CRCNS: Multimodal network interactions for internal state dynamics of resiliency

Resiliency to stress is crucial to the health of individuals and of society. While the detrimental effects of prolonged stress are well-documented, there remains a critical knowledge gap regarding the mechanisms underlying individual resilience to adverse outcomes. The circuits underlying stress resiliency, and how resilient individuals tap into these circuits to maintain normal behavioral function, are not fully understood. Furthermore, resiliency occurs over both long and short timescales and likely emerges from interactions across neural subsystems that span molecules to whole-brain connectivity. To unravel these complex mechanisms, we will use a multidisciplinary approach that combines time course experiments, machine learning, signal processing, and statistical inference to integrate multidimensional data from the transcriptome to the whole-brain connectome to characterize how circuit activity gives rise to resiliency. We will collect behavioral, neuroimaging, electrophysiological, gene expression, and biomarker data from well-validated mouse models of resilience. We will also develop new computational tools to characterize the multiscale network dynamics of genes, molecules, and circuits that underlie the neurobiological substrates of resilience. We will validate these relationships in another mouse strain to determine the specificity of our model. This will be the first study to provide a comprehensive understanding of interactions between neural circuits and their molecular, genomic, and neuroanatomical contexts in animal models of resilience. If successful, our findings could yield insights into unifying principles of how neural mechanisms interact to generate behavioral phenotypes.