Targeted stimulation of the brain has the potential to treat mental illnesses but designing an appropriate protocol requires a multitude of choices.  I will describe an approach to help design the stimulation protocol by identifying electrical dynamics across many brain regions that relate to illness states or behaviors, defined as electrical connectome, or electome, networks.  The observed electrical activity of the brain is modeled as a superposition of activity from the latent electome networks, where the weights on the latent networks predict disease state, behavior, or outcomes.  These electome networks are interpretable in their power and coherence relationships between brain regions, forming an explainable artificial intelligence approach for neural data that facilitates future experimental design.  I will highlight how this approach has been used to successfully design stimulation protocols in the context of an animal model of major depressive disorder and appetitive social behavior.

Learning Objectives:

1. Define an additive model

2. Explain how decompositions of neural activity can be linked with disease states