Estimation of respiration‐induced noise fluctuations from undersampled multislice fMRI data†

Abstract

Functional MRI time series data are known to be contaminated by highly structured noise due to physiological fluctuations. Significant components of this noise are at frequencies greater than those critically sampled in standard multislice imaging protocols and are therefore aliased into the activation spectrum, compromising the estimation of functional activations and the determination of their significance. However, in this work it is demonstrated that unaliased noise information is available in multislice data, and can be used to estimate and reduce noise due to high-frequency respiratory-related fluctuations. The analysis of functional magnetic resonance imaging (fMRI) time series data is complicated by the fact that the noise is not Gaussian (1-5). This is a consequence of the fact that the dominant contributions to the noise in fMRI are signal variations produced by physiological processes, rather than by the thermal noise. These physiologically related signal fluctuations are generally quite complicated and can have significant power over a wide range of frequencies. Moreover, these fluctuations can be correlated with one another, producing sidebands with significant power. The dominant contributions appear to be highfrequency fluctuations related to the quasiperiodic processes of respiration and cardiac pulsations, and low-frequency fluctuations, which can result from slow drifts in the time series, but have also been hypothesized to be related to noise correlations produced by the hemodynamic response of the brain (1,6).In spite of the complexities of the spectrum of noise fluctuations, estimation of functional activation can still be relatively straightforward even in the presence of such fluctuations, provided that its spectrum is not overlapped by that of the noise fluctuations, and that the data sampling rate is sufficient to critically sample the spectrum of the noise. If these conditions hold, standard methods of filtering can be applied (7) to reduce unwanted noise components. Unfortunately, while it is possible to collect data in a single slice at a rate sufficient to critically sample high-frequency physiological fluctuations, virtually no fMRI experiments are actually done in this way. Rather, multislice acquisitions are performed in order to achieve adequate spatial coverage. With the typical imaging parameters used in multislice studies, the sampling rate for each slice is not sufficient to critically sample the physiological fluctuations, and the resulting time series noise is contaminated by aliased spectral components from the high-frequency physiological fluctuations. This not only reduces both functional signal-to-noise ratio (SNR) and significance of the estimates, but makes improper the use of many well-developed standard estimation techniques based on Gaussian noise models.However, in this work we show that it is possible to obtain unaliased information about the noise structure directly from multislice echo-planar imaging (EPI) fMRI time series data. This is achieved by noting two i...

False activation in the brain ventricles related to task-correlated breathing in fMRI speech and motor paradigms

“…The aforementioned studies suggest variations in breathing pattern that are correlated with the stimulus presentation can lead to activation artefact, and ultimately compromise accurate detection of regions truly activated by the task of interest. If any phase of the respiratory cycle (inspiration or expiration) is task-correlated, it could result in activation artefact since the fMRI signal fluctuates with respiration [3,12]. Given that exercise and speech tasks can be associated with head motion and alterations to regular breathing they present a challenge in fMRI.…”

“…Several studies have demonstrated and tried to accommodate for MRI signal fluctuation with respiration [3,[11][12][13]. Bulk susceptibility or air volume variation in the lungs correlates with variations in the static magnetic field and is a significant source of respiratory noise [11,13].…”

Partitioning of Physiological Noise Signals in the Brain with Concurrent Near-Infrared Spectroscopy and fMRI

“…These measurements do not examine blood parameters directly, and are at best an indirect measure of the corresponding cerebral blood changes that affect fMRI signals, due to the complicated hemodynamic responses that affect both the shapes and delays in the BOLD waveform. Some studies have concentrated on using shorter repetition time (TR) (500 milliseconds) to avoid the aliasing problem altogether (Frank et al, 2001), but this requires a significant reduction in the spatial resolution or extent of brain coverage of the fMRI data.…”

Brain Imaging

“…The cut-off window must be chosen carefully to avoid removing signals of interest together with the noise. In addition, there are now methods to attempt to correct physiological noise at source, such as the retrospective correction of physiological motion effects in fMRI (RETROICOR) (Glover et al, 2000;Frank et al, 2001).…”