Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.
PLoS One. 2019 Feb 11;14(2):e0211699. doi: 10.1371/journal.pone.0211699. eCollection 2019.
Depression is one of the most common and important neuropsychiatric symptoms in Parkinson's disease and often becomes worse as Parkinson's disease progresses. However, the underlying mechanisms of depression in Parkinson's disease are not clear. The aim of our study was to find genetic features related to depression in Parkinson's disease using an imaging genetics approach and to construct an analytical model for predicting the degree of depression in Parkinson's disease. The neuroimaging and genotyping data were obtained from an openly accessible database. We computed imaging features through connectivity analysis derived from tractography of diffusion tensor imaging. The imaging features were used as intermediate phenotypes to identify genetic variants according to the imaging genetics approach. We then constructed a linear regression model using the genetic features from imaging genetics approach to describe clinical scores indicating the degree of depression. As a comparison, we constructed other models using imaging features and genetic features based on references to demonstrate the effectiveness of our imaging genetics model. The models were trained and tested in a five-fold cross-validation. The imaging genetics approach identified several brain regions and genes known to be involved in depression, with the potential to be used as meaningful biomarkers. Our proposed model using imaging genetic features predicted and explained the degree of depression in Parkinson's disease appropriately (adjusted R2 larger than 0.6 over five training folds) and with a lower error and higher correlation than with other models over five test folds.
抑郁症是帕金森病中最常见和最重要的神经精神症状之一，随着帕金森病的进展往往会变得更糟。然而，帕金森病中抑郁症的潜在机制尚不清楚。我们的研究旨在使用影像遗传学方法寻找与帕金森病相关的抑郁的遗传特征，并构建用于预测帕金森病抑郁程度的分析模型。神经影像学和基因分型数据来自公开可访问的数据库。我们通过弥散张量成像的轨迹连通性分析来计算成像特征。将成像特征作为中间表型，根据影像遗传学方法识别遗传变异。然后，我们使用影像遗传学方法的遗传特征构建线性回归模型，以描述表明抑郁程度的临床评分。作为比较，我们使用基于参考文献的影像特征和遗传特征构建了其他模型，以证明我们的影像遗传学模型的有效性。在五重交叉验证中对模型进行了训练和测试。影像遗传学方法确定了一些已知与抑郁症相关的大脑区域和基因，它们有可能成为有意义的生物标志物。我们提出的使用影像遗传特征的模型适当地预测和解释了帕金森病中的抑郁程度（五个训练折叠中的调整 R2 大于 0.6），并且在五个测试折叠中，其误差较低，相关性较高。
