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 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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Publication Details

Journal
Translational Psychiatry
Published
2024-12-18
DOI
https://doi.org/10.1038/s41398-024-03200-7
Citations
3
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
1.03

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article

Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model

Sangma Xie, Yunchun Chen, Kunchi Li, Chaoyue Ding et al.
3 citations
Translational Psychiatry
Functional Brain Connectivity Studies
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article

Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model

Sangma Xie, Yunchun Chen, Kunchi Li, Chaoyue Ding, Yaojing Chen, Peng Li, Jun Yan, Hao Yan, Huaning Wang, Lin Lü, Yongfeng Yang, Luxian Lv, Zhanjun Zhang, Huiling Wang, Hongxing Zhang, Dai Zhang, Jun Chen, Yuqing Sun, Tianzi Jiang, Bing Liu
article en
3 citations

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.

Translational PsychiatryVol. 14(1)
McGovern Institute for Brain Research (US), Chinese Academy of Sciences (CN), Peking University (CN), Beijing Normal University (CN), Wuhan University (CN), Beijing Academy of Artificial Intelligence (CN), Henan Psychiatric Hospital (CN), Institute of Automation (CN), Chinese Institute for Brain Research (CN), Zhejiang Lab (CN), Renmin Hospital of Wuhan University (CN), Xijing Hospital (CN), Peking University Sixth Hospital (CN), University of Chinese Academy of Sciences (CN), Hangzhou Dianzi University (CN), Henan Medical University (CN), Air Force Medical University (CN)
National Natural Science Foundation of China, Beijing Normal University, Beijing Nova Program
Openalex Percentile: Top 23%
Functional Brain Connectivity Studies
1.03
DOIOpen Access PDFOpenAlex
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