Aaron Alexander-Bloch

The insula is a heterogeneous region associated with a range of functional domains including sensory, cognitive, and emotional processing. Cytoarchitectural and functional studies have described an antero-posterior axis of insula organization. However, the insula's functional development remains sparsely described. We delineated the development of insula resting-state functional connectivity across 562 individuals (6-22 years) using generalized additive models and population value decomposition. We show insulo-cortical functional connectivity development differs across the insula: anterior subregions have predominantly decreasing connectivity across the age range examined, whereas posterior subregions have variable connectivity that aligns with the sensorimotor-association axis. Vertex-level analysis revealed that age-related trajectories followed an antero-posterior axis that aligned with estimates of insula microstructure. Our results emphasize that the development of insula connectivity is spatially heterogeneous and aligns with known cytoarchitecture. The normative insula development described here provides a foundation for investigating how emerging psychopathology may be linked to variation in insula development.

Background: 22q11.2 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 magnetic resonance imaging (MRI) without sedation, features common in 22q11DS and other rare genetic disorders. 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. Methods: In this retrospective, cross-sectional study, we 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 patient 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. Results: Patients with 22q11DS showed widespread reductions in brain volumes (max Cohen’s 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 centile. Spatial patterns of cortical volume and surface area correlated with expression of genes within the 22q11.2 locus. In exploratory analyses, syndrome-specific growth chart deviations in cerebellar volumes relative to 22q11DS peers were nominally associated with lower language scores across two independent assessment methods ( p < 0.03), suggesting potential prognostic utility. Conclusions: This 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.

Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations. However, a limitation to date has been the performance of existing segmentation tools for measuring various brain phenotypes in clinical scans. In particular, many tools underperform in infant scans due to morphological and physical changes such as rapid myelination. Here, we introduce ClinSeg: a robust segmentation approach tailored to early-life clinical MRIs with variable orientation, resolution, and contrast. We leverage existing registration and synthetic data generation tools to construct a training corpus for a 3d U-Net spanning anatomical and contrast diversity, including scans with morphological abnormalities from a pediatric hospital. Validated against manual segmentations, ClinSeg outperforms existing models in infancy while matching them in childhood and adolescence. Finally, ClinSeg enables the construction of reference brain growth trajectories in 11,699 individuals from 0-21 years of age, leading to the detection of more nuanced age-related findings in clinical groups.

Background: Children and young people (CYP) with intellectual disability (ID) frequently have co-occurring neurodevelopmental (ND) and mental health (MH) difficulties. While copy number variants (CNVs) are identified as an important aetiology of ID, it is unclear whether and how CNV risk scores predict ND and MH characteristics within the CNV-associated ID population. Methods: We analysed data from the UK-based IMAGINE-ID cohort of CYP (aged 4–19 years) with ID and clinically-reported CNVs (N = 1,640). CNVs were annotated with Gencode 19 in ENSEMBL to calculate CNV risk scores, including summed probability of loss-of-function intolerance (pLI) and dosage sensitivity. Multivariate regression models examined the prediction of CNV variables and inheritance on ND and MH characteristics, assessed via the Development and Well-Being Assessment (DAWBA). Post-hoc analyses explored CNV variable stratification (lower vs. higher range pLI). Results: Higher summed pLI scores (indexing CNV genes’ intolerance to loss of function) unexpectedly predicted fewer MH difficulties and a lower likelihood of ND diagnoses, even after accounting for demographic factors and CNV inheritance. Post-hoc analyses identified a threshold effect. Within the lower pLI range, higher pLI scores were associated with greater MH difficulties, consistent with findings from population-based samples. In contrast, within the higher pLI range, higher pLI scores were associated with fewer MH difficulties (among individuals more likely to have severe ID). Conclusion: These findings challenge the assumption that CNV genomic “risk scores” universally predict ND and MH difficulties. Instead, within CNV-associated ID, complex relationships exist between CNV risk scores, inheritance and phenotypes. These insights emphasise the necessity of integrating genomic results with familial and developmental context to understand individual vulnerabilities and support needs.

