ArticleCommunications biology2024
Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A neuroimaging biomarker for disease staging in clinically diagnosed Alzheimer's disease.BMC medicine · 2026Article
- A Preliminary Mechanics-Informed Machine Learning Framework for Objective Assessment of Parkinson's Disease and Rehabilitation Outcomes.Diagnostics (Basel, Switzerland) · 2025Article
- Understanding heterogeneity in psychiatric disorders: A method for identifying subtypes and parsing comorbidity.Psychiatry and clinical neurosciences · 2025Article
- ModFus-PD: synergizing cross-modal attention and contrastive learning for enhanced multimodal diagnosis of Parkinson's disease.Frontiers in computational neuroscience · 2025Article
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Authors and funding
17 authors.
Funding
Abstract
Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 PD patients and 366 controls, aiming to disentangle PD-specific neuroanatomical alterations. The results reveal that these neuroanatomical alterations in PD are correlated with individual differences in dopamine transporter binding deficit, neurodegeneration biomarkers, and clinical severity and progression. The correlation with clinical severity is verified in an external cohort. Notably, certain proteins in the cerebrospinal fluid are strongly associated with PD-specific features, particularly those involved in the immune function. The most notable neuroanatomical alterations are observed in both subcortical and temporal regions. Our findings provide deeper insights into the patterns of brain atrophy in PD and potential underlying molecular mechanisms, paving the way for earlier patient stratification and the development of treatments to slow down neurodegeneration.
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