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The role of physiological noise in resting-state functional connectivity
NeuroImage 62 (2012) 864–870
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Review
The role of physiological noise in resting-state functional connectivity Rasmus M. Birn Department of Psychiatry, University of Wisconsin Madison, Madison, WI, 53719, USA
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Article history: Accepted 1 January 2012 Available online 8 January 2012 Keywords: functional Magnetic Resonance Imaging (fMRI) Functional connectivity Resting-state Physiological noise
a b s t r a c t Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (b 0.1 Hz) fluctuations in the MRI signal. These correlated fluctuations occur even when the subject is “at rest” (not asked to perform any particular task) and result from spontaneous neuronal activity synchronized within multiple distinct networks of brain regions. This estimate of connectivity, however, can be influenced by physiological noise, such as cardiac and respiratory fluctuations. This brief review looks at the effect of physiological noise on estimates of resting-state functional connectivity, discusses ways to remove physiological noise, and provides a personal recollection of the early developments in these approaches. This review also discusses the importance of physiological noise correction and provides a summary of evidence demonstrating that functional connectivity does have a neuronal underpinning and cannot purely be the result of physiological noise. © 2012 Published by Elsevier Inc.
Contents Introduction . . . . . . . . . . . . . . . . . . . . The role of physiological noise . . . . . . . . . . Removal of physiological noise . . . . . . . . . Global signal regression . . . . . . . . . . . . . Current physiological noise correction techniques . Is resting-state functional connectivity neuronal or Does physiological noise correction matter? . . . Conclusion . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . .
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Introduction The last few years have seen a virtual explosion in the number of studies using MRI to investigate not only the activity of different brain regions, but also the functional connectivity between them (Fig. 1). This functional connectivity can be estimated by measuring the correlation of low frequency (b0.1 Hz) MRI signal fluctuations between two or more brain regions. The rationale is that functionally related brain regions show synchronized increases and decreases in neuronal activity resulting in correlated MRI signal fluctuations. What is remarkable is that these synchronized signal fluctuations can be observed even when the subject is “at rest” and not explicitly asked to perform any task. These correlated signal fluctuations at rest likely reflect both unconstrained mental activity (e.g. “mind
E-mail address: [email protected]. 1053-8119/$ – see front matter © 2012 Published by Elsevier Inc. doi:10.1016/j.neuroimage.2012.01.016
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wandering”) as well as spontaneous neuronal fluctuations, and have been shown to reliably map multiple functional networks in the brain. This unique ability to non-invasively measure the functional relationships of different regions in the brain, combined with the relative ease at which such scans can be acquired, has a huge clinical and neuroscience potential, allowing us to improve our understanding of both the typical and atypical brain. This field is only just beginning, with new applications and improved methodology and technology appearing every week. The ability to map functional connectivity with MRI during the resting state was first discovered by Biswal et al. (1995). By looking more closely at “what's in the noise” (Weisskoff et al., 1993), Biswal et al. found that signal fluctuations in the left and right primary motor areas were significantly correlated even when the subject was not performing any motor task. Further work by a number of research groups (reviewed in more detail elsewhere in this issue) provided important foundational work for this technique, demonstrating
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Year Fig. 1. Number of publications per year on resting-state functional connectivity. This graph was generated from Scopus (http://www.scopus.com) using the following search criterion: (“functional Magnetic Resonance Imaging” OR “functional MRI” OR “fMRI”) AND (((rest OR resting) AND connectivity) OR “resting state” OR “spontaneous fluctuations” OR “intrinsic fluctuations”).
that these correlated signal fluctuations occurred at low temporal frequencies (b0.1 Hz), that they occurred in a number of different cortical and subcortical brain systems, and that they had similar properties as task-related blood oxygenation level dependent (BOLD) responses (Biswal et al., 1997; Cordes et al., 2001; Lowe et al., 1998; Peltier and Noll, 2002). Remarkably, it would take more than 10 years before resting-state functional connectivity was finally accepted and adopted by the larger neuroimaging community. The role of physiological noise The primary challenge researchers faced in these earlier years was to convince others that this measure of functional connectivity observed during rest did indeed reflect networks of neuronal activity. After all, functional connectivity was being derived from the correlation of MRI signal fluctuations, and there are many reasons why two regions of the brain could have similar MRI signal fluctuations. For example, head motion results in correlated signal changes at the edges of the brain and in regions with large spatial variations in image contrast. In addition, other physiological processes such as cardiac pulsation and respiration can lead to correlated signal fluctuations in the brain. Cardiac pulsation results in brain tissue movement and inflow effects leading to correlated signal fluctuations primarily in and near large blood vessels (Dagli et al., 1999). The movement of the chest wall during breathing results in magnetic field changes that distort the MR image acquired of the brain (Brosch et al., 2002; Raj et al., 2001). Cerebrospinal fluid (CSF) flow is modulated by both the cardiac and respiratory cycles, resulting in additional signal changes. Improper spoiling of the magnetization after the acquisition of an image (e.g. by not applying strong enough crusher gradients at the end of acquisition) can result in steady-state free precession (SSFP) signals which are modulated by cardiac, respiratory, or other B0 fluctuations and thus lead to correlated signal changes (Zhao et al., 2000). The potential confound of physiological fluctuations on estimates of functional connectivity was recognized early on. In order to focus on the correlation of low frequency (b0.1 Hz) fluctuations in the MRI signal, the earliest studies of functional connectivity used a very short TR and filtered out physiological fluctuations occurring at about 1 Hz (cardiac) and 0.3 Hz (respiration) (Biswal et al., 1996). The drawback with this approach is that only a few slices of the brain could be imaged at such a fast rate. Whole brain imaging required a much longer TR (about 2–3 s), resulting in an aliasing of cardiac and respiratory fluctuations to much lower frequencies. A simple low pass filter (e.g. b0.1 Hz) will not remove cardiac and respiratory-induced fluctuations when the MRI data is acquired with a TR of 2 s or above. Thus, researchers turned to retrospective correction techniques, initially
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developed for task fMRI, that use heart beat and respiration recordings during the scan to regress out the physiologically induced fluctuations (Glover et al., 2000; Hu et al., 1995; Josephs et al., 1997). Because these techniques take into account the phase of the respiration and cardiac cycle at which each image is acquired, respiration and cardiac induced signal fluctuations can be reduced despite aliasing or variations in cycling rate during the acquisition. There are, however, additional physiological (cardiac and respiratory-related) fluctuations that are not removed by these techniques. Variations in breathing depth and rate, for example, lead to alterations in arterial CO2 levels. Since CO2 is a potent vasodilator, decreased breathing depth and/or rate leads to an increased blood flow and, thus, an increase in the BOLD signal (Birn et al., 2006; Modarreszadeh and Bruce, 1994; Van den Aardweg and Karemaker, 2002; Wise et al., 2004). These alterations are part of a complex feedback circuit — increased arterial CO2 levels activate chemoreceptors that increase subsequent breathing depth and rate. This results in increased CO2 exhalation and a reduction of arterial CO2. Studies have shown that fluctuations of arterial CO2 (as measured by a person's end-tidal CO2) vary with a cycle of about 0.03 Hz (Modarreszadeh and Bruce, 1994; Van den Aardweg and Karemaker, 2002). Therefore these changes in arterial CO2 lead to low frequency (0.03 Hz) fluctuations in the fMRI signal. FMRI studies have also shown that the variability in heart rate is correlated with MRI signal changes throughout gray matter (Chang et al., 2009; Shmueli et al., 2007). Furthermore, cerebral blood vessels exhibit vasomotion, a spontaneous low frequency (~0.1 Hz) oscillation in vascular tone and blood flow (Hudetz et al., 1998). Successful measurement of functional connectivity requires that all of these sources of non-neuronal fluctuations be addressed. My own research addressing the influence of artifactual fluctuations, particularly respiration changes, on functional connectivity began as a combination of serendipity and a motivated student. In 2004 a Howard Hughes fellow, Jason Diamond, came to work in Peter Bandettini's research section where I was a staff scientist. Jason had worked on fMRI projects as an undergraduate at Washington University in St. Louis with Harold Burton, and he was eager to continue to work with fMRI, particularly on resting-state functional connectivity. At that time, Greicius et al. had recently published a paper showing that regions of the “default mode network” (DMN) were functionally connected at rest (Greicius et al., 2003). This DMN which includes the posterior cingulate, anterior cingulate/medial prefrontal cortex (mPFC), and bilateral parietal areas, had been previously shown to deactivate consistently during a range of cognitive tasks (McKiernan et al., 2003; Raichle et al., 2001). In other words, these were areas that were more active at rest — hence the label of “default mode” network. Jason was interested in working with me to determine whether there were any differences between the regions that generally deactivate during tasks and areas that were functionally connected at rest. During my time in graduate school, I had interacted with both Bharat Biswal and Beth Meyerand (whose lab later focused extensively on methodological developments in resting-state functional connectivity), and I had seen the lengths they had gone to in order to filter out other sources of noise, such as cardiac and respiratory fluctuations. At that time, measuring the subject's heart beat and respiration often required custom setups or interfaces with the MRI scanner. I had also seen the difficulty they had in convincing others that this connectivity was truly neuronal in origin. I therefore cautioned Jason that undertaking this research would not be trivial. Fortunately, Jason was adamant about pursuing this research. The first step was finding a task that would give consistent deactivations in the DMN. Pilot experiments we ran showed that a word/non-word discrimination task (with words/non-words drawn from the English Lexicon Project (Balota et al., 2007)) resulted in robust deactivations in regions of the DMN. We then assessed the connectivity between these DMN brain regions during a resting state.
