with ASD and 15 matched-controls underwent resting-state and task-based fMRI, during which participants discriminated emotional states from point light displays (PLDs). Intrinsic FC of the right pSTS was further examined using 584 (278 ASD/306 controls) resting-state data of the Autism Brain Imaging Data Exchange (ABIDE). Participants with ASD were less accurate than controls in recognizing emotional states from PLDs. Analyses revealed pronounced ASD-related reductions both in task-based activity and resting-state iFC of the right pSTS with fronto-parietal areas typically encompassing the action observation network (AON). Notably, pSTS-hypo-activity was related to pSTS-hypo-connectivity, and both measures were predictive of emotion recognition performance with each measure explaining a unique part of the variance. Analyses with the large independent ABIDE dataset replicated reductions in pSTS-iFC to fronto-parietal regions. These findings provide novel evidence that pSTS hypo-activity and hypo-connectivity with the fronto-parietal AON are linked to the social deficits characteristic of ASD. PMID:24078018

Robust prediction of individual creative ability from brain functional connectivity.

PubMed

Beaty, Roger E; Kenett, Yoed N; Christensen, Alexander P; Rosenberg, Monica D; Benedek, Mathias; Chen, Qunlin; Fink, Andreas; Qiu, Jiang; Kwapil, Thomas R; Kane, Michael J; Silvia, Paul J

2018-01-30

People's ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis-connectome-based predictive modeling-to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences ( r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems-intrinsic functional networks that tend to work in opposition-suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

Strengthening of Top-Down Frontal Cognitive Control Networks Underlying the Development of Inhibitory Control: An fMRI Effective Connectivity Study

PubMed Central

Hwang, Kai; Velanova, Katerina; Luna, Beatriz

2010-01-01

The ability to voluntarily inhibit responses to task irrelevant stimuli, which is a fundamental component of cognitive control, has a protracted development through adolescence. Prior human developmental imaging studies have found immaturities in localized brain activity in children and adolescents. However, little is known about how these regions integrate with age to form the distributed networks known to support cognitive control. In the present study, we used Granger Causality analysis to characterize developmental changes in effective connectivity underlying inhibitory control (antisaccade task) compared to reflexive responses (prosaccade task) in human participants. By childhood few top-down connectivity were evident with increased parietal interconnectivity. By adolescence connections from prefrontal cortex increased and parietal interconnectivity decreased in number. From adolescence to adulthood there was evidence of increased number and strength of frontal connections to cortical regions as well as subcortical regions. Taken together, results suggest that developmental improvements in inhibitory control may be supported by age related enhancements in top-down effective connectivity between frontal, oculomotor and subcortical regions. PMID:21084608

Reading impairment in schizophrenia: dysconnectivity within the visual system.

PubMed

Vinckier, Fabien; Cohen, Laurent; Oppenheim, Catherine; Salvador, Alexandre; Picard, Hernan; Amado, Isabelle; Krebs, Marie-Odile; Gaillard, Raphaël

2014-01-01

Patients with schizophrenia suffer from perceptual visual deficits. It remains unclear whether those deficits result from an isolated impairment of a localized brain process or from a more diffuse long-range dysconnectivity within the visual system. We aimed to explore, with a reading paradigm, the functioning of both ventral and dorsal visual pathways and their interaction in schizophrenia. Patients with schizophrenia and control subjects were studied using event-related functional MRI (fMRI) while reading words that were progressively degraded through word rotation or letter spacing. Reading intact or minimally degraded single words involves mainly the ventral visual pathway. Conversely, reading in non-optimal conditions involves both the ventral and the dorsal pathway. The reading paradigm thus allowed us to study the functioning of both pathways and their interaction. Behaviourally, patients with schizophrenia were selectively impaired at reading highly degraded words. While fMRI activation level was not different between patients and controls, functional connectivity between the ventral and dorsal visual pathways increased with word degradation in control subjects, but not in patients. Moreover, there was a negative correlation between the patients' behavioural sensitivity to stimulus degradation and dorso-ventral connectivity. This study suggests that perceptual visual deficits in schizophrenia could be related to dysconnectivity between dorsal and ventral visual pathways. © 2013 Published by Elsevier Ltd.

Joint representation of consistent structural and functional profiles for identification of common cortical landmarks.

PubMed

Zhang, Shu; Zhao, Yu; Jiang, Xi; Shen, Dinggang; Liu, Tianming

2018-06-01

In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series

PubMed Central

Fransson, Peter

2016-01-01

Abstract Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box–Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed. PMID:27784176

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

PubMed

Thompson, William Hedley; Fransson, Peter

2016-12-01

Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

Transient alcohol craving suppression by rTMS of dorsal anterior cingulate: an fMRI and LORETA EEG study.

PubMed

De Ridder, Dirk; Vanneste, Sven; Kovacs, Silvia; Sunaert, Stefan; Dom, Geert

2011-05-27

It has recently become clear that alcohol addiction might be related to a brain dysfunction, in which a genetic background and environmental factors shape brain mechanisms involved with alcohol consumption. Craving, a major component determining relapses in alcohol abuse has been linked to abnormal activity in the orbitofrontal cortex, dorsal anterior cingulated cortex (dACC) and amygdala. We report the results of a patient who underwent rTMS targeting the dACC using a double cone coil in an attempt to suppress very severe intractable alcohol craving. Functional imaging studies consisting of fMRI and resting state EEG were performed before rTMS, after successful rTMS and after unsuccessful rTMS with relapse. Craving was associated with EEG beta activity and connectivity between the dACC and PCC in the patient in comparison to a healthy population, which disappeared after successful rTMS. Cue induced worsening of craving pre-rTMS activated the ACC-vmPFC and PCC on fMRI, as well as the nucleus accumbens area, and lateral frontoparietal areas. The nucleus accumbens, ACC-vmPFC and PCC activation disappeared on fMRI following successful rTMS. Relapse was associated with recurrence of ACC and PCC EEG activity, but in gamma band, in comparison to a healthy population. On fMRI nucleus accumbens, ACC and PCC activation returned to the initial activation pattern. A pathophysiological approach is described to suppress alcohol craving temporarily by rTMS directed at the anterior cingulate. Linking functional imaging changes to craving intensity suggests this approach warrants further exploration. Crown Copyright © 2011. Published by Elsevier Ireland Ltd. All rights reserved.

