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Abstract #3952

Global Signal Regression in Resting-state fMRI Pre-processing Improves Classification Accuracy

Kaibin Xu1,2,3, Yong Yang1,2, Yong Liu1,2, Bing Liu1,2, Ming Song1,2, Jun Chen4, Yunchun Chen5, Hua Guo6, Peng Li7,8, Lin Lu7,8, Luxian Lv9,10, Ping Wan6, Huaning Wang5, Huiling Wang4, Hao Yan7,8, Jun Yan7,8, Yongfeng Yang9,10, Hongxing Zhang9,11, Dai Zhang7,8,12, and Tianzi Jiang1,2,13,14,15

1Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 2National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 3University of Chinese Academy of Sciences, Beijing, China, 4Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China, 5Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China, 6Zhumadian Psychiatric Hospital, Zhumadian, China, 7Peking University Sixth Hospital / Institute of Mental Health, Beijing, China, 8Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China, 9Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China, 10Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China, 11Department of Psychology, Xinxiang Medical University, Xinxiang, China, 12Center for Life Sciences / PKU-IDG / McGovern Institute for Brain Research, Peking University, Beijing, China, 13Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China, 14Queensland Brain Institute, University of Queensland, Brisbane, Australia, 15CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Beijing, China

Global signal regression (GSR) is under debate whether or not influences the interpretation of functional connectivity (FC). However, few studies have compared and discussed the classification performance of GSR on a large dataset. We used a large dataset of resting-state fMRI data with 1082 subjects to test whether GSR influences the FC-based classification performance. We reached 81.35%-84.36% test accuracy using nested cross-validation. We tested the contribution of GSR, feature whitening and classifiers to the classification accuracy variance using three-way ANOVA and found significant main effects only for the GSR factor (F=7.14, P=0.0089). The results suggest GSR improves the classification accuracy.

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