Researchers from the Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC have developed an AI model that helps fill gaps in brain wiring maps made with diffusion MRI, a scan that traces the brain's nerve fiber pathways, which could help researchers study how key brain connections change with aging and disease. The model, described in a study published in Medical Image Analysis, targets the brainstem and the orbitofrontal region just above the eyes, two areas where distortions in the scanner's magnetic field can erase much of the signal. The team trained and tested the model using scans from 1,315 participants in the UK Biobank and 679 participants in the Lifespan Human Connectome Project Aging study (HCP-Aging), two large studies of mostly older adults. The model, FOD-Diffusion, builds on a type of generative AI called a diffusion model, which creates images by starting with random noise and removing it step by step. The data it restores, called fiber orientation distributions, consist of 45 linked 3D maps of the brain for each scan. To test accuracy, the team hid sections of the brainstem in scans with minimal signal loss and asked the model to reconstruct them; across 43 test participants, the model's main fiber directions differed from the original data.