  Deep learning (DL) for predicting Alzheimer's disease (AD) has provided
timely intervention in disease progression yet still demands attentive
interpretability to explain how their DL models make definitive decisions.
Recently, counterfactual reasoning has gained increasing attention in medical
research because of its ability to provide a refined visual explanatory map.
However, such visual explanatory maps based on visual inspection alone are
insufficient unless we intuitively demonstrate their medical or neuroscientific
validity via quantitative features. In this study, we synthesize the
counterfactual-labeled structural MRIs using our proposed framework and
transform it into a gray matter density map to measure its volumetric changes
over the parcellated region of interest (ROI). We also devised a lightweight
linear classifier to boost the effectiveness of constructed ROIs, promoted
quantitative interpretation, and achieved comparable predictive performance to
DL methods. Throughout this, our framework produces an ``AD-relatedness index''
for each ROI and offers an intuitive understanding of brain status for an
individual patient and across patient groups with respect to AD progression.
