Gabrielle Dagasso ^1,2,3,4* Matthias Wilms ^2,4,5,6 Sarah J. MacEachern ^2,4,5 Nils Daniel Forkert ^1,2,4,7

• ¹ Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
• ² Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
• ³ Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada
• ⁴ Alberta Children's Hospital, Calgary, Alberta, Canada
• ⁵ Department of Pediatrics, Cumming School fo Medicine, University of Calgary, Calgary, Alberta, Canada
• ⁶ Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
• ⁷ Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

Quantitative global or regional brain imaging measurements, known as imaging-specific or -derived phenotypes (IDPs), are commonly used in genotype-phenotype association studies to explore the genomic architecture of the brain and how it may be affected by neurological diseases (e.g., Alzheimer's disease), mental health (e.g., depression), and neurodevelopmental disorders (e.g., attention-deficit hyperactivity disorder [ADHD]). For this purpose, medical images have been used as IDPs using a voxelwise or global approach via principal component analysis. However, these methods have limitations related to multiple testing or the inability to isolate high variation regions, respectively. To address these limitations, this study investigates a localized, principal component analysis-like approach for dimensionality reduction of cross-sectional T1-weighted MRI datasets utilizing diffeomorphic morphometry. This approach can reduce the dimensionality of images while preserving spatial information and enables the inclusion of spatial locality in the analysis. In doing so, this method can be used to explore morphometric brain changes across specific components and spatial scales of interest and to identify associations with genome regions. For a first clinical feasibility analysis, this method was applied to data from the Adolescent Brain Cognitive Development (ABCD) study, wherein meaningful associations of specific morphometric features with genome regions were identified within data from adolescents with ADHD (n=1359), obsessive-compulsive disorder (n=1752), and depression (n=1766). In summary, the localized, principal component analysis-like approach can reduce the dimensionality of medical images while still being able to identify meaningful local brain region alterations that are associated with genomic markers across multiple scales. The proposed method can be applied to various image types and can be easily integrated in many genotype-phenotype association study setups.