Elsevier

NeuroImage

Volume 40, Issue 2, 1 April 2008, Pages 644-654
NeuroImage

The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration

https://doi.org/10.1016/j.neuroimage.2007.11.059Get rights and content

Abstract

Changes in the subject’s breathing rate or depth, such as a breath-hold challenge, can cause significant MRI signal changes. However, the response function that best models breath-holding-induced signal changes, as well as those resulting from a wider range of breathing variations including those occurring during rest, has not yet been determined. Respiration related signal changes appear to be slower than neuronally induced BOLD signal changes and are not modeled accurately using the typical hemodynamic response functions used in fMRI. In this study, we derive a new response function to model the average MRI signal changes induced by variations in the respiration volume (breath-to-breath changes in the respiration depth and rate). This was done by averaging the response to a series of single deep breaths performed once every 40 s amongst otherwise constant breathing. The new “respiration response function” consists of an early overshoot followed by a later undershoot (peaking at approximately 16 s), and accurately models the MRI signal changes resulting from breath-holding as well as cued depth and rate changes.

Introduction

Time series of MRI signal changes measured in functional MRI (fMRI) can be strongly influenced by many factors, including changes in the subject’s breathing rate and/or depth over time. This can be seen most clearly in studies involving periods of breath-holding, where a breath-hold of even a few seconds can cause signal changes of several percent (Abbott et al., 2005, Kastrup et al., 1999a, Kastrup et al., 1999b, Kwong et al., 1995, Li et al., 1999, Stillman et al., 1995, Thomason et al., 2005). More recently, studies have shown that even subtle variations in breathing depth and rate that occur naturally during rest can result in significant signal changes (Birn et al., 2006, Wise et al., 2004). These signal changes arise from a number of hypothesized mechanisms. First, a number of brain regions are activated in association with voluntary changes in breathing (McKay et al., 2003). In addition to these neuronally induced blood oxygenation level-dependent (BOLD) signal changes, there are a number of non-neuronal, artifactual, signal changes. One source of these artifactual signal changes is the breathing cycle itself: the motion of the chest during breathing causes changes in the magnetic field, which leads to image distortions (Brosch et al., 2002, Raj et al., 2001). Secondly, changes in the depth and rate of breathing result in variations in the arterial level of CO2, a potent vasodilator (Van den Aardweg and Karemaker, 2002). Fluctuations in breathing therefore cause either vasodilation or vasoconstriction, resulting in blood flow and oxygenation changes. These changes typically occur at very low temporal frequencies (< 0.1 Hz), and are not removed by typical physiological correction routines (Glover et al., 2000, Josephs et al., 1997). These additional physiologically induced fluctuations can impede the detection of functional activation, or they can result in additional false positives if the breathing changes are correlated with the task. Furthermore, these breathing-related fluctuations are particularly problematic for resting-state connectivity analyses, which rely on the correlation of time-series between brain regions to infer a functional connection. As demonstrated in previous studies, the fluctuations in breathing during rest generally occur at similar frequencies (∼ 0.03 Hz) and in similar brain regions as those implicated in resting-state default-mode network activity (Birn et al., 2006, Modarreszadeh and Bruce, 1994, Van den Aardweg and Karemaker, 2002, Wise et al., 2004). Therefore, in order to obtain resting-state activity maps that reflect fluctuations in neuronal activity exclusively, it is vital that these respiration-induced fluctuations are modeled or removed from the data. Finally, respiratory challenges, such as breath-holding, have been suggested as ways to measure relative baseline venous blood volume across the brain, which can be used to calibrate the BOLD signal. All of these analyses – correcting for false positives and negatives, improving functional connectivity analysis, and mapping the relative amplitude of respiration-induced signal changes – require that we know precisely how a change in respiration affects the MRI signal.

Previously, respiration changes have been modeled in one of 3 ways – (1) as the timing of breathing changes convolved with a hemodynamic response function derived from BOLD activation dynamics (a gamma-variate function, or the default hemodynamic response function included in SPM) (Abbott et al., 2005, Thomason et al., 2005, Thomason et al., 2007); (2) by time-shifting a boxcar waveform representing the cue for breathing changes (e.g. cues for breath-holding) (Kastrup et al., 1999a, Kastrup et al., 1999b, Li et al., 1999); or (3) by time-shifting an estimate of the respiration volume per unit time (Birn et al., 2006). It is not apparent whether the use of a relatively rapid activation-derived BOLD signal change response function is necessarily the best model for the slower respiration-induced changes. First, the flow and BOLD changes induced by variations in breathing are mediated in part by levels of arterial oxygen saturation, intrathoracic pressure changes, and variations in arterial CO2 (Thomason et al., 2005). These changes in arterial CO2 do not occur immediately after a change in the breathing volume or rate, but may take several seconds to develop (Van den Aardweg and Bruce, 2002). Additionally, signal changes induced by an administration of CO2 have been observed to result in relatively slow signal changes, with a time constant of approximately 45 s and a delay of approximately 6 s (Corfield et al., 2001, Poulin et al., 1996). Since the time constants of these signal changes are not the same as neuronal-activation-induced BOLD changes, it is unlikely that a gamma-variate impulse response, with time constants originally derived from the BOLD fMRI signal change to a 1-s visual stimulus, accurately models the MR signal response to respiration changes. Finally, the MR signal response to breath-holding has often shown a strong bimodal response, with an early signal decrease followed by a later overshoot, particularly when the breath-hold is performed after inspiration. The correlation of the MRI time course with an estimate of the respiration volume per time changes during rest has also suggested a bimodal response to breathing changes, with a positive correlation at short latencies, and an even larger negative correlation at longer latencies (Birn et al., 2006). In other words, a decrease in the breathing depth or rate results in an initial decrease in signal followed by a strong overshoot, while an increase in the breathing depth or rate results in an initial overshoot with a later decrease. All of these findings and observations suggest that the MRI signal response to variations in respiration has a longer time constant and is potentially more complex than the BOLD fMRI response to activation, and should therefore be modeled with a different response function.

