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Background <p>Bipolar disorder (BD) is a chronic psychiatric illness with high recurrence and disability rates. Identifying neuroimaging biomarkers may improve diagnostic precision and treatment prediction.</p> Methods <p>The resting-state functional MRI (rs-fMRI) data were acquired from patients with BD and age-, sex-, and education-matched healthy controls (HCs), with a subset of patients completing a three-month pharmacological follow-up. Dynamic degree centrality (dDC) was calculated using a sliding-window approach to characterize time-varying whole-brain network integration. Group differences were assessed at baseline and post-treatment. Support vector machine (SVM) models were used to evaluate the discriminative ability of dDC features. Imaging–transcriptomic association analyses were conducted using gene expression data from the Allen Human Brain Atlas to explore potential molecular mechanisms.</p> Results <p>Compared with HCs, patients with BD at baseline showed significantly reduced dDC variability in the cerebellar Crus I and Crus II. After treatment, increased dDC variability emerged in the bilateral anterior cingulate and paracingulate gyri. The SVM classifier based on dDC features achieved moderate performance in distinguishing BD patients from HCs. Transcriptomic analyses indicated that genes associated with dDC alterations were mainly enriched in synaptic signaling and immune–inflammatory pathways, with several hub genes identified in protein–protein interaction networks.</p> Conclusion <p>BD is characterized by disrupted temporal dynamics of whole-brain network integration involving cerebellar and cingulate regions, which appear partially modulated by pharmacological treatment. Integrating dynamic network metrics with transcriptomic data provides complementary insights into the neurobiological and molecular substrates of BD and supports the potential of dDC as a neuroimaging biomarker.</p>

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Abnormal dynamics of brain networks in bipolar disorder: a dynamic degree centrality and imaging–transcriptomic investigation

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Chunguo Zhang,
  • Leyi Zhang,
  • Jiaquan Liang,
  • Yiding Han,
  • Haohao Yan,
  • Wei Huang,
  • Xiaoling Li,
  • Chaohua Tang,
  • Jinbing Xu,
  • Yangpan Ou,
  • Guojun Xie,
  • Wenbin Guo

摘要

Background

Bipolar disorder (BD) is a chronic psychiatric illness with high recurrence and disability rates. Identifying neuroimaging biomarkers may improve diagnostic precision and treatment prediction.

Methods

The resting-state functional MRI (rs-fMRI) data were acquired from patients with BD and age-, sex-, and education-matched healthy controls (HCs), with a subset of patients completing a three-month pharmacological follow-up. Dynamic degree centrality (dDC) was calculated using a sliding-window approach to characterize time-varying whole-brain network integration. Group differences were assessed at baseline and post-treatment. Support vector machine (SVM) models were used to evaluate the discriminative ability of dDC features. Imaging–transcriptomic association analyses were conducted using gene expression data from the Allen Human Brain Atlas to explore potential molecular mechanisms.

Results

Compared with HCs, patients with BD at baseline showed significantly reduced dDC variability in the cerebellar Crus I and Crus II. After treatment, increased dDC variability emerged in the bilateral anterior cingulate and paracingulate gyri. The SVM classifier based on dDC features achieved moderate performance in distinguishing BD patients from HCs. Transcriptomic analyses indicated that genes associated with dDC alterations were mainly enriched in synaptic signaling and immune–inflammatory pathways, with several hub genes identified in protein–protein interaction networks.

Conclusion

BD is characterized by disrupted temporal dynamics of whole-brain network integration involving cerebellar and cingulate regions, which appear partially modulated by pharmacological treatment. Integrating dynamic network metrics with transcriptomic data provides complementary insights into the neurobiological and molecular substrates of BD and supports the potential of dDC as a neuroimaging biomarker.