{"ID":2834178,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03202","arxiv_id":"2512.03202","title":"Quality assurance of the Federal Interagency Traumatic Brain Injury Research (FITBIR) MRI database to enable integrated multi-site analysis","abstract":"The Federal Interagency Traumatic Brain Injury Research (FITBIR) database is a centralized data repository for traumatic brain injury (TBI) research. It includes over 45,529 magnetic resonance images (MRI) from 6,211 subjects (9,229 imaging sessions) across 26 studies with heterogeneous organization formats, contrasts, acquisition parameters, and demographics. In this work, we organized all available structural and diffusion MRI from FITBIR along with relevant demographic information into the Brain Imaging Data Structure. We analyzed whole-brain mean fractional anisotropy, mean diffusivity, total intracranial volume, and the volumes of 132 regions of interest using UNesT segmentations. There were 4,868 subjects (7,035 sessions) with structural MRI and 2,666 subjects (3,763 sessions) with diffusion MRI following quality assurance and harmonization. We modeled profiles for these metrics across ages with generalized additive models for location, scale, and shape (GAMLSS) and found significant differences in subjects with TBI compared to controls in volumes of 54 regions of the brain (q\u003c0.05, likelihood ratio test with false discovery rate correction).","short_abstract":"The Federal Interagency Traumatic Brain Injury Research (FITBIR) database is a centralized data repository for traumatic brain injury (TBI) research. It includes over 45,529 magnetic resonance images (MRI) from 6,211 subjects (9,229 imaging sessions) across 26 studies with heterogeneous organization formats, contrasts,...","url_abs":"https://arxiv.org/abs/2512.03202","url_pdf":"https://arxiv.org/pdf/2512.03202v1","authors":"[\"Adam M. Saunders\",\"Michael E. Kim\",\"Gaurav Rudravaram\",\"Elyssa M. McMaster\",\"Chloe Scholten\",\"Simon Vandekar\",\"Tonia S. Rex\",\"François Rheault\",\"Bennett A. Landman\"]","published":"2025-12-02T19:59:35Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[\"Diffusion Model\",\"Generative Adversarial Network\"]","has_code":false}