Machine learning identifies novel markers predicting functional decline in older adults.
Machine learning identifies novel markers predicting functional decline in older adults.
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DOI：
10.1093/braincomms/fcab140
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发表时间：
2021-07
影响因子：
4.8
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Valerio KE;Prieto S;Hasselbach AN;Moody JN;Hayes SM;Hayes JP;Alzheimer’s Disease Neuroimaging Initiative
单独进行日常生活中的工具性活动（例如支付账单、记住约会和购物）的能力随着年龄的增长而下降，但老年人的下降速度存在显着的个体差异。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的干预措施以延长独立性至关重要。先前的研究表明，认知测量、神经影像学和基于液体的生物标志物可以预测功能衰退。然而，变量的先验选择可能会导致某些变量的高估，并排除其他可能具有预测性的变量。在这项研究中，我们使用机器学习技术从阿尔茨海默病神经影像倡议数据集中选择了各种基线变量，这些变量最能预测个体在两年内的功能衰退。该样本包括 398 名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、结构 MRI、氟脱氧葡萄糖-PET、神经认知和基于遗传/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式中最能预测测试样本功能下降的单个变量。在所检查的五种方式中，神经认知测量显示预测功能衰退的准确性最高（准确度 = 74.2%；曲线下面积 = 0.77），其次是氟脱氧葡萄糖-PET（准确度 = 70.8%；曲线下面积 = 0.66）。预测功能衰退最具区分能力的个体变量包括日常认知问卷中的语言伙伴报告、ADAS13 以及使用氟脱氧葡萄糖 PET 检测的左角回活动。这三个变量共同解释了功能衰退总方差的 32%。综上所述，机器学习模型识别出可能参与语义信息的处理、检索和概念整合的新型生物标志物，并预测评估后两年的功能衰退。这些发现可用于探索日常认知作为一种非侵入性、成本和时间有效的工具来预测未来功能衰退的临床效用。瓦莱里奥等。等人。使用机器学习来研究功能衰退的预测因素。他们发现，与人口统计、MRI、FDG-PET 和液体生物标志物测量相比，神经认知测量显示出最佳的预测能力。研究结果进一步确定语义信息处理是一种新颖的结构，对于预测功能衰退可能至关重要。
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, a priori 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. Valerio et. al. used machine learning to study predictors of functional decline. They found that neurocognitive measures showed the best predictive ability compared to demographic, MRI, FDG-PET, and fluid biomarker measures. Findings further identified semantic information processing as a novel construct that may be critical for predicting decline in function.
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影响因子：
168.9
作者：
Christensen, Kaare;Doblhammer, Gabriele;Rau, Roland;Vaupel, James W.
通讯作者：
Vaupel, James W.
DOI：
10.1016/j.jalz.2011.03.005
发表时间：
2011-05
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
McKhann GM;Knopman DS;Chertkow H;Hyman BT;Jack CR Jr;Kawas CH;Klunk WE;Koroshetz WJ;Manly JJ;Mayeux R;Mohs RC;Morris JC;Rossor MN;Scheltens P;Carrillo MC;Thies B;Weintraub S;Phelps CH
通讯作者：
Phelps CH
影响因子：
5.1
作者：
Ba, M.;Ng, K. P.;Yu, L.
通讯作者：
Yu, L.
影响因子：
3.7
作者：
Cui Y;Liu B;Luo S;Zhen X;Fan M;Liu T;Zhu W;Park M;Jiang T;Jin JS;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
5.7
作者：
Gray, Katherine R.;Aljabar, Paul;Heckemann, Rolf A.;Hammers, Alexander;Rueckert, Daniel
通讯作者：
Rueckert, Daniel