Temporal variability of brain networks predicts individual differences in bistable perception

Abstract The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case–control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.

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Identifying reproducible individual differences in childhood functional brain networks: An ABCD study

Developmental Cognitive Neuroscience ◽

10.1016/j.dcn.2019.100706 ◽

2019 ◽

Vol 40 ◽

pp. 100706 ◽

Cited By ~ 18

Author(s):

Scott Marek ◽

Brenden Tervo-Clemmens ◽

Ashley N. Nielsen ◽

Muriah D. Wheelock ◽

Ryland L. Miller ◽

...

Keyword(s):

Individual Differences ◽

Brain Networks ◽

Functional Brain ◽

Functional Brain Networks

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General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

NeuroImage ◽

10.1016/j.neuroimage.2019.01.068 ◽

2019 ◽

Vol 189 ◽

pp. 516-532 ◽

Cited By ~ 59

Author(s):

Maxwell L. Elliott ◽

Annchen R. Knodt ◽

Megan Cooke ◽

M. Justin Kim ◽

Tracy R. Melzer ◽

...

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Resting State ◽

Brain Networks ◽

Functional Brain ◽

Functional Brain Networks ◽

And Task

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Unifying the Notions of Modularity and Core–Periphery Structure in Functional Brain Networks during Youth

Cerebral Cortex ◽

10.1093/cercor/bhz150 ◽

2019 ◽

Vol 30 (3) ◽

pp. 1087-1102

Author(s):

Shi Gu ◽

Cedric Huchuan Xia ◽

Rastko Ciric ◽

Tyler M Moore ◽

Ruben C Gur ◽

...

Keyword(s):

Individual Differences ◽

Brain Function ◽

Brain Networks ◽

Brain Regions ◽

Imaging Data ◽

Modular Architecture ◽

Functional Brain ◽

Rich Club ◽

Functional Brain Networks ◽

Integrative Processing

AbstractAt rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core–periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core–periphery structure. Here, we leverage a recently-developed model-based approach—the weighted stochastic block model—that simultaneously uncovers modular and core–periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core–periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.

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Individual differences in the temporal variability of medically unexplained symptom reporting

British Journal of Health Psychology ◽

10.1348/135910704773891069 ◽

2004 ◽

Vol 9 (2) ◽

pp. 219-240 ◽

Cited By ~ 10

Author(s):

Eamonn Ferguson ◽

Helen J. Cassaday ◽

James Erskind ◽

Gemma Delahaye

Keyword(s):

Individual Differences ◽

Temporal Variability ◽

Symptom Reporting ◽

Unexplained Symptom ◽

Medically Unexplained Symptom ◽

Medically Unexplained

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Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

PLoS ONE ◽

10.1371/journal.pone.0242985 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242985

Author(s):

Howard Muchen Hsu ◽

Zai-Fu Yao ◽

Kai Hwang ◽

Shulan Hsieh

Keyword(s):

Individual Differences ◽

Brain Networks ◽

Network Connectivity ◽

Brain Regions ◽

Motor Inhibition ◽

Attention Network ◽

Functional Brain ◽

Graph Theoretic ◽

Dorsal Attention Network ◽

Functional Brain Networks

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

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Linking flexibility of brain networks to cognitive development in preschool children

10.1101/2020.03.24.005074 ◽

2020 ◽

Author(s):

Lily Chamakura ◽

Syed Naser Daimi ◽

Katsumi Watanabe ◽

Joydeep Bhattacharya ◽

Goutam Saha

Keyword(s):

Preschool Children ◽

Brain Development ◽

Cognitive Ability ◽

Temporal Variability ◽

Short Term Memory ◽

Brain Networks ◽

Brain Network ◽

Memory And Attention ◽

Functional Brain Networks ◽

Human Adults

AbstractRecent studies of functional connectivity networks (FCNs) suggest that the reconfiguration of brain network across time, both at rest and during task, is linked with cognition in human adults. In this study, we tested this prediction, i.e. cognitive ability is associated with a flexible brain network in preschool children of 3-4 years - a critical age, representing a ‘blossoming period’ for brain development. We recorded magnetoen-cephalogram (MEG) data from 88 preschoolers, and assessed their cognitive ability by a battery of cognitive tests. We estimated FCNs obtained from the source reconstructed MEG recordings, and characterized the temporal variability at each node using a novel path-based measure of temporal variability; the latter captures reconfiguration of the node’s interactions to the rest of the network across time. Using connectome predictive modeling, we demonstrated that the temporal variability of fronto-temporal nodes in the dynamic FCN can reliably predict out-of-scanner performance of short-term memory and attention distractability in novel participants. Further, we observed that the network-level temporal variability increased with age, while individual nodes exhibited an inverse relationship between temporal variability and node centrality. These results demonstrate that functional brain networks, and especially their reconfiguration ability, are important to cognition at an early but a critical stage of human brain development.

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The diversity and multiplexity of edge communities within and between brain systems

10.1101/2020.05.05.067777 ◽

2020 ◽

Author(s):

Youngheun Jo ◽

Farnaz Zamani Esfahlani ◽

Joshua Faskowitz ◽

Evgeny J. Chumin ◽

Olaf Sporns ◽

...

Keyword(s):

Cerebral Cortex ◽

Individual Differences ◽

Human Brain ◽

Cognitive Processes ◽

Brain Networks ◽

Brain Regions ◽

System Level ◽

Future Research ◽

Behavior Development ◽

Using Data

The human brain is composed of regions that can be grouped into functionally specialized systems. These systems transiently couple and decouple across time to support complex cognitive processes. Recently, we proposed an edge-centric model of brain networks whose elements can be clustered to reveal communities of connections whose co-fluctuations are correlated across time. It remains unclear, however, how these co-fluctuation patterns relate to traditionally-defined brain systems. Here, we address this question using data from the Midnight Scan Club. We show that edge communities transcend traditional definitions of brain systems, forming a multiplexed network in which all pairs of brain systems are linked to one another by at least two distinct edge communities. Mapping edge communities back to individual brain regions and deriving a novel distance metric to describe the similarity of regions’ “edge community profiles”, we then demonstrate that the within-system similarity of profiles is heterogeneous across systems. Specifically, we find that heteromodal association areas exhibit significantly greater diversity of edge communities than primary sensory systems. Next, we cluster the entire cerebral cortex according to the similarity of regions’ edge community profiles, revealing systematic differences between traditionally-defined systems and the detected clusters. Specifically, we find that regions in heteromodal systems exhibit dissimilar edge community profiles and are more likely to form their own clusters. Finally, we show show that edge communities are highly personalized and can be used to identify individual subjects. Collectively, our work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brain’s system level architecture. Our work provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.

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