Diffusion MRI-based Estimation of Cortical Architecture via Machine Learning (DECAM) enhanced by cortical label vectors

Motivation: Advanced diffusion MRI (dMRI) has enabled noninvasive assessment of cortical measures conventionally only available from neuropathology. Analytical dMRI models are limited by restrictive model assumptions. Goal(s): In this study, we develop Diffusion-MRI based Estimation of Cortical Architecture using Machine-learning (DECAM), a translational framework of “noninvasive neuropathology” that can quantify cortical architecture based on dMRI. Approach: DECAM incorporates cortical label vectors to address the challenge of achieving perfect MRI-histology registration in primate brains due to their complex morphology. Results: By providing high-fidelity, reproducible whole-brain soma density maps validated with histology, DECAM paves the way for data-driven noninvasive histology for potential applications such as Alzheimer’s. Impact: DECAM is the first translational framework and robust pipeline that addresses the challenge of estimating high-fidelity whole-brain soma density in primate brains with complex morphology. DECAM paves the way for data-driven noninvasive histology for potential applications such as Alzheimer’s.