The technical premise rests on normative modeling, a statistical framework that has become one of the most influential tools in modern neuroimaging. Instead of comparing patients with a small control group and asking whether a brain region is, on average, larger or smaller, normative modeling fits reference curves — much like the growth charts pediatricians use for height and weight — that describe the expected range of brain measures at every age. Each individual scan is then assigned a centile, or deviation score, quantifying how far that person’s brain structure sits from the population norm. Applied to thousands of scans, this approach turns a single clinical MRI into a rich phenotypic fingerprint, one that can flag extreme deviations and map where in the brain they concentrate.

What makes the new work distinctive is its source of data. Prospective neuroimaging studies routinely exclude individuals with severe cognitive impairment, significant medical comorbidities, or an inability to tolerate a research MRI session without sedation — features that are common in 22q11.2 deletion syndrome and other rare genetic disorders. Those exclusions quietly narrow the phenotypic spectrum that research cohorts can see, limiting how generalizable the findings are to the full population of affected patients. Clinical scans, by contrast, are acquired for medical reasons and include precisely the patients that research studies tend to leave out. In this retrospective, cross-sectional study, the team analyzed 92 patients with 22q11.2 deletion syndrome, aged six months to 21 years and 49 percent female, alongside 252 matched clinical controls, all drawn from hospital records at the Children’s Hospital of Philadelphia.

The reference curves themselves were derived from 1,995 clinical scans, a large normative sample that allowed the researchers to estimate age-specific expectations for a battery of structural brain measures: total gray matter volume, white matter volume, subcortical gray matter volumes, intracranial volume, cortical thickness, and regional cortical surface area and volume. The statistical machinery behind these charts, generalized additive models for location, scale, and shape, is the same family of models used to construct conventional child growth standards, and it can capture how both the average and the variability of brain measures change across development. Every patient scan was then scored against these population norms, producing deviation maps that could be compared across individuals and aggregated to characterize the syndrome as a whole.

The headline result is a picture of widespread structural reduction. Patients with 22q11.2 deletion syndrome showed broad decreases in brain volumes, with the largest effect reaching a Cohen’s d of −1.31 — a very large effect size by any neuroimaging standard — and reductions in cortical surface area peaking at d = −0.71. Cortical thickness, however, moved in the opposite direction, increasing to d = 0.39. That combination is technically meaningful: reduced surface area with preserved or increased thickness suggests that the cortex in this syndrome is affected in its areal expansion rather than simply being thinned, a dissociation that implicates specific developmental processes in the folding and areal patterning of the cerebral sheet. Subcortical structures and white matter were also reduced, consistent with the syndrome’s known impact on deep gray matter nuclei and connecting pathways.

Because clinical samples invite skepticism about scan quality and processing, the team validated their findings against an entirely independent source: the ENIGMA-22q research consortium, a multinational collaboration that has assembled prospectively collected research MRI data from 22q11.2 deletion syndrome patients across dozens of sites. The spatial convergence was striking. Regional patterns of volume and surface area deviations in the clinical cohort correlated with those in the research cohort at r = 0.61 to 0.87, indicating that the two approaches — one built on routine hospital scans, the other on carefully standardized research acquisitions — were seeing the same underlying biology. This is a critical proof of principle that heterogeneous clinical imaging, processed with appropriate quality controls, can robustly characterize brain structure in rare genetic disorders at a scale and inclusiveness that research cohorts alone cannot match.

Within-syndrome heterogeneity emerged as a central theme. Forty percent of patients showed at least one global brain measure below the 2.5th centile of the population norm — a rate of extreme deviation far exceeding what would be expected by chance — yet individual patients varied enormously in which measures were affected and how severely. Standard growth charts, the authors note, can identify patients who deviate from typical development, but they cannot distinguish a patient whose brain is small because of the syndrome from one who is disproportionately affected relative to their genetic peers. That distinction matters clinically, because the patients who sit at the extreme tail of their own syndrome’s distribution may face different risks and may need different monitoring than those who cluster near the syndrome’s average.

To address that gap, the team generated syndrome-specific growth charts — reference curves built from the 22q11.2 deletion syndrome patients themselves, so that each patient’s brain measures could be scored against the expectations of their genetic peers rather than the general population. In exploratory analyses, deviations in cerebellar volume relative to other 22q11.2 deletion syndrome patients were nominally associated with lower language scores across two independent assessment methods, the Clinical Evaluation of Language Fundamentals and the Preschool Language Scales, with p-values below 0.03. The cerebellum, long appreciated for its role in motor coordination, has increasingly been implicated in language and cognition, and the finding hints that cerebellar development may carry prognostic information about cognitive and linguistic outcomes in this syndrome. The authors are careful to frame these analyses as exploratory, but the framework they introduce — scoring individual brain structure against syndrome-specific norms and testing whether those scores predict outcomes — is a novel route toward risk stratification in rare neurogenetic conditions.

The study also reached back to the genome itself. Spatial patterns of cortical volume and surface area deviation across the cortex correlated with the expression of genes located within the 22q11.2 locus, as mapped by the Allen Human Brain Atlas, a publicly available resource that charts where thousands of genes are expressed across the human brain. This gene-expression alignment suggests that the regional pattern of brain alteration is not random but reflects, at least in part, the spatial distribution of the very genes whose absence defines the syndrome. It is a technically elegant convergence: the deletion removes a set of genes, those genes are expressed unevenly across the cortex, and the cortex deviates unevenly in exactly the regions where the deleted genes are most active.

The implications extend well beyond a single microdeletion. 22q11.2 deletion syndrome affects an estimated one in roughly 2,000 to 4,000 births and is among the most common copy number variants involving the brain, and its study has long served as a model for understanding how CNVs shape neuropsychiatric risk, including schizophrenia, autism spectrum disorders, and attention-deficit/hyperactivity disorder. If routine clinical MRI can be transformed into syndrome-specific brain charts for this condition, the same pipeline — normative modeling from large clinical reference samples, deviation scoring, validation against research consortia, and linkage to cognitive outcomes — could in principle be applied to other rare genetic disorders whose patients are systematically underrepresented in research imaging. The work, funded by the National Institute of Mental Health, was conducted under ethical review at CHOP and Penn, with retrospective use of clinical scans determined exempt because it involved secondary use of existing data with low risk to human subjects. What the study delivers, above all, is a scalable framework: a way to turn the scans hospitals already acquire into quantitative, individualized maps of brain development that can include the very patients research studies have historically left behind.

Subject of Research: Brain structure in 22q11.2 deletion syndrome mapped with clinical neuroimaging and normative modeling

Article Title: Charting brain structure in 22q11.2 deletion syndrome with clinical neuroimaging

Article References: Jung, B., Schmitt, J. E., Seidlitz, J., Schabdach, J. M., Karandikar, S., Crowley, T. B., Dorfschmidt, L., Mandal, A. S., Zimmerman, D., Williams, R. M. S., Prem, S., Levitis, E., Gardner, M., Cyr, K., Padmanabhan, V., Taylor, J. H., Ruparel, K., Boen, R., Bearden, C. E., … O’Hara, R. (2026). Charting brain structure in 22q11.2 deletion syndrome with clinical neuroimaging. Genome Medicine. https://doi.org/10.1186/s13073-026-01788-7

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01788-7

Keywords: 22q11.2 deletion syndrome, normative modeling, clinical neuroimaging, ENIGMA-22q, Genome Medicine, brain growth charts, schizophrenia risk, copy number variant, Charting, brain, structure, deletion