title: Edgewise and subgraph-level tests for brain networks.
abstract: Resting-state functional magnetic resonance image is a useful technique for investigating brain functional connectivity at rest. In this work, we develop flexible regression models and methods for determining differences in resting-state functional connectivity as a function of age, gender, drug intervention, or neuropsychiatric disorders. We propose two complementary methods for identifying changes of edges and subgraphs. (i) For detecting changes of edges, we select the optimal model at each edge and then conduct contrast tests to identify the effects of the important variables while controlling the familywise error rate. (ii) We adopt the network-based statistics method to improve power by incorporating the graph topological structure. Both methods have wide applications for low signal-to-noise ratio data. We propose stability criteria for the choice of threshold in the network-based statistics procedure and utilize efficient massive parallel procedure to speed up the estimation and inference procedure. Results from our simulation studies show that the thresholds chosen by the proposed stability criteria outperform the Bonferroni threshold. To demonstrate applicability, we use both methods in the context of the Oxytocin and Aging Study to determine effects of age, gender, and drug treatment on resting-state functional connectivity, as well as in the context of the Autism Brain Imaging Data Exchange Study to determine effects of autism spectrum disorder on functional connectivity at rest. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, FL, U.S.A.. huaihouchen@ufl.edu.|Center for Cognitive Aging and Memory (CAM), Institute on Aging, McKnight Brain Institute, Department of Aging Geriatric Research, University of Florida, Gainesville, FL, U.S.A.. huaihouchen@ufl.edu.
authors.forename: Huaihou
authors.initials: H
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, FL, U.S.A.
authors.forename: Bingxin
authors.initials: B
authors.lastname: Zhao
authors.affiliation: Center for Cognitive Aging and Memory (CAM), Institute on Aging, McKnight Brain Institute, Department of Aging Geriatric Research, University of Florida, Gainesville, FL, U.S.A.
authors.forename: Eric C
authors.initials: EC
authors.lastname: Porges
authors.affiliation: Center for Cognitive Aging and Memory (CAM), Institute on Aging, McKnight Brain Institute, Department of Aging Geriatric Research, University of Florida, Gainesville, FL, U.S.A.
authors.forename: Ronald A
authors.initials: RA
authors.lastname: Cohen
authors.affiliation: Center for Cognitive Aging and Memory (CAM), Institute on Aging, McKnight Brain Institute, Department of Aging Geriatric Research, University of Florida, Gainesville, FL, U.S.A.|Department of Psychology, University of Florida, Gainesville, FL, U.S.A.
authors.forename: Natalie C
authors.initials: NC
authors.lastname: Ebner
mesh_terms: D000067877:Autism Spectrum Disorder / Q000000981:diagnostic imaging; D001921:Brain; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D009434:Neural Pathways
keywords: graphical structure; network-based statistics; resting-state brain functional connectivity