Brain stiffness is known to decrease in subjects with Alzheimer’s disease (AD). However, previously reported stiffness estimates were heavily weighted toward white matter. Here we investigate the sensitivity of cortical-centric stiffness measurements for detecting AD pathophysiology, given that the cortex is the primary site of pathology. Using a neural network-based inversion algorithm, cortical-centric measurements are highly repeatable with test-retest errors of less than 2% on average. With respect to AD, the medial temporal lobe region of interest is found to best discriminate those with dementia from cognitively normal subjects, and performs better than previously reported methods.