Driving brain state transitions via Adaptive Local Energy Control Model
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 fun...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925000230 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591001696337920 |
|---|---|
| author | Rong Yao Langhua Shi Yan Niu HaiFang Li Xing Fan Bin Wang |
| author_facet | Rong Yao Langhua Shi Yan Niu HaiFang Li Xing Fan Bin Wang |
| author_sort | Rong Yao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f58ca13625b8405b8b097f08426736cc |
| institution | Kabale University |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-f58ca13625b8405b8b097f08426736cc2025-01-23T05:26:25ZengElsevierNeuroImage1095-95722025-02-01306121023Driving brain state transitions via Adaptive Local Energy Control ModelRong Yao0Langhua Shi1Yan Niu2HaiFang Li3Xing Fan4Bin Wang5College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, ChinaCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, ChinaCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, ChinaCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, ChinaCorresponding authors.; College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, ChinaCorresponding authors.; College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, ChinaThe 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.http://www.sciencedirect.com/science/article/pii/S1053811925000230Hetero-state transitionAdaptive Local Energy ControlNetwork control theoryLocal control setsSchizophreniaBipolar disorder |
| spellingShingle | Rong Yao Langhua Shi Yan Niu HaiFang Li Xing Fan Bin Wang Driving brain state transitions via Adaptive Local Energy Control Model NeuroImage Hetero-state transition Adaptive Local Energy Control Network control theory Local control sets Schizophrenia Bipolar disorder |
| title | Driving brain state transitions via Adaptive Local Energy Control Model |
| title_full | Driving brain state transitions via Adaptive Local Energy Control Model |
| title_fullStr | Driving brain state transitions via Adaptive Local Energy Control Model |
| title_full_unstemmed | Driving brain state transitions via Adaptive Local Energy Control Model |
| title_short | Driving brain state transitions via Adaptive Local Energy Control Model |
| title_sort | driving brain state transitions via adaptive local energy control model |
| topic | Hetero-state transition Adaptive Local Energy Control Network control theory Local control sets Schizophrenia Bipolar disorder |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925000230 |
| work_keys_str_mv | AT rongyao drivingbrainstatetransitionsviaadaptivelocalenergycontrolmodel AT langhuashi drivingbrainstatetransitionsviaadaptivelocalenergycontrolmodel AT yanniu drivingbrainstatetransitionsviaadaptivelocalenergycontrolmodel AT haifangli drivingbrainstatetransitionsviaadaptivelocalenergycontrolmodel AT xingfan drivingbrainstatetransitionsviaadaptivelocalenergycontrolmodel AT binwang drivingbrainstatetransitionsviaadaptivelocalenergycontrolmodel |