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My research at that time focused primarily on the physiology and spatial heterogeneity of the BOLD response. Prior work had shown that more quantitative information about neuronal metabolism could be obtained by calibrating the BOLD response using an administration of CO2 (Davis et al., 1998; Hoge et al., 1999), which increases the cerebral blood flow (CBF) throughout the brain. Administration of CO2, however, is cumbersome, and we therefore looked at breathholding as a simpler way to globally increase CBF. In the spring of 2004, I heard a talk at the International Society for Magnetic Resonance in Medicine (ISMRM) conference by Richard Wise who showed that fluctuations in end-tidal CO2 during rest were significantly correlated with fluctuations in the MRI signal. In this talk as well as in the subsequent paper, he also suggested that these signal fluctuations could cause problems for estimates of resting-state functional connectivity (Wise et al., 2004). I was struck by the similarity of these maps of signal changes from resting breathing variations to maps we had obtained in our breath-holding studies, and I therefore set out to investigate the role of breathing variations in our recently acquired resting-state data. We had not collected measures of endtidal CO2, but we did have measures of respiration depth and rate recorded from a pneumatic belt placed around the subject's chest during the scan. In order to approximate the variations in arterial CO2 induced by changes in breathing depth and rate, we computed the respiration volume divided by the period (the respiration volume per time, RVT). This RVT time course was then shifted in time and fit to the MR data. The map of the amplitude of RVT-related signal changes looked remarkably similar to both Wise et al.'s work on end-tidal CO2 fluctuations and our prior breath-holding studies with large signal changes in the posterior cingulate, inferior occipital cortex, near large vessels, and to a certain extent throughout gray matter (see Fig. 2A). I walked over to Jason Diamond's cubicle to share this interesting result and noticed that he was looking at a similar map on his computer screen. Jason, however, was not investigating breathing related changes but had computed the functional connectivity with a seed region in the posterior cingulate, similar to the earlier analysis from Greicius et al. (2003). We compared our two maps more closely (see Fig. 2), and realized that respiration changes (in this case measured by RVT) indeed caused great problems for estimates of functional connectivity. Removal of physiological noise In our next set of experiments, we set out to characterize this respiration artifact and find ways to reduce its influence on functional connectivity measures. In addition to the lexical decision making
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task and resting run, we had the subjects try to breathe at a more constant depth and rate by following a visual display that indicated when to breathe in and out. The resulting maps of functional connectivity to the posterior cingulate showed a more focal pattern of connectivity with significantly connected regions that agreed more closely with areas that were deactivated during the lexical task. The extent to which this still constituted a “resting state” instead of a task involving visual activity and breathing control is of course up for debate. In addition, requiring subjects to control their breathing negates many of the advantages of resting-state fMRI in which the subject is not required to perform a task. We therefore also looked at ways to remove the measured fluctuations from the data. Regressing out signal changes related to respiration variations requires that the estimated RVT time course be shifted in time since a change in breathing depth or rate causes a CO2 change, and resultant blood flow change, later in time. Conversely, it is possible that variations in brain activity are driving the changes in respiration. We therefore allowed the latency of the estimated RVT time course to vary from − 10 to +15 s and for each voxel in the brain found the shift that resulted in the greatest positive and the greatest negative correlation between the (shifted) RVT time course and the fMRI time series signal. We found that the correlation was predominantly negative (i.e. fMRI signal increases resulting from decreases in respiration depth) at an average latency of 5.4 s, and a smaller positive correlation at an average latency of −0.9 s (see Fig. 3). Furthermore, these latencies varied across the brain. While the shifted RVT time course was significantly correlated with fMRI time series fluctuations, regressing out this shifted RVT time course did not significantly alter the functional connectivity maps. In part this may be due to a sudden change in breathing, such as a deep breath, which results not only in a delayed but also in a prolonged fMRI signal change. Regressing out only a shifted RVT time course therefore did not fully remove the respiration-related signal changes. Our next experiments set out to measure this respiration response function by averaging the response to repeated single deep breaths spaced 30–40 s apart. We found that this respiration response function, when convolved with the RVT time course, accurately fit other cued breathing variations, such as cued depth changes, cued rate changes, or breath-holding. However, our studies indicated that the response function convolved with the RVT time course during rest did not accurately fit resting-state fMRI signal fluctuations. More recent work by Chang et al. combines the removal of both respiration volume and heart rate changes (RVHRcor) and finds that including both respiration volume and heart rate variations as nuisance regressors explains significantly more variance than either
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Fig. 2. A) fMRI signal correlated with respiration volume per time (RVT) changes, B) functional connectivity with a seed region in the posterior cingulate from a group of 10 subjects. Adapted from Birn et al., 2006.
