Is fMRI 'noise' really noise? resting state nuisance regressors remove variance with network structure

Article English OPEN
Bright, Molly G. ; Murphy, Kevin (2015)
  • Publisher: Elsevier
  • Journal: NeuroImage, volume 114, pages 158-169 (issn: 1053-8119, eissn: 1095-9572)
  • Related identifiers: doi: 10.1016/j.neuroimage.2015.03.070, pmc: PMC4461310
  • Subject: Resting state | Connectivity | Motion | BF | Noise correction | Regression | Cognitive Neuroscience | Article | FMRI | Neurology | R1

Noise correction is a critical step towards accurate mapping of resting state BOLD fMRI connectivity. Noise sources related to head motion or physiology are typically modelled by nuisance regressors, and a generalised linear model is applied to regress out the associated signal variance. In this study, we use independent component analysis (ICA) to characterise the data variance typically discarded in this pre-processing stage in a cohort of 12 healthy volunteers. The signal variance removed by 24, 12, 6, or only 3 head motion parameters demonstrated network structure typically associated with functional connectivity, and certain networks were discernable in the variance extracted by as few as 2 physiologic regressors. Simulated nuisance regressors, unrelated to the true data noise, also removed variance with network structure, indicating that any group of regressors that randomly sample variance may remove highly structured “signal” as well as “noise.” Furthermore, to support this we demonstrate that random sampling of the original data variance continues to exhibit robust network structure, even when as few as 10% of the original volumes are considered. Finally, we examine the diminishing returns of increasing the number of nuisance regressors used in pre-processing, showing that excessive use of motion regressors may do little better than chance in removing variance within a functional network. It remains an open challenge to understand the balance between the benefits and confounds of noise correction using nuisance regressors.
  • References (37)
    37 references, page 1 of 4

    Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M., 2005. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc., B 360, 1001-1013.;2-5.

    Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008. The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. NeuroImage 40, 644-654. neuroimage.2007.11.059.

    Birn, R.M., Molloy, E.K., Patriat, R., Parker, T., Meier, T.B., Kirk, G.R., Nair, V.A., Meyerand, M.E., Prabhakaran, V., 2013. The effect of scan length on the reliability of restingstate fMRI connectivity estimates. NeuroImage 83, 550-558. 1016/j.neuroimage.2013.05.099.

    Bright, M.G., Murphy, K., 2013a. Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data. NeuroImage 64, 526-537. http://dx.doi. org/10.1016/j.neuroimage.2012.09.043.

    Bright, M.G., Murphy, K., 2013b. Reliable quantification of BOLD fMRI cerebrovascular reactivity despite poor breath-hold performance. NeuroImage 83C, 559-568. http://dx.

    Bright, M.G., Bulte, D.P., Jezzard, P., Duyn, J.H., 2009. Characterization of regional heterogeneity in cerebrovascular reactivity dynamics using novel hypocapnia task and BOLD fMRI. NeuroImage 48, 166-175.

    Bullmore, E.T., Brammer, M.J., Rabe-Hesketh, S., Curtis, V.A., Morris, R.G., Williams, S.C., Sharma, T., McGuire, P.K., 1999. Methods for diagnosis and treatment of stimuluscorrelated motion in generic brain activation studies using fMRI. Hum. Brain Mapp. 7, 38-48.

    Chang, C., Cunningham, J.P., Glover, G.H., 2009. Influence of heart rate on the BOLD signal: the cardiac response function. NeuroImage 44, 857-869. neuroimage.2008.09.029.

    Churchill, N.W., Yourganov, G., Oder, A., Tam, F., Graham, S.J., Strother, S.C., 2012. Optimizing preprocessing and analysis pipelines for single-subject fMRI: 2. Interactions with ICA, PCA, task contrast and inter-subject heterogeneity. PLoS ONE 7, e31147. http://

    Cox, R.W., 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162-173.

  • Related Research Results (1)
  • Metrics
    views in OpenAIRE
    views in local repository
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    Online Research @ Cardiff - IRUS-UK 0 55
Share - Bookmark