Poster No:
696
Submission Type:
Abstract Submission
Authors:
Madeline Garvey1, J. Shane Kippenhan2, Michael Gregory2, Philip Kohn3, Tiffany Nash2, Isaac Sebenius4, Carolyn Mervis5, Daniel Eisenberg3, Anna Kelemen6, Darby Krugel6, Petra Vértes7, Edward Bullmore8, Karen Berman3
Institutions:
1National Institute of Mental Health, Washington, DC, 2NIMH, Bethesda, MD, 3NIMH/NIH, Bethesda, MD, 4Department of Psychiatry, Cambridge University, Cambridge, Cambridge, Cambridge, 5University of Pennsylvania, Philadelphia, PA, 6National Institute of Mental Health, Bethesda, MD, 7University of Cambridge, Cambridge, Cambridgeshire, 8Department of Psychiatry, Cambridge, Cambridge
First Author:
Co-Author(s):
Isaac Sebenius
Department of Psychiatry, Cambridge University, Cambridge
Cambridge, Cambridge
Anna Kelemen
National Institute of Mental Health
Bethesda, MD
Darby Krugel
National Institute of Mental Health
Bethesda, MD
Petra Vértes
University of Cambridge
Cambridge, Cambridgeshire
Introduction:
Recent methodological advances have established a role for structural similarity networks in quantifying individual-level, in-vivo characterizations of regional brain measures, and in revealing patterns of brain network architecture [1]. These networks have been shown to correlate with underlying regional cytoarchitectural and gene expression properties and are hypothesized to reflect the synchronicity of developmental processes [1]. However, the impact of neurobehaviorally-relevant genetic variation on these structural similarity phenotypes remains unclear. Working within a human copy number variant (CNV) in which the affected genes are known and the behavioral phenotypes are well-characterized has the potential to bridge this gap. One such rare CNV involves Williams syndrome (WS), caused by hemideletion of ~26 genes at chromosomal locus 7q11.23. Behaviorally, people with WS have significant problems with visuospatial construction, relative strengths in expressive language [2], and increased social drive along with non-social anxiety [3]. Here we used Morphometric INverse Divergence (MIND), a multimodal measure [1], to characterize structural similarity brain phenotypes of children and adolescents with WS compared to typically developing controls (TDs).
Methods:
Participants with WS (n=30, mean age=12.5±3.9 years, 21 females) and age- and sex- matched TDs (n=60, age=12.5±3.4 years, 42 females) each completed three T1-weighted ME-MPRAGE scans that were visually inspected for quality and averaged together to create a single image. Scans were then intensity-normalized and processed through Freesurfer 7.1.1's recon-all pipeline to generate anatomic surface-based quantifications of five different cortical features (mean curvature, surface area, volume, sulcal depth, and cortical thickness) for 68 cortical brain regions, which were parcellated in each participant's native space within the Desikan-Killiany atlas. Regional population density distribution curves for all five features for each region were compared to all other regions using the Kullback-Leibler divergence measure, which generates an estimate of the similarities between multivariate distributions of multiple features between each pair of cortical regions as a value between 0 and 1. Resulting edge-weight similarity matrices were then summed for all connections for each region to generate an estimate of each cortical region's similarity to all other regions, for each individual. General linear models controlling for age and sex were tested for case-control differences in regional anatomical similarity in all 68 regions and were Bonferroni-corrected for multiple comparisons.
Results:
Structural similarity differences between WS and TD groups met the statistical threshold in eight regions: bilateral fusiform gyri, bilateral isthmi of the cingulate, bilateral postcentral gyri, the left posterior cingulate gyrus, and the right superior parietal region. Interestingly, among these regions, those with positive t-statistics (postcentral and parietal regions), indicating greater similarity with the rest of the brain and under-differentiation in individuals with WS, primarily subserve visuomotor functions; while regions with negative t-statistics (fusiform and cingulate regions), indicating less similarity with the rest of the brain and greater specialization in individuals with WS, primarily subserve social processes.
Conclusions:
The present findings reveal that genetically-driven structural brain similarity in individuals with WS is most pronounced in visuomotor and social regions in a manner consistent with our prior work showing functional differences in these regions [4] and with the well-characterized WS behavioral phenotype. Future work will relate these patterns to underlying transcriptomic signatures, providing a powerful framework for understanding how genetic factors influence brain architecture, function, and behavior in a coordinated and integrated manner.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Genetics:
Neurogenetic Syndromes 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Multivariate Approaches
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Keywords:
Congenital
Cortex
Development
Morphometrics
Pediatric Disorders
STRUCTURAL MRI
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
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Free Surfer
Provide references using APA citation style.
1. Sebenius, I., Seidlitz, J., Warrier, V., & others. (2023). Robust estimation of cortical similarity networks from brain MRI. Nature Neuroscience, 26(10), 1461–1471. https://doi.org/10.1038/s41593-023-01376-7
2. Mervis, C. B., Robinson, B. F., Bertrand, J., Morris, C. A., Klein-Tasman, B. P., & Armstrong, S. C. (2000). The Williams syndrome cognitive profile. Brain and Cognition, 44(3), 604–628. https://doi.org/10.1006/brcg.2000.1232
3. Klein-Tasman, B. P., & Mervis, C. B. (2003). Distinctive personality characteristics of 8-, 9-, and 10-year-olds with Williams syndrome. Developmental Neuropsychology, 23(1–2), 269–290. https://doi.org/10.1080/87565641.2003.9651895
4. Garvey, M. H., Nash, T., Kippenhan, J. S., & others. (2024). Contrasting neurofunctional correlates of face- and visuospatial-processing in children and adolescents with Williams syndrome: Convergent results from four fMRI paradigms. Scientific Reports, 14, 10304. https://doi.org/10.1038/s41598-024-60460-5
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