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Fig. 3. Correlation with the respiration per volume time (RVT) in a representative subject. A) Z-scores of regions negatively correlated with the RVT; B) Regions positively correlated with the RVT. C) and D) Z-score of correlating the RVT with fMRI time course (for two sample voxels, as indicated by the arrows) at multiple shifts between the RVT and the time-series. Positive shifts indicate a shift of the RVT forward in time relative to the fMRI data (i.e. fMRI signal changes following respiration changes). Adapted from Birn et al., 2006.
regressor alone (Chang et al., 2009). Furthermore, including measures of end-tidal CO2 explains even more noise, suggesting that the different measurements (respiration volume, heart rate, and end-tidal CO2) to a certain degree explain different aspects of the noise (Chang and Glover, 2009). Most of these physiological noise removal techniques require an independent measurement of the pulse and respiration (e.g., using a pulse oximeter and respiration belt). However, some techniques have been developed that do not require these ancillary measures. When the TR is short enough to avoid aliasing of cardiac and respiratory fluctuations, physiological noise can be reduced by temporal low-pass filtering, or by using other more sophisticated methods (e.g. IMPACT (Chuang and Chen, 2001)). There are also strategies that work for longer TRs. CORSICA is a technique that uses spatial independent component analysis (ICA) to identify and remove signal fluctuations that match spatially with patterns of known physiological noise (Perlbarg et al., 2007). PESTICA, a technique by Beall et al., (Beall and Lowe, 2007) estimates the physiological fluctuations from the fMRI data using a temporal ICA approach. Physiological fluctuations can also be reduced by including nuisance regressors derived from the data itself, for example from regions such as CSF, white matter, or edges of the brain that are unlikely to show neuronal-induced BOLD signal fluctuations (Birn et al., 2009; Jo et al., 2010; Weissenbacher et al., 2009). Global signal regression We were initially surprised that the maps of functional connectivity of the default mode network from our study (Fig. 2B) showed more extensive correlations across the brain than that reported previously by Greicius et al. (Greicius et al., 2003). Looking more closely at the methods employed by Greicius et al., we noticed an additional preprocessing step — regressing out the average signal over the whole brain. When we performed this global signal regression, the regions significantly correlated with the posterior cingulate became more focal in space and matched more closely with the areas that were deactivated during the lexical task. This increased sparseness and improved correspondence to task-deactivated areas may be the result of a reduction of the relatively global signal fluctuations induced by breathing variations, which tend to occur throughout gray matter (Wise et al., 2004). Likely because of the improved sparseness of the functional connectivity maps, global signal regression has now become a common step in processing functional connectivity data. In addition, this preprocessing step makes a certain amount of sense — we are not really interested in signal changes that are constant over the whole brain, and certain physiological artifacts (e.g. respiration related changes) occur relatively globally over the whole brain. The problem with global signal regression is that the global signal necessarily contains a portion of the signal of interest as well. Removing
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this global signal can therefore remove some of the signal of interest and distort the measure of functional connectivity. Furthermore, it has been shown that global signal regression can induce anticorrelations with the seed region being investigated (Murphy et al., 2009; Weissenbacher et al., 2009). This finding led to some heated debate and controversy — an earlier study by Fox et al. had shown that at rest the default mode network (or “task-negative” network) was anti-correlated with other brain regions that generally show increased activity during goal directed tasks (the “task positive” network, encompassing areas involved in attention and various cognitive tasks) (Fox et al., 2005, 2009). The observation that the negative interplay between these two networks continues even during rest (e.g. activity in the default mode network increasing as activity in attention areas decreases) makes a certain amount of sense. However, since the global signal was removed from the data as a preprocessing step, it is unclear whether this observed anti-correlation is due to the opposing interaction of two neuronal networks, or whether it is simply introduced by this preprocessing step. Many subsequent studies failed to see this anti-correlation without performing global signal regression (Murphy et al., 2009; Weissenbacher et al., 2009). Note that this finding does not mean that the observed anticorrelation between task-positive and task-negative networks is necessarily due to global signal regression. Observed anti-correlations after performing global signal regression may have been introduced by this processing step, or alternatively the true anti-correlation between these two networks may have been revealed by removing global noise. Current physiological noise correction techniques Because the average signal over the whole brain contains the spontaneous neuronal fluctuations of interest and because of the previously mentioned potential difficulties with global signal regression, investigators have started to use the average signals over the white matter and CSF as nuisance regressors. The rationale is that these regions of the brain are less influenced by neuronal-induced BOLD changes and are therefore more reflective of non-neuronal signal changes (i.e. “noise”). A study by H-J Jo et al., for example investigated the influence of various nuisance regressors on resulting functional connectivity maps and found that significant variance in the data could be explained by using estimates of subject motion, as well as averaged signals from eroded white matter and ventricular CSF masks (Jo et al., 2010). Furthermore, they showed that in some cases, using a local estimate of white matter signal surrounding each voxel as a nuisance regressor resulted in a better reduction of noise, particularly for spatially heterogeneous artifacts. A study by Anderson et al. found additional benefit from including signals from the soft tissue of the face (phase-shifted soft tissue regression, PSTcor) (Anderson et al., 2011). A quantitative comparison of different preprocessing strategies by Weissenbacher et al. also found that using average white matter and CSF signals as nuisance regressors improved the specificity of the connectivity maps, but they further noted that including the global signal as a nuisance regressor resulted in an even greater increase in specificity (Weissenbacher et al., 2009). The optimal set of nuisance regressors and preprocessing steps that can produce both the most robust functional connectivity maps as well as reliably reveal individual differences in functional connectivity is therefore still an active area of investigation. Is resting-state functional connectivity neuronal or just an artifact? Our work, as well as that of other groups, has shown how similar maps of functional connectivity appear to maps that represent purely respiration-related signal changes. Does this mean that all functional connectivity results are due to artifacts and not reflective of correlated neuronal activity? While I started out skeptical about some functional connectivity results, I have become convinced that when
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performed properly, resting-state fMRI can give us reliable estimates of the functional connectivity for a number of reasons. First, correlations are observed between functionally related brain areas. Second, early work on resting-state connectivity showed that correlated signal fluctuations in functionally related brain regions are not due to cardiac pulsations and respiration but instead occur at much lower temporal frequencies (Biswal et al., 1995; Cordes et al., 2001; Kiviniemi et al., 2005). Third, these fluctuations have similar characteristics as neuronally-induced BOLD responses (e.g. TE dependence, attenuation of the response during hypercapnia) (Biswal et al., 1997; Peltier and Noll, 2002). While other respiration-related fluctuations, such as those resulting from changes in respiration volume and depth, do share the properties of a BOLD response, regressing out these fluctuations, using recorded respirations, recorded endtidal CO2, or the averaged global, white matter, or CSF signals, did not remove the correlation between regions of the default mode network (Birn et al., 2006). If anything, removing cardiac and respiratory-related fluctuations made the map of connectivity to the posterior cingulate look more like the map of areas deactivated during tasks. In addition, a recent experiment by C. Madjar et al. reduced fluctuations in arterial CO2 levels during normal breathing by using a computer-controlled respiration system (Slessarev et al., 2007). This study showed that correlated fluctuations in the default mode remain (and in fact increase) even when the arterial CO2 levels, as measured by end-tidal CO2, are held more constant (Madjar et al., 2011). Fourth, correlations are observed in multiple distinct functional brain networks (e.g. motor, visual, auditory, attention, default-mode, etc.) (Beckmann et al., 2005; Damoiseaux et al., 2006; De Luca et al., 2006; Smith et al., 2009). It was suggested early in the study of resting-state functional connectivity that correlated fluctuations (e.g. between left and right motor cortices) may simply reflect similar vascular structure in these regions. However, for this vascular argument to hold, left and right motor cortices would have to have similar vascular structure that is distinct from the vascular structure of the visual cortex, which again is distinct from the vascular structure of the auditory system, the default mode network, attention areas, and so on. It is highly unlikely that the similarity of vascular structure could give rise to independent networks of correlated fluctuations that map so closely onto known functional networks. Fifth, studies have shown that a disruption in structural connectivity leads to a reduction in resting-state functional connectivity. A study by Quigley et al. showed that subjects with callosal agenesis had reduced or no functional connectivity between left and right sensorimotor cortices (Quigley et al., 2003). A subsequent study by Johnston et al. investigated the functional connectivity before and after a callosotomy and found that interhemispheric functional connectivity was lost after sectioning the corpus callosum, while intra-hemispheric connectivity remained intact (Johnston et al., 2008). Sixth, a resting-state functional connectivity study by Krienen and Buckner showed that the left cerebellum was more strongly coupled with the right primary motor cortex, and conversely that the right cerebellum was more strongly coupled with the left motor cortex (Krienen and Buckner, 2009). The connectivity of left and right lateral prefrontal cortices showed a similar contralateral correlation with different regions in the cerebellum. This contralateral correlation is expected based on known neuronal connections and cannot be explained by either head motion or similarities in the cerebrovasculature, since the cerebellum and sensorimotor cortex are each supplied by its own major artery. Seventh, correlated fluctuations in neuronal activity have been demonstrated with other modalities, such as EEG (Laufs et al., 2003), ECoG (Miller et al., 2009), and even direct recordings in animal models (Leopold and Logothetis, 2003; Leopold et al., 2003; Shmuel and Leopold, 2008). Finally, low frequency fluctuations in the fMRI signal during rest have been linked to individual differences in behavior or pathology (Di Martino et al., 2009; Fox et al., 2007; Garrity et al., 2007; Greicius, 2008; Kennedy and Courchesne, 2008; Kim et al., 2010).