Joint brain connectivity estimation from diffusion and functional MRI data

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

[Seeking the aetiology of autistic spectrum disorder. Part 2: Functional neuroimaging].

PubMed

Bryńska, Anita

2012-01-01

Multiple functional imaging techniques help to a better understanding of the neurobiological basis of autism-spectrum disorders (ASD). The early functional imaging studies on ASD focused on task-specific methods related to core symptom domains and explored patterns of activation in response to face processing, theory of mind tasks, language processing and executive function tasks. On the other hand, fMRI research in ASD focused on the development of functional connectivity methods and has provided evidence of alterations in cortical connectivity in ASD and establish autism as a disorder of under-connectivity among the brain regions participating in cortical networks. This atypical functional connectivity in ASD results in inefficiency and poor integration of processing in network connections to achieve task performance. The goal of this review is to summarise the actual neuroimaging functional data and examine their implication for understanding of the neurobiology of ASD.

Remembering beauty: roles of orbitofrontal and hippocampal regions in successful memory encoding of attractive faces.

PubMed

Tsukiura, Takashi; Cabeza, Roberto

2011-01-01

Behavioral data have shown that attractive faces are better remembered but the neural mechanisms of this effect are largely unknown. To investigate this issue, female participants were scanned with event-related functional MRI (fMRI) while rating the attractiveness of male faces. Memory for the faces was tested after fMRI scanning and was used to identify successful encoding activity (subsequent memory paradigm). As expected, attractive faces were remembered better than other faces. The study yielded three main fMRI findings. First, activity in the right orbitofrontal cortex increased linearly as a function of attractiveness ratings. Second, activity in the left hippocampus increased as a function of subsequent memory (subsequent misses

Remembering beauty: Roles of orbitofrontal and hippocampal regions in successful memory encoding of attractive faces

PubMed Central

Tsukiura, Takashi; Cabeza, Roberto

2010-01-01

Behavioral data have shown that attractive faces are better remembered but the neural mechanisms of this effect are largely unknown. To investigate this issue, female participants were scanned with event-related functional MRI (fMRI) while rating the attractiveness of male faces. Memory for the faces was tested after fMRI scanning and was used to identify successful encoding activity (subsequent memory paradigm). As expected, attractive faces were remembered better than other faces. The study yielded three main fMRI findings. First, activity in the right orbitofrontal cortex increased linearly as a function of attractiveness ratings. Second, activity in the left hippocampus increased as a function of subsequent memory (subsequent misses

Diffeomorphic functional brain surface alignment: Functional demons.

PubMed

Nenning, Karl-Heinz; Liu, Hesheng; Ghosh, Satrajit S; Sabuncu, Mert R; Schwartz, Ernst; Langs, Georg

2017-08-01

Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects. Copyright © 2017 Elsevier Inc. All rights reserved.

Longitudinal functional brain imaging study in early course schizophrenia before and after cognitive enhancement therapy.

PubMed

Keshavan, Matcheri S; Eack, Shaun M; Prasad, Konasale M; Haller, Chiara S; Cho, Raymond Y

2017-05-01

Schizophrenia is characterized by impaired -social and non social cognition both of which lead to functional deficits. These deficits may benefit from cognitive remediation, but the neural underpinnings of such improvements have not been clearly delineated. We conducted a functional magnetic resonance (fMRI) study in early course schizophrenia patients randomly assigned to cognitive enhancement therapy (CET) or enriched supportive therapy (EST) and treated for two years. Imaging data over three time points including fMRI blood oxygen level dependent (BOLD) data were acquired during performance of a cognitive control paradigm, the Preparing to Overcome Prepotency (POP) task, and functional connectivity data, were analyzed. During the two years of treatment, CET patients showed a continual increase in BOLD activity in the right dorsolateral prefrontal cortex (DLPFC), whereas EST patients tended to show no change in prefrontal brain function throughout treatment. Increases in right DLPFC activity were modestly associated with improved neurocognition (β = .14, p = .041), but not social cognition. Functional connectivity analyses showed reduced connectivity between the DLPFC and the anterior cingulate cortex (ACC) in CET compared to EST over the two years of treatment, which was associated with neurocognitive improvement. These findings suggest that CET leads to enhanced neural activity in brain regions mediating cognitive control and increased efficiency in prefrontal circuits; such changes may be related to the observed therapeutic effects of CET on neurocognitive function. Copyright © 2017. Published by Elsevier Inc.

The Dual-Loop Model and the Human Mirror Neuron System: an Exploratory Combined fMRI and DTI Study of the Inferior Frontal Gyrus.

