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Identifying and utilising various biomarkers for tracking Alzheimer's disease(AD) progression have received many recent attentions and enable helpingclinicians make the prompt decisions. Traditional progression models focus onextracting morphological biomarkers in regions of interest (ROIs) from MRI/PETimages, such as regional average cortical thickness and regional volume. Theyare effective but ignore the relationships between brain ROIs over time, whichwould lead to synergistic deterioration. For exploring the synergisticdeteriorating relationship between these biomarkers, in this paper, we proposea novel spatio-temporal similarity measure based multi-task learning approachfor effectively predicting AD progression and sensitively capturing thecritical relationships between biomarkers. Specifically, we firstly define atemporal measure for estimating the magnitude and velocity of biomarker changeover time, which indicate a changing trend(temporal). Converting this trendinto the vector, we then compare this variability between biomarkers in aunified vector space(spatial). The experimental results show that compared withdirectly ROI based learning, our proposed method is more effective inpredicting disease progression. Our method also enables performing longitudinalstability selection to identify the changing relationships between biomarkers,which play a key role in disease progression. We prove that the synergisticdeteriorating biomarkers between cortical volumes or surface areas have asignificant effect on the cognitive prediction.

Spatio-Temporal Similarity Measure based Multi-Task Learning for Predicting Alzheimer's Disease Progression using MRI Data