Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning

Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning

Abstract Cognitive impairments are prevalent in Parkinson’s disease (PD), but the underlying mechanisms of their development are unknown. In this study, we aimed to predict global cognition (GC) in PD with machine learning (ML) using structural neuroimaging, genetics and clinical and demographic cha...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Mehrafarin Ramezani, Pauline Mouches, Eunjin Yoon, Deepthi Rajashekar, Jennifer A. Ruskey, Etienne Leveille, Kristina Martens, Mekale Kibreab, Tracy Hammer, Iris Kathol, Nadia Maarouf, Justyna Sarna, Davide Martino, Gerald Pfeffer, Ziv Gan-Or, Nils D. Forkert, Oury Monchi
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/204558a8b15c462ebcd859ce0e968136
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:Abstract Cognitive impairments are prevalent in Parkinson’s disease (PD), but the underlying mechanisms of their development are unknown. In this study, we aimed to predict global cognition (GC) in PD with machine learning (ML) using structural neuroimaging, genetics and clinical and demographic characteristics. As a post-hoc analysis, we aimed to explore the connection between novel selected features and GC more precisely and to investigate whether this relationship is specific to GC or is driven by specific cognitive domains. 101 idiopathic PD patients had a cognitive assessment, structural MRI and blood draw. ML was performed on 102 input features including demographics, cortical thickness and subcortical measures, and several genetic variants (APOE, MAPT, SNCA, etc.). Using the combination of RRELIEFF and Support Vector Regression, 11 features were found to be predictive of GC including sex, rs894280, Edinburgh Handedness Inventory, UPDRS-III, education, five cortical thickness measures (R-parahippocampal, L-entorhinal, R-rostral anterior cingulate, L-middle temporal, and R-transverse temporal), and R-caudate volume. The rs894280 of SNCA gene was selected as the most novel finding of ML. Post-hoc analysis revealed a robust association between rs894280 and GC, attention, and visuospatial abilities. This variant indicates a potential role for the SNCA gene in cognitive impairments of idiopathic PD.

Ejemplares similares