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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Published in:Translational psychiatry Vol. 14; no. 1; pp. 496 - 14
Main Authors: Ding, Chaoyue, Sun, Yuqing, Li, Kunchi, Xie, Sangma, Yan, Hao, Li, Peng, Yan, Jun, Chen, Jun, Wang, Huiling, Wang, Huaning, Chen, Yunchun, Yang, Yongfeng, Lv, Luxian, Zhang, Hongxing, Lu, Lin, Zhang, Dai, Chen, Yaojing, Zhang, Zhanjun, Jiang, Tianzi, Liu, Bing
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 18.12.2024
Nature Publishing Group
Subjects:
38/39
59/36
59/57
631/378/340
692/699/476/1799
Adult
Behavioral Sciences
Biological Psychology
Brain - physiopathology
Deep Learning
Female
Humans
Male
Medicine
Medicine & Public Health
Models, Neurological
Neurosciences
Pharmacotherapy
Psychiatry
Schizophrenia
Schizophrenia - physiopathology
ISSN:2158-3188, 2158-3188
Online Access:Get full text
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Summary: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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ISSN:2158-3188
2158-3188
DOI:10.1038/s41398-024-03200-7