Reference intervals, defined as intervals containing a new observation with a specified probability relative to reference data, would be clinically useful in assessing brain magnetic resonance imaging (MRI). Brain charts, which are estimates of MRI phenotypes across covariates such as age and sex, can be used to construct reference intervals. However, the reference data used to fit intervals often differ from a new sample in terms of study design, MRI acquisition, and image preprocessing. Application of MRI reference intervals to new samples remains a challenging problem. Here, we propose a new method called reference interval calibration via conFormal prediction (ReForm) that adjusts reference intervals for a new sample. Our method builds on recent work in conformal prediction, which yields intervals with guaranteed coverage for new observations. Through resampling experiments in Lifespan Brain Chart Consortium cortical thickness data, we compare ReFormed reference intervals with refitting intervals, statistical harmonization methods, and model-based adjustment of intervals. Notably for patient privacy concerns, ReForm does not require sharing of reference data. Yet, our empirical results demonstrate that ReForm controls FPR similarly or better than alternative methods that require sharing reference data. Finally, we provide recommendations for practical applications of ReForm and an R package (https://github.com/andy1764/ReForm) for calibrating reference intervals using ReForm.

Late-onset Alzheimer's disease (LOAD) is the most common cause of dementia in older adults, with specific genomic copy number variants (CNVs) implicated in its pathology. However, the aggregate burden of genome-wide CNVs in dementia and age-related neuropathologies is uncharacterized. This study investigated the association between genome-wide CNV scores (CNV-S) and dementia, as well as LOAD-related neuropathologies, in 1011 elderly participants (mean age 88.06) from two ongoing US-based longitudinal clinical-pathological cohort studies who were initially dementia-free and consented to brain donation upon death. Participants exhibited varying cognitive statuses at death (429 dementia, 258 mild cognitive impairment, 324 cognitively normal). We evaluated effects of (1) eight individual-level CNV-S based on gene loss intolerance and dosage sensitivity; (2) a single nucleotide polymorphism (SNP)-based LOAD polygenic score (PGS-LOAD) calculated using Bayesian continuous shrinkage; and (3) covariates (age, sex, and education). Outcomes included cognitive scores across 19 tests, clinical diagnoses of Alzheimer's disease or mild cognitive impairment, and four LOAD-related neuropathologies assessed postmortem. Analyses identified 4867 CNVs (3918 deletions, 949 duplications) mapped to 3211 genes. Higher deletion CNV-S were significantly associated with increased cerebrovascular pathologies (pLI: β = 0.14, 95% CI [0.08, 0.21]; LOEUF: β = 0.14, 95% CI [0.08, 0.20]; pHI: β = 0.15, 95% CI [0.08, 0.21]; binarized pHI: β = 0.14, 95% CI [0.08, 0.21]). Models predicting cerebral atherosclerosis that included deletion CNV-S significantly outperformed models based on only PGS-LOAD (R² increase: 0.02). These findings suggest that genome-wide CNV burden, particularly deletions in dosage-sensitive genes, contributes to cerebrovascular pathology in aging. CNV-S may augment existing LOAD genetic risk models by capturing vascular pathways distinct from traditional SNP-based risk.

The cerebellum contains most of the brain’s neurons and supports many functions, yet how it changes with age remains unclear. Here we used three brain imaging studies spanning 47,000 adults and examined how different parts of the cerebellum age and their relation to cognition. We characterized cerebellar aging using volumetry and the T1-weighted/T2-weighted ratio, and corroborated these findings with quantitative magnetic resonance imaging in an independent sample. We show a spatially heterogeneous pattern of aging in which specific association and motor-related regions show steeper relationships with age than other lobules. Greater cerebellar volume was associated with higher cognitive scores with increasing age, suggesting that cerebellar structure may provide brain reserve that helps maintain function despite aging. In patients with Alzheimer’s disease, cerebellar volume was linked to cognition in individuals with lower amyloid burden, especially in those carrying two copies of the APOE -ε4 risk gene. This supports a threshold-reserve model, in which the cerebellum helps sustain cognition until pathology becomes widespread. These results show that the cerebellum has an active role in healthy cognitive aging and resilience.

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. Key Points: Question: In genetically confirmed leukodystrophies, can anatomical MRI measurements benchmarked against brain growth charts identify neuroanatomical patterns that correlate with clinical function and disease severity? Findings: In this cross-sectional neuroimaging study of six leukodystrophies, imaging-derived quantitative phenotypes benchmarked against brain growth charts revealed neuroanatomical patterns of volume loss consistent with previously-reported qualitative changes for each disorder. These patterns of regional volume loss correlated with measures of clinical function, particularly in POLR3-related leukodystrophy, TUBB4A-related leukodystrophy, and Aicardi-Goutières Syndrome. Meaning: Brain growth charts may be a valuable tool for characterizing the patterns of involvement across different leukodystrophies. Furthermore, this approach may facilitate the use of atrophy as a biomarker for assessing disease severity in clinical trials.