Resting-State Functional Magnetic Resonance Imaging: The Impact of Regression Analysis

Chia Jung Yeh, Yu Sheng Tseng, Yi Ru Lin, Shang Yueh Tsai, Teng Yi Huang*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

14 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)117-123
Number of pages7
JournalJournal of Neuroimaging
Volume25
Issue number1
DOIs
StatePublished - 01 01 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014 by the American Society of Neuroimaging.

Keywords

  • Default mode network
  • Functional connectivity
  • Regression
  • Resting state
  • RsfMRI

Fingerprint

Dive into the research topics of 'Resting-State Functional Magnetic Resonance Imaging: The Impact of Regression Analysis'. Together they form a unique fingerprint.

Cite this