A Data-Driven Closed-Loop Control Approach to Drive NeuralState Transitions for Mechanistic Insight
Repetitive negative thinking (RNT) is a transdiagnostic risk factor for mood disorders, consistently associated with altered biological substrates, including functional connectivity in key brain networks. As a stable cognitive feature linked to vulnerability across disorders, RNT presents a compelling target for intervention. However, leveraging RNT as a modifiable mechanism requires a deeper understanding of its causal neural dynamics and how targeted modulation can induce adaptive change. This study introduces a data-driven framework combining dynamical system reconstruction (DSR) with model predictive control (MPC) to infer optimal control policies for transitioning between resting and sad mood brain states, based on functional magnetic resonance imaging (fMRI) data. Using generative DSR models trained on fMRI data from participants with a remitted major depressive disorder (rMDD) and matched healthy controls from a resting state period and a sad mood induction task, we reconstruct nonlinear brain dynamics and derive region-specific control strategies for transitioning from resting to sad mood states. Our results demonstrate that small brain regions, such as the subgenual anterior cingulate cortex (sgACC), exhibit higher controllability, requiring less energy to drive state transitions. Notably, rMDD group shows reduced control energy demands and stronger neural connectivity, particularly relating to dorsolateral prefrontal cortex (DLPFC)-hippocampal pathways, suggesting heightened susceptibility to relapse into negative mood states. These findings highlight the potential of closed-loop control approaches to uncover mechanistic insights into RNT and inform targeted interventions for mood disorders in the future.