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The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is aneuropsychological tool that has been designed to assess the severity ofcognitive symptoms of dementia. Personalized prediction of the changes inADAS-Cog scores could help in timing therapeutic interventions in dementia andat-risk populations. In the present work, we compared single and multitasklearning approaches to predict the changes in ADAS-Cog scores based onT1-weighted anatomical magnetic resonance imaging (MRI). In contrast to mostmachine learning-based prediction methods ADAS-Cog changes, we stratified thesubjects based on their baseline diagnoses and evaluated the predictionperformances in each group. Our experiments indicated a positive relationshipbetween the predicted and observed ADAS-Cog score changes in each diagnosticgroup, suggesting that T1-weighted MRI has a predictive value for evaluatingcognitive decline in the entire AD continuum. We further studied whethercorrection of the differences in the magnetic field strength of MRI wouldimprove the ADAS-Cog score prediction. The partial least square-based domainadaptation slightly improved the prediction performance, but the improvementwas marginal. In summary, this study demonstrated that ADAS-Cog change couldbe, to some extent, predicted based on anatomical MRI. Based on this study, therecommended method for learning the predictive models is a single-taskregularized linear regression due to its simplicity and good performance. Itappears important to combine the training data across all subject groups forthe most effective predictive models.

Comparison of single and multitask learning for predicting cognitive decline based on MRI data