• Chia-Jung Yeh, Yu-Sheng Tseng, Yi-Ru Lin, Shang-Yueh Tsai, and Teng-Yi Huang.
• Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.
• J Neuroimaging. 2015 Jan 1;25(1):117-23.

PurposeTo investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis.Materials And MethodsTwenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods.ResultsThe results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas.ConclusionrsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear.Copyright © 2014 by the American Society of Neuroimaging.

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