NEST: Network Enrichment Significance Testing of Brain-Phenotype Associations

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):

Simon Vandekar  
Vanderbilt University
Aaron Alexander-Bloch  
University of Pennsylvania, Children's Hospital of Philadelphia
Armin Raznahan  
National Institute of Mental Health
Mingyao Li  
University of Pennsylvania
Raquel Gur  
University of Pennsylvania
Ruben Gur  
University of Pennsylvania
David Roalf  
University of Pennsylvania
Min Tae M. Park  
University of Toronto, McGill University
Mallar Chakravarty  
McGill University, Cerebral Imaging Centre
Erica Baller  
University of Pennsylvania
Kristin Linn  
University of Pennsylvania
Theodore Satterthwaite  
University of Pennsylvania
Russell Shinohara  
University of Pennsylvania

First Author:

Sarah Weinstein  
Temple University

Presenting Author:

Sarah Weinstein  
University of Pennsylvania

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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