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Inferring brain-wide interactions using data-constrained recurrent neural network models

View ORCID ProfileMatthew G. Perich, Charlotte Arlt, Sofia Soares, Megan E. Young, View ORCID ProfileClayton P. Mosher, View ORCID ProfileJuri Minxha, Eugene Carter, View ORCID ProfileUeli Rutishauser, View ORCID ProfilePeter H. Rudebeck, View ORCID ProfileChristopher D. Harvey, View ORCID ProfileKanaka Rajan
doi: https://doi.org/10.1101/2020.12.18.423348
Matthew G. Perich
1Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Charlotte Arlt
2Harvard Medical School, Boston, MA, USA
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Sofia Soares
2Harvard Medical School, Boston, MA, USA
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Megan E. Young
1Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Clayton P. Mosher
1Icahn School of Medicine at Mount Sinai, New York, NY, USA
3Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Juri Minxha
3Cedars-Sinai Medical Center, Los Angeles, CA, USA
4California Institute of Technology, Pasadena, CA
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Eugene Carter
1Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Ueli Rutishauser
3Cedars-Sinai Medical Center, Los Angeles, CA, USA
4California Institute of Technology, Pasadena, CA
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Peter H. Rudebeck
1Icahn School of Medicine at Mount Sinai, New York, NY, USA
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  • ORCID record for Peter H. Rudebeck
Christopher D. Harvey
2Harvard Medical School, Boston, MA, USA
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Kanaka Rajan
1Icahn School of Medicine at Mount Sinai, New York, NY, USA
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  • For correspondence: kanaka.rajan@mssm.edu
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ABSTRACT

Behavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of interregion communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
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-NC-ND 4.0 International license.
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Posted December 21, 2020.
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Inferring brain-wide interactions using data-constrained recurrent neural network models
Matthew G. Perich, Charlotte Arlt, Sofia Soares, Megan E. Young, Clayton P. Mosher, Juri Minxha, Eugene Carter, Ueli Rutishauser, Peter H. Rudebeck, Christopher D. Harvey, Kanaka Rajan
bioRxiv 2020.12.18.423348; doi: https://doi.org/10.1101/2020.12.18.423348
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Inferring brain-wide interactions using data-constrained recurrent neural network models
Matthew G. Perich, Charlotte Arlt, Sofia Soares, Megan E. Young, Clayton P. Mosher, Juri Minxha, Eugene Carter, Ueli Rutishauser, Peter H. Rudebeck, Christopher D. Harvey, Kanaka Rajan
bioRxiv 2020.12.18.423348; doi: https://doi.org/10.1101/2020.12.18.423348

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