Does physiological noise correction matter? Despite numerous studies demonstrating that the heart beat and respiration lead to correlated fluctuations in certain brain regions, many studies do not perform any physiological noise correction aside from regressing out global, white matter, and/or CSF signals. Nevertheless, the resulting networks appear to be consistent, reliable, and conform to known task-related networks (Damoiseaux et al., 2006; De Luca et al., 2006; Shehzad et al., 2009; Smith et al., 2009). This raises the question of whether physiological noise correction is really necessary. A recent study by H.J. Jo et al., for example, determined that averaged over gray matter, cardiac and respiratory regressors modeled in RETROICOR account for a small amount of time course variance (R 2 b 0.05), but that this improvement was offset by the loss of degrees of freedom (Jo et al., 2010). The spatial specificity of functional connectivity, however, has been clearly demonstrated to be influenced by aliased physiological noise (Lowe et al., 1998). Furthermore, the influence of physiological noise on estimates of functional connectivity may depend on the precise region being studied. A study by Dagli et al. found that significant cardiac fluctuations were found on average in 27.5 +/− 8.0% of the brain voxels. Removing these fluctuations resulted in a variance reduction of roughly 10–40%, depending on the region being investigated (Dagli et al., 1999). Estimates of functional connectivity from seed regions near pulsatile vessels should therefore be improved by physiological noise correction. Our examination of the functional connectivity of the default mode network clearly demonstrated the influence that respiration variations can have on the estimated functional connectivity of the posterior cingulate. A recent study by Vogt et al. also showed that RETROICOR resulted in a significant reduction in time course variance and increase in the detection of activation to a painful stimulus (Vogt et al., 2011). The bottom line is that there is evidence that physiological noise can affect our estimates of functional connectivity. Additional analysis steps should therefore be taken to account for this noise, such as regressing out physiologically induced fluctuations, or ruling out the contribution of physiological noise due to the spatial pattern of correlation. Significant progress has been made in developing routines to remove physiological noise. Interestingly, however, maps of temporal variance before vs. after physiological noise correction often still show similar spatial patterns, with the greatest fluctuations occurring in ventricles and large vessels, indicating a need to further improve and refine physiological noise correction techniques (Birn et al., 2006; Glover et al., 2000). An important caveat to regressing out cardiac and respiratoryrelated fluctuations is that these signals may be correlated with variations in neuronal activity. Heart rate variability, for example, has long been used as a measure of emotional arousal and autonomic nervous system activity. In addition, changes in emotional state or arousal are often tied to breathing variations (Shea, 1996). In these cases, regressing out RVT or HR time courses could remove neuronal effects of interest. Using physiological measures, particularly heart rate variability and respiration volume and rate fluctuations, as nuisance regressors should therefore be done with caution, particularly when the behaviors and brain activation associated with these physiological responses are of interest. Conclusion Resting-state functional connectivity is an important, promising, and exciting new technique for understanding the organization and function of the brain. A growing number of studies have demonstrated that this technique can provide robust and reliable estimates of the functional relationship between different brain regions, and the clinical applications of this powerful technique are only just beginning. However, there are potential confounds of which researchers should
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be aware, such as the influence of physiological noise. A number of good tools exist for minimizing the influence of physiological noise and other confounds. Recordings of the heart beat and respiration are now relatively straightforward on many scanners, and removing modeled waveforms based on these recorded signals has been shown to reduce variance in many brain areas. The largest increases in specificity of functional connectivity maps at present, however, appear to come from the removal of nuisance variables derived from the data itself, such as the average signal fluctuations in the white matter and CSF. In part this advantage of data-driven noise reduction methods may be due to incomplete or inaccurate models of cardiac and respiratory-related signal changes. It is currently unclear to what extent these data-driven nuisance regression techniques can account for all of cardiac and respiratory related noise, or whether this data driven approach is complementary to model-based methods, particularly as models of physiological signal changes are improved. The refinement of physiological noise removal tools and the development of more advanced correction techniques are therefore likely to continue in the future. Acknowledgments Many thanks to Jason Diamond for helpful discussions and for starting me down the path of studying functional connectivity. Also, a heartfelt thanks to Peter Bandettini for motivating me to pursue a research career in fMRI and for his support over the years. The research described here was supported in part by the National Institute of Mental Health Intramural Research Program. R.M. Birn is currently supported in part by NIH grant RC1MH090912 as well as the HealthEmotions Research Institute. References Anderson, J.S., Druzgal, T.J., Lopez-Larson, M., Jeong, E.K., Desai, K., Yurgelun-Todd, D., 2011. Network anticorrelations, global regression, and phase-shifted soft tissue correction. Hum. Brain Mapp. 32, 919–934. Balota, D.A., Yap, M.J., Cortese, M.J., Hutchison, K.A., Kessler, B., Loftis, B., Neely, J.H., Nelson, D.L., Simpson, G.B., Treiman, R., 2007. The English Lexicon Project. Behav. Res. Meth. 39, 445–459. Beall, E.B., Lowe, M.J., 2007. Isolating physiologic noise sources with independently determined spatial measures. NeuroImage 37, 1286–1300. Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M., 2005. Investigations into restingstate connectivity using independent component analysis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 1001–1013. Birn, R.M., Diamond, J.B., Smith, M.A., Bandettini, P.A., 2006. Separating respiratoryvariation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage 31, 1536–1548. Birn, R.M., Murphy, K., Handwerker, D.A., Bandettini, P.A., 2009. fMRI in the presence of task-correlated breathing variations. NeuroImage 47, 1092–1104. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541. Biswal, B., DeYoe, A.E., Hyde, J.S., 1996. Reduction of physiological fluctuations in fMRI using digital filters. Magn. Reson. Med. 35, 107–113. Biswal, B., Hudetz, A.G., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1997. Hypercapnia reversibly suppresses low-frequency fluctuations in the human motor cortex during rest using echo-planar MRI. J. Cereb. Blood Flow Metab. 17, 301–308. Brosch, J.R., Talavage, T.M., Ulmer, J.L., Nyenhuis, J.A., 2002. Simulation of human respiration in fMRI with a mechanical model. IEEE Trans. Biomed. Eng. 49, 700–707. Chang, C., Glover, G.H., 2009. Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI. NeuroImage 47, 1381–1393. Chang, C., Cunningham, J.P., Glover, G.H., 2009. Influence of heart rate on the BOLD signal: the cardiac response function. NeuroImage 44, 857–869. Chuang, K.H., Chen, J.H., 2001. IMPACT: image-based physiological artifacts estimation and correction technique for functional MRI. Magn. Reson. Med. 46, 344–353. Cordes, D., Haughton, V.M., Arfanakis, K., Carew, J.D., Turski, P.A., Moritz, C.H., Quigley, M.A., Meyerand, M.E., 2001. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am. J. Neuroradiol. 22, 1326–1333. Dagli, M.S., Ingeholm, J.E., Haxby, J.V., 1999. Localization of cardiac-induced signal change in fMRI. NeuroImage 9, 407–415. Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M., Beckmann, C.F., 2006. Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U. S. A. 103, 13848–13853. Davis, T.L., Kwong, K.K., Weisskoff, R.M., Rosen, B.R., 1998. Calibrated functional MRI: mapping the dynamics of oxidative metabolism. Proc. Natl. Acad. Sci. U. S. A. 95, 1834–1839.
