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Modelling and Interpreting Network Dynamics

View ORCID ProfileAnkit N. Khambhati, Ann E. Sizemore, Richard F. Betzel, Danielle S. Bassett
doi: https://doi.org/10.1101/124016
Ankit N. Khambhati
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
2Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA, 19104
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  • ORCID record for Ankit N. Khambhati
Ann E. Sizemore
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
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Richard F. Betzel
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
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Danielle S. Bassett
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
2Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA, 19104
3Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104
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  • For correspondence: dsb@seas.upenn.edu
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Abstract

Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted April 04, 2017.
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Modelling and Interpreting Network Dynamics
Ankit N. Khambhati, Ann E. Sizemore, Richard F. Betzel, Danielle S. Bassett
bioRxiv 124016; doi: https://doi.org/10.1101/124016
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Modelling and Interpreting Network Dynamics
Ankit N. Khambhati, Ann E. Sizemore, Richard F. Betzel, Danielle S. Bassett
bioRxiv 124016; doi: https://doi.org/10.1101/124016

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