Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders
The Hopf whole-brain model, based on structural connectivity, overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters, quantifying dynamic brain characteristics in healthy and diseased states. Traditional parameter fitting...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Research |
| Online Access: | https://spj.science.org/doi/10.34133/research.0648 |
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| author | Qian-Yun Zhang Chun-Wang Su Qiang Luo Celso Grebogi Zi-Gang Huang Junjie Jiang |
| author_facet | Qian-Yun Zhang Chun-Wang Su Qiang Luo Celso Grebogi Zi-Gang Huang Junjie Jiang |
| author_sort | Qian-Yun Zhang |
| collection | DOAJ |
| description | The Hopf whole-brain model, based on structural connectivity, overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters, quantifying dynamic brain characteristics in healthy and diseased states. Traditional parameter fitting techniques lack precision, restricting broader use. To address this, we validated parameter fitting methods using simulated networks and synthetic models, introducing improvements such as individual-specific initialization and optimized gradient descent, which reduced individual data loss. We also developed an approximate loss function and gradient adjustment mechanism, enhancing parameter fitting accuracy and stability. Applying this refined method to datasets for major depressive disorder (MDD) and autism spectrum disorder (ASD), we identified differences in brain regions between patients and healthy controls, explaining related anomalies. This rigorous validation is crucial for clinical application, paving the way for precise neuropathological identification and novel treatments in neuropsychiatric research, demonstrating substantial potential in clinical neurology. |
| format | Article |
| id | doaj-art-db7f86dfaac6410da7ceccad808cade3 |
| institution | OA Journals |
| issn | 2639-5274 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Research |
| spelling | doaj-art-db7f86dfaac6410da7ceccad808cade32025-08-20T02:25:17ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742025-01-01810.34133/research.0648Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental DisordersQian-Yun Zhang0Chun-Wang Su1Qiang Luo2Celso Grebogi3Zi-Gang Huang4Junjie Jiang5Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi’an Jiaotong University, Xi’an, China.Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi’an Jiaotong University, Xi’an, China.National Clinical Research Center for Aging and Medicine at Huashan Hospital, Fudan University, Shanghai 200433, China.Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK.Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi’an Jiaotong University, Xi’an, China.Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi’an Jiaotong University, Xi’an, China.The Hopf whole-brain model, based on structural connectivity, overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters, quantifying dynamic brain characteristics in healthy and diseased states. Traditional parameter fitting techniques lack precision, restricting broader use. To address this, we validated parameter fitting methods using simulated networks and synthetic models, introducing improvements such as individual-specific initialization and optimized gradient descent, which reduced individual data loss. We also developed an approximate loss function and gradient adjustment mechanism, enhancing parameter fitting accuracy and stability. Applying this refined method to datasets for major depressive disorder (MDD) and autism spectrum disorder (ASD), we identified differences in brain regions between patients and healthy controls, explaining related anomalies. This rigorous validation is crucial for clinical application, paving the way for precise neuropathological identification and novel treatments in neuropsychiatric research, demonstrating substantial potential in clinical neurology.https://spj.science.org/doi/10.34133/research.0648 |
| spellingShingle | Qian-Yun Zhang Chun-Wang Su Qiang Luo Celso Grebogi Zi-Gang Huang Junjie Jiang Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders Research |
| title | Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders |
| title_full | Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders |
| title_fullStr | Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders |
| title_full_unstemmed | Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders |
| title_short | Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders |
| title_sort | adaptive whole brain dynamics predictive method relevancy to mental disorders |
| url | https://spj.science.org/doi/10.34133/research.0648 |
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