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NeuroImage journal homepage: www.elsevier.com/locate/ynimg
Review
The role of physiological noise in resting-state functional connectivity Rasmus M. Birn Department of Psychiatry, University of Wisconsin Madison, Madison, WI, 53719, USA
a r t i c l e
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Article history: Accepted 1 January 2012 Available online 8 January 2012 Keywords: functional Magnetic Resonance Imaging (fMRI) Functional connectivity Resting-state Physiological noise
a b s t r a c t Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (b 0.1 Hz) fluctuations in the MRI signal. These correlated fluctuations occur even when the subject is “at rest” (not asked to perform any particular task) and result from spontaneous neuronal activity synchronized within multiple distinct networks of brain regions. This estimate of connectivity, however, can be influenced by physiological noise, such as cardiac and respiratory fluctuations. This brief review looks at the effect of physiological noise on estimates of resting-state functional connectivity, discusses ways to remove physiological noise, and provides a personal recollection of the early developments in these approaches. This review also discusses the importance of physiological noise correction and provides a summary of evidence demonstrating that functional connectivity does have a neuronal underpinning and cannot purely be the result of physiological noise. © 2012 Published by Elsevier Inc.
Contents Introduction . . . . . . . . . . . . . . . . . . . . The role of physiological noise . . . . . . . . . . Removal of physiological noise . . . . . . . . . Global signal regression . . . . . . . . . . . . . Current physiological noise correction techniques . Is resting-state functional connectivity neuronal or Does physiological noise correction matter? . . . Conclusion . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . .
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Introduction The last few years have seen a virtual explosion in the number of studies using MRI to investigate not only the activity of different brain regions, but also the functional connectivity between them (Fig. 1). This functional connectivity can be estimated by measuring the correlation of low frequency (b0.1 Hz) MRI signal fluctuations between two or more brain regions. The rationale is that functionally related brain regions show synchronized increases and decreases in neuronal activity resulting in correlated MRI signal fluctuations. What is remarkable is that these synchronized signal fluctuations can be observed even when the subject is “at rest” and not explicitly asked to perform any task. These correlated signal fluctuations at rest likely reflect both unconstrained mental activity (e.g. “mind
E-mail address: [email protected]. 1053-8119/$ – see front matter © 2012 Published by Elsevier Inc. doi:10.1016/j.neuroimage.2012.01.016
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wandering”) as well as spontaneous neuronal fluctuations, and have been shown to reliably map multiple functional networks in the brain. This unique ability to non-invasively measure the functional relationships of different regions in the brain, combined with the relative ease at which such scans can be acquired, has a huge clinical and neuroscience potential, allowing us to improve our understanding of both the typical and atypical brain. This field is only just beginning, with new applications and improved methodology and technology appearing every week. The ability to map functional connectivity with MRI during the resting state was first discovered by Biswal et al. (1995). By looking more closely at “what's in the noise” (Weisskoff et al., 1993), Biswal et al. found that signal fluctuations in the left and right primary motor areas were significantly correlated even when the subject was not performing any motor task. Further work by a number of research groups (reviewed in more detail elsewhere in this issue) provided important foundational work for this technique, demonstrating
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Number of publications / yr 350
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Year Fig. 1. Number of publications per year on resting-state functional connectivity. This graph was generated from Scopus (http://www.scopus.com) using the following search criterion: (“functional Magnetic Resonance Imaging” OR “functional MRI” OR “fMRI”) AND (((rest OR resting) AND connectivity) OR “resting state” OR “spontaneous fluctuations” OR “intrinsic fluctuations”).
that these correlated signal fluctuations occurred at low temporal frequencies (b0.1 Hz), that they occurred in a number of different cortical and subcortical brain systems, and that they had similar properties as task-related blood oxygenation level dependent (BOLD) responses (Biswal et al., 1997; Cordes et al., 2001; Lowe et al., 1998; Peltier and Noll, 2002). Remarkably, it would take more than 10 years before resting-state functional connectivity was finally accepted and adopted by the larger neuroimaging community. The role of physiological noise The primary challenge researchers faced in these earlier years was to convince others that this measure of functional connectivity observed during rest did indeed reflect networks of neuronal activity. After all, functional connectivity was being derived from the correlation of MRI signal fluctuations, and there are many reasons why two regions of the brain could have similar MRI signal fluctuations. For example, head motion results in correlated signal changes at the edges of the brain and in regions with large spatial variations in image contrast. In addition, other physiological processes such as cardiac pulsation and respiration can lead to correlated signal fluctuations in the brain. Cardiac pulsation results in brain tissue movement and inflow effects leading to correlated signal fluctuations primarily in and near large blood vessels (Dagli et al., 1999). The movement of the chest wall during breathing results in magnetic field changes that distort the MR image acquired of the brain (Brosch et al., 2002; Raj et al., 2001). Cerebrospinal fluid (CSF) flow is modulated by both the cardiac and respiratory cycles, resulting in additional signal changes. Improper spoiling of the magnetization after the acquisition of an image (e.g. by not applying strong enough crusher gradients at the end of acquisition) can result in steady-state free precession (SSFP) signals which are modulated by cardiac, respiratory, or other B0 fluctuations and thus lead to correlated signal changes (Zhao et al., 2000). The potential confound of physiological fluctuations on estimates of functional connectivity was recognized early on. In order to focus on the correlation of low frequency (b0.1 Hz) fluctuations in the MRI signal, the earliest studies of functional connectivity used a very short TR and filtered out physiological fluctuations occurring at about 1 Hz (cardiac) and 0.3 Hz (respiration) (Biswal et al., 1996). The drawback with this approach is that only a few slices of the brain could be imaged at such a fast rate. Whole brain imaging required a much longer TR (about 2–3 s), resulting in an aliasing of cardiac and respiratory fluctuations to much lower frequencies. A simple low pass filter (e.g. b0.1 Hz) will not remove cardiac and respiratory-induced fluctuations when the MRI data is acquired with a TR of 2 s or above. Thus, researchers turned to retrospective correction techniques, initially
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developed for task fMRI, that use heart beat and respiration recordings during the scan to regress out the physiologically induced fluctuations (Glover et al., 2000; Hu et al., 1995; Josephs et al., 1997). Because these techniques take into account the phase of the respiration and cardiac cycle at which each image is acquired, respiration and cardiac induced signal fluctuations can be reduced despite aliasing or variations in cycling rate during the acquisition. There are, however, additional physiological (cardiac and respiratory-related) fluctuations that are not removed by these techniques. Variations in breathing depth and rate, for example, lead to alterations in arterial CO2 levels. Since CO2 is a potent vasodilator, decreased breathing depth and/or rate leads to an increased blood flow and, thus, an increase in the BOLD signal (Birn et al., 2006; Modarreszadeh and Bruce, 1994; Van den Aardweg and Karemaker, 2002; Wise et al., 2004). These alterations are part of a complex feedback circuit — increased arterial CO2 levels activate chemoreceptors that increase subsequent breathing depth and rate. This results in increased CO2 exhalation and a reduction of arterial CO2. Studies have shown that fluctuations of arterial CO2 (as measured by a person's end-tidal CO2) vary with a cycle of about 0.03 Hz (Modarreszadeh and Bruce, 1994; Van den Aardweg and Karemaker, 2002). Therefore these changes in arterial CO2 lead to