磁共振成像
心理学
逻辑回归
神经影像学
神经心理学
脑脊液
曲线下面积
内科学
验证性因素分析
阿尔茨海默病
认知
疾病
医学
结构方程建模
神经科学
机器学习
放射科
计算机科学
作者
Min Chang,Charles J. Brainerd
标识
DOI:10.1080/13803395.2022.2115015
摘要
We studied the ability of latent factor scores to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and investigated whether multimodal factor scores improve predictive power, relative to single-modal factor scores.We conducted exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) of the baseline data of MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to generate factor scores for three data modalities: neuropsychological (NP), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF). Factor scores from single or multiple modalities were entered in logistic regression models to predict MCI to AD conversion for 160 ADNI subjects over a 2-year interval.NP factors attained an area under the curve (AUC) of .80, with a sensitivity of .66 and a specificity of .77. MRI factors reached a comparable level of performance (AUC = .80, sensitivity = .66, specificity = .78), whereas CSF factors produced weaker prediction (AUC = .70, sensitivity = .56, specificity = .79). Combining NP factors with MRI or CSF factors produced better prediction than either MRI or CSF factors alone. Similarly, adding MRI factors to NP or CSF factors produced improvements in prediction relative to NP or CSF factors alone. However, adding CSF factors to either NP or MRI factors produced no improvement in prediction.Latent factor scores provided good accuracy for predicting MCI to AD conversion. Adding NP or MRI factors to factors from other modalities enhanced predictive power but adding CSF factors did not.
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