PubMed

Hamzei, Farsin; Vry, Magnus-Sebastian; Saur, Dorothee; Glauche, Volkmar; Hoeren, Markus; Mader, Irina; Weiller, Cornelius; Rijntjes, Michel

2016-05-01

The inferior frontal gyrus (IFG) is active during both goal-directed action and while observing the same motor act, leading to the idea that also the meaning of a motor act (action understanding) is represented in this "mirror neuron system" (MNS). However, in the dual-loop model, based on dorsal and ventral visual streams, the MNS is thought to be a function of the dorsal steam, projecting to pars opercularis (BA44) of IFG, while recent studies suggest that conceptual meaning and semantic analysis are a function of ventral connections, projecting mainly to pars triangularis (BA45) of IFG. To resolve this discrepancy, we investigated action observation (AO) and imitation (IMI) using fMRI in a large group of subjects. A grasping task (GR) assessed the contribution from movement without AO. We analyzed connections of the MNS-related areas within IFG with postrolandic areas with the use of activation-based DTI. We found that action observation with imitation are mainly a function of the dorsal stream centered on dorsal part of BA44, but also involve BA45, which is dorsally and ventrally connected to the same postrolandic regions. The current finding suggests that BA45 is the crucial part where the MNS and the dual-loop system interact. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Functional asymmetry and effective connectivity of the auditory system during speech perception is modulated by the place of articulation of the consonant- A 7T fMRI study

PubMed Central

Specht, Karsten; Baumgartner, Florian; Stadler, Jörg; Hugdahl, Kenneth; Pollmann, Stefan

2014-01-01

To differentiate between stop-consonants, the auditory system has to detect subtle place of articulation (PoA) and voice-onset time (VOT) differences between stop-consonants. How this differential processing is represented on the cortical level remains unclear. The present functional magnetic resonance (fMRI) study takes advantage of the superior spatial resolution and high sensitivity of ultra-high-field 7 T MRI. Subjects were attentively listening to consonant–vowel (CV) syllables with an alveolar or bilabial stop-consonant and either a short or long VOT. The results showed an overall bilateral activation pattern in the posterior temporal lobe during the processing of the CV syllables. This was however modulated strongest by PoA such that syllables with an alveolar stop-consonant showed stronger left lateralized activation. In addition, analysis of underlying functional and effective connectivity revealed an inhibitory effect of the left planum temporale (PT) onto the right auditory cortex (AC) during the processing of alveolar CV syllables. Furthermore, the connectivity result indicated also a directed information flow from the right to the left AC, and further to the left PT for all syllables. These results indicate that auditory speech perception relies on an interplay between the left and right ACs, with the left PT as modulator. Furthermore, the degree of functional asymmetry is determined by the acoustic properties of the CV syllables. PMID:24966841

Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury

PubMed Central

Wu, Xuehai; Zou, Qihong; Hu, Jin; Tang, Weijun; Mao, Ying; Gao, Liang; Zhu, Jianhong; Jin, Yi; Wu, Xin; Lu, Lu; Zhang, Yaojun; Zhang, Yao; Dai, Zhengjia; Gao, Jia-Hong; Weng, Xuchu; Northoff, Georg; Giacino, Joseph T.; He, Yong

2015-01-01

For accurate diagnosis and prognostic prediction of acquired brain injury (ABI), it is crucial to understand the neurobiological mechanisms underlying loss of consciousness. However, there is no consensus on which regions and networks act as biomarkers for consciousness level and recovery outcome in ABI. Using resting-state fMRI, we assessed intrinsic functional connectivity strength (FCS) of whole-brain networks in a large sample of 99 ABI patients with varying degrees of consciousness loss (including fully preserved consciousness state, minimally conscious state, unresponsive wakefulness syndrome/vegetative state, and coma) and 34 healthy control subjects. Consciousness level was evaluated using the Glasgow Coma Scale and Coma Recovery Scale-Revised on the day of fMRI scanning; recovery outcome was assessed using the Glasgow Outcome Scale 3 months after the fMRI scanning. One-way ANOVA of FCS, Spearman correlation analyses between FCS and the consciousness level and recovery outcome, and FCS-based multivariate pattern analysis were performed. We found decreased FCS with loss of consciousness primarily distributed in the posterior cingulate cortex/precuneus (PCC/PCU), medial prefrontal cortex, and lateral parietal cortex. The FCS values of these regions were significantly correlated with consciousness level and recovery outcome. Multivariate support vector machine discrimination analysis revealed that the FCS patterns predicted whether patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%, and the most discriminative region was the PCC/PCU. These findings suggest that intrinsic functional connectivity patterns of the human posteromedial cortex could serve as a potential indicator for consciousness level and recovery outcome in individuals with ABI. SIGNIFICANCE STATEMENT Varying degrees of consciousness loss and recovery are commonly observed in acquired brain injury patients, yet the underlying neurobiological mechanisms remain elusive. Using a large sample of patients with varying degrees of consciousness loss, we demonstrate that intrinsic functional connectivity strength in many brain regions, especially in the posterior cingulate cortex and precuneus, significantly correlated with consciousness level and recovery outcome. We further demonstrate that the functional connectivity pattern of these regions can predict patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%. Our study thus provides potentially important biomarkers of acquired brain injury in clinical diagnosis, prediction of recovery outcome, and decision making for treatment strategies for patients with severe loss of consciousness. PMID:26377477

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.

Modelling brain activations and connectivity of pain modulated by having a loved one nearby

NASA Astrophysics Data System (ADS)

Tamam, Sofina; Ahmad, Asma Hayati; Kamil, Wan Ahmad

2018-06-01

This study is to model the connectivity between activated areas in the brain associated with pain responses in the presence and absence of a loved one. We used Th:YAG laser targeted onto the dorsum of the right hand of 17 Malay-female participants (mean age 20.59; SD 2.85 years) in two conditions: (1) in the absence of a loved one in the functional magnetic resonance imaging (fMRI) room (Alone condition), and (2) in the presence of a loved one (Support condition). The laser-induced pain stimuli were delivered according to an fMRI paradigm utilising blocked design comprising 15 blocks of activity and 15 blocks of rest. Brain activations and connectivity were analysed using statistical parametric mapping (SPM), dynamic causal modelling (DCM) and Bayesian model selection (BMS) analyses. Individual responses to pain were found to be divided into two categories: (1) Love Hurts (participants who reported more pain in the presence of a loved one) involved activations in thalamus (THA), parahippocampal gyrus (PHG) and hippocampus (HIP); and (2) Love Heals (participants who reported less pain in the presence of a loved one) involved activations in all parts of cingulate cortex. BMS showed that Love Heals could be represented by a cortical network involving the area of anterior cingulate cortex (ACC), middle cingulate cortex (MCC) and posterior cingulate cortex (PCC) in the intrinsic connectivity of ACC → PCC → MCC and ACC → MCC. There was no optimal model to explain the increase in pain threshold when accompanied by the loved one in Love Hurts. The present study reveals a new possible cortical network for the reduction of pain by having a loved one nearby.

