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Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography

Nature Biomedical Engineering volume 3, pages 902–916 (2019)Cite this article

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Abstract

Electrocorticography (ECoG) data can be used to estimate brain-wide connectivity patterns. Yet, the invasiveness of ECoG, incomplete cortical coverage, and variability in electrode placement across individuals make the network analysis of ECoG data challenging. Here, we show that the architecture of whole-brain ECoG networks and the factors that shape it can be studied by analysing whole-brain, interregional and band-limited ECoG networks from a large cohort—in this case, of individuals with medication-resistant epilepsy. Using tools from network science, we characterized the basic organization of ECoG networks, including frequency-specific architecture, segregated modules and the dependence of connection weights on interregional Euclidean distance. We then used linear models to explain variabilities in the connection strengths between pairs of brain regions, and to highlight the joint role, in shaping the brain-wide organization of ECoG networks, of communication along white matter pathways, interregional Euclidean distance and correlated gene expression. Moreover, we extended these models to predict out-of-sample, single-subject data. Our predictive models may have future clinical utility; for example, by anticipating the effect of cortical resection on interregional communication.

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Fig. 1: Processing pipeline for group-level ECoG FC matrices.
Fig. 2: Relationship between group-level ECoG and BOLD FC.
Fig. 3: Relationship between group-level ECoG modules and canonical systems.
Fig. 4: Distance dependence of ECoG FC and community properties.
Fig. 5: Predicting AECoG with search information, Euclidean distance and gene expression correlations.
Fig. 6: Predicting single-subject AECoG.

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Data availability

The main data supporting the results of this study are available within the paper and its Supplementary Information files. All source data collected from the subjects are available on request via http://memory.psych.upenn.edu/RAM_Public_Data.

Code availability

All code is available from the authors upon reasonable request.

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Acknowledgements

The resting-state fMRI and diffusion imaging data collection was funded by the Army Research Office through contract number W911NF-14-1-0679 awarded to D.S.B. R.F.B. and J.S. were supported by grants awarded to D.S.B., including awards from the John D. and Catherine T. MacArthur Foundation, Alfred P. Sloan Foundation, ISI Foundation, Paul G. Allen Family Foundation, Army Research Laboratory (W911NF-10-2-0022), Army Research Office (Bassett-W911NF-14-1-0679, Grafton-W911NF-16-1-0474 and DCIST-W911NF-17-2-0181), Office of Naval Research, National Institute of Mental Health (2-R01-DC-009209-11, R01-MH112847, R01-MH107235 and R21-M MH-106799), National Institute of Child Health and Human Development (1R01HD086888-01), National Institute of Neurological Disorders and Stroke (R01 NS099348) and National Science Foundation (BCS-1441502, BCS-1430087, NSF PHY-1554488 and BCS-1631550). A.E.K. was supported by a grant awarded to D.S.B. from the Army Research Laboratory through contract number W911NF-10-2-0022. J.D.M. acknowledges support from the National Institute of Health (award 1-DP5-OD021352). This content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. We also thank M. Kahana and the group associated with the DARPA Restoring Active Memory programme, who were responsible for collecting and publicly sharing the ECoG data used in this paper.

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Authors and Affiliations

  1. Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Richard F. Betzel, Ari E. Kahn, Jonathan Soffer & Danielle S. Bassett

  2. Department of Psychology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    John D. Medaglia

  3. Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ari E. Kahn & Daniel R. Schonhaut

  4. Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  5. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  6. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  7. Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

Authors
  1. Richard F. Betzel
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  2. John D. Medaglia
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  3. Ari E. Kahn
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  4. Jonathan Soffer
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  5. Daniel R. Schonhaut
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  6. Danielle S. Bassett
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Contributions

R.F.B. and D.S.B. designed the study, performed all analyses and wrote the initial draft of the manuscript. J.D.M., A.E.K. and J.S. contributed to and processed the MRI data. D.R.S. helped with the Gene Ontology analysis. All authors wrote the final draft of the manuscript.

Corresponding author

Correspondence to Danielle S. Bassett.

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The authors declare no competing Interests.

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Betzel, R.F., Medaglia, J.D., Kahn, A.E. et al. Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography. Nat Biomed Eng 3, 902–916 (2019). https://doi.org/10.1038/s41551-019-0404-5

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  • DOI: https://doi.org/10.1038/s41551-019-0404-5

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