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Targeted stimulation of the brain has the potential to treat mentalillnesses. We propose an approach to help design the stimulation protocol byidentifying electrical dynamics across many brain regions that relate toillness states. We model multi-region electrical activity as a superposition ofactivity from latent networks, where the weights on the latent networks relateto an outcome of interest. In order to improve on drawbacks of latent factormodeling in this context, we focus on supervised autoencoders (SAEs), which canimprove predictive performance while maintaining a generative model. We explainwhy SAEs yield improved predictions, describe the distributional assumptionsunder which SAEs are an appropriate modeling choice, and provide modelingconstraints to ensure biological relevance of the learned network. We use theanalysis strategy to find a network associated with stress that characterizes agenotype associated with bipolar disorder. This discovered network aligns witha previously used stimulation technique, providing experimental validation ofour approach.

Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders