ArticleScientific reports2021
Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson's disease using machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 4 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis.International journal of molecular sciences · 2024Pooled it
- The identification of cognitive impairment in Parkinson's disease using biofluids, neuroimaging, and artificial intelligence.Frontiers in neuroscience · 2024Pooled it
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- Predicting dementia in people with Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Research progress of multimodal biomarkers in the early diagnosis of mild cognitive impairment in Parkinson's disease.Frontiers in neurology · 2025Review
- Fifty Years of Handedness Research: A Neurological and Methodological Update.Brain sciences · 2024Article
- Neurocognitive Impairment and Social Cognition in Parkinson's Disease Patients.Neurology international · 2024Review
- Transcriptome sequencing of circular RNA reveals the involvement of hsa-SCMH1_0001 in the pathogenesis of Parkinson's disease.CNS neuroscience & therapeutics · 2024Article
- Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.Scientific reports · 2024Article
- Genetic background of cognitive decline in Parkinson's disease.Frontiers in cognition · 2024Review
- Genetic risk assessment based on association and prediction studies.Scientific reports · 2023Article
- A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci.Scientific reports · 2022Article
- Internetwork Connectivity Predicts Cognitive Decline in Parkinson's and Is Altered by Genetic Variants.Frontiers in aging neuroscience · 2022Article
- The Impact ofLife (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
17 authors at 4 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.