Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease

Alzheimer’s disease (AD) is the most common form of de-mentia that causes progressive impairment of memory and other cognitive functions. Multivariate regression models have been studied in AD for revealing relationships between neuroimaging measures and cognitive scores to understand how structural changes in brain can influence cognitive sta-tus. Existing regression methods, however, do not explic-itly model dependence relation among multiple scores de-rived from a single cognitive test. It has been found that such dependence can deteriorate the performance of these methods. To overcome this limitation, we propose an effi-cient sparse Bayesian multi-task learning algorithm, which adaptively learns and exploits the dependence to achieve improved prediction performance. The proposed algorithm is applied to a real world neuroimaging study in AD to pre-dict cognitive performance using MRI scans. The effective-ness of the proposed algorithm is demonstrated by its supe-rior prediction performance over multiple state-of-the-art competing methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are con-sistent with prior knowledge. 1.