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Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Nature Neuroscience volume 23, pages 1644–1654 (2020)Cite this article

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Abstract

Network neuroscience has relied on a node-centric network model in which cells, populations and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. In this study, we developed an edge-centric network model that generates constructs ‘edge time series’ and ‘edge functional connectivity’ (eFC). Using network analysis, we show that, at rest, eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We show that eFC is systematically modulated by variation in sensory input. In future work, the edge-centric approach could be useful for identifying novel biomarkers of disease, characterizing individual variation and mapping the architecture of highly resolved neural circuits.

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Fig. 1: Derivation of the eFC matrix.
Fig. 2: Organization of the eFC matrix.
Fig. 3: Intra- and inter-participant similarity of eFC across scan sessions.
Fig. 4: Edge communities.
Fig. 5: Edge community entropy and overlap.
Fig. 6: System-level similarity of edge communities.
Fig. 7: Effect of passive movie watching on eFC.

Data availability

All imaging data come from publicly available, open access repositories. Human Connectome Project data can be accessed at https://db.humanconnectome.org/app/template/Login.vm after signing a data use agreement. Midnight Scan Club data can be accessed via OpenNeuro at https://openneuro.org/datasets/ds000224/versions/1.0.1. The Healthy Brain Network Serial Scanning Initiative data can be accessed at https://fcon_1000.projects.nitrc.org/indi/hbn_ssi/download.html.

Code availbility

Code to compute eFC and its related derivatives has been made available at https://github.com/brain-networks.

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Acknowledgements

This research was supported by the Indiana University Office of the Vice President for Research Emerging Area of Research Initiative, Learning: Brains, Machines and Children (F.Z.E. and R.F.B.). This material is based on work supported by the National Science Foundation Graduate Research Fellowship under grant no. 1342962 (J.F.). This research was supported, in part, by the Lilly Endowment, through its support for the Indiana University Pervasive Technology Institute and, in part, by the Indiana METACyt Initiative. The Indiana METACyt Initiative at Indiana University was also supported, in part, by the Lilly Endowment. Data were provided, in part, by the Human Connectome Project, WU-Minn Consortium (principal investigators: D. Van Essen and K. Ugurbil; 1U54MH091657), funded by the 16 National Institues of Health (NIH) institutes and centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. We thank B. Mišić for reading an early version of this manuscript.

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Authors and Affiliations

  1. Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA

    Joshua Faskowitz, Farnaz Zamani Esfahlani, Youngheun Jo, Olaf Sporns & Richard F. Betzel

  2. Program in Neuroscience, Indiana University, Bloomington, IN, USA

    Joshua Faskowitz, Olaf Sporns & Richard F. Betzel

  3. Cognitive Science Program, Indiana University, Bloomington, IN, USA

    Olaf Sporns & Richard F. Betzel

  4. Network Science Institute, Indiana University, Bloomington, IN, USA

    Olaf Sporns & Richard F. Betzel

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  1. Joshua Faskowitz
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  2. Farnaz Zamani Esfahlani
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  3. Youngheun Jo
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  4. Olaf Sporns
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Contributions

J.F. and R.F.B. conceived of the study, processed data, carried out all analyses and wrote the first draft of the manuscript. F.Z.E., Y.J. and O.S. contributed to project direction via discussion. All authors helped revise and write the submitted manuscript.

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Correspondence to Richard F. Betzel.

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Peer review information Nature Neuroscience thanks Lucina Uddin, Andrew Zalesky, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Faskowitz, J., Esfahlani, F.Z., Jo, Y. et al. Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nat Neurosci 23, 1644–1654 (2020). https://doi.org/10.1038/s41593-020-00719-y

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