Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model
Neurodynamic models that simulate how micro-level alterations propagate upward to impact macroscopic neural circuits and overall brain function may offer valuable insights into the pathological mechanisms of schizophrenia (SCZ). In this study, we integrated a neurodynamic model with the classical Co...
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| Veröffentlicht in: | Translational psychiatry Jg. 14; H. 1; S. 496 - 14 |
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| Sprache: | Englisch |
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18.12.2024
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| ISSN: | 2158-3188, 2158-3188 |
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| Abstract | Neurodynamic models that simulate how micro-level alterations propagate upward to impact macroscopic neural circuits and overall brain function may offer valuable insights into the pathological mechanisms of schizophrenia (SCZ). In this study, we integrated a neurodynamic model with the classical Contrastive Variational Autoencoder (CVAE) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states. Firstly, we demonstrated the robust fitting of the model within our multi-site dataset. Subsequently, by employing representational similarity analysis and a deep learning classifier, we confirmed the specificity and disorder-related information capturing ability of SCZ-specific features. Moreover, analysis of the attractor characteristics of the neurodynamic system revealed significant differences in attractor space patterns between SCZ-specific states and shared states. Finally, we utilized Partial Least Squares (PLS) regression to examine the multivariate mapping relationship between SCZ-specific features and symptoms, identifying two sets of correlated modes implicating unique molecular mechanisms: one mode corresponding to negative and general symptoms, and another mode corresponding to positive symptoms. Our results provide valuable insights into disorder-specific neurodynamic features and states associated with SCZ, laying the foundation for understanding the intricate pathophysiology of this disorder. |
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| AbstractList | Neurodynamic models that simulate how micro-level alterations propagate upward to impact macroscopic neural circuits and overall brain function may offer valuable insights into the pathological mechanisms of schizophrenia (SCZ). In this study, we integrated a neurodynamic model with the classical Contrastive Variational Autoencoder (CVAE) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states. Firstly, we demonstrated the robust fitting of the model within our multi-site dataset. Subsequently, by employing representational similarity analysis and a deep learning classifier, we confirmed the specificity and disorder-related information capturing ability of SCZ-specific features. Moreover, analysis of the attractor characteristics of the neurodynamic system revealed significant differences in attractor space patterns between SCZ-specific states and shared states. Finally, we utilized Partial Least Squares (PLS) regression to examine the multivariate mapping relationship between SCZ-specific features and symptoms, identifying two sets of correlated modes implicating unique molecular mechanisms: one mode corresponding to negative and general symptoms, and another mode corresponding to positive symptoms. Our results provide valuable insights into disorder-specific neurodynamic features and states associated with SCZ, laying the foundation for understanding the intricate pathophysiology of this disorder. Neurodynamic models that simulate how micro-level alterations propagate upward to impact macroscopic neural circuits and overall brain function may offer valuable insights into the pathological mechanisms of schizophrenia (SCZ). In this study, we integrated a neurodynamic model with the classical Contrastive Variational Autoencoder (CVAE) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states. Firstly, we demonstrated the robust fitting of the model within our multi-site dataset. Subsequently, by employing representational similarity analysis and a deep learning classifier, we confirmed the specificity and disorder-related information capturing ability of SCZ-specific features. Moreover, analysis of the attractor characteristics of the neurodynamic system revealed significant differences in attractor space patterns between SCZ-specific states and shared states. Finally, we utilized Partial Least Squares (PLS) regression to examine the multivariate mapping relationship between SCZ-specific features and symptoms, identifying two sets of correlated modes implicating unique molecular mechanisms: one mode corresponding to negative and general symptoms, and another mode corresponding to positive symptoms. Our results provide valuable insights into disorder-specific neurodynamic features and states associated with SCZ, laying the foundation for understanding the intricate pathophysiology of this disorder.Neurodynamic models that simulate how micro-level alterations propagate upward to impact macroscopic neural circuits and overall brain function may offer valuable insights into the pathological mechanisms of schizophrenia (SCZ). In this study, we integrated a neurodynamic model with the classical Contrastive Variational Autoencoder (CVAE) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states. Firstly, we demonstrated the robust fitting of the model within our multi-site dataset. Subsequently, by employing representational similarity analysis and a deep learning classifier, we confirmed the specificity and disorder-related information capturing ability of SCZ-specific features. Moreover, analysis of the attractor characteristics of the neurodynamic system revealed significant differences in attractor space