Simultaneous tDCS-fMRI Identifies Resting State Networks Correlated with Visual Search Enhancement.

PubMed

Callan, Daniel E; Falcone, Brian; Wada, Atsushi; Parasuraman, Raja

2016-01-01

This study uses simultaneous transcranial direct current stimulation (tDCS) and functional MRI (fMRI) to investigate tDCS modulation of resting state activity and connectivity that underlies enhancement in behavioral performance. The experiment consisted of three sessions within the fMRI scanner in which participants conducted a visual search task: Session 1: Pre-training (no performance feedback), Session 2: Training (performance feedback given), Session 3: Post-training (no performance feedback). Resting state activity was recorded during the last 5 min of each session. During the 2nd session one group of participants underwent 1 mA tDCS stimulation and another underwent sham stimulation over the right posterior parietal cortex. Resting state spontaneous activity, as measured by fractional amplitude of low frequency fluctuations (fALFF), for session 2 showed significant differences between the tDCS stim and sham groups in the precuneus. Resting state functional connectivity from the precuneus to the substantia nigra, a subcortical dopaminergic region, was found to correlate with future improvement in visual search task performance for the stim over the sham group during active stimulation in session 2. The after-effect of stimulation on resting state functional connectivity was measured following a post-training experimental session (session 3). The left cerebellum Lobule VIIa Crus I showed performance related enhancement in resting state functional connectivity for the tDCS stim over the sham group. The ability to determine the relationship that the relative strength of resting state functional connectivity for an individual undergoing tDCS has on future enhancement in behavioral performance has wide ranging implications for neuroergonomic as well as therapeutic, and rehabilitative applications.

Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence

PubMed Central

Alamian, Golnoush; Hincapié, Ana-Sofía; Combrisson, Etienne; Thiery, Thomas; Martel, Véronique; Althukov, Dmitrii; Jerbi, Karim

2017-01-01

Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations. PMID:28367127

Neural correlates of the natural observation of an emotionally loaded video

PubMed Central

Gonzalez-Santos, Leopoldo

2018-01-01

Studies based on a paradigm of free or natural viewing have revealed characteristics that allow us to know how the brain processes stimuli within a natural environment. This method has been little used to study brain function. With a connectivity approach, we examine the processing of emotions using an exploratory method to analyze functional magnetic resonance imaging (fMRI) data. This research describes our approach to modeling stress paradigms suitable for neuroimaging environments. We showed a short film (4.54 minutes) with high negative emotional valence and high arousal content to 24 healthy male subjects (36.42 years old; SD = 12.14) during fMRI. Independent component analysis (ICA) was used to identify networks based on spatial statistical independence. Through this analysis we identified the sensorimotor system and its influence on the dorsal attention and default-mode networks, which in turn have reciprocal activity and modulate networks described as emotional. PMID:29883494

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

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

Cerebro-cerebellar functional connectivity profile of an epilepsy patient with periventricular nodular heterotopia.

PubMed

Emiliano, Santarnecchi; Giampaolo, Vatti; Daniela, Marino; Nicola, Polizzotto; Alfonso, Cerase; Raffaele, Rocchi; Alessandro, Rossi

2012-09-01

Periventricular nodular heterotopia (PNH) is a rare malformation of cortical development often associated with drug resistant focal onset epilepsy. The link between nodules and neocortex have been demonstrated with depth electrodes investigations showing that seizures may arise from both structures. In the last years fMRI resting-state (fMRI-RS) have received a surge in interest due to its capability to track non-invasively physiological and pathological relevant differences in brain network organization. We performed a cerebro-cerebellar voxel-wise and region-of-interest resting state fMRI (RS-fMRI) functional connectivity analysis in a seizure-free epilepsy patient with a PNH in the right temporal horn. Our finding confirms a spontaneous synchronization between PNH and its surrounding cortex, specifically in the inferior temporal, fusiform and occipital gyrus. We also found a significant connectivity with bilateral cerebellum, more intense and widespread on the PNH cerebellar contralateral lobule. RS-fMRI confirmed its potential as a promising tool for non-invasive mapping of cortical and subcortical brain functional organization. Copyright © 2012 Elsevier B.V. All rights reserved.

Executive-affective connectivity in smokers viewing anti-smoking images: an fMRI study.

PubMed

Dinh-Williams, Laurence; Mendrek, Adrianna; Dumais, Alexandre; Bourque, Josiane; Potvin, Stéphane

2014-12-30

Despite knowledge of the harmful consequences of smoking on health, tobacco users continue to smoke. Neuroimaging studies have begun to provide insight into the mechanisms underlying this response. Regions involved in executive control and affective processing/persuasion are activated when viewing the negative value of smoking, but these systems can interact in ways that promote or hinder its impact on behavior. The goal of this functional magnetic resonance imaging (fMRI) study was to examine the dynamics between these systems during the processing of images designed to elicit a negative emotional response regarding tobacco smoking in a group of current smokers. Thirty chronic smokers passively viewed aversive smoking-related, aversive nonsmoking-related and neutral images presented in a block design while being scanned. Functional connectivity analyses showed that the left inferior frontal gyrus (IFG) is negatively associated to activity in medial frontal, cingulate, limbic, subcortical and parietal regions in chronic smokers during the processing of aversive smoking-related material, a pattern that was significantly greater when stimuli were drug-related compared with when they were nondrug-related. Our results suggest that individuals with tobacco dependence present different patterns of functional connectivity depending on whether the aversive stimuli are smoking- or nonsmoking-related. Activity in the left inferior frontal gyrus may act to down-regulate corresponding activity in regions key to an affective and persuasive response during the processing of anti-smoking material. This mechanism may reduce the extent to which "feeling bad" brings about a change in behavior. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Network Modeling for Functional Magnetic Resonance Imaging (fMRI) Signals during Ultra-Fast Speech Comprehension in Late-Blind Listeners

