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Driving brain state transitions via Adaptive Local Energy Control Model
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Journal Article
Driving brain state transitions via Adaptive Local Energy Control Model
2025
Overview
•The ALECM considers the complex interactions along the white matter network.•The ALECM reveals that SZ and BD require higher energy for Hetero-state transition.•The ALECM successfully induced Hetero-state transition in the patients' brains.
The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions. Existing studies have primarily focused on analyzing the energy required for brain state transitions, which are driven either by the single brain region or by all brain regions. However, they often neglect the critical question of how the whole brain responds to external control inputs that are driven by control energy from multiple brain regions, which limits their application value in guiding clinical neurostimulation. In this paper, we proposed the Adaptive Local Energy Control Model (ALECM) to explore brain state transitions, which considers the complex interactions of the whole brain along the white matter network when external control inputs are applied to multiple regions. It not only quantifies the energy required for state transitions but also predicts their outcomes based on local control. Our results indicated that patients with Schizophrenia (SZ) and Bipolar Disorder (BD) required more energy to drive the brain state transitions from the pathological state to the healthy baseline state, which is defined as Hetero-state transition. Importantly, we successfully induced Hetero-state transition in the patients' brains by using the ALECM, with subnetworks or specific brain regions serving as local control sets. Eventually, the network similarity between patients and healthy subjects reached baseline levels. These offer evidence that the ALECM can effectively quantify the cost characteristics of brain state transitions, providing a theoretical foundation for accurately predicting the efficacy of electromagnetic perturbation therapies in the future.
Publisher
Elsevier Inc,Elsevier Limited,Elsevier
Subject
/ Adult
/ Bipolar Disorder - diagnostic imaging
/ Bipolar Disorder - physiopathology
/ Energy
/ Female
/ Humans
/ Magnetic Resonance Imaging - methods
/ Male
/ Nerve Net - diagnostic imaging
/ Schizophrenia - diagnostic imaging
/ Schizophrenia - physiopathology
/ White Matter - diagnostic imaging
