Retrospective estimation and correction of physiological artifacts in fMRI by direct extraction of physiological activity from MR data

Le, Tuong Huu; Hu, Xiaoping

doi:10.1002/mrm.1910350305

Cited by 129 publications

(96 citation statements)

References 18 publications

(11 reference statements)

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“…The EPI images were corrected for geometrical distortion and Nyquist ghost artifacts using the multi-echo reference method (Schmithorst et al, 2001). The reconstructed EPI data were corrected for drift using quadratic baseline correction on a pixel-by-pixel basis (Hu et al, 1995;Le & Hu, 1996), co-registered to further reduce the effects of motion artifacts (Thevenaz & Unser, 1998), and transformed into Talairach coordinates (Talairach & Tournoux, 1988) using a linear affine transformation shown previously to be valid for individuals 5 to 18 years of age (Muzik & Chugani, 2000;Wilke et al, 2002).…”

Section: Fmri Data Acquisition and Analysesmentioning

confidence: 99%

Long-term neural processing of attention following early childhood traumatic brain injury: fMRI and neurobehavioral outcomes

Kramer

Chiu

Walz

et al. 2008

J Inter Neuropsych Soc

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Attentional deficits are common and significant sequelae of pediatric traumatic brain injury (TBI). However, little is known about how the underlying neural processes that support different components of attention are affected. The present study examined brain activation patterns using f MRI in a group of young children who sustained a TBI in early childhood (n 5 5; mean age 5 9.4), and a group of age-matched control children with orthopedic injuries (OI) (n 5 8) during a continuous performance task (CPT). Four children in the TBI group had moderate injuries, and one had a severe injury. Performance on the CPT task did not differ between groups. Both TBI and OI children activated similar networks of brain regions relevant to sustained attention processing, but the TBI group demonstrated several areas of significantly greater activation relative to controls, including frontal and parietal regions. These findings of over-activation of the relevant attention network in the TBI group contrast with those obtained in imaging studies of Attention-Deficit0Hyperactivity Disorder where under-activation of the attention network has been documented. This study provides evidence that young children's brains function differently following a traumatic brain injury, and that these differences persist for years after the injury. (JINS, 2008, 14, 424-435.)

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Section: Fmri Data Acquisition and Analysesmentioning

confidence: 99%

Long-term neural processing of attention following early childhood traumatic brain injury: fMRI and neurobehavioral outcomes

Kramer

Chiu

Walz

et al. 2008

J Inter Neuropsych Soc

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“…These may operate in k-space (Hu et al, 1995;Le and Hu, 1996;Wowk et al, 1997) or in image space (Chuang and Chen, 2001;Deckers et al, 2006;Glover et al, 2000) with the latter being the preferred method since changes made in k-space affect all the voxels in the reconstructed images. This makes spatially localized noise difficult to remove and may induce spatial correlations.…”

Section: Introductionmentioning

confidence: 99%

“…Other methods utilize the MRI data itself to estimate the noise (Le and Hu, 1996;Lowe and Sakaie, 2006;Wowk et al, 1997). Some of the methods are designed for straightforward data correction (Glover et al, 2000) but most can be extended to perform 'nuisance variable regression' (Birn et al, 2006a;Lund et al, 2006) in which the physiological noise measures (or models derived from them) are included as regressors in a general linear model (GLM) regression analysis.…”

Section: Introductionmentioning

confidence: 99%

Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

et al. 2007

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Heart rate fluctuations occur in the low frequency region (< 0.1 Hz) probed in functional magnetic resonance imaging (fMRI) studies of resting-state functional connectivity and most fMRI block paradigms, and may be related to low frequency blood-oxygenation-level-dependent (BOLD) signal fluctuations. To investigate this hypothesis, temporal correlations between cardiac rate and restingstate fMRI signal timecourses were assessed at 3 Tesla. Resting-state BOLD fMRI and accompanying physiological data were acquired and analyzed using cross-correlation and regression. Time-shifted cardiac rate timecourses were included as regressors in addition to established physiological regressors (RETROICOR (Glover et al., 2000) and respiration volume per unit time (Birn et al., 2006b)). Significant correlations between the cardiac rate and BOLD signal timecourses were revealed, particularly negative correlations in gray matter at time-shifts of 6-12 seconds and positive correlations at time shifts of 30-42 seconds (TR = 6 s). Regressors consisting of cardiac rate timecourses shifted by delays of between 0 and 24 seconds explained an additional 1 % of the BOLD signal variance on average over the whole brain across 9 subjects, a similar additional variance to that explained by respiration volume per unit time and RETROICOR regressors, even when used in combination with these other physiological regressors. This suggests that including such time-shifted cardiac rate regressors will be beneficial for explaining physiological noise variance and will thereby improve the statistical power in future task-based and resting-state fMRI studies.

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“…In addition, time courses from external monitoring systems are not necessarily the most helpful data, since the processes by which the MR signal varies as a result of these processes is complicated and their relationship to the MR signal variations is not straightforward. What is desired are estimates of the MR signal fluctuations themselves (9). Several investigators have utilized the phase information to estimate the physiologically induced signal fluctuations.…”

Section: Discussionmentioning

confidence: 99%

“…Both of these are, of course, manifestations of magnitude and phase variations in the k-space data actually acquired. A remarkable fact that has been noted before is that these physiologically induced k-space phase variations are relatively uncontaminated in the phase channel, and can be utilized for retrospective correction of either k-space phase or image space magnitude time series data (9,10). The fluctuations in the k-space phase produced by respiratory processes are relatively global, and therefore are expected to be present to some degree in most k-space components.…”

Section: Structure Of Fmri Noisementioning

confidence: 95%

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

Frank

Buxton

Wong

2001

Magnetic Resonance in Med

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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...

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Retrospective estimation and correction of physiological artifacts in fMRI by direct extraction of physiological activity from MR data

Cited by 129 publications

References 18 publications

Long-term neural processing of attention following early childhood traumatic brain injury: fMRI and neurobehavioral outcomes

Long-term neural processing of attention following early childhood traumatic brain injury: fMRI and neurobehavioral outcomes

Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

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

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