PubMed Central

Dietrich, Susanne; Hertrich, Ingo; Ackermann, Hermann

2015-01-01

In many functional magnetic resonance imaging (fMRI) studies blind humans were found to show cross-modal reorganization engaging the visual system in non-visual tasks. For example, blind people can manage to understand (synthetic) spoken language at very high speaking rates up to ca. 20 syllables/s (syl/s). FMRI data showed that hemodynamic activation within right-hemispheric primary visual cortex (V1), bilateral pulvinar (Pv), and left-hemispheric supplementary motor area (pre-SMA) covaried with their capability of ultra-fast speech (16 syllables/s) comprehension. It has been suggested that right V1 plays an important role with respect to the perception of ultra-fast speech features, particularly the detection of syllable onsets. Furthermore, left pre-SMA seems to be an interface between these syllabic representations and the frontal speech processing and working memory network. So far, little is known about the networks linking V1 to Pv, auditory cortex (A1), and (mesio-) frontal areas. Dynamic causal modeling (DCM) was applied to investigate (i) the input structure from A1 and Pv toward right V1 and (ii) output from right V1 and A1 to left pre-SMA. As concerns the input Pv was significantly connected to V1, in addition to A1, in blind participants, but not in sighted controls. Regarding the output V1 was significantly connected to pre-SMA in blind individuals, and the strength of V1-SMA connectivity correlated with the performance of ultra-fast speech comprehension. By contrast, in sighted controls, not understanding ultra-fast speech, pre-SMA did neither receive input from A1 nor V1. Taken together, right V1 might facilitate the “parsing” of the ultra-fast speech stream in blind subjects by receiving subcortical auditory input via the Pv (= secondary visual pathway) and transmitting this information toward contralateral pre-SMA. PMID:26148062

Network Modeling for Functional Magnetic Resonance Imaging (fMRI) Signals during Ultra-Fast Speech Comprehension in Late-Blind Listeners.

PubMed

Dietrich, Susanne; Hertrich, Ingo; Ackermann, Hermann

2015-01-01

In many functional magnetic resonance imaging (fMRI) studies blind humans were found to show cross-modal reorganization engaging the visual system in non-visual tasks. For example, blind people can manage to understand (synthetic) spoken language at very high speaking rates up to ca. 20 syllables/s (syl/s). FMRI data showed that hemodynamic activation within right-hemispheric primary visual cortex (V1), bilateral pulvinar (Pv), and left-hemispheric supplementary motor area (pre-SMA) covaried with their capability of ultra-fast speech (16 syllables/s) comprehension. It has been suggested that right V1 plays an important role with respect to the perception of ultra-fast speech features, particularly the detection of syllable onsets. Furthermore, left pre-SMA seems to be an interface between these syllabic representations and the frontal speech processing and working memory network. So far, little is known about the networks linking V1 to Pv, auditory cortex (A1), and (mesio-) frontal areas. Dynamic causal modeling (DCM) was applied to investigate (i) the input structure from A1 and Pv toward right V1 and (ii) output from right V1 and A1 to left pre-SMA. As concerns the input Pv was significantly connected to V1, in addition to A1, in blind participants, but not in sighted controls. Regarding the output V1 was significantly connected to pre-SMA in blind individuals, and the strength of V1-SMA connectivity correlated with the performance of ultra-fast speech comprehension. By contrast, in sighted controls, not understanding ultra-fast speech, pre-SMA did neither receive input from A1 nor V1. Taken together, right V1 might facilitate the "parsing" of the ultra-fast speech stream in blind subjects by receiving subcortical auditory input via the Pv (= secondary visual pathway) and transmitting this information toward contralateral pre-SMA.

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.

Individualized Functional Parcellation of the Human Amygdala Using a Semi-supervised Clustering Method: A 7T Resting State fMRI Study.

PubMed

Zhang, Xianchang; Cheng, Hewei; Zuo, Zhentao; Zhou, Ke; Cong, Fei; Wang, Bo; Zhuo, Yan; Chen, Lin; Xue, Rong; Fan, Yong

2018-01-01

The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo . In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions.

Verbal working memory-related functional connectivity alterations in boys with attention-deficit/hyperactivity disorder and the effects of methylphenidate.

PubMed

Wu, Zhao-Min; Bralten, Janita; An, Li; Cao, Qing-Jiu; Cao, Xiao-Hua; Sun, Li; Liu, Lu; Yang, Li; Mennes, Maarten; Zang, Yu-Feng; Franke, Barbara; Hoogman, Martine; Wang, Yu-Feng

2017-08-01

Few studies have investigated verbal working memory-related functional connectivity patterns in participants with attention-deficit/hyperactivity disorder (ADHD). Thus, we aimed to compare working memory-related functional connectivity patterns in healthy children and those with ADHD, and study effects of methylphenidate (MPH). Twenty-two boys with ADHD were scanned twice, under either MPH (single dose, 10 mg) or placebo, in a randomised, cross-over, counterbalanced placebo-controlled design. Thirty healthy boys were scanned once. We used fMRI during a numerical n-back task to examine functional connectivity patterns in case-control and MPH-placebo comparisons, using independent component analysis. There was no significant difference in behavioural performance between children with ADHD, treated with MPH or placebo, and healthy controls. Compared with controls, participants with ADHD under placebo showed increased functional connectivity within fronto-parietal and auditory networks, and decreased functional connectivity within the executive control network. MPH normalized the altered functional connectivity pattern and significantly enhanced functional connectivity within the executive control network, though in non-overlapping areas. Our study contributes to the identification of the neural substrates of working memory. Single dose of MPH normalized the altered brain functional connectivity network, but had no enhancing effect on (non-impaired) behavioural performance.

