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Activity Number:
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26
- Statistics in Imaging: Student Award Session
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #312349
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Title:
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Extracting Brain Disease Related Connectome Subgraphs by Adaptive Dense Graph Discovery
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Author(s):
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Qiong Wu* and James Waltz and Shuo Chen
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Companies:
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University of Maryland and Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland and University of Maryland, School of Medicine
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Keywords:
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brain connectome;
densest subgraph;
likelihood-based criterion;
network topology;
permutation test;
schizophrenia
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Abstract:
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Group-level brain connectome analysis has attracted increasing interest in neuropsychiatric research with the goal of identifying connectomic subnetworks that are systematically associated with brain disorders. However, extracting disease-related subnetworks has been challenging, because no prior knowledge is available regarding the sizes and locations of the subnetworks. In addition, neuroimaging data is often mixed with substantial noise that can further obscure informative subnetwork detection. We propose a likelihood-based adaptive dense graph discovery (ADG) method to extract disease-related subgraphs from the whole brain connectome data. Our method is robust to both false positive and false negative errors of edge-wise inference and thus can lead to the more accurate discovery of latent disease-related subnetworks. We develop computationally efficient algorithms and derive theoretical results to guarantee the convergence properties of ADG. We apply the proposed approach to a brain connectome study for schizophrenia research and identify well-organized and biologically meaningful subnetworks that exhibit schizophrenia-related thalamo-sensory connectivity abnormality.
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Authors who are presenting talks have a * after their name.
