Data_Sheet_1_Predicting the occurrence of mild cognitive impairment in Parkinson’s disease using structural MRI data.docx

IntroductionMild cognitive impairment (MCI) is a common symptom observed in individuals with Parkinson’s disease (PD) and a main risk factor for progressing to dementia. Our objective was to identify early anatomical brain changes that precede the transition from healthy cognition to MCI in PD.
MethodsStructural T1-weighted magnetic resonance imaging data of PD patients with healthy cognition at baseline were downloaded from the Parkinson’s Progression Markers Initiative database. Patients were divided into two groups based on the annual cognitive assessments over a 5-year time span: (i) PD patients with unstable healthy cognition who developed MCI over a 5-year follow-up (PD-UHC, n = 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC, n = 52). These 52 PD-SHC were selected among 192 PD-SHC patients using propensity score matching method to have similar demographic and clinical characteristics with PD-UHC at baseline. Seventy-five percent of these were used to train a support vector machine (SVM) algorithm to distinguish between the PD-UHC and PD-SHC groups, and tested on the remaining 25% of individuals. Shapley Additive Explanations (SHAP) feature analysis was utilized to identify the most informative brain regions in SVM classifier.
ResultsThe average accuracy of classifying PD-UHC vs. PD-SHC was 80.76%, with 82.05% sensitivity and 79.48% specificity using 10-fold cross-validation. The performance was similar in the hold-out test sets with all accuracy, sensitivity, and specificity at 76.92%. SHAP analysis showed that the most influential brain regions in the prediction model were located in the frontal, occipital, and cerebellar regions as well as midbrain.
DiscussionOur machine learning-based analysis yielded promising results in identifying PD individuals who are at risk of cognitive decline from the earliest disease stage and revealed the brain regions which may be linked to the prospective cognitive decline in PD before clinical symptoms emerge.

引言
轻度认知障碍（Mild cognitive impairment, MCI）是帕金森病（Parkinson’s disease, PD）患者常见的临床表现，也是进展为痴呆的主要危险因素。本研究旨在识别帕金森病患者从认知正常进展为轻度认知障碍之前的早期脑部解剖结构变化。

方法
从帕金森病进展标记物倡议（Parkinson’s Progression Markers Initiative）数据库下载基线认知正常的帕金森病患者的结构T1加权磁共振成像数据。根据5年随访期间的年度认知评估结果，将受试者分为两组：(i) 5年随访期间进展为轻度认知障碍的认知不稳定型帕金森病患者（PD-UHC，n=52）；(ii) 同期维持认知稳定正常的帕金森病患者（PD-SHC，n=52）。本研究从192名认知稳定正常的帕金森病患者中，通过倾向性得分匹配法筛选出52名与PD-UHC组基线人口学及临床特征匹配的PD-SHC受试者。其中75%的样本用于训练支持向量机（support vector machine, SVM）分类器以区分PD-UHC与PD-SHC组，剩余25%的样本用于模型测试。本研究采用夏普利可加解释（Shapley Additive Explanations, SHAP）特征分析方法，识别SVM分类器中最具信息价值的脑区。

结果
采用10折交叉验证时，PD-UHC与PD-SHC分类的平均准确率为80.76%，灵敏度为82.05%，特异度为79.48%。在留出测试集上的表现与之相近，准确率、灵敏度与特异度均为76.92%。SHAP分析显示，预测模型中影响力最高的脑区位于额叶、枕叶、小脑及中脑。

讨论
本研究基于机器学习的分析在从帕金森病最早期阶段识别认知衰退风险人群方面获得了颇具前景的结果，并揭示了临床症状出现前可能与帕金森病前瞻性认知衰退相关的脑区。