  Identifying and utilising various biomarkers for tracking Alzheimer's disease
(AD) progression have received many recent attentions and enable helping
clinicians make the prompt decisions. Traditional progression models focus on
extracting morphological biomarkers in regions of interest (ROIs) from MRI/PET
images, such as regional average cortical thickness and regional volume. They
are effective but ignore the relationships between brain ROIs over time, which
would lead to synergistic deterioration. For exploring the synergistic
deteriorating relationship between these biomarkers, in this paper, we propose
a novel spatio-temporal similarity measure based multi-task learning approach
for effectively predicting AD progression and sensitively capturing the
critical relationships between biomarkers. Specifically, we firstly define a
temporal measure for estimating the magnitude and velocity of biomarker change
over time, which indicate a changing trend(temporal). Converting this trend
into the vector, we then compare this variability between biomarkers in a
unified vector space(spatial). The experimental results show that compared with
directly ROI based learning, our proposed method is more effective in
predicting disease progression. Our method also enables performing longitudinal
stability selection to identify the changing relationships between biomarkers,
which play a key role in disease progression. We prove that the synergistic
deteriorating biomarkers between cortical volumes or surface areas have a
significant effect on the cognitive prediction.
