A Data-Driven Closed-Loop Control Approach to Drive Neural State Transitions for Mechanistic Insight.

Altered affective state dynamics are a characteristic feature of depression and can persist beyond symptomatic remission. Individuals with remitted major depressive disorder (rMDD) often show heightened reactivity to negative mood states and reduced efficiency in recovering from them, consistent with changes in affective dynamics after remission. These patterns may reflect alterations in the brain's capacity to flexibly shift between neural states that support distinct affective modes. Characterizing the dynamical mechanisms that govern transitions into and out of experimentally induced affective states is therefore essential for understanding vulnerability to recurrence and informing mechanistic interventions. We developed a data-driven framework combining dynamical system reconstruction (DSR) with model-based control to infer optimal control policies for transitions between resting and sad mood brain states using functional magnetic resonance imaging (fMRI) data. Nonlinear DSR models trained on individuals with rMDD and healthy controls (HC) yielded region-specific, state-dependent control strategies. Small regions (e.g., sgACC, NAcc) showed higher controllability, requiring less energy for state transitions. Notably, rMDD participants required less control energy than HC to shift both into and, to a more spatially restricted extent, out of sad mood states. Despite reaching the resting state target with similar proximity, however, they remained closer to the sad mood distribution when returning to rest, reflecting a residual bias toward the sad mood distribution. Elevated coupling in rMDD, especially toward the DLPFC, was linked to lower control energy, suggesting that stronger network coupling facilitates transitions. These findings indicate rMDD dynamics that ease entry into sad mood states but impede full disengagement. More broadly, they demonstrate how closed-loop control applied to data-driven dynamical models can provide mechanistic insight into brain state transitions and inform future hypotheses about cognitive vulnerability or compensatory processes.