Global signal regression improves model performance of connectome-based predictive modeling

Physiologic significance of the global signal and the use (or omission) global signal regression (GSR) in fMRI data preprocessing remain controversial. Connectome-based predictive modeling(CPM) is one of the most commonly used machine-learning models. The effect of GSR on the performance of the CPM model is not well understood. We performed two preprocessing procedures for fMRI data: GSR and without GSR, and we used different brain atlases to construct CPM models to predict age, full-scale, performance and verbal IQ. We found that GSR can improve the predictive performance of CPM, at least for age, full-scale, performance and verbal IQ .