Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan
The development of the human brain is a complex, lifelong process during which collective behaviors of neurons exhibit self-organizing dynamics. Metastable states play a crucial role in understanding the complex dynamical mechanisms of the brain, and analyzing them helps to reveal the mechanisms of...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925001211 |
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| author | Hao Guo Yu-Xuan Liu Yao Li Qi-Li Guo Zhi-Peng Hao Yan-Li Yang Jing Wei |
| author_facet | Hao Guo Yu-Xuan Liu Yao Li Qi-Li Guo Zhi-Peng Hao Yan-Li Yang Jing Wei |
| author_sort | Hao Guo |
| collection | DOAJ |
| description | The development of the human brain is a complex, lifelong process during which collective behaviors of neurons exhibit self-organizing dynamics. Metastable states play a crucial role in understanding the complex dynamical mechanisms of the brain, and analyzing them helps to reveal the mechanisms of functional changes in the brain throughout development and aging. Specifically, global metastable state provides a overall perspective on the flexibility of brain reorganization, while the evolution trajectories of transient functional patterns capture detailed changes in brain activity. The leading eigenvector dynamics analysis (LEiDA) method significantly reduces the dimensionality of data and is widely used to capture the temporal trajectory characteristics of transient functional patterns, i.e., metastable brain states. However, LEiDA's linear dimensionality reduction of high-dimensional raw brain data may overlook non-linear information and lose some relationships between features. We developed a framework based on Phase Coherence Graph Autoencoder (PCGAE) that employs graph autoencoders (GAE) for non-linear dimensionality reduction of phase coherence matrices. This approach clusters to identify more distinct metastable brain states and is applied to the analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data across the human lifespan. This paper investigates age-related differences and continuity changes from different perspectives: metastable state indicators and state trajectory indicators (occurrence probability, lifetime, and state transition metrics). Global metastable state shows a linear decline with age, while both linear and quadratic effects of age-related changes are observed in detailed state metastable and state trajectory indicators. Finally, the proposed feature extraction scheme demonstrates good classification performance for categorizing brain age groups. These findings can help us understand the self-organizing reorganization characteristics associated with aging and their complex dynamic changes, providing new insights into brain development throughout the entire lifespan. |
| format | Article |
| id | doaj-art-3b4f1e561b7542fd9bad91c55cc1d708 |
| institution | OA Journals |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-3b4f1e561b7542fd9bad91c55cc1d7082025-08-20T02:09:07ZengElsevierNeuroImage1095-95722025-04-0131012111910.1016/j.neuroimage.2025.121119Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespanHao Guo0Yu-Xuan Liu1Yao Li2Qi-Li Guo3Zhi-Peng Hao4Yan-Li Yang5Jing Wei6College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Software, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; Corresponding authors.School of Information, Shanxi University of Finance and Economics, Taiyuan 030024, China; Corresponding authors.The development of the human brain is a complex, lifelong process during which collective behaviors of neurons exhibit self-organizing dynamics. Metastable states play a crucial role in understanding the complex dynamical mechanisms of the brain, and analyzing them helps to reveal the mechanisms of functional changes in the brain throughout development and aging. Specifically, global metastable state provides a overall perspective on the flexibility of brain reorganization, while the evolution trajectories of transient functional patterns capture detailed changes in brain activity. The leading eigenvector dynamics analysis (LEiDA) method significantly reduces the dimensionality of data and is widely used to capture the temporal trajectory characteristics of transient functional patterns, i.e., metastable brain states. However, LEiDA's linear dimensionality reduction of high-dimensional raw brain data may overlook non-linear information and lose some relationships between features. We developed a framework based on Phase Coherence Graph Autoencoder (PCGAE) that employs graph autoencoders (GAE) for non-linear dimensionality reduction of phase coherence matrices. This approach clusters to identify more distinct metastable brain states and is applied to the analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data across the human lifespan. This paper investigates age-related differences and continuity changes from different perspectives: metastable state indicators and state trajectory indicators (occurrence probability, lifetime, and state transition metrics). Global metastable state shows a linear decline with age, while both linear and quadratic effects of age-related changes are observed in detailed state metastable and state trajectory indicators. Finally, the proposed feature extraction scheme demonstrates good classification performance for categorizing brain age groups. These findings can help us understand the self-organizing reorganization characteristics associated with aging and their complex dynamic changes, providing new insights into brain development throughout the entire lifespan.http://www.sciencedirect.com/science/article/pii/S1053811925001211Resting state fMRIEntire lifespanPhase coherence graph autoencoderMetastable state |
| spellingShingle | Hao Guo Yu-Xuan Liu Yao Li Qi-Li Guo Zhi-Peng Hao Yan-Li Yang Jing Wei Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan NeuroImage Resting state fMRI Entire lifespan Phase coherence graph autoencoder Metastable state |
| title | Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan |
| title_full | Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan |
| title_fullStr | Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan |
| title_full_unstemmed | Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan |
| title_short | Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan |
| title_sort | self organizing dynamic research based on phase coherence graph autoencoders analysis of brain metastable states across the lifespan |
| topic | Resting state fMRI Entire lifespan Phase coherence graph autoencoder Metastable state |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925001211 |
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