COMMSVAE: LEARNING THE BRAIN'S MACROSCALE COMMUNICATION DYNAMICS USING COUPLED SE-QUENTIAL VAES

Abstract

Communication within or between complex systems is commonplace in the natural sciences and fields such as graph neural networks. The brain is a perfect example of such a complex system, where communication between brain regions is constantly being orchestrated. To analyze communication, the brain is often split up into anatomical regions that each perform certain computations. These regions must interact and communicate with each other to perform tasks and support higher-level cognition. On a macroscale, these regions communicate through signal propagation along the cortex and along white matter tracts over longer distances. When and what types of signals are communicated over time is an unsolved problem and is often studied using either functional or structural data. In this paper, we propose a non-linear generative approach to communication from functional data. We address three issues with common connectivity approaches by explicitly modeling the directionality of communication, finding communication at each timestep, and encouraging sparsity. To evaluate our model, we simulate temporal data that has sparse communication between nodes embedded in it and show that our model can uncover the expected communication dynamics. Subsequently, we apply our model to temporal neural data from multiple tasks and show that our approach models communication that is more specific to each task. The specificity of our method means it can have an impact on the understanding of psychiatric disorders, which are believed to be related to highly specific communication between brain regions compared to controls. In sum, we propose a general model for dynamic communication learning on graphs, and show its applicability to a subfield of the natural sciences, with potential widespread scientific impact.

1. INTRODUCTION

Characterizing macroscale communication between brain regions is a complex and difficult problem, but is necessary to understand the connection between brain activity and behavior. The effect of neural systems on each other, or connectivity, has also been linked to psychiatric disorders, such as schizophrenia Friston (2002) . Hence, gaining a deeper understanding of the dynamics underlying the communication between brain regions is both from the perspective of understanding how our brain facilitates higher-order cognition and also to provide insight into and consequently how psychiatric disorders arise. Static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC), computed respectively as the Pearson correlation between regional activation timeseries over the full scan duration (sFNC) or on shorter sliding windows (dFNC), are among the most widely reported measures of connectivity between brain regions Hutchison et al. (2013) . These approaches calculate the correlation between the timeseries of each brain region to find their coherence, either across the full timeseries (sFNC) or by windowing the timeseries and calculating the correlation within each window (dFNC). Although extensions have been proposed Hutchison et al. (2013) , along with more complex connectivity measures, such as wavelet coherence Yaesoubi et al. (2015) , multiplication of temporal derivates Shine et al. (2015) , and Granger causality Roebroeck et al. (2005); Seth et al. (2015) , Pearson correlation remains the most prevalent measure of brain network connectivity. And even the less commonly employed metrics have issues stemming from some combination of sensitivity to noise, linearity, symmetry, or coarse timescales. Most im-

