Beheshti Iman, Ko Ji Hyun
Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
PrairieNeuro Research Centre, Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB, Canada.
Front Neurosci. 2024 Apr 18;18:1375395. doi: 10.3389/fnins.2024.1375395. eCollection 2024.
Mild 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.
Structural 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, = 52), and (ii) PD patients who maintained stable healthy cognitive function over the same period (PD-SHC, = 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.
The 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.
Our 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.
轻度认知障碍（MCI）是帕金森病（PD）患者中常见的症状，也是发展为痴呆症的主要危险因素。我们的目标是确定在PD患者从健康认知转变为MCI之前早期的脑部解剖结构变化。
从帕金森病进展标志物倡议数据库下载基线时认知健康的PD患者的结构T1加权磁共振成像数据。根据5年期间的年度认知评估将患者分为两组：（i）在5年随访中发展为MCI的认知健康不稳定的PD患者（PD-UHC，n = 52），以及（ii）在同一时期保持稳定认知功能的PD患者（PD-SHC，n = 52）。使用倾向评分匹配方法从192例PD-SHC患者中选择这52例PD-SHC，使其在基线时具有与PD-UHC相似的人口统计学和临床特征。其中75%用于训练支持向量机（SVM）算法以区分PD-UHC和PD-SHC组，并在其余25%的个体上进行测试。使用Shapley附加解释（SHAP）特征分析来识别SVM分类器中最具信息性的脑区。
使用10倍交叉验证对PD-UHC与PD-SHC进行分类的平均准确率为80.76%，灵敏度为82.05%，特异性为79.48%。在保留测试集中的表现相似，所有准确率、灵敏度和特异性均为76.92%。SHAP分析表明，预测模型中最具影响力的脑区位于额叶、枕叶、小脑区域以及中脑。
我们基于机器学习的分析在识别最早疾病阶段有认知衰退风险的PD个体方面取得了有前景的结果，并揭示了在临床症状出现之前可能与PD患者未来认知衰退相关的脑区。
