A critical explanatory gap in clinical neuroscience lies in our mechanistic understanding of how systems-level neuroimaging biomarkers emerge from underlying microcircuit-level perturbations associated with a disease state. I will describe a computational psychiatry approach leveraging biophysically-based computational models of large-scale brain dynamics (in particular, resting-state functional connectivity) and their potential integration with clinical and pharmacological neuroimaging. These models highlighs the importance of local circuit properties, and their regional heterogeneity, in shaping the emergent functional connectivity. Important extensions for clinical neuroscience applications include model fitting at the individual-subject level, and simulation of pharmacological effects on brain dynamics. Combined with data analytic approaches parsing clinical variation and linking it to neural variation, these computational modeling approaches hold promise to inform development of personalized therapeutics.
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