The effect of respiration variations on independent component analysis results of resting state functional connectivity

Authors

  • Rasmus M. Birn,

    Corresponding author
    1. Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, Maryland
    • Laboratory of Brain and Cognition, National Institute of Mental Health, 10 Center Dr., Bldg 10, Rm 1D80, Bethesda, MD 20892-1148
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  • Kevin Murphy,

    1. Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, Maryland
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  • Peter A. Bandettini

    1. Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, Maryland
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Abstract

The analysis of functional connectivity in fMRI can be severely affected by cardiac and respiratory fluctuations. While some of these artifactual signal changes can be reduced by physiological noise correction routines, signal fluctuations induced by slower breath-to-breath changes in the depth and rate of breathing are typically not removed. These slower respiration-induced signal changes occur at low frequencies and spatial locations similar to the fluctuations used to infer functional connectivity, and have been shown to significantly affect seed-ROI or seed-voxel based functional connectivity analysis, particularly in the default mode network. In this study, we investigate the effect of respiration variations on functional connectivity maps derived from independent component analysis (ICA) of resting-state data. Regions of the default mode network were identified by deactivations during a lexical decision task. Variations in respiration were measured independently and correlated with the MRI time series data. ICA appears to separate the default mode network and the respiration-related changes in most cases. In some cases, however, the component automatically identified as the default mode network was the same as the component identified as respiration-related. Furthermore, in most cases the time series associated with the default mode network component was still significantly correlated with changes in respiration volume per time, suggesting that current methods of ICA may not completely separate respiration from the default mode network. An independent measure of the respiration provides valuable information to help distinguish the default mode network from respiration-related signal changes, and to assess the degree of residual respiration related effects. Hum Brain Mapp 2008. © 2008 Wiley-Liss, Inc.

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