ArticleFrontiers in aging neuroscience2020
Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease.
Article in Frontiers in aging neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Abnormal topological organization of structural covariance networks in early-stage Parkinson's disease patients with autonomic dysfunction.Quantitative imaging in medicine and surgery · 2025Article
- Multimodal MRI analysis of COVID-19 effects on pediatric brain.Scientific reports · 2025Article
- Convergent reductions in interhemispheric functional, structural and callosal connectivity in Parkinson's disease.Frontiers in aging neuroscience · 2025Article
- Abnormal topological structure of structural covariance networks based on fractal dimension in noise induced hearing loss.Scientific reports · 2024Article
- Abnormal Topological Organization of Structural Covariance Networks in Patients with Temporal Lobe Epilepsy Comorbid Sleep Disorder.Brain sciences · 2023Article
- Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.Heliyon · 2023Article
- The complex interplay of hypoxia and sleep disturbance in gray matter structure alterations in obstructive sleep apnea patients.Frontiers in aging neuroscience · 2023Article
- Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features.Frontiers in aging neuroscience · 2022Article
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8 authors.
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Abstract
Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.
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