CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs

by   Eloy Geenjaar, et al.

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.


page 1

page 2

page 3

page 4


Local White Matter Architecture Defines Functional Brain Dynamics

Large bundles of myelinated axons, called white matter, anatomically con...

DBGDGM: Dynamic Brain Graph Deep Generative Model

Graphs are a natural representation of brain activity derived from funct...

Modeling Spatio-Temporal Dynamics in Brain Networks: A Comparison of Graph Neural Network Architectures

Comprehending the interplay between spatial and temporal characteristics...

Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling

The characterisation of the brain as a functional network in which the c...

The impact of input node placement in the controllability of brain networks

Network control theory can be used to model how one should steer the bra...

Learning Interpretable Models for Coupled Networks Under Domain Constraints

Modeling the behavior of coupled networks is challenging due to their in...

Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks

Neuroscientists have enjoyed much success in understanding brain functio...

Please sign up or login with your details

Forgot password? Click here to reset