Abstract Number:
1661
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Sarah Weinstein (1), Simon Vandekar (2), Aaron Alexander-Bloch (3), Armin Raznahan (4), Mingyao Li (5), Raquel Gur (5), Ruben Gur (5), David Roalf (5), Min Tae M. Park (6), Mallar Chakravarty (7), Erica Baller (5), Kristin Linn (5), Theodore Satterthwaite (5), Russell Shinohara (5)
Institutions:
(1) Temple University, Philadelphia, PA, USA, (2) Vanderbilt University, Nashville, TN, USA, (3) University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, PA, USA, (4) National Institute of Mental Health, Bethesda, MD, USA, (5) University of Pennsylvania, Philadelphia, PA, USA, (6) University of Toronto, McGill University, Toronto, ON and Montreal QC, Canada, (7) McGill University, Cerebral Imaging Centre, Montreal, QC, Canada
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Maps of canonical functional brain networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about the spatial structure of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from Gene Set Enrichment Analysis (GSEA, Subramanian et al. 2005), a method widely used in genomics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose Network Enrichment Significance Testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks. We apply NEST to study associations involving structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.
Keywords:
enrichment|permutation testing|neuroimaging|brain networks|spatial data|
Sponsors:
Biometrics Section
Tracks:
Computational Neuroscience
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