Structure–function relationships during segregated and integrated network states of human brain functional connectivity
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Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.
KeywordsSegregation and integration Structural connectivity Time-resolved functional connectivity Resting state Networks Connectomics
Data were provided in part by the Human Connectome Project (HCP), 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. The authors would like to thank Marcel A. de Reus for constructing structural networks from the HCP data.
Compliance with ethical standards
This study was supported by the Japan Society for the Promotion of Science Postdoctoral Fellowship for Research Abroad (H28-150), the National Science Foundation/Integrative Graduate Education and Research Traineeship Training Program in the Dynamics of Brain-Body-Environment Systems at Indiana University (0903495), the National Key Basic Research and Development Program (973 Program; 2015CB351702), the Natural Sciences Foundation of China (81471740 and 81220108014), the CAS K.C. Wong Education Foundation, the J.S. McDonnell Foundation (22002082), and the National Institutes of Health (R01 AT009036-01).
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants (in public data sets) included in the study.
- Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus CL, Vaughn BV for the American Academy of Sleep Medicine (2015) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, version 2.2, American Academy of Sleep Medicine, Darien, ILGoogle Scholar
- Fukushima M, Betzel RF, He Y, de Reus MA, van den Heuvel MP, Zuo XN, Sporns O (2017) Fluctuations between high- and low-modularity topology in time-resolved functional connectivity. Neuroimage. doi: 10.1016/j.neuroimage.2017.08.044
- Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, Della Penna S, Duyn JH, Glover GH, Gonzalez-Castillo J, Handwerker DA, Keilholz S, Kiviniemi V, Leopold DA, de Pasquale F, Sporns O, Walter M, Chang C (2013) Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80:360–378CrossRefPubMedGoogle Scholar
- Miller RL, Adalı T, Levin-Schwartz Y, Calhoun VD (2017) Resting-state fMRI dynamics and null models: perspectives, sampling variability, and simulations. bioRxiv. doi: 10.1101/153411
- Yeo BTT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zollei L, Polimeni JR, Fischl B, Liu H, Buckner RL (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125–1165CrossRefPubMedGoogle Scholar