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Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression

Molecular Psychiatry volume 30, pages 5150–5160 (2025)Cite this article

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Abstract

Aberrant functional connectivity (FC) between brain networks has been indicated closely associated with bipolar disorder (BD). However, the previous findings of specific brain network connectivity patterns have been inconsistent, and the clinical utility of FCs for predicting treatment outcomes in bipolar depression was underexplored. To identify robust neuro-biomarkers of bipolar depression, a connectome-based analysis was conducted on resting-state functional MRI (rs-fMRI) data of 580 bipolar depression patients and 116 healthy controls (HCs). A subsample of 148 patients underwent a 4-week quetiapine treatment with post-treatment clinical assessment. Adopting machine learning, a predictive model based on pre-treatment brain connectome was then constructed to predict treatment response and identify the efficacy-specific networks. Distinct brain network connectivity patterns were observed in bipolar depression compared to HCs. Elevated intra-network connectivity was identified within the default mode network (DMN), sensorimotor network (SMN), and subcortical network (SC); and as to the inter-network connectivity, increased FCs were between the DMN, SMN and frontoparietal (FPN), ventral attention network (VAN), and decreased FCs were between the SC and cortical networks, especially the DMN and FPN. And the global network topology analyses revealed decreased global efficiency and increased characteristic path length in BD compared to HC. Further, the support vector regression model successfully predicted the efficacy of quetiapine treatment, as indicated by a high correspondence between predicted and actual HAMD reduction ratio values (r(df=147)=0.4493, p = 2*10−4). The identified efficacy-specific networks primarily encompassed FCs between the SMN and SC, and between the FPN, DMN, and VAN. These identified networks further predicted treatment response with r = 0.3940 in the subsequent validation with an independent cohort (n = 43). These findings presented the characteristic aberrant patterns of brain network connectome in bipolar depression and demonstrated the predictive potential of pre-treatment network connectome for quetiapine response. Promisingly, the identified connectivity networks may serve as functional targets for future precise treatments for bipolar depression.

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Fig. 1: Flowchart of data analysis.
Fig. 2: Characteristic brain network connectivity alterations in bipolar depression patients compared to healthy controls.
Fig. 3: Differences in global topological metrics of brain networks between bipolar depression patients and healthy controls.
Fig. 4: Quetiapine efficacy prediction model and positive and negative weighted networks.
Fig. 5: Positive and negative efficacy correlation networks.

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Data availability

The MATLAB code of this study is available upon reasonable request from the corresponding author. The datasets that support the findings are not publicly available due to patients’ privacy and ethical restrictions.

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Acknowledgements

The authors are grateful to all participants for their cooperation, and related staff in the First Affiliated Hospital of Zhejiang University School of Medicine and the Department of Psychology, University of Chinese Academy of Sciences. The study was supported by the projects including the National Key Research and Development Program of China (2023YFC2506200), the Research Project of Jinan Microecological Biomedicine Shandong Laboratory (No. JNL-2023001B), the Zhejiang Provincial Key Research and Development Program (No. 2021C03107), the Leading Talent of Scientific and Technological Innovation—“Ten Thousand Talents Program” of Zhejiang Province (No. 2021R52016), the Innovation team for precision diagnosis and treatment of major brain diseases (No. 2020R01001), the Chinese Medical Education Association (2022KTZ004).

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Author notes

  1. These authors contributed equally: Caixi Xi, Bin Lu, Xiaonan Guo.

Authors and Affiliations

  1. Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Caixi Xi, Xiaonan Guo, Zeyu Qin & Shaohua Hu

  2. Nanhu Brain-computer Interface institute, Hangzhou, China

    Caixi Xi, Xiaonan Guo, Zeyu Qin & Shaohua Hu

  3. CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China

    Bin Lu & Chaogan Yan

  4. Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China

    Chaogan Yan

  5. The Zhejiang Key Laboratory of Precision Psychiatry, Hangzhou, China

    Shaohua Hu

  6. MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou, China

    Shaohua Hu

  7. Brain Research Institute of Zhejiang University, Hangzhou, China

    Shaohua Hu

  8. The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China

    Shaohua Hu

  9. Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, China

    Shaohua Hu

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  1. Caixi Xi
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Contributions

Shaohua Hu, Chaogan Yan and Caixi Xi designed the study; Caixi Xi, Bin Lu, and Xiaonan Guo performed the data processing and analysis, conducted the statistics of results, and wrote the paper together; Caixi Xi and Zeyu Qin contributed to sample collection and data curations; Caixi Xi, Zeyu Qin and Shaohua hu revised the manuscript. All authors have critically reviewed and approved the final version of the manuscript.

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Correspondence to Chaogan Yan or Shaohua Hu.

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The authors declare no competing interests.

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All methods in this study were performed in accordance with the relevant guidelines and regulations. The study was approved by the Ethics Committee of the First Affiliated Hospital of Zhejiang University (NCT05480150, 2017-397). All participants provided written informed consent after thoroughly understanding the study procedures. The authors have also obtained written informed consent for publication of the images.

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Xi, C., Lu, B., Guo, X. et al. Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression. Mol Psychiatry 30, 5150–5160 (2025). https://doi.org/10.1038/s41380-025-03099-6

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