  Targeted stimulation of the brain has the potential to treat mental
illnesses. We propose an approach to help design the stimulation protocol by
identifying electrical dynamics across many brain regions that relate to
illness states. We model multi-region electrical activity as a superposition of
activity from latent networks, where the weights on the latent networks relate
to an outcome of interest. In order to improve on drawbacks of latent factor
modeling in this context, we focus on supervised autoencoders (SAEs), which can
improve predictive performance while maintaining a generative model. We explain
why SAEs yield improved predictions, describe the distributional assumptions
under which SAEs are an appropriate modeling choice, and provide modeling
constraints to ensure biological relevance of the learned network. We use the
analysis strategy to find a network associated with stress that characterizes a
genotype associated with bipolar disorder. This discovered network aligns with
a previously used stimulation technique, providing experimental validation of
our approach.
