Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression.
Commun Biol
; 7(1): 954, 2024 Aug 07.
Article
en En
| MEDLINE
| ID: mdl-39112797
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedad de Parkinson
/
Índice de Severidad de la Enfermedad
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Imagen por Resonancia Magnética
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Progresión de la Enfermedad
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Aprendizaje Automático
Límite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Commun Biol
Año:
2024
Tipo del documento:
Article
País de afiliación:
China