Objective: This study aims to construct a reliable neuroimaging dataset to assess the impact of GNAO1 mutations on brain development.

Background: GNAO1-related disorders (GNAO1-RD) are ultra-rare conditions characterized by epilepsy, movement disorders—including dyskinetic crises—psychomotor delay/intellectual disability, and frequent gastrostomy use. While clinical recognition of GNAO1-RD has expanded, structural brain abnormalities remain poorly characterized due to the scarcity of systematic neuroimaging studies. Current knowledge on neuroimaging findings in GNAO1-RD is fragmented, largely based on individual case reports or small case series. A standardized evaluation of brain structure alterations in a larger cohort is essential to refine the phenotypic spectrum and understand potential genotype-phenotype correlations.

Method: We retrospectively reviewed MRI data from 35 patients with genetically confirmed GNAO1-RD, including 24 from our institution and 11 from seven other centers. T1-weighted images were assessed for quality, ensuring sufficient resolution for volumetric analysis (<1.3 mm³). A total of 79 MRI sessions were identified, of which 67 (84%) passed quality control. Brain volume measurements were performed using a methodology adapted to heterogeneous, multi-center neuroimaging data.

Results: Preliminary volumetric analysis of nine patients aged 4–8 years (mean = 6.6 years) revealed significant reductions in two brain structures compared to age-matched controls: the cerebellar crus X (mean relative volume deficit = 76%) and the entorhinal cortex (mean relative volume deficit = 48%). These volume reductions were consistently bilateral, suggesting a potential structural hallmark of GNAO1-RD.

Conclusion: This study demonstrates the feasibility of constructing a standardized neuroimaging database for rare disorders using clinical MRI data. Our findings suggest that GNAO1 mutations may lead to measurable structural changes, particularly in regions involved in motor coordination and cognitive processing. Further studies in larger cohorts are warranted to explore the clinical significance of these abnormalities and their potential role in disease progression. This approach may also serve as a model for investigating neuroimaging biomarkers in other ultra-rare neurological disorders.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/constructing-a-multicenter-neuroimaging-dataset-to-unveil-structural-brain-alterations-in-gnao1-related-disorders/