Here, we demonstrate how well-established methods for system identification in control systems engineering may be applied to functional magnetic resonance imaging (fMRI) data to extract generative computational models of human brain circuits. These provide two quantitative measures of direct relevance for psychiatric disorders: a circuit’s sensitivity to external perturbation and its dysregulation.

Using a control systems approach to prefrontal-limbic regulation, we use data-driven modeling to simulate how circuit dynamics affects approach-avoidance behavior, with implications for clinical anxiety.

We use Bayesian modeling to evaluate how decisions are made under uncertainty, in environments with critical yet ambiguous information.

In the real world, threat is often difficult to discern, particularly for the high-stakes decision-making ubiquitous to the predatory environment in which our brains evolved. Using EEG we measure circuit dynamics in the processing of ambiguous threat perception.

We introduce the theory of optimal control in neural circuitry, and characterize, in a concrete sense, what it means to apply control circuits to psychiatry and neuroscience. We show how this concept can benefit clinicians, theorists, and experimentalists.