High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer’s disease progression prediction

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by pro-gressive impairment of memory and other cognitive functions. Regression analy-sis has been studied to relate neuroimaging measures to cognitive status. However, whether these measures have further predictive power to infer a trajectory of cog-nitive performance over time is still an under-explored but important topic in AD research. We propose a novel high-order multi-task learning model to address this issue. The proposed model explores the temporal correlations existing in imag-ing and cognitive data by structured sparsity-inducing norms. The sparsity of the model enables the selection of a small number of imaging measures while main-taining high prediction accuracy. The empirical studies, using the longitudinal imaging and cognitive data of the ADNI cohort, have yielded promising results.