low frequency (0.03 Hz) fluctuations in the fMRI signal. FMRI studies have also shown that the variability in heart rate is correlated with MRI signal changes throughout gray matter (Chang et al., 2009; Shmueli et al., 2007). Furthermore, cerebral blood vessels exhibit vasomotion, a spontaneous low frequency (~0.1 Hz) oscillation in vascular tone and blood flow (Hudetz et al., 1998). Successful measurement of functional connectivity requires that all of these sources of non-neuronal fluctuations be addressed. My own research addressing the influence of artifactual fluctuations, particularly respiration changes, on functional connectivity began as a combination of serendipity and a motivated student. In 2004 a Howard Hughes fellow, Jason Diamond, came to work in Peter Bandettini's research section where I was a staff scientist. Jason had worked on fMRI projects as an undergraduate at Washington University in St. Louis with Harold Burton, and he was eager to continue to work with fMRI, particularly on resting-state functional connectivity. At that time, Greicius et al. had recently published a paper showing that regions of the “default mode network” (DMN) were functionally connected at rest (Greicius et al., 2003). This DMN which includes the posterior cingulate, anterior cingulate/medial prefrontal cortex (mPFC), and bilateral parietal areas, had been previously shown to deactivate consistently during a range of cognitive tasks (McKiernan et al., 2003; Raichle et al., 2001). In other words, these were areas that were more active at rest — hence the label of “default mode” network. Jason was interested in working with me to determine whether there were any differences between the regions that generally deactivate during tasks and areas that were functionally connected at rest. During my time in graduate school, I had interacted with both Bharat Biswal and Beth Meyerand (whose lab later focused extensively on methodological developments in resting-state functional connectivity), and I had seen the lengths they had gone to in order to filter out other sources of noise, such as cardiac and respiratory fluctuations. At that time, measuring the subject's heart beat and respiration often required custom setups or interfaces with the MRI scanner. I had also seen the difficulty they had in convincing others that this connectivity was truly neuronal in origin. I therefore cautioned Jason that undertaking this research would not be trivial. Fortunately, Jason was adamant about pursuing this research. The first step was finding a task that would give consistent deactivations in the DMN. Pilot experiments we ran showed that a word/non-word discrimination task (with words/non-words drawn from the English Lexicon Project (Balota et al., 2007)) resulted in robust deactivations in regions of the DMN. We then assessed the connectivity between these DMN brain regions during a resting state.
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My research at that time focused primarily on the physiology and spatial heterogeneity of the BOLD response. Prior work had shown that more quantitative information about neuronal metabolism could be obtained by calibrating the BOLD response using an administration of CO2 (Davis et al., 1998; Hoge et al., 1999), which increases the cerebral blood flow (CBF) throughout the brain. Administration of CO2, however, is cumbersome, and we therefore looked at breathholding as a simpler way to globally increase CBF. In the spring of 2004, I heard a talk at the International Society for Magnetic Resonance in Medicine (ISMRM) conference by Richard Wise who showed that fluctuations in end-tidal CO2 during rest were significantly correlated with fluctuations in the MRI signal. In this talk as well as in the subsequent paper, he also suggested that these signal fluctuations could cause problems for estimates of resting-state functional connectivity (Wise et al., 2004). I was struck by the similarity of these maps of signal changes from resting breathing variations to maps we had obtained in our breath-holding studies, and I therefore set out to investigate the role of breathing variations in our recently acquired resting-state data. We had not collected measures of endtidal CO2, but we did have measures of respiration depth and rate recorded from a pneumatic belt placed around the subject's chest during the scan. In order to approximate the variations in arterial CO2 induced by changes in breathing depth and rate, we computed the respiration volume divided by the period (the respiration volume per time, RVT). This RVT time course was then shifted in time and fit to the MR data. The map of the amplitude of RVT-related signal changes looked remarkably similar to both Wise et al.'s work on end-tidal CO2 fluctuations and our prior breath-holding studies with large signal changes in the posterior cingulate, inferior occipital cortex, near large vessels, and to a certain extent throughout gray matter (see Fig. 2A). I walked over to Jason Diamond's cubicle to share this interesting result and noticed that he was looking at a similar map on his computer screen. Jason, however, was not investigating breathing related changes but had computed the functional connectivity with a seed region in the posterior cingulate, similar to the earlier analysis from Greicius et al. (2003). We compared our two maps more closely (see Fig. 2), and realized that respiration changes (in this case measured by RVT) indeed caused great problems for estimates of functional connectivity. Removal of physiological noise In our next set of experiments, we set out to characterize this respiration artifact and find ways to reduce its influence on functional connectivity measures. In addition to the lexical decision making
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task and resting run, we had the subjects try to breathe at a more constant depth and rate by following a visual display that indicated when to breathe in and out. The resulting maps of functional connectivity to the posterior cingulate showed a more focal pattern of connectivity with significantly connected regions that agreed more closely with areas that were deactivated during the lexical task. The extent to which this still constituted a “resting state” instead of a task involving visual activity and breathing control is of course up for debate. In addition, requiring subjects to control their breathing negates many of the advantages of resting-state fMRI in which the subject is not required to perform a task. We therefore also looked at ways to remove the measured fluctuations from the data. Regressing out signal changes related to respiration variations requires that the estimated RVT time course be shifted in time since a change in breathing depth or rate causes a CO2 change, and resultant blood flow change, later in time. Conversely, it is possible that variations in brain activity are driving the changes in respiration. We therefore allowed the latency of the estimated RVT time course to vary from − 10 to +15 s and for each voxel in the brain found the shift that resulted in the greatest positive and the greatest negative correlation between the (shifted) RVT time course and the fMRI time series signal. We found that the correlation was predominantly negative (i.e. fMRI signal increases resulting from decreases in respiration depth) at an average latency of 5.4 s, and a smaller positive correlation at an average latency of −0.9 s (see Fig. 3). Furthermore, these latencies varied across the brain. While the shifted RVT time course was significantly correlated with fMRI time series fluctuations, regressing out this shifted RVT time course did not significantly alter the functional connectivity maps. In part this may be due to a sudden change in breathing, such as a deep breath, which results not only in a delayed but also in a prolonged fMRI signal change. Regressing out only a shifted RVT time course therefore did not fully remove the respiration-related signal changes. Our next experiments set out to measure this respiration response function by averaging the response to repeated single deep breaths spaced 30–40 s apart. We found that this respiration response function, when convolved with the RVT time course, accurately fit other cued breathing variations, such as cued depth changes, cued rate changes, or breath-holding. However, our studies indicated that the response function convolved with the RVT time course during rest did not accurately fit resting-state fMRI signal fluctuations. More recent work by Chang et al. combines the removal of both respiration volume and heart rate changes (RVHRcor) and finds that including both respiration volume and heart rate variations as nuisance regressors explains significantly more variance than either
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Fig. 2. A) fMRI signal correlated with respiration volume per time (RVT) changes, B) functional connectivity with a seed region in the posterior cingulate from a group of 10 subjects. Adapted from Birn et al., 2006.