patterns between SCZ-specific states and shared states. Finally, we utilized Partial Least Squares (PLS) regression to examine the multivariate mapping relationship between SCZ-specific features and symptoms, identifying two sets of correlated modes implicating unique molecular mechanisms: one mode corresponding to negative and general symptoms, and another mode corresponding to positive symptoms. Our results provide valuable insights into disorder-specific neurodynamic features and states associated with SCZ, laying the foundation for understanding the intricate pathophysiology of this disorder. Abstract Neurodynamic models that simulate how micro-level alterations propagate upward to impact macroscopic neural circuits and overall brain function may offer valuable insights into the pathological mechanisms of schizophrenia (SCZ). In this study, we integrated a neurodynamic model with the classical Contrastive Variational Autoencoder (CVAE) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states. Firstly, we demonstrated the robust fitting of the model within our multi-site dataset. Subsequently, by employing representational similarity analysis and a deep learning classifier, we confirmed the specificity and disorder-related information capturing ability of SCZ-specific features. Moreover, analysis of the attractor characteristics of the neurodynamic system revealed significant differences in attractor space patterns between SCZ-specific states and shared states. Finally, we utilized Partial Least Squares (PLS) regression to examine the multivariate mapping relationship between SCZ-specific features and symptoms, identifying two sets of correlated modes implicating unique molecular mechanisms: one mode corresponding to negative and general symptoms, and another mode corresponding to positive symptoms. Our results provide valuable insights into disorder-specific neurodynamic features and states associated with SCZ, laying the foundation for understanding the intricate pathophysiology of this disorder. |
| ArticleNumber | 496 |
| Author | Zhang, Hongxing Jiang, Tianzi Xie, Sangma Li, Kunchi Chen, Jun Lv, Luxian Lu, Lin Yan, Hao Wang, Huiling Ding, Chaoyue Yang, Yongfeng Zhang, Dai Liu, Bing Wang, Huaning Sun, Yuqing Zhang, Zhanjun Li, Peng Chen, Yunchun Chen, Yaojing Yan, Jun |
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Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) – sequence: 6 givenname: Peng surname: Li fullname: Li, Peng organization: Institute of Mental Health, Peking University Sixth Hospital, Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) – sequence: 7 givenname: Jun surname: Yan fullname: Yan, Jun organization: Institute of Mental Health, Peking University Sixth Hospital, Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) – sequence: 8 givenname: Jun surname: Chen fullname: Chen, Jun organization: Department of Radiology, Renmin Hospital of Wuhan University – sequence: 9 givenname: Huiling surname: Wang fullname: Wang, Huiling organization: Department of Psychiatry, Renmin Hospital of Wuhan University – sequence: 10 givenname: Huaning orcidid: 0000-0003-1981-4293 surname: Wang fullname: Wang, Huaning organization: Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University – sequence: 11 givenname: Yunchun surname: Chen fullname: Chen, Yunchun organization: Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University – sequence: 12 givenname: Yongfeng orcidid: 0000-0003-0358-0752 surname: Yang fullname: Yang, Yongfeng organization: Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan – sequence: 13 givenname: Luxian orcidid: 0000-0002-3963-660X surname: Lv fullname: Lv, Luxian organization: Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan – sequence: 14 givenname: Hongxing orcidid: 0000-0003-1412-1359 surname: Zhang fullname: Zhang, Hongxing organization: Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Henan Key Lab of Biological Psychiatry of Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan – sequence: 15 givenname: Lin orcidid: 0000-0003-0742-9072 surname: Lu fullname: Lu, Lin organization: Institute of Mental Health, Peking University Sixth Hospital, Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) – sequence: 16 givenname: Dai surname: Zhang fullname: Zhang, Dai organization: Institute of Mental Health, Peking University Sixth Hospital, Key Laboratory of Mental Health, Ministry of Health, and National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) – sequence: 17 givenname: Yaojing surname: Chen fullname: Chen, Yaojing organization: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, IDG/McGovern Institute for Brain Research, Beijing Normal University – sequence: 18 givenname: Zhanjun orcidid: 0000-0001-7266-4218 surname: Zhang fullname: Zhang, Zhanjun email: zhang_rzs@bnu.edu.cn organization: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, IDG/McGovern Institute for Brain Research, Beijing Normal University – sequence: 19 givenname: Tianzi orcidid: 0000-0002-0607-3775 surname: Jiang fullname: Jiang, Tianzi email: jiangtz@nlpr.ia.ac.cn organization: School of Artificial Intelligence, University of Chinese Academy of Sciences, Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Research Center for Augmented Intelligence, Zhejiang Lab, Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences – sequence: 20 givenname: Bing orcidid: 0000-0003-2029-5187 surname: Liu fullname: Liu, Bing email: bing.liu@bnu.edu.cn organization: State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, IDG/McGovern Institute for Brain Research, Beijing Normal University, Chinese Institute for Brain Research |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39695106$$D View this record in MEDLINE/PubMed |
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| Title | Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model |
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