Cardiac-induced physiologic noise in tissue is a direct observation of cardiac-induced fluctuations

Bhattacharyya, Pallab K.; Lowe, Mark J.

doi:10.1016/j.mri.2003.08.003

Cited by 71 publications

(51 citation statements)

References 12 publications

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“…[102;103]). The use of short repetition times (TR) has been suggested to critically sample the large cardiac and respiratory noise for its removal [104]. However, this impedes the acquisition of full brain data and also may increase the noise in CSF regions where steady-state free precession variation artifacts are possible [105].…”

Section: Confounding Factorsmentioning

confidence: 99%

Assessing functional connectivity in the human brain by fMRI

Rogers

Morgan

Newton

et al. 2007

Magnetic Resonance Imaging

408

279

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Functional magnetic resonance imaging is widely used to detect and delineate regions of the brain that change their level of activation in response to specific stimuli and tasks. Simple activation maps depict only the average level of engagement of different regions within distributed systems. FMRI potentially can reveal additional information about the degree by which components of large-scale neural systems are functionally coupled together to achieve specific tasks. In order to better understand how brain regions contribute to functionally connected circuits, it is necessary to record activation maps either as a function of different conditions, at different times or in different subjects. Data obtained under different conditions may then be analyzed by a variety of techniques to infer correlations and couplings between nodes in networks. Several different multivariate statistical methods have been adapted and applied to analyze the variations within such data. An approach of particular interest that is suited to studies of connectivity within single subjects makes use of acquisitions of runs of MRI images obtained while the brain is in a so-called steady state, either at rest (i.e. without any specific stimulus or task) or in a condition of continuous activation. The interregional correlations between fluctuations of MRI signal potentially reveal functional connectivity. Recent studies have established that interregional correlations between different components of circuits in each of the visual, language, motor and working memory systems can be detected in the resting state. The correlations at baseline are changed during the performance of a continuous task. In this review the various methods available for assessing connectivity are described and evaluated.

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Section: Confounding Factorsmentioning

confidence: 99%

Assessing functional connectivity in the human brain by fMRI

Rogers

Morgan

Newton

et al. 2007

Magnetic Resonance Imaging

408

279

View full text Add to dashboard Cite

show abstract

“…The minimization of physiological noise as a confound in such studies has been a preoccupation since their inception (Biswal et al, 1996;Fukunaga et al, 2006b;Lowe and Sakaie, 2006;Vogt et al, 2006). Several physiological noise sources have been identified, including effects related to the cardiac (Bhattacharyya and Lowe, 2004;Dagli et al, 1999) and respiratory (Birn et al, 2006b;Wise et al, 2004) cycles. Variations related to arterial carbon dioxide fluctuations (Wise et al, 2004) and residual movement artifacts not accounted for by rigid body registration (Lund et al, 2005) also contribute.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have demonstrated that there are fMRI signal changes associated with the pulsatile cardiac motion (both at the cardiac frequency and its harmonics) (Bhattacharyya and Lowe, 2004;Biswal et al, 1996;Dagli et al, 1999;). Most of the physiological noise reduction methods model the contribution from the cardiac motion by determining the relative timing of each image volume (or slice (Liston et al, 2006;Vogt et al, 2006)) within the cardiac cycle.…”

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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“…Apart from movement, other sources of noise include physiological signals such as cardiac and respiratory signals (Bhattacharyya and Lowe, 2004;Birn et al, 2006;. This can be especially problematic when it is correlated with the signal of interest, such as when the variable of interest increases breathing or heart rate, due to increased excitement or anxiety.…”

Section: Physiological Sources Of Noisementioning

confidence: 99%