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Fig. 3. Correlation with the respiration per volume time (RVT) in a representative subject. A) Z-scores of regions negatively correlated with the RVT; B) Regions positively correlated with the RVT. C) and D) Z-score of correlating the RVT with fMRI time course (for two sample voxels, as indicated by the arrows) at multiple shifts between the RVT and the time-series. Positive shifts indicate a shift of the RVT forward in time relative to the fMRI data (i.e. fMRI signal changes following respiration changes). Adapted from Birn et al., 2006.
regressor alone (Chang et al., 2009). Furthermore, including measures of end-tidal CO2 explains even more noise, suggesting that the different measurements (respiration volume, heart rate, and end-tidal CO2) to a certain degree explain different aspects of the noise (Chang and Glover, 2009). Most of these physiological noise removal techniques require an independent measurement of the pulse and respiration (e.g., using a pulse oximeter and respiration belt). However, some techniques have been developed that do not require these ancillary measures. When the TR is short enough to avoid aliasing of cardiac and respiratory fluctuations, physiological noise can be reduced by temporal low-pass filtering, or by using other more sophisticated methods (e.g. IMPACT (Chuang and Chen, 2001)). There are also strategies that work for longer TRs. CORSICA is a technique that uses spatial independent component analysis (ICA) to identify and remove signal fluctuations that match spatially with patterns of known physiological noise (Perlbarg et al., 2007). PESTICA, a technique by Beall et al., (Beall and Lowe, 2007) estimates the physiological fluctuations from the fMRI data using a temporal ICA approach. Physiological fluctuations can also be reduced by including nuisance regressors derived from the data itself, for example from regions such as CSF, white matter, or edges of the brain that are unlikely to show neuronal-induced BOLD signal fluctuations (Birn et al., 2009; Jo et al., 2010; Weissenbacher et al., 2009). Global signal regression We were initially surprised that the maps of functional connectivity of the default mode network from our study (Fig. 2B) showed more extensive correlations across the brain than that reported previously by Greicius et al. (Greicius et al., 2003). Looking more closely at the methods employed by Greicius et al., we noticed an additional preprocessing step — regressing out the average signal over the whole brain. When we performed this global signal regression, the regions significantly correlated with the posterior cingulate became more focal in space and matched more closely with the areas that were deactivated during the lexical task. This increased sparseness and improved correspondence to task-deactivated areas may be the result of a reduction of the relatively global signal fluctuations induced by breathing variations, which tend to occur throughout gray matter (Wise et al., 2004). Likely because of the improved sparseness of the functional connectivity maps, global signal regression has now become a common step in processing functional connectivity data. In addition, this preprocessing step makes a certain amount of sense — we are not really interested in signal changes that are constant over the whole brain, and certain physiological artifacts (e.g. respiration related changes) occur relatively globally over the whole brain. The problem with global signal regression is that the global signal necessarily contains a portion of the signal of interest as well. Removing
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this global signal can therefore remove some of the signal of interest and distort the measure of functional connectivity. Furthermore, it has been shown that global signal regression can induce anticorrelations with the seed region being investigated (Murphy et al., 2009; Weissenbacher et al., 2009). This finding led to some heated debate and controversy — an earlier study by Fox et al. had shown that at rest the default mode network (or “task-negative” network) was anti-correlated with other brain regions that generally show increased activity during goal directed tasks (the “task positive” network, encompassing areas involved in attention and various cognitive tasks) (Fox et al., 2005, 2009). The observation that the negative interplay between these two networks continues even during rest (e.g. activity in the default mode network increasing as activity in attention areas decreases) makes a certain amount of sense. However, since the global signal was removed from the data as a preprocessing step, it is unclear whether this observed anti-correlation is due to the opposing interaction of two neuronal networks, or whether it is simply introduced by this preprocessing step. Many subsequent studies failed to see this anti-correlation without performing global signal regression (Murphy et al., 2009; Weissenbacher et al., 2009). Note that this finding does not mean that the observed anticorrelation between task-positive and task-negative networks is necessarily due to global signal regression. Observed anti-correlations after performing global signal regression may have been introduced by this processing step, or alternatively the true anti-correlation between these two networks may have been revealed by removing global noise. Current physiological noise correction techniques Because the average signal over the whole brain contains the spontaneous neuronal fluctuations of interest and because of the previously mentioned potential difficulties with global signal regression, investigators have started to use the average signals over the white matter and CSF as nuisance regressors. The rationale is that these regions of the brain are less influenced by neuronal-induced BOLD changes and are therefore more reflective of non-neuronal signal changes (i.e. “noise”). A study by H-J Jo et al., for example investigated the influence of various nuisance regressors on resulting functional connectivity maps and found that significant variance in the data could be explained by using estimates of subject motion, as well as averaged signals from eroded white matter and ventricular CSF masks (Jo et al., 2010). Furthermore, they showed that in some cases, using a local estimate of white matter signal surrounding each voxel as a nuisance regressor resulted in a better reduction of noise, particularly for spatially heterogeneous artifacts. A study by Anderson et al. found additional benefit from including signals from the soft tissue of the face (phase-shifted soft tissue regression, PSTcor) (Anderson et al., 2011). A quantitative comparison of different preprocessing strategies by Weissenbacher et al. also found that using average white matter and CSF signals as nuisance regressors improved the specificity of the connectivity maps, but they further noted that including the global signal as a nuisance regressor resulted in an even greater increase in specificity (Weissenbacher et al., 2009). The optimal set of nuisance regressors and preprocessing steps that can produce both the most robust functional connectivity maps as well as reliably reveal individual differences in functional connectivity is therefore still an active area of investigation. Is resting-state functional connectivity neuronal or just an artifact? Our work, as well as that of other groups, has shown how similar maps of functional connectivity appear to maps that represent purely respiration-related signal changes. Does this mean that all functional connectivity results are due to artifacts and not reflective of correlated neuronal activity? While I started out skeptical about some functional connectivity results, I have become convinced that when
