Driving brain state transitions via Adaptive Local Energy Control Model

Area of Science:

• Neuroscience
• Network Science
• Computational Psychiatry

Background:

• Brain state transitions are fundamental to brain function and dysfunction.
• Network control theory provides a framework for studying brain dynamics.
• Existing models often overlook multi-region control inputs for brain state transitions.

Purpose of the Study:

• To develop a novel model (ALECM) for exploring brain state transitions under multi-region control.
• To quantify the energy required for brain state transitions and predict outcomes.
• To investigate brain state transitions in patients with Schizophrenia and Bipolar Disorder.

Main Methods:

• Proposed the Adaptive Local Energy Control Model (ALECM).
• Applied ALECM to analyze brain state transitions considering white matter network interactions.
• Investigated Hetero-state transitions (pathological to healthy state) in patient groups.

Main Results:

• Schizophrenia and Bipolar Disorder patients require significantly more energy for Hetero-state transitions.
• ALECM successfully induced Hetero-state transitions in patient brains using local control sets.
• Network similarity between patients and healthy subjects improved to baseline levels post-intervention.

Conclusions:

• ALECM effectively quantifies the energy costs of brain state transitions.
• The model offers a theoretical basis for predicting the efficacy of electromagnetic perturbation therapies.
• ALECM provides insights into the neural mechanisms underlying psychiatric disorders.