Valerio Kate E, Prieto Sarah, Hasselbach Alexander N, Moody Jena N, Hayes Scott M, Hayes Jasmeet P
Department of Psychology, The Ohio State University, Columbus, OH 43210, USA.
Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH 43210, USA.
Brain Commun. 2021 Jun 26;3(3):fcab140. doi: 10.1093/braincomms/fcab140. eCollection 2021 Jul.
The ability to carry out instrumental activities of daily living, such as paying bills, remembering appointments and shopping alone decreases with age, yet there are remarkable individual differences in the rate of decline among older adults. Understanding variables associated with a decline in instrumental activities of daily living is critical to providing appropriate intervention to prolong independence. Prior research suggests that cognitive measures, neuroimaging and fluid-based biomarkers predict functional decline. However, selection of variables can lead to the over-valuation of certain variables and exclusion of others that may be predictive. In this study, we used machine learning techniques to select a wide range of baseline variables that best predicted functional decline in two years in individuals from the Alzheimer's Disease Neuroimaging Initiative dataset. The sample included 398 individuals characterized as cognitively normal or mild cognitive impairment. Support vector machine classification algorithms were used to identify the most predictive modality from five different data modality types (demographics, structural MRI, fluorodeoxyglucose-PET, neurocognitive and genetic/fluid-based biomarkers). In addition, variable selection identified individual variables across all modalities that best predicted functional decline in a testing sample. Of the five modalities examined, neurocognitive measures demonstrated the best accuracy in predicting functional decline (accuracy = 74.2%; area under the curve = 0.77), followed by fluorodeoxyglucose-PET (accuracy = 70.8%; area under the curve = 0.66). The individual variables with the greatest discriminatory ability for predicting functional decline included partner report of language in the Everyday Cognition questionnaire, the ADAS13, and activity of the left angular gyrus using fluorodeoxyglucose-PET. These three variables collectively explained 32% of the total variance in functional decline. Taken together, the machine learning model identified novel biomarkers that may be involved in the processing, retrieval, and conceptual integration of semantic information and which predict functional decline two years after assessment. These findings may be used to explore the clinical utility of the Everyday Cognition as a non-invasive, cost and time effective tool to predict future functional decline.
进行日常生活工具性活动的能力，如支付账单、记住预约和独自购物，会随着年龄的增长而下降，但老年人在下降速度上存在显著的个体差异。了解与日常生活工具性活动下降相关的变量对于提供适当干预以延长独立性至关重要。先前的研究表明，认知测量、神经影像学和基于体液的生物标志物可预测功能下降。然而，变量的选择可能导致对某些变量的高估以及对其他可能具有预测性的变量的排除。在本研究中，我们使用机器学习技术从阿尔茨海默病神经影像学倡议数据集的个体中选择了一系列最能预测两年内功能下降的基线变量。样本包括398名被表征为认知正常或轻度认知障碍的个体。支持向量机分类算法用于从五种不同的数据模态类型（人口统计学、结构磁共振成像、氟脱氧葡萄糖正电子发射断层扫描、神经认知和基于基因/体液的生物标志物）中识别最具预测性的模态。此外，变量选择确定了所有模态中最能预测测试样本功能下降的个体变量。在所检查的五种模态中，神经认知测量在预测功能下降方面表现出最佳准确性（准确率 = 74.2%；曲线下面积 = 0.77），其次是氟脱氧葡萄糖正电子发射断层扫描（准确率 = 70.8%；曲线下面积 = 0.66）。对功能下降具有最大判别能力的个体变量包括日常认知问卷中语言的伴侣报告、ADAS13以及使用氟脱氧葡萄糖正电子发射断层扫描测量的左侧角回的活动。这三个变量共同解释了功能下降总方差的32%。总体而言，机器学习模型识别出了可能参与语义信息处理、检索和概念整合的新型生物标志物，这些生物标志物可预测评估后两年的功能下降。这些发现可用于探索日常认知作为一种非侵入性、成本效益高且省时的工具预测未来功能下降的临床效用。
