81 research outputs found

    Resting-state fMRI confounds and cleanup

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    The goal of resting-state functional magnetic resonance imaging (fMRI) is to investigate the brain's functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain ā€œat restā€ as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of fMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state fMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO2 concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state fMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state fMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline

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

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    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
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