Adaptive cyclic physiologic noise modeling and correction in functional MRI.

PubMed

Beall, Erik B

2010-03-30

Physiologic noise in BOLD-weighted MRI data is known to be a significant source of the variance, reducing the statistical power and specificity in fMRI and functional connectivity analyses. We show a dramatic improvement on current noise correction methods in both fMRI and fcMRI data that avoids overfitting. The traditional noise model is a Fourier series expansion superimposed on the periodicity of parallel measured breathing and cardiac cycles. Correction using this model results in removal of variance matching the periodicity of the physiologic cycles. Using this framework allows easy modeling of noise. However, using a large number of regressors comes at the cost of removing variance unrelated to physiologic noise, such as variance due to the signal of functional interest (overfitting the data). It is our hypothesis that there are a small variety of fits that describe all of the significantly coupled physiologic noise. If this is true, we can replace a large number of regressors used in the model with a smaller number of the fitted regressors and thereby account for the noise sources with a smaller reduction in variance of interest. We describe these extensions and demonstrate that we can preserve variance in the data unrelated to physiologic noise while removing physiologic noise equivalently, resulting in data with a higher effective SNR than with current corrections techniques. Our results demonstrate a significant improvement in the sensitivity of fMRI (up to a 17% increase in activation volume for fMRI compared with higher order traditional noise correction) and functional connectivity analyses. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin

PubMed Central

Carhart-Harris, Robin L.; Erritzoe, David; Williams, Tim; Stone, James M.; Reed, Laurence J.; Colasanti, Alessandro; Tyacke, Robin J.; Leech, Robert; Malizia, Andrea L.; Murphy, Kevin; Hobden, Peter; Evans, John; Feilding, Amanda; Wise, Richard G.; Nutt, David J.

2012-01-01

Psychedelic drugs have a long history of use in healing ceremonies, but despite renewed interest in their therapeutic potential, we continue to know very little about how they work in the brain. Here we used psilocybin, a classic psychedelic found in magic mushrooms, and a task-free functional MRI (fMRI) protocol designed to capture the transition from normal waking consciousness to the psychedelic state. Arterial spin labeling perfusion and blood-oxygen level-dependent (BOLD) fMRI were used to map cerebral blood flow and changes in venous oxygenation before and after intravenous infusions of placebo and psilocybin. Fifteen healthy volunteers were scanned with arterial spin labeling and a separate 15 with BOLD. As predicted, profound changes in consciousness were observed after psilocybin, but surprisingly, only decreases in cerebral blood flow and BOLD signal were seen, and these were maximal in hub regions, such as the thalamus and anterior and posterior cingulate cortex (ACC and PCC). Decreased activity in the ACC/medial prefrontal cortex (mPFC) was a consistent finding and the magnitude of this decrease predicted the intensity of the subjective effects. Based on these results, a seed-based pharmaco-physiological interaction/functional connectivity analysis was performed using a medial prefrontal seed. Psilocybin caused a significant decrease in the positive coupling between the mPFC and PCC. These results strongly imply that the subjective effects of psychedelic drugs are caused by decreased activity and connectivity in the brain's key connector hubs, enabling a state of unconstrained cognition. PMID:22308440

Toward Dysfunctional Connectivity: A Review of Neuroimaging Findings in Pediatric Major Depressive Disorder

PubMed Central

Hulvershorn, Leslie; Cullen, Kathryn; Anand, Amit

2011-01-01

Child and adolescent psychiatric neuroimaging research typically lags behind similar advances in adult disorders. While the pediatric depression imaging literature is less developed, a recent surge in interest has created the need for a synthetic review of this work. Major findings from pediatric volumetric and functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and resting state functional connectivity studies converge to implicate a corticolimbic network of key areas that work together to mediate the task of emotion regulation. Imaging the brain of children and adolescents with unipolar depression began with volumetric studies of isolated brain regions that served to identify key prefrontal, cingulate and limbic nodes of depression-related circuitry elucidated from more recent advances in DTI and functional connectivity imaging. Systematic review of these studies preliminarily suggests developmental differences between findings in youth and adults, including prodromal neurobiological features, along with some continuity across development. PMID:21901425

Hearing voices in the resting brain: A review of intrinsic functional connectivity research on auditory verbal hallucinations

PubMed Central

Alderson-Day, Ben; McCarthy-Jones, Simon; Fernyhough, Charles

2018-01-01

Resting state networks (RSNs) are thought to reflect the intrinsic functional connectivity of brain regions. Alterations to RSNs have been proposed to underpin various kinds of psychopathology, including the occurrence of auditory verbal hallucinations (AVH). This review outlines the main hypotheses linking AVH and the resting state, and assesses the evidence for alterations to intrinsic connectivity provided by studies of resting fMRI in AVH. The influence of hallucinations during data acquisition, medication confounds, and movement are also considered. Despite a large variety of analytic methods and designs being deployed, it is possible to conclude that resting connectivity in the left temporal lobe in general and left superior temporal gyrus in particular are disrupted in AVH. There is also preliminary evidence of atypical connectivity in the default mode network and its interaction with other RSNs. Recommendations for future research include the adoption of a common analysis protocol to allow for more overlapping datasets and replication of intrinsic functional connectivity alterations. PMID:25956256

Multivariate Heteroscedasticity Models for Functional Brain Connectivity.

PubMed

Seiler, Christof; Holmes, Susan

2017-01-01

Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI). We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP) comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.

Normalisation of brain connectivity through compensatory behaviour, despite congenital hand absence.

