RT Journal Article
SR Electronic
T1 Inferring brain-wide interactions using data-constrained recurrent neural network models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.12.18.423348
DO 10.1101/2020.12.18.423348
A1 Perich, Matthew G.
A1 Arlt, Charlotte
A1 Soares, Sofia
A1 Young, Megan E.
A1 Mosher, Clayton P.
A1 Minxha, Juri
A1 Carter, Eugene
A1 Rutishauser, Ueli
A1 Rudebeck, Peter H.
A1 Harvey, Christopher D.
A1 Rajan, Kanaka
YR 2020
UL http://biorxiv.org/content/early/2020/12/21/2020.12.18.423348.abstract
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.