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Multimodal brain network topology and enhanced computer-aided diagnosis in Parkinson’s Disease: a systematic review and meta-analysis
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  • Published: 07 January 2026

Multimodal brain network topology and enhanced computer-aided diagnosis in Parkinson’s Disease: a systematic review and meta-analysis

  • Chao Zuo1,2 na1,
  • Wenxiong Liu1,3 na1,
  • Huan Lan1,4,
  • Li Chen1,2,
  • Nannan Li5,
  • Yuying Yan5,
  • Li Li6,
  • Chunyan Luo1,2,
  • Graham J. Kemp7,
  • Su Lui1,2,
  • Xueling Suo1,4 &
  • …
  • Qiyong Gong1,8 

npj Digital Medicine , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Computational biology and bioinformatics
  • Diseases
  • Neurology
  • Neuroscience

Abstract

Parkinson’s disease (PD) is increasingly recognized as a brain network-disconnection syndrome. However, there is little consistent evidence on multimodal global topological alterations and their diagnostic value. We systematically searched PubMed, Embase and Web of Science up to March 2025 for articles reporting brain network topology in PD, to which we applied a multilevel random-effects meta-analyses with robust variance estimation to account for statistical dependencies. Our case-control meta-analysis included 80 studies (42 fMRI, 25 dMRI, 10 EEG, 4 sMRI, 3 others) involving 3736 PD patients and 2384 healthy controls. Compared to controls, PD patients showed lower structural and functional network segregation, especially when cognitively impaired. Structural network integration was also lower in PD, such deficits appearing to correlate with disease progression. Drug and network construction strategies were identified as potential moderating factors. Our diagnostic meta-analysis of 10 studies yielded a pooled diagnostic odds ratio of 16.4 and a pooled area under the curve of 0.86, with better diagnostic performance observed in studies using combined network metrics. These results support the clinical relevance of topological metrics in PD as potential biomarkers for disease characterization, prognosis and patient stratification, and underscore the importance of methodological harmonization and prospective validation in future research.

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Data availability

All the data included in this study are available within the paper and its supplementary information files. The codes used in this paper are available on GitHub: https://github.com/chao9791/PD-brain-network-topological-properties-alterations.

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Acknowledgements

This research was supported by the National Key R&D Program of China (No. 2022YFC2009904), National Natural Science Foundation of China (Grant Nos. 82001800), Young Elite Scientists Sponsorship Program by China Association for Science and Technology (CAST) (No. 2022QNRC001), and Sichuan Science and Technology Program (No. 2025ZNSFSC0661). The authors would like to express their sincere gratitude to Dr Angeliki Zarkali, Dr Kathy Dujardin, Dr Chuanxi Tang, Dr Muthuraman Muthuraman, and Dr Madhura Ingalhalikar for generously providing data and/or additional information essential to the completion of this study.

Author information

Author notes

  1. These authors contributed equally: Chao Zuo, Wenxiong Liu.

Authors and Affiliations

  1. Department of Radiology, Huaxi MR Research Center (HMRRC), Institution of Radiology and Medical Imaging, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Chao Zuo, Wenxiong Liu, Huan Lan, Li Chen, Chunyan Luo, Su Lui, Xueling Suo & Qiyong Gong

  2. Psychoradiology Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Chao Zuo, Li Chen, Chunyan Luo & Su Lui

  3. Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Wenxiong Liu

  4. Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China

    Huan Lan & Xueling Suo

  5. Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Nannan Li & Yuying Yan

  6. Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, China

    Li Li

  7. Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK

    Graham J. Kemp

  8. Xiamen Key Lab of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China

    Qiyong Gong

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Contributions

X.L.S. and Q.Y.G. designed the study. C.Z., W.X.L., X.L.S., and H.L. contributed to the literature search, data collection and interpretation. C.Z. contributed to statistical analysis of case-control meta-analysis. W.X.L. contributed to statistical analysis of diagnostic meta-analysis. C.Z. and W.X.L. drafted the manuscript. L.C., N.L., Y.Y., L.L., C.L., G.J.K., S.L., X.L.S., and Q.Y.G. critically revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xueling Suo or Qiyong Gong.

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Zuo, C., Liu, W., Lan, H. et al. Multimodal brain network topology and enhanced computer-aided diagnosis in Parkinson’s Disease: a systematic review and meta-analysis. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-025-02301-x

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  • Received: 27 July 2025

  • Accepted: 18 December 2025

  • Published: 07 January 2026

  • DOI: https://doi.org/10.1038/s41746-025-02301-x

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