Fluctuations between high- and low-modularity topology in time-resolved functional connectivity
Fukushima, Makoto; Betzel, Richard F.; He, Ye; de Reus, Marcel A; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf
(2018) NeuroImage, volume 180, issue Pt B, pp. 406 - 416
(Article)
Abstract
Modularity is an important topological attribute for functional brain networks. Recent human fMRI studies have reported that modularity of functional networks varies not only across individuals being related to demographics and cognitive performance, but also within individuals co-occurring with fluctuations in network properties of functional connectivity, estimated over short time
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intervals. However, characteristics of these time-resolved functional networks during periods of high and low modularity have remained largely unexplored. In this study we investigate basic spatiotemporal properties of time-resolved networks in the high and low modularity periods during rest, with a particular focus on their spatial connectivity patterns, temporal homogeneity and test-retest reliability. We show that spatial connectivity patterns of time-resolved networks in the high and low modularity periods are represented by increased and decreased dissociation of the default mode network module from task-positive network modules, respectively. We also find that the instances of time-resolved functional connectivity sampled from within the high (respectively, low) modularity period are relatively homogeneous (respectively, heterogeneous) over time, indicating that during the low modularity period the default mode network interacts with other networks in a variable manner. We confirmed that the occurrence of the high and low modularity periods varies across individuals with moderate inter-session test-retest reliability and that it is correlated with previously-reported individual differences in the modularity of functional connectivity estimated over longer timescales. Our findings illustrate how time-resolved functional networks are spatiotemporally organized during periods of high and low modularity, allowing one to trace individual differences in long-timescale modularity to the variable occurrence of network configurations at shorter timescales.
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Keywords: Networks, Resting state, Time-resolved functional connectivity, Modularity, Connectomics, Datasets as Topic, Humans, Brain/physiology, Time, Connectome/methods, Magnetic Resonance Imaging, Algorithms, Image Processing, Computer-Assisted/methods, Models, Neurological, Nerve Net/physiology, Neurology, Cognitive Neuroscience, Review, Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural
ISSN: 1053-8119
Publisher: Academic Press Inc.
Note: Funding Information: M.F. was supported by a Uehara Memorial Foundation Postdoctoral Fellowship and a Japan Society for the Promotion of Science Postdoctoral Fellowship for Research Abroad. O.S. was supported by the J.S. McDonnell Foundation ( 22002082 ) and the National Institutes of Health ( R01 AT009036-01 ). R.F.B. was supported by the National Science Foundation/Integrative Graduate Education and Research Traineeship Training Program in the Dynamics of Brain-Body-Environment Systems at Indiana University ( 0903495 ). X.N.Z was supported by the National Key Basic Research and Development Program (973 Program; 2015CB351702 ) and the Natural Sciences Foundation of China ( 81471740 , 81220108014 ). X.N.Z. and O.S. are members of an international collaboration team (trial stage) supported by the CAS K.C. Wong Education Foundation . Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. Funding Information: M.F. was supported by a Uehara Memorial Foundation Postdoctoral Fellowship and a Japan Society for the Promotion of Science Postdoctoral Fellowship for Research Abroad. O.S. was supported by the J.S. McDonnell Foundation (22002082) and the National Institutes of Health (R01 AT009036-01). R.F.B. was supported by the National Science Foundation/Integrative Graduate Education and Research Traineeship Training Program in the Dynamics of Brain-Body-Environment Systems at Indiana University (0903495). X.N.Z was supported by the National Key Basic Research and Development Program (973 Program; 2015CB351702) and the Natural Sciences Foundation of China (81471740, 81220108014). X.N.Z. and O.S. are members of an international collaboration team (trial stage) supported by the CAS K.C. Wong Education Foundation. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. Publisher Copyright: © 2017 The Authors
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