Therefore, the goal of the present study was to derive more disentangled SPARE-BA and SPARE-AD scores by testing the following two approaches: (i) refine the training samples for both machine learning models by implementing strict molecular diagnostic criteria using amyloid and tau measurements and (ii) add Alzheimer’s disease brains in the training of the SPARE-BA model, which would lead the model to learn ageing patterns with brain features least affected by Alzheimer’s disease. This evidence concerns the gene MAPT and early-onset autosomal dominant Alzheimer disease.