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Activity Number: 26
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #317206
Title: A Parsimonious Differential Brain Connectivity Network Detection Method
Author(s): Shuo Chen*
Companies: University of Maryland
Keywords: Brain Imaging ; Network ; Biomarker ; parsimony ; fMRI ; connectivity
Abstract:

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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