PURPOSE: To 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 METHODS: Twenty 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. RESULTS: The 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. CONCLUSION: rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear.
PURPOSE: To 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 METHODS: Twenty 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. RESULTS: The 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. CONCLUSION: rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear.
Authors: James W Ibinson; Keith M Vogt; Kevin B Taylor; Shiv B Dua; Christopher J Becker; Marco Loggia; Ajay D Wasan Journal: Brain Connect Date: 2015-08-03
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Authors: Leonhard Schilbach; Birgit Derntl; Andre Aleman; Svenja Caspers; Mareike Clos; Kelly M J Diederen; Oliver Gruber; Lydia Kogler; Edith J Liemburg; Iris E Sommer; Veronika I Müller; Edna C Cieslik; Simon B Eickhoff Journal: Schizophr Bull Date: 2016-03-02 Impact factor: 9.306
Authors: Jürgen Hänggi; Deborah A Vitacco; Leonie M Hilti; Roger Luechinger; Bernd Kraemer; Peter Brugger Journal: Brain Behav Date: 2017-02-23 Impact factor: 2.708