Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Neuroimage. 2020 Oct 1;219:116938. doi: 10.1016/j.neuroimage.2020.116938. Epub 2020 Jun 2.
Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised functional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject.
从大脑解剖磁共振成像预测受试者年龄有可能提供大脑变化的敏感总结，表明不同的神经退行性疾病。然而，现有研究通常忽略了这些预测的不确定性。在这项工作中，我们通过应用功能数据分析方法考虑到了这种不确定性。我们提出了一种基于阿尔茨海默病神经影像学倡议（ADNI）中认知正常（CN）受试者的年龄对大脑结构的惩罚功能分位数回归模型，并将其用于预测轻度认知障碍（MCI）和阿尔茨海默病（AD）患者的大脑年龄。与大脑年龄预测文献中提供仅有点预测的机器学习方法不同，我们模型的结果是每个受试者的预测区间。
