Motivation: Due to the complexity of brain tissue, single-contrast MRI approaches often lack the sensitivity and specificity to identify a specific pathology. Goal(s): Our goal was to develop a method to predict specific Alzheimer's disease (AD) pathologies or their cellular features from diffusion-T2 relaxation correlation density distributions. Approach: We adopted a multimodal approach by integrating and co-registering histological and MRI datasets from 12 human donors with mixed AD diagnoses. We used elastic net-regularized linear regression to predict histological stains from the diffusion-T2 density distribution. Results: We could predict voxelwise amyloid beta, phosphorylated tau, microglia, and myelin content (cross-validated R2=0.32,0.55,0.49,0.60 and Pearson correlation 0.57,0.74,0.70,0.78, respectively). Impact: Ex vivo diffusion-T2 relaxation correlation MRI can be used to detect multiple Alzheimer's disease pathologies. This approach could lead the way for advanced in vivo characterization of pathologies related to aging and dementia.
Manninen et al. (Tue,) studied this question.