Abstract Details
Activity Number:
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26
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #317206
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Title:
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A Parsimonious Differential Brain Connectivity Network Detection Method
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Author(s):
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Shuo Chen*
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Companies:
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University of Maryland
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Keywords:
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Brain Imaging ;
Network ;
Biomarker ;
parsimony ;
fMRI ;
connectivity
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Abstract:
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Differential brain connectivity network may be associated with neurological disorder status or psychological experimental conditions in fMRI studies. We present a new statistical method for parsimonious differential brain connectivity network detection, which seeks to capture most significantly differentially expressed connectivity edges within a smaller number of nodes. By constraining the number of nodes, the detected differential connectivity network could include less false positive edges and gain extra statistical power by allowing edges to borrow strength with each other. We implement the optimization by using spectral graph models. We control the family-wise error rate of the detected network by conducting permutation tests. The new method is evaluated and compared with existing models by using a simulation study and a resting state fMRI case-control study.
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Authors who are presenting talks have a * after their name.
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