Keywords: Microstructure, Diffusion/other diffusion imaging techniques, Diffusion analysis and visualization, biomarkers, cortical architecture, non-invasive virtual histology
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.
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