Spatial Patterns of Longitudinal Gray Matter Change as Predictors of Concurrent Cognitive Decline in Amyloid Positive Healthy Subjects.

A substantial proportion of cognitively healthy elders (HC) show abnormally high amyloid-β (Aβ) deposition, a major pathology of Alzheimer's disease (AD). These subjects are at increased risk of Alzheimer's disease (AD) dementia, and biomarkers are needed to predict their cognitive deterioration. Here we used relevance vector regression (RVR), a pattern-recognition method, to predict concurrent cognitive decline on the basis of longitudinal gray matter (GM) changes, within two a priori, meta-analytically defined functional networks subserving episodic memory and executive function. Ninety-six HC subjects were assessed annually for three years with structural MRI and cognitive tests within the Alzheimer's Disease Neuroimaging Initiative. Presence of abnormal biomarker values of Aβ (Aβ+) were determined with cerebrospinal fluid and amyloid-PET (HC-Aβ+, n=30; with n=66 for normal HC-Aβ-). Using leave-one-out cross-validation, we found that in HC-Aβ+ patterns of GM changes within both networks predicted decline in episodic memory (r=0.61, p<0.001; r=0.40, p=0.03), but not executive function. In HC-Aβ-, GM changes within the executive function network predicted decline in executive function (r=0.44, p<0.001). Previously established region-of-interest (ROI)-based predictors such as changes in hippocampal volume, within an AD-signature multi-ROI, or total GM volume were not predictive of cognitive decline in any group or cognitive domain. RVR analyses unrestricted to the a priori networks yielded compatible results with the restricted case. In conclusion, RVR-derived patterns of subtle cortical GM changes are biomarker candidates of concurrent cognitive decline in aging and subjects at risk for AD.