In a previous study, we showed that a time-shifted estimate of the temporal changes in respiration volume per time (RVT) is significantly correlated with MRI signal variations (Birn et al., 2006). However, fully removing the variance in the MRI signal induced by respiration changes requires a function that is not only roughly correlated with the response, but one that matches the precise temporal shape of the induced signal change. A sudden change in breathing rate or depth, for example, results in a relatively slow flow and oxygenation change. Regressing a shifted estimate of the respiration volume per time out of the MRI signal time series therefore leaves a significant amount of residual variation, which can still cause problems in resting-state connectivity analyses (Birn et al., 2006). In addition, this approach is problematic if the respiration changes are correlated with a task being investigated in a study. Allowing for a variable time-shift of the RVT time course would result in true task-related BOLD responses as being falsely classified as artifact, and a resultant decreased ability to detect true activation. If, however, the respiration changes result in CO2 mediated responses that are slower than activation-induced BOLD responses, separating respiration-induced from activation-induced changes more cleanly and completely may be possible, even if breathing changes are correlated with the task.

The goal of this study is to determine the transfer function between respiration changes and MRI signal changes. This transfer function is estimated by having the subject perform a series of single deep breaths, spaced 30–40 s apart, during otherwise constant respiration rate and depth. The rationale for this is that a single deep breath will result not only in known magnetic field changes, which occur during the breath, but also in other physiological changes, such as a transient decrease in arterial CO2, evident in a series of breaths and their associated rate and depth changes. Three breathing manipulations – depth changes, rate changes, and breath-holding – are studied. In addition, we test whether this “respiration response function” can accurately predict the fluctuations in the MRI signal resulting from RVT fluctuations at rest.

Section snippets

Subjects and imaging parameters

Eleven normal, healthy, right-handed volunteers were scanned under an Institutional Review Board (IRB) approved protocol after obtaining informed consent (ages: 23–40 years, mean age 31.8 ± 6.2 years, 6 females). Time series of T2*-weighted echo-planar MR images were acquired on a 3-T General Electric (GE) MR scanner (Waukesha, WI) using an 8-channel GE receive coil with whole body RF excitation. A limited coverage of six 5-mm-thick axial slices positioned at the level of the visual cortex was

Results

Repeated single deep breaths, series of cued deep breaths, cued rate increases, and breath-holding all resulted in significant signal changes in gray matter and regions that are known from previous studies to contain a higher baseline venous blood volume fraction (see Fig. 1, Fig. 2). A single deep breath resulted in a bimodal response with an early signal increase, peaking at 3 s, followed by a pronounced undershoot of even greater magnitude, peaking at 16 s. This response was fit well by a

Discussion

The goal of this study was to find a respiration response function that can best describe the average respiration-induced response function across the brain, in a similar way that the typical hemodynamic response functions used in fMRI data analysis were derived to model the average hemodynamic BOLD fMRI response.

In this study, we derived an estimate of the “impulse response function” for respiration-induced MRI signal changes by using what could approximately be considered to be an impulse (or

Conclusion

Using the average response to a single deep breath, we have determined a new “respiration response function” that can be used to model respiration-induced signal changes across a range of cued breathing manipulations. This response function provides a significantly better fit, on average, to the signal changes induced by cued breathing variations than hemodynamic response functions typically used to model BOLD fMRI signal changes. When latency is allowed to vary, the fit is improved, which may

Acknowledgment

This research was funded by the National Institutes of Mental Health Intramural Research Program.

References (28)

  • R.G. Wise et al.

    Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal

    NeuroImage

    (2004)
  • D.F. Abbott et al.

    Brief breath holding may confound functional magnetic resonance imaging studies

    Hum. Brain Mapp.

    (2005)
  • BandettiniP.A. et al.

    A hypercapnia-based normalization method for improved spatial localization of human brain activation with fMRI

    NMR Biomed.

    (1997)
  • D.H. Brainard

    The psychophysics toolbox

    Spat. Vis.

    (1997)
  • Cited by (0)

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