Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems

Understanding how multiple brain regions interact to produce behavior is a major challenge in systems neuroscience, with many regions causally implicated in common tasks such as sensory processing and decision-making. Moreover, neural dynamics are nonlinear and non-stationary, exhibiting switches both within and across trials. Here we propose multi-region switching dynamical systems (MR-SDS), a switching nonlinear state space model that decomposes multi-region neural dynamics into local and cross-region components. MR-SDS includes directed interactions between brain regions, allowing for estimation of state-dependent communication signals and sensory inputs effects. We show that our model accurately recovers latent trajectories, vector fields underlying switching nonlinear dynamics, and cross-region communication profiles in three simulations. We then apply our method to two large-scale, multi-region neural datasets involving mouse decision-making. The first includes hundreds of neurons per region, recorded simultaneously at single-cell-resolution across 3 distal cortical regions. The second is a mesoscale widefield dataset of 8 adjacent cortical regions imaged across both hemispheres. On these multi-region datasets, MR-SDS outperforms existing models, including multi-region recurrent switching linear models, and reveals multiple distinct dynamical states and a rich set of cross-region communication profiles.