Abstract #3952

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

Kaibin Xu^1,2,3, Yong Yang^1,2, Yong Liu^1,2, Bing Liu^1,2, Ming Song^1,2, Jun Chen⁴, Yunchun Chen⁵, Hua Guo⁶, Peng Li^7,8, Lin Lu^7,8, Luxian Lv^9,10, Ping Wan⁶, Huaning Wang⁵, Huiling Wang⁴, Hao Yan^7,8, Jun Yan^7,8, Yongfeng Yang^9,10, Hongxing Zhang^9,11, Dai Zhang^7,8,12, and Tianzi Jiang^1,2,13,14,15

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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