Interpretable Factor Models of Latent Brain Networks Associated with Stress and Depression

Abstract

A major component of most psychiatric disorders is that they affect the internal mental state of the individual.Modern psychiatric research primarily depends on self-report to measure internal state in humans and on behavior to measure internal state in animals. Both of these can be unreliable measures of internal mental state. In order to facilitate psychiatric research, we need models of mental state that are based on neural activity. Such models have proven challenging to design because they must be able to distill the complexity of neural activity distributed across many brain regions. This dissertation describes models that solve this problem, by representing such neural activity as a sum of contributions from many distinct sub-networks in the brain. We call these sub-network electrical functional connectome (electome) networks. Here, I show that electome networks can be used to distinguish between mental states in mice. One of these electome networks distinguishes mice that show depressive symptoms from those that do not, and can be used as a measure depressive phenotype. Electome networks represent a new class of tools that give brain researchers a new way to measure internal mental state and relate it back to brain activity.