Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science, Korea.
Sci Rep. 2017 Apr 21;7:46700. doi: 10.1038/srep46700.
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.
帕金森病（PD）是一种进行性神经退行性疾病，与潜在的遗传因素和神经影像学发现有关。现有的与 PD 基因组相关的神经影像学研究大多集中在某些候选基因上。我们的研究目的是构建一个使用遗传和神经影像学特征的线性回归模型，与传统方法相比，更好地预测临床评分。我们从研究数据库中获得了神经影像学和 DNA 基因分型数据。连通性分析用于识别能够区分健康对照组（HC）和 PD 组的神经影像学特征。应用称为影像学遗传学的遗传和影像学信息的联合分析来研究遗传变异体。然后，我们比较了在回归框架中结合不同的遗传变异体和神经影像学特征对运动障碍协会赞助的统一帕金森病评定量表（MDS-UPDRS）进行预测的效用。关联皮层、运动皮层、丘脑和苍白球显示出 HC 和 PD 组之间明显不同的连通性。影像学遗传学分析确定 PARK2、PARK7、HtrA2、GIGYRF2 和 SNCA 是与影像学表型显著相关的遗传变异体。与仅使用遗传或神经影像学信息的其他模型相比，结合遗传和神经影像学特征的线性回归模型预测 MDS-UPDRS 的误差更低，与实际 MDS-UPDRS 的相关性更高。