PubMed

Hahamy, Avital; Sotiropoulos, Stamatios N; Henderson Slater, David; Malach, Rafael; Johansen-Berg, Heidi; Makin, Tamar R

2015-01-06

Previously we showed, using task-evoked fMRI, that compensatory intact hand usage after amputation facilitates remapping of limb representations in the cortical territory of the missing hand (Makin et al., 2013a). Here we show that compensatory arm usage in individuals born without a hand (one-handers) reflects functional connectivity of spontaneous brain activity in the cortical hand region. Compared with two-handed controls, one-handers showed reduced symmetry of hand region inter-hemispheric resting-state functional connectivity and corticospinal white matter microstructure. Nevertheless, those one-handers who more frequently use their residual (handless) arm for typically bimanual daily tasks also showed more symmetrical functional connectivity of the hand region, demonstrating that adaptive behaviour drives long-range brain organisation. We therefore suggest that compensatory arm usage maintains symmetrical sensorimotor functional connectivity in one-handers. Since variability in spontaneous functional connectivity in our study reflects ecological behaviour, we propose that inter-hemispheric symmetry, typically observed in resting sensorimotor networks, depends on coordinated motor behaviour in daily life.

A task-related and resting state realistic fMRI simulator for fMRI data validation

NASA Astrophysics Data System (ADS)

Hill, Jason E.; Liu, Xiangyu; Nutter, Brian; Mitra, Sunanda

2017-02-01

After more than 25 years of published functional magnetic resonance imaging (fMRI) studies, careful scrutiny reveals that most of the reported results lack fully decisive validation. The complex nature of fMRI data generation and acquisition results in unavoidable uncertainties in the true estimation and interpretation of both task-related activation maps and resting state functional connectivity networks, despite the use of various statistical data analysis methodologies. The goal of developing the proposed STANCE (Spontaneous and Task-related Activation of Neuronally Correlated Events) simulator is to generate realistic task-related and/or resting-state 4D blood oxygenation level dependent (BOLD) signals, given the experimental paradigm and scan protocol, by using digital phantoms of twenty normal brains available from BrainWeb (http://brainweb.bic.mni.mcgill.ca/brainweb/). The proposed simulator will include estimated system and modelled physiological noise as well as motion to serve as a reference to measured brain activities. In its current form, STANCE is a MATLAB toolbox with command line functions serving as an open-source add-on to SPM8 (http://www.fil.ion.ucl.ac.uk/spm/software/spm8/). The STANCE simulator has been designed in a modular framework so that the hemodynamic response (HR) and various noise models can be iteratively improved to include evolving knowledge about such models.

Alternations of functional connectivity in amblyopia patients: a resting-state fMRI study

NASA Astrophysics Data System (ADS)

Wang, Jieqiong; Hu, Ling; Li, Wenjing; Xian, Junfang; Ai, Likun; He, Huiguang

2014-03-01

Amblyopia is a common yet hard-to-cure disease in children and results in poor or blurred vision. Some efforts such as voxel-based analysis, cortical thickness analysis have been tried to reveal the pathogenesis of amblyopia. However, few studies focused on alterations of the functional connectivity (FC) in amblyopia. In this study, we analyzed the abnormalities of amblyopia patients by both the seed-based FC with the left/right primary visual cortex and the network constructed throughout the whole brain. Experiments showed the following results: (1)As for the seed-based FC analysis, FC between superior occipital gyrus and the primary visual cortex was found to significantly decrease in both sides. The abnormalities were also found in lingual gyrus. The results may reflect functional deficits both in dorsal stream and ventral stream. (2)Two increased functional connectivities and 64 decreased functional connectivities were found in the whole brain network analysis. The decreased functional connectivities most concentrate in the temporal cortex. The results suggest that amblyopia may be caused by the deficits in the visual information transmission.

The Neural Processing of Second Language Comprehension Modulated by the Degree of Proficiency: A Listening Connected Speech fMRI Study

PubMed Central

Hesling, Isabelle; Dilharreguy, Bixente; Bordessoules, Martine; Allard, Michèle

2012-01-01

While the neural network encompassing the processing of the mother tongue (L1) is well defined and has revealed the existence of a bilateral ventral pathway and a left dorsal pathway in which 3 loops have been defined, the question of the processing of a second language (L2) is still a matter of debate. Among variables accounting for the discrepancies in results, the degree of L2 proficiency appears to be one of the main factors. The present study aimed at assessing both pathways in L2, making it possible to determine the degree of mastery of the different speech components (prosody, phonology, semantics and syntax) that are intrinsically embedded within connected speech and that vary according to the degree of proficiency using high degrees of prosodic information. Two groups of high and moderate proficiency in L2 performed an fMRI comprehension task in L1 and L2. The modifications in brain activity observed within the dorsal and the ventral pathways according to L2 proficiency suggest that different processes of L2 are supported by differences in the integrated activity within distributed networks that included the left STSp, the left Spt and the left pars triangularis. PMID:22927897

Music Listening modulates Functional Connectivity and Information Flow in the Human Brain.

PubMed

Karmonik, Christof; Brandt, Anthony; Anderson, Jeff; Brooks, Forrest; Lytle, Julie; Silverman, Elliott; Frazier, Jeff T

2016-07-27

Listening to familiar music has recently been reported to be beneficial during recovery from stroke. A better understanding of changes in functional connectivity and information flow is warranted in order to further optimize and target this approach through music therapy. Twelve healthy volunteers listened to seven different auditory samples during an fMRI scanning session: a musical piece chosen by the volunteer that evokes a strong emotional response (referred to as: "self-selected emotional"), two unfamiliar music pieces (Invention #1 by J. S. Bach* and Gagaku - Japanese classical opera, referred to as "unfamiliar"), the Bach piece repeated with visual guidance (DML: Directed Music Listening) and three spoken language pieces (unfamiliar African click language, an excerpt of emotionally charged language, and an unemotional reading of a news bulletin). Functional connectivity and betweenness (BTW) maps, a measure for information flow, were created with a graph-theoretical approach. Distinct variation in functional connectivity was found for different music pieces consistently for all subjects. Largest brain areas were recruited for processing self-selected music with emotional attachment or culturally unfamiliar music. Maps of information flow correlated significantly with fMRI BOLD activation maps (p<0.05). Observed differences in BOLD activation and functional connectivity may help explain previously observed beneficial effects in stroke recovery, as increased blood flow to damaged brain areas stimulated by active engagement through music listening may have supported a state more conducive to therapy.

