ArticleAnnals of translational medicine2022
Surface-based morphological patterns associated with neuropsychological performance, symptom severity, and treatment response in Parkinson's disease.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Parkinson's disease severity is encoded by non-linear oscillatory interactions across cortical-subcortical networks.Scientific reports · 2026Article
- Cortical gyrification deficits in early-stage Parkinson's disease: the importance of bradykinesia.Brain communications · 2026Article
- Machine learning models for diagnosing Alzheimer's disease using brain cortical complexity.Frontiers in aging neuroscience · 2024Article
- Association of Cortical Gyrification With Imaging and Serum Biomarkers in Patients With Parkinson Disease.Neurology · 2023Article
- Multimodal imaging-based diagnostic approach for MRI-negative posterior cortex epilepsy.Therapeutic advances in neurological disorders · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Surface-based cortical morphological patterns provide insight into the neural mechanisms of Parkinson's disease (PD). Explorations of the relationship between these patterns and the clinical assessment and treatment effects could be used to inform early intervention and treatment planning. Methods: We recruited 78 PD patients who underwent presurgical evaluation and 55 healthy controls. We assessed neocortical sulcal depth, gyrification index, and fractal dimension and applied a general linear model using the multivariate Hotelling's Results: The surface-based neuroimaging pattern of PD included decreases in morphological metrics in the gyrus (left: F=4.32; right: F=4.13), insular lobe (left: F=4.87; right: F=4.53), paracentral lobe (left: F=4.01; right: F=4.26), left posterior cingulate cortex (F=4.48), and left occipital lobe (F=4.27, P<0.01). This pattern was significantly associated with cognitive performance and motor symptoms (P<0.01). The machine learning model using morphological metrics was able to predict the drug response in the tremor score (R=-0.34, P<0.01) and postural instability and gait disorders score (R=0.24, P=0.04). Conclusions: We identified the surface-based neuroimaging pattern associated with PD and explored its association with clinical assessment. Our findings suggest that these morphological indicators have potential value in informing personalized medicine and patient management.
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