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Quantifying control circuit regulation in the human brain.

Kumar, R.; Strey, H. H.; Mujica-Parodi, L. R.

2021-03-30 neuroscience
10.1101/2021.03.30.437729 bioRxiv
Show abstract

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 circuits sensitivity to external perturbation and its dysregulation.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.