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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Chao Zuo,
  • Wenxiong Liu,
  • Huan Lan,
  • Li Chen,
  • Nannan Li,
  • Yuying Yan,
  • Li Li,
  • Chunyan Luo,
  • Graham J. Kemp,
  • Su Lui,
  • Xueling Suo,
  • Qiyong Gong

摘要

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.