Trajectory drift as a measure of neural circuit dysregulation

Psychiatric disorders are thought to result from dysregulated brain circuits, yet human neuroimaging currently lacks standardized methods for quantifying neural dysregulation. Here, we present a scalable framework for extracting fMRI-derived (generative) control circuits, then use circuit trajectories to estimate their control error. Using synthetic circuits, we first demonstrate that our framework accurately identifies each circuit’s architecture and models its dynamics by estimation of transfer functions. As a use case, we then apply the framework to human task-based functional MRI data (UK Biobank, N=19,831). In a purely data-driven manner, without priors, our framework identified thalamus-linked prefrontal-limbic and ventral stream subcircuits, selectively engaged during sensori-motor processing of affective and non-affective stimuli. Finally, we demonstrate that circuit-wide dysregulation, defined by degree of drift from healthy trajectories, tracks symptom severity for neuroticism (ventral subcircuit), depression (prefrontal-limbic subcircuit), and bipolar disorder (full circuit).

For more information, see:

Sultan SF, Skiena S, Mujica-Parodi LR. Quantifying dysregulation of fMRI-derived control circuits for computational psychiatry

Trajectory Drift as a Measure of Feedback Control Error, and Thus Circuit Dysregulation. (a) In a classic engineering control application, such as autopilot, a vehicle corrects for deviations from its desired trajectory through negative feedback. As such, the difference between the autopilot’s actual versus desired trajectories provides a measure of its control error or dysregulation. (b) As per the autopilot example, we use trajectory drift as control error to calculate dysregulation across fMRI-derived control circuits, and demonstrate its application for three psychiatric use cases: neuroticism, depression, and bipolar disorder. (c) Schematic of the pipeline for the discovery of circuit architecture and dynamics from human fMRI and simulated time series using Time-Varying Autoregressive Model with Exogenous Inputs (TVARX) and other state-space models. We use trajectory drift between predicted and actual trajectories to quantify circuit dysregulation across subjects with varying degrees of severity for psychiatric symptoms.