%0 Journal Article
%T Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure.
%A Vandewouw MM
%A Norris-Brilliant A
%A Rahman A
%A Assimopoulos S
%A Morton SU
%A Kushki A
%A Cunningham S
%A King E
%A Goldmuntz E
%A Miller TA
%A Thomas NH
%A Adams HR
%A Cleveland J
%A Cnota JF
%A Ellen Grant P
%A Goldberg CS
%A Huang H
%A Li JS
%A McQuillen P
%A Porter GA
%A Roberts AE
%A Russell MW
%A Seidman CE
%A Tivarus ME
%A Chung WK
%A Hagler DJ
%A Newburger JW
%A Panigrahy A
%A Lerch JP
%A Gelb BD
%A Anagnostou E
%J Neuroimage
%V 297
%N 0
%D 2024 Jul 4
%M 38968977
%F 7.4
%R 10.1016/j.neuroimage.2024.120721
%X Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.