Abstract #2368
Diagnosis of Schizophrenia using CBF Measures as a Classification Feature A FBIRN Phase 3 Multisite ASL Study at 3T
David Shin 1 , Burak Ozyurt 1 , Jerod Rasmussen 2 , Juan Bustillo 3 , Theodorus Van Erp 2 , Jatin Vaidya 4 , Daniel Mathalon 5 , Bryon Mueller 6 , James Voyvodic 7 , Douglas Greve 8 , Judith Ford 5 , Gary Glover 9 , Gregory Brown 1 , Steven Potkin 2 , and Thomas Liu 1
1
University of California, San Diego, La
Jolla, CA, United States,
2
University
of California, Irvine, Irvine, CA, United States,
3
University
of New Mexico, Albuquerque, NM, United States,
4
University
of Iowa, Iowa City, IA, United States,
5
University
of California, San Francisco, San Francisco, CA, United
States,
6
University
of Minnesota, Twin Cities, Minneapolis, MN, United
States,
7
Duke
University, Durham, NC, United States,
8
Harvard
Medical School, Massachusetts General Hospital,
Charlestown, MA, United States,
9
Stanford
University, Stanford, CA, United States
Resting state CBF maps (n=234) collected from the
multisite FBIRN Phase 3 Study
(http://www.birncommunity.org) were used as learning
features in a support vector machines (SVM)
classification technique to evaluate its performance in
differentiating Schizophrenic patients (SCZ) from
healthy controls (CNT). For feature extraction, clusters
of voxels with group differences (SCZ vs. CNT) were
first identified (t-test, p<0.01) and the mean CBF
values across these clusters were used as the training
data. The sensitivity, specificity, and accuracy for the
leave-one-out cross validation were found to be 75.9%,
75.5%, and 75.7%, respectively. The results suggest that
the CBF map acquired from a 5-minute ASL scan combined
with SVM may be a useful complimentary tool for
diagnosis of SCZ.
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