Dataset Information

Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions.

ABSTRACT: Large-scale analysis of functional MRI data has revealed that brain regions can be grouped into stable "networks" or communities. In many instances, the communities are characterized as relatively disjoint. Although recent work indicates that brain regions may participate in multiple communities (for example, hub regions), the extent of community overlap is poorly understood. To address these issues, here we investigated large-scale brain networks based on "rest" and task human functional MRI data by employing a mixed-membership Bayesian model that allows each brain region to belong to all communities simultaneously with varying membership strengths. The approach allowed us to 1) compare the structure of disjoint and overlapping communities; 2) determine the relationship between functional diversity (how diverse is a region's functional activation repertoire) and membership diversity (how diverse is a region's affiliation to communities); 3) characterize overlapping community structure; 4) characterize the degree of non-modularity in brain networks; 5) study the distribution of "bridges", including bottleneck and hub bridges. Our findings revealed the existence of dense community overlap that was not limited to "special" hubs. Furthermore, the findings revealed important differences between community organization during rest and during specific task states. Overall, we suggest that dense overlapping communities are well suited to capture the flexible and task dependent mapping between brain regions and their functions.

SUBMITTER: Najafi M

PROVIDER: S-EPMC4915991 | biostudies-other | 2016 Jul

REPOSITORIES: biostudies-other

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Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions.

Najafi Mahshid M McMenamin Brenton W BW Simon Jonathan Z JZ Pessoa Luiz L

NeuroImage 20160426

Large-scale analysis of functional MRI data has revealed that brain regions can be grouped into stable "networks" or communities. In many instances, the communities are characterized as relatively disjoint. Although recent work indicates that brain regions may participate in multiple communities (for example, hub regions), the extent of community overlap is poorly understood. To address these issues, here we investigated large-scale brain networks based on "rest" and task human functional MRI da ...[more]

PMID: 27129758

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Project description:ImportanceAnhedonia can present in children and predict detrimental clinical outcomes.ObjectiveTo map anhedonia in children onto changes in intrinsic large-scale connectivity and task-evoked activation and to probe the specificity of these changes in anhedonia against other clinical phenotypes (low mood, anxiety, and attention-deficit/hyperactivity disorder [ADHD]).Design, setting, and participantsFunctional magnetic resonance imaging (fMRI) data were from the first annual release of the Adolescent Brain Cognitive Development study, collected between September 2016 and September 2017 and analyzed between April and September 2018. Cross-sectional data of children aged 9 to 10 years from unreferred, community samples during rest (n?=?2878) and during reward anticipation (n?=?2874) and working memory (n?=?2745) were analyzed.Main outcomes and measuresAlterations in fMRI data during rest, reward anticipation, and working memory were examined, using both frequentist and Bayesian approaches. Functional MRI connectivity within large-scale networks, between networks, and between networks and subcortical regions were examined during rest. Functional MRI activation were examined during reward anticipation and working memory using the monetary incentive delayed and N-back tasks, respectively.ResultsAmong 2878 children with adequate-quality resting-state fMRI data (mean [SD] age, 10.03 [0.62] years; 1400 girls [48.6%]), children with anhedonia (261 [9.1%]), compared with those without anhedonia (2617 [90.9%]), showed hypoconnectivity among various large-scale networks and subcortical regions, including between the arousal-related cingulo-opercular network and reward-related ventral striatum area (mean [SD] with anhedonia, 0.08 [0.10] vs without anhedonia,?0.10 [0.10]; t2,876?=?3.33; P?<?.001; q[false discovery rate]?=?0.03; ln[Bayes factor10]?=?2.85). Such hypoconnectivity did not manifest among children with low mood (277 of 2878 [9.62%]), anxiety (109 of 2878 [3.79%]), or ADHD (459 of 2878 [15.95%]), suggesting specificity. Similarly, among 2874 children (mean [SD] age, 10.03 [0.62] years; 1414 girls?[49.2%]) with high-quality task-evoked fMRI data, children with anhedonia (248 of 2874 [8.63%]) demonstrated hypoactivation during reward anticipation in various areas, including the dorsal striatum and areas of the cingulo-opercular network. This hypoactivity was not found among children with low mood (268 of 2874 [9.32%]), anxiety (90 of 2874 [3.13%]), or ADHD (473 of 2874 [16.46%]). Moreover, we also found context- and phenotype-specific double dissociations; while children with anhedonia showed altered activation during reward anticipation (but not working memory), those with ADHD showed altered activation during working memory (but not reward anticipation).Conclusions and relevanceUsing the Adolescent Brain Cognitive Development study data set, phenotype-specific alterations were found in intrinsic large-scale connectivity and task-evoked activation in children with anhedonia. The hypoconnectivity at rest and hypoactivation during reward anticipation complementarily map anhedonia onto aberrations in neural-cognitive processes: lack of intrinsic reward-arousal integration during rest and diminishment of extrinsic reward-arousal activity during reward anticipation. These findings help delineate the pathophysiological underpinnings of anhedonia in children.

Dataset Information

Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions.

Publications

Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions.

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