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

Niklas Emonds1,2, Evelyn Herberg2, Martin Fungisai Gerchen3,4,5

  • 1Hector Institute for AI in Psychiatry & Department of Psychiatry and Psychotherapy, Central Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.

Human Brain Mapping
|July 15, 2026
PubMed
Summary

Individuals with remitted depression show altered brain dynamics, making it easier to enter sad states but harder to leave them. This suggests a residual bias towards negative moods, impacting recurrence vulnerability.

Keywords:
affective state transitionsbrain network controllabilityclosed‐loop neuromodulationdynamical systems reconstructionmajor depressive disorderoptimal control

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Affective Science

Background:

  • Altered affective state dynamics persist in remitted major depressive disorder (rMDD).
  • Individuals with rMDD exhibit heightened reactivity to negative moods and slower recovery.
  • Neural state transition flexibility may underlie these affective dynamic changes.

Purpose of the Study:

  • To characterize dynamical mechanisms governing brain state transitions in rMDD.
  • To infer optimal control policies for mood state shifts using fMRI data.
  • To understand neural underpinnings of vulnerability to depression recurrence.

Main Methods:

  • Developed a framework combining dynamical system reconstruction (DSR) and model-based control.
  • Applied nonlinear DSR models to fMRI data from rMDD participants and healthy controls (HC).
  • Inferred region-specific, state-dependent control strategies for transitions between resting and sad mood states.

Main Results:

  • rMDD participants required less energy to shift into and out of sad mood states compared to HC.
  • Specific brain regions (sgACC, NAcc) demonstrated higher controllability.
  • rMDD showed a residual bias toward sad mood states upon return to rest, despite similar proximity to the resting state target.
  • Elevated network coupling in rMDD, particularly towards the DLPFC, correlated with lower control energy.

Conclusions:

  • rMDD dynamics facilitate entry into sad states but hinder complete disengagement.
  • Findings suggest a residual bias towards negative affective states in rMDD.
  • The study demonstrates the utility of closed-loop control applied to DSR for mechanistic insights into brain state transitions and cognitive vulnerability.