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<p>There is growing interest in utilizing dynamic methods to investigate psychiatric disorders, particularly the transient dynamic approaches. However, current research predominantly focuses on dynamic abnormalities within a single psychiatric disorder compared to healthy controls, without considering the shared and specific features across different psychiatric conditions. The dynamic abnormality across psychiatric disorders remains unclear. In this study, we employed Co-activation Pattern (CAP) method to investigate the transient configurations of brain activity across different psychiatric conditions, including schizophrenia (SZ, <i>n</i> = 37); bipolar I disorder (BD, <i>n</i> = 40); attention-deficit/hyperactivity disorder (ADHD, <i>n</i> = 37), and healthy controls (HC, <i>n</i> = 110). By conducting k-means clustering analysis, we identified 10 transient activation patterns. Our findings reveal that the specificity of psychiatric disorders is reflected in the transition probabilities between states, with distinct state transition patterns observed across different disorders. Notably, abnormal state transitions are concentrated in the core states (State 1 and State 2), highlighting the common dynamic abnormalities across psychiatric conditions. These core states involve the activation of the attention network and the sensorimotor network and show significant associations with the functional gradient. Furthermore, we found that abnormalities in state transitions are associated with cognitive behavior. Overall, this work provides a dynamic network perspective for understanding the shared and specific characteristic of psychiatric disorders.</p>

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Coactivation patterns reveal the abnormality of dynamic state transitions between different psychiatric disorders

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月1日

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  • Lianjie Niu,
  • Wenshi Li,
  • Yongtao Bai,
  • Keke Fang,
  • Shaoqiang Han,
  • Peng Liu,
  • Jinrong Qu,
  • Xianfu Sun

摘要

There is growing interest in utilizing dynamic methods to investigate psychiatric disorders, particularly the transient dynamic approaches. However, current research predominantly focuses on dynamic abnormalities within a single psychiatric disorder compared to healthy controls, without considering the shared and specific features across different psychiatric conditions. The dynamic abnormality across psychiatric disorders remains unclear. In this study, we employed Co-activation Pattern (CAP) method to investigate the transient configurations of brain activity across different psychiatric conditions, including schizophrenia (SZ, n = 37); bipolar I disorder (BD, n = 40); attention-deficit/hyperactivity disorder (ADHD, n = 37), and healthy controls (HC, n = 110). By conducting k-means clustering analysis, we identified 10 transient activation patterns. Our findings reveal that the specificity of psychiatric disorders is reflected in the transition probabilities between states, with distinct state transition patterns observed across different disorders. Notably, abnormal state transitions are concentrated in the core states (State 1 and State 2), highlighting the common dynamic abnormalities across psychiatric conditions. These core states involve the activation of the attention network and the sensorimotor network and show significant associations with the functional gradient. Furthermore, we found that abnormalities in state transitions are associated with cognitive behavior. Overall, this work provides a dynamic network perspective for understanding the shared and specific characteristic of psychiatric disorders.