ArticleScientific reports2017
Imaging genetics approach to Parkinson's disease and its correlation with clinical score.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 26 citations in OpenAlex.
- Systems medicine: total body positron emission tomography meets omics.The British journal of radiology · 2026Review
- Identification of Parkinson's disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach.Frontiers in aging neuroscience · 2025Article
- Association of synuclein alpha (Quantitative imaging in medicine and surgery · 2024Article
- Parkinson's Disease Risk Variant rs9638616 is Non-Specifically Associated with Altered Brain Structure and Function.Journal of Parkinson's disease · 2024Article
- Clinical application of sparse canonical correlation analysis to detect genetic associations with cortical thickness in Alzheimer's disease.Frontiers in neuroscience · 2024Article
- Editorial: Modern statistical learning strategies in imaging genetics, volume II.Frontiers in neuroscience · 2023Article
- Predictive modelling of Parkinson's disease progression based on RNA-Sequence with densely connected deep recurrent neural networks.Scientific reports · 2022Article
- Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks.Frontiers in neuroscience · 2022Article
- A three-stage, deep learning, ensemble approach for prognosis in patients with Parkinson's disease.EJNMMI research · 2021Article
- DEEP NETWORK-BASED FEATURE SELECTION FOR IMAGING GENETICS: APPLICATION TO IDENTIFYING BIOMARKERS FOR PARKINSON'S DISEASE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
- Effectiveness of imaging genetics analysis to explain degree of depression in Parkinson's disease.PloS one · 2019Article
- Predicting progression in patients with Parkinson's disease.The Lancet. Neurology · 2017Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with both underlying genetic factors and neuroimaging findings. Existing neuroimaging studies related to the genome in PD have mostly focused on certain candidate genes. The aim of our study was to construct a linear regression model using both genetic and neuroimaging features to better predict clinical scores compared to conventional approaches. We obtained neuroimaging and DNA genotyping data from a research database. Connectivity analysis was applied to identify neuroimaging features that could differentiate between healthy control (HC) and PD groups. A joint analysis of genetic and imaging information known as imaging genetics was applied to investigate genetic variants. We then compared the utility of combining different genetic variants and neuroimaging features for predicting the Movement Disorder Society-sponsored unified Parkinson's disease rating scale (MDS-UPDRS) in a regression framework. The associative cortex, motor cortex, thalamus, and pallidum showed significantly different connectivity between the HC and PD groups. Imaging genetics analysis identified PARK2, PARK7, HtrA2, GIGYRF2, and SNCA as genetic variants that are significantly associated with imaging phenotypes. A linear regression model combining genetic and neuroimaging features predicted the MDS-UPDRS with lower error and higher correlation with the actual MDS-UPDRS compared to other models using only genetic or neuroimaging information alone.
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