Normative modeling for quantitative brain MRI phenotyping and biomarker discovery for pediatric leukodystrophies.

Importance: Leukodystrophies are a heterogeneous group of genetic disorders affecting the white matter of the brain, often presenting with overlapping clinical features but differing in neuroanatomical involvement. There is a critical need for quantitative tools to characterize disease burden and support diagnosis, severity stratification, and clinical trial readiness. Objective: To characterize shared and distinct neuroanatomical patterns across six genetically confirmed leukodystrophies using anatomical MRI-derived phenotypes benchmarked against brain growth charts, and to assess the utility of this methodological approach for identifying imaging biomarkers of disease severity. Design: Cross-sectional neuroimaging study using retrospective clinical MRI data. Setting: Multicenter study incorporating data from the Global Leukodystrophy Initiative Clinical Trials Network (GLIA-CTN) and control data from the Children's Hospital of Philadelphia. Participants: The study included 434 MRI scan sessions from 274 patients with genetically confirmed leukodystrophies (Pelizaeus-Merzbacher disease, Metachromatic leukodystrophy, Alexander disease, Aicardi-Goutières syndrome, TUBB4A-related leukodystrophies, and POLR3-related leukodystrophy). Control MRI data (7628 scans from 7205 subjects) were drawn from the Scans with Limited Imaging Pathology cohort at the Children's Hospital of Philadelphia. Exposures: All MRI scans underwent automated segmentation using deep learning segmentation tools to derive global and regional brain volumes. Normative models of brain development ("brain growth charts") were generated for the control cohort using generalized additive models for location, scale, and shape. Centile scores were then calculated for leukodystrophy subjects to quantify deviations from typical development. Main Outcomes and Measures: Centile scores for global and regional brain volumes were compared across leukodystrophy subtypes to identify disease-specific neuroanatomical patterns and to evaluate their potential utility for severity stratification. Results: Distinct patterns of neuroanatomical deviation were observed across leukodystrophy subtypes. Certain leukodystrophies showed preferential involvement of specific cortical or subcortical regions, while others displayed more diffuse volume loss. Centile scores demonstrated potential for differentiating disease subtypes and stratifying individuals by severity. Preliminary longitudinal data suggest centile scores may also track progression over time. Conclusions and Relevance: This study demonstrates the feasibility and utility of MRI profiling of individuals with leukodystrophy using anatomical MRI-derived phenotypes benchmarked against brain growth charts. The approach enables data-driven, quantitative characterization of structural brain abnormalities, offering a scalable method for phenotyping, diagnosis, and future use in clinical trials.