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...
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Nature Portfolio
2021
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oai:doaj.org-article:204558a8b15c462ebcd859ce0e9681362021-12-02T13:34:51ZInvestigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning10.1038/s41598-021-84316-42045-2322https://doaj.org/article/204558a8b15c462ebcd859ce0e9681362021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84316-4https://doaj.org/toc/2045-2322Abstract 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.Mehrafarin RamezaniPauline MouchesEunjin YoonDeepthi RajashekarJennifer A. RuskeyEtienne LeveilleKristina MartensMekale KibreabTracy HammerIris KatholNadia MaaroufJustyna SarnaDavide MartinoGerald PfefferZiv Gan-OrNils D. ForkertOury MonchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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Medicine R Science Q 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 Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Mehrafarin Ramezani |
| title |
Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning |
| title_short |
Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning |
| title_full |
Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning |
| title_fullStr |
Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning |
| title_full_unstemmed |
Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning |
| title_sort |
investigating the relationship between the snca gene and cognitive abilities in idiopathic parkinson’s disease using machine learning |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/204558a8b15c462ebcd859ce0e968136 |
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