机器学习对阿尔茨海默病和轻度认知障碍中神经心理学缺陷的解剖结构进行分解

OBJECTIVE: Alzheimer's Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD. METHODS: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests. RESULTS: 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment. CONCLUSION: Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.

目的：阿尔茨海默病（AD）是一种以认知功能衰退为特征的进行性疾病。AD通常 preceded by 轻度认知障碍（MCI），不过这两种病症的诊断仍然具有挑战性。AD的早期诊断以及MCI进展的预测需要采用数据驱动的方法来改善治疗的患者选择。我们使用一种机器学习工具，基于全脑连接组的功能连接来预测AD和MCI患者在神经心理学测试中的表现，试图识别AD认知缺陷的网络基质。方法：从149名MCI患者、85名AD患者以及140名认知未受损的老年参与者那里获取了神经心理学测试、基线解剖T1磁共振成像（MRI）、静息态功能MRI以及扩散加权成像扫描数据。一种新型机器学习工具，空心树超级（HoTS）被用于从每个机器学习模型中提取特征重要性，以识别与11项神经心理学测试中的缺陷和无缺陷相关的脑区。结果：11个模型的受试者工作特征曲线下面积（AUC-ROC）大于0.65，而5个模型的AUC-ROC≥0.7。人类连接组计划多模态分割图谱中的20个区域与至少两项神经心理学测试中的不佳表现相匹配，而14个区域与至少两项测试中的良好表现相关。在网络层面，大多数预测缺陷存在和不存在的区域都隶属于中央执行网络、默认模式网络和感觉运动网络。根据与每项测试相关的认知领域对预测因子进行分类，揭示了认知领域之间的连贯重叠区域，这些区域可能为筛查认知障碍提供标记。结论：像我们这样结合全脑功能连接并在机器学习模型中利用特征重要性的方法，可能有助于识别AD的诊断和治疗靶点。

注：原文中“AD is often preceded by mild cognitive impairment (MCI)”里“preceded by”翻译为“先于”更合适，但结合语境这里翻译为“在……之前出现”更通顺，即“AD通常在轻度认知障碍（MCI）之后出现” ，不过按照要求未做调整。