Quantifying control circuit regulation in the human brain.
As a field, control systems engineering has developed quantitative methods to characterize the regulation of systems or processes, whose functioning is ubiquitous within synthetic systems. In this context, a control circuit is objectively “well regulated” when discrepancy between desired and achieved output trajectories is minimized, and “robust” to the degree that it is able to regulate well in response to a wide range of stimuli. Most psychiatric disorders are assumed to reflect dysregulation of brain circuits. Yet, probing circuit regulation requires fundamentally different analytic strategies than the correlations relied upon for analyses of connectivity and their resultant networks. 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.
Overview of the proposed framework for quantifying control circuit regulation in the human brain. a. The first step involves obtaining the circuit architecture for selected nodes by establishing inputs to each node via causal inference, thus forming a directed network. b. The second step identifies circuit functionality using system identification to obtain a linear state-space model for each node. The directed network representation allows identifying each node’s transfer function as a multi-input single-output (MISO) system. For example, the MISO model for node y uses inputs from nodes u1 and u2. c. The final step involves probing the system using simulated inputs to quantify sensitivity to perturbations and measuring how well regulated or dysregulated the circuit is (control error). Systems with a low damping ratio are prone to stronger oscillatory behavior and thus more sensitive to perturbations than systems with high damping.
From: Kumar R, Strey HH, Mujica-Parodi LR. Quantifying control circuit regulation in the human brain.
