PT - JOURNAL ARTICLE AU - Perich, Matthew G. AU - Arlt, Charlotte AU - Soares, Sofia AU - Young, Megan E. AU - Mosher, Clayton P. AU - Minxha, Juri AU - Carter, Eugene AU - Rutishauser, Ueli AU - Rudebeck, Peter H. AU - Harvey, Christopher D. AU - Rajan, Kanaka TI - Inferring brain-wide interactions using data-constrained recurrent neural network models AID - 10.1101/2020.12.18.423348 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.12.18.423348 4099 - http://biorxiv.org/content/early/2020/12/21/2020.12.18.423348.short 4100 - http://biorxiv.org/content/early/2020/12/21/2020.12.18.423348.full AB - 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 StatementThe authors have declared no competing interest.