Comparison of single and multi-task learning for predicting cognitive decline based on MRI data

Abstract

Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in the timing of therapeutic interventions in dementia and at-risk populations. In the present work, we compared single- and multi-task learning approaches to predict the changes in ADAS-Cog scores based on T1-weighted anatomical magnetic resonance imaging (MRI). In contrast to most machine learning-based methods to predict the changes in ADAS-Cog, we stratified the subjects based on their baseline diagnoses and evaluated the prediction performances in each group. Our experiments indicated a positive relationship between predicted and observed ADAS-Cog score changes in each diagnostic group suggesting that T1-weighted MRI has predictive value for evaluating cognitive decline in the whole AD continuum. We further studied whether correction of the differences in magnetic field strength of MRI would improve ADAS-Cog score prediction. The partial least square-based domain adaptation improved slightly prediction performance, but the improvement was marginal. In sum, this study demonstrated that ADAS-Cog change can be, to some extent, predicted based on anatomical MRI. Based on this study, the recommended method for learning the predictive models is a single-task regularized linear regression owing to its simplicity and good performance. It appears important to combine the training data across all subject groups for the most effective predictive models.