CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
This work addresses a specific problem in neuroscience for understanding brain communication dynamics, with potential applications in psychiatric disorder research, though it appears incremental as it builds on existing VAE and graph neural network techniques.
The authors tackled the problem of modeling dynamic communication between brain regions from functional data by proposing CommsVAE, a coupled sequential VAE approach that explicitly models directionality, timestep-level communication, and sparsity. They demonstrated that their model successfully uncovers embedded communication dynamics in simulations and produces more task-specific communication patterns in neural data compared to existing methods.
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.