Characterization of resting state activity in MCI individuals

PubMed Central

Cieri, Filippo; Cera, Nicoletta

2013-01-01

Objectives. Aging is the major risk factor for Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). The aim of this study was to identify novel modifications of brain functional connectivity in MCI patients. MCI individuals were compared to healthy elderly subjects. Methods. We enrolled 37 subjects (age range 60–80 y.o.). Of these, 13 subjects were affected by MCI and 24 were age-matched healthy elderly control (HC). Subjects were evaluated with Mini Mental State Examination (MMSE), Frontal Assessment Battery (FAB), and prose memory (Babcock story) tests. In addition, with functional Magnetic Resonance Imaging (fMRI), we investigated resting state network (RSN) activities. Resting state (Rs) fMRI data were analyzed by means of Independent Component Analysis (ICA). Subjects were followed-up with neuropsychological evaluations for three years. Results. Rs-fMRI of MCI subjects showed increased intrinsic connectivity in the Default Mode Network (DMN) and in the Somatomotor Network (SMN). Analysis of the DMN showed statistically significant increased activation in the posterior cingulate cortex (PCC) and left inferior parietal lobule (lIPL). During the three years follow-up, 4 MCI subjects converted to AD. The subset of MCI AD-converted patients showed increased connectivity in the right Inferior Parietal Lobule (rIPL). As for SMN activity, MCI and MCI-AD converted groups showed increased level of connectivity in correspondence of the right Supramarginal Gyrus (rSG). Conclusions. Our findings indicate alterations of DMN and SMN activity in MCI subjects, thereby providing potential imaging-based markers that can be helpful for the early diagnosis and monitoring of these patients. PMID:24010015

Painful Heat Reveals Hyperexcitability of the Temporal Pole in Interictal and Ictal Migraine States

PubMed Central

Moulton, E. A.; Becerra, L.; Maleki, N.; Pendse, G.; Tully, S.; Hargreaves, R.; Burstein, R.

2011-01-01

During migraine attacks, alterations in sensation accompanying headache may manifest as allodynia and enhanced sensitivity to light, sound, and odors. Our objective was to identify physiological changes in cortical regions in migraine patients using painful heat and functional magnetic resonance imaging (fMRI) and the structural basis for such changes using diffusion tensor imaging (DTI). In 11 interictal patients, painful heat threshold + 1°C was applied unilaterally to the forehead during fMRI scanning. Significantly greater activation was identified in the medial temporal lobe in patients relative to healthy subjects, specifically in the anterior temporal pole (TP). In patients, TP showed significantly increased functional connectivity in several brain regions relative to controls, suggesting that TP hyperexcitability may contribute to functional abnormalities in migraine. In 9 healthy subjects, DTI identified white matter connectivity between TP and pulvinar nucleus, which has been related to migraine. In 8 patients, fMRI activation in TP with painful heat was exacerbated during migraine, suggesting that repeated migraines may sensitize TP. This article investigates a nonclassical role of TP in migraineurs. Observed temporal lobe abnormalities may provide a basis for many of the perceptual changes in migraineurs and may serve as a potential interictal biomarker for drug efficacy. PMID:20562317

Cortical activation during power grip task with pneumatic pressure gauge: an fMRI study

NASA Astrophysics Data System (ADS)

Mohamad, M.; Mardan, N. H.; Ismail, S. S.

2017-05-01

Aging is associated with a decline in cognitive and motor function. But, the relationships with motor performance are less well understood. In this study, functional magnetic resonance imaging (fMRI) was used to assess cortical activation in older adults. This study employed power grip task that utilised block paradigm consisted of alternate 30s rest and active. A visual cue was used to pace the hand grip movement that clenched a cylindrical rubber bulb connected with pressure pneumatic gauge that measure the pressure (Psi). The objective of this study is determined the brain areas activated during motor task and the correlation between percentage signal change of each motor area (BA 4 and 6) and hand grip pressure. Result showed there was a significant difference in mean percentage signal change in BA 4 and BA 6 in both hemispheres and negative correlation obtained in BA 4 and BA 6. These results indicate that a reduced ability in the motor networks contribute to age-related decline in motor performance.

CEREBRA: a 3-D visualization tool for brain network extracted from fMRI data.

PubMed

Nasir, Baris; Yarman Vural, Fatos T

2016-08-01

In this paper, we introduce a new tool, CEREBRA, to visualize the 3D network of human brain, extracted from the fMRI data. The tool aims to analyze the brain connectivity by representing the selected voxels as the nodes of the network. The edge weights among the voxels are estimated by considering the relationships among the voxel time series. The tool enables the researchers to observe the active brain regions and the interactions among them by using graph theoretic measures, such as, the edge weight and node degree distributions. CEREBRA provides an interactive interface with basic display and editing options for the researchers to study their hypotheses about the connectivity of the brain network. CEREBRA interactively simplifies the network by selecting the active voxels and the most correlated edge weights. The researchers may remove the voxels and edges by using local and global thresholds selected on the window. The built-in graph reduction algorithms are then eliminate the irrelevant regions, voxels and edges and display various properties of the network. The toolbox is capable of space-time representation of the voxel time series and estimated arc weights by using the animated heat maps.