Leveraging Clinical Neuroimaging to Chart Brain Structure in 22q11.2 Deletion Syndrome

Background22q11.2 deletion syndrome (22q11DS) is a common microdeletion associated with widespread brain alterations and elevated risk for schizophrenia and other neuropsychiatric conditions. Prospective research studies often exclude individuals with severe cognitive impairment, medical comorbidities, or inability to tolerate research MRI without sedation, features common in 22q11DS. This limits both the generalizability of neuroimaging findings and our understanding of the full phenotypic spectrum. Moreover, while standard brain growth charts quantify deviation from typical development, they cannot identify patients who are disproportionately affected relative to their genetic peers, limiting clinical utility for risk stratification. Leveraging clinical MRI data offers a scalable approach to address these gaps. MethodsWe analyzed 92 patients with 22q11DS (age 0.5-21 years, 49% female) and 252 matched clinical controls. Using normative modeling derived from 1,995 reference clinical scans, we quantified individual-level brain deviations from population norms. We validated clinical findings against the independent ENIGMA-22q research consortium, characterized rates of extreme structural deviations to assess within-syndrome heterogeneity, correlated spatial patterns of brain alterations with gene expression from the Allen Human Brain Atlas, and generated syndrome-specific growth charts to test whether deviations from syndrome-specific norms predicted cognitive and language outcomes. ResultsPatients with 22q11DS showed widespread reductions in brain volumes (max Cohens d=-1.31) and cortical surface area (d=-0.71) with increased cortical thickness (d=0.39). These findings were highly convergent with the ENIGMA-22q research cohort (r=0.61-0.87). Forty percent of patients showed at least one global brain measure below the 2.5th percentile. Spatial patterns of cortical volume and surface area correlated with the expression of genes within the 22q11.2 locus. Critically, syndrome-specific growth charts revealed that smaller cerebellar volume relative to 22q11DS peers predicted lower language scores across two independent assessment methods (p<0.03), demonstrating potential prognostic utility. ConclusionsThis study provides a critical proof of principle for using heterogeneous clinical imaging to robustly characterize brain structure in rare genetic disorders. Syndrome-specific growth charts provide a novel framework to quantify within-syndrome variability and demonstrate potential prognostic value by linking individual brain structure to cognitive outcomes.