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performed properly, resting-state fMRI can give us reliable estimates of the functional connectivity for a number of reasons. First, correlations are observed between functionally related brain areas. Second, early work on resting-state connectivity showed that correlated signal fluctuations in functionally related brain regions are not due to cardiac pulsations and respiration but instead occur at much lower temporal frequencies (Biswal et al., 1995; Cordes et al., 2001; Kiviniemi et al., 2005). Third, these fluctuations have similar characteristics as neuronally-induced BOLD responses (e.g. TE dependence, attenuation of the response during hypercapnia) (Biswal et al., 1997; Peltier and Noll, 2002). While other respiration-related fluctuations, such as those resulting from changes in respiration volume and depth, do share the properties of a BOLD response, regressing out these fluctuations, using recorded respirations, recorded endtidal CO2, or the averaged global, white matter, or CSF signals, did not remove the correlation between regions of the default mode network (Birn et al., 2006). If anything, removing cardiac and respiratory-related fluctuations made the map of connectivity to the posterior cingulate look more like the map of areas deactivated during tasks. In addition, a recent experiment by C. Madjar et al. reduced fluctuations in arterial CO2 levels during normal breathing by using a computer-controlled respiration system (Slessarev et al., 2007). This study showed that correlated fluctuations in the default mode remain (and in fact increase) even when the arterial CO2 levels, as measured by end-tidal CO2, are held more constant (Madjar et al., 2011). Fourth, correlations are observed in multiple distinct functional brain networks (e.g. motor, visual, auditory, attention, default-mode, etc.) (Beckmann et al., 2005; Damoiseaux et al., 2006; De Luca et al., 2006; Smith et al., 2009). It was suggested early in the study of resting-state functional connectivity that correlated fluctuations (e.g. between left and right motor cortices) may simply reflect similar vascular structure in these regions. However, for this vascular argument to hold, left and right motor cortices would have to have similar vascular structure that is distinct from the vascular structure of the visual cortex, which again is distinct from the vascular structure of the auditory system, the default mode network, attention areas, and so on. It is highly unlikely that the similarity of vascular structure could give rise to independent networks of correlated fluctuations that map so closely onto known functional networks. Fifth, studies have shown that a disruption in structural connectivity leads to a reduction in resting-state functional connectivity. A study by Quigley et al. showed that subjects with callosal agenesis had reduced or no functional connectivity between left and right sensorimotor cortices (Quigley et al., 2003). A subsequent study by Johnston et al. investigated the functional connectivity before and after a callosotomy and found that interhemispheric functional connectivity was lost after sectioning the corpus callosum, while intra-hemispheric connectivity remained intact (Johnston et al., 2008). Sixth, a resting-state functional connectivity study by Krienen and Buckner showed that the left cerebellum was more strongly coupled with the right primary motor cortex, and conversely that the right cerebellum was more strongly coupled with the left motor cortex (Krienen and Buckner, 2009). The connectivity of left and right lateral prefrontal cortices showed a similar contralateral correlation with different regions in the cerebellum. This contralateral correlation is expected based on known neuronal connections and cannot be explained by either head motion or similarities in the cerebrovasculature, since the cerebellum and sensorimotor cortex are each supplied by its own major artery. Seventh, correlated fluctuations in neuronal activity have been demonstrated with other modalities, such as EEG (Laufs et al., 2003), ECoG (Miller et al., 2009), and even direct recordings in animal models (Leopold and Logothetis, 2003; Leopold et al., 2003; Shmuel and Leopold, 2008). Finally, low frequency fluctuations in the fMRI signal during rest have been linked to individual differences in behavior or pathology (Di Martino et al., 2009; Fox et al., 2007; Garrity et al., 2007; Greicius, 2008; Kennedy and Courchesne, 2008; Kim et al., 2010).
Does physiological noise correction matter? Despite numerous studies demonstrating that the heart beat and respiration lead to correlated fluctuations in certain brain regions, many studies do not perform any physiological noise correction aside from regressing out global, white matter, and/or CSF signals. Nevertheless, the resulting networks appear to be consistent, reliable, and conform to known task-related networks (Damoiseaux et al., 2006; De Luca et al., 2006; Shehzad et al., 2009; Smith et al., 2009). This raises the question of whether physiological noise correction is really necessary. A recent study by H.J. Jo et al., for example, determined that averaged over gray matter, cardiac and respiratory regressors modeled in RETROICOR account for a small amount of time course variance (R 2 b 0.05), but that this improvement was offset by the loss of degrees of freedom (Jo et al., 2010). The spatial specificity of functional connectivity, however, has been clearly demonstrated to be influenced by aliased physiological noise (Lowe et al., 1998). Furthermore, the influence of physiological noise on estimates of functional connectivity may depend on the precise region being studied. A study by Dagli et al. found that significant cardiac fluctuations were found on average in 27.5 +/− 8.0% of the brain voxels. Removing these fluctuations resulted in a variance reduction of roughly 10–40%, depending on the region being investigated (Dagli et al., 1999). Estimates of functional connectivity from seed regions near pulsatile vessels should therefore be improved by physiological noise correction. Our examination of the functional connectivity of the default mode network clearly demonstrated the influence that respiration variations can have on the estimated functional connectivity of the posterior cingulate. A recent study by Vogt et al. also showed that RETROICOR resulted in a significant reduction in time course variance and increase in the detection of activation to a painful stimulus (Vogt et al., 2011). The bottom line is that there is evidence that physiological noise can affect our estimates of functional connectivity. Additional analysis steps should therefore be taken to account for this noise, such as regressing out physiologically induced fluctuations, or ruling out the contribution of physiological noise due to the spatial pattern of correlation. Significant progress has been made in developing routines to remove physiological noise. Interestingly, however, maps of temporal variance before vs. after physiological noise correction often still show similar spatial patterns, with the greatest fluctuations occurring in ventricles and large vessels, indicating a need to further improve and refine physiological noise correction techniques (Birn et al., 2006; Glover et al., 2000). An important caveat to regressing out cardiac and respiratoryrelated fluctuations is that these signals may be correlated with variations in neuronal activity. Heart rate variability, for example, has long been used as a measure of emotional arousal and autonomic nervous system activity. In addition, changes in emotional state or arousal are often tied to breathing variations (Shea, 1996). In these cases, regressing out RVT or HR time courses could remove neuronal effects of interest. Using physiological measures, particularly heart rate variability and respiration volume and rate fluctuations, as nuisance regressors should therefore be done with caution, particularly when the behaviors and brain activation associated with these physiological responses are of interest. Conclusion Resting-state functional connectivity is an important, promising, and exciting new technique for understanding the organization and function of the brain. A growing number of studies have demonstrated that this technique can provide robust and reliable estimates of the functional relationship between different brain regions, and the clinical applications of this powerful technique are only just beginning. However, there are potential confounds of which researchers should
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be aware, such as the influence of physiological noise. A number of good tools exist for minimizing the influence of physiological noise and other confounds. Recordings of the heart beat and respiration are now relatively straightforward on many scanners, and removing modeled waveforms based on these recorded signals has been shown to reduce variance in many brain areas. The largest increases in specificity of functional connectivity maps at present, however, appear to come from the removal of nuisance variables derived from the data itself, such as the average signal fluctuations in the white matter and CSF. In part this advantage of data-driven noise reduction methods may be due to incomplete or inaccurate models of cardiac and respiratory-related signal changes. It is currently unclear to what extent these data-driven nuisance regression techniques can account for all of cardiac and respiratory related noise, or whether this data driven approach is complementary to model-based methods, particularly as models of physiological signal changes are improved. The refinement of physiological noise removal tools and the development of more advanced correction techniques are therefore likely to continue in the future. Acknowledgments Many thanks to Jason Diamond for helpful discussions and for starting me down the path of studying functional connectivity. Also, a heartfelt thanks to Peter Bandettini for motivating me to pursue a research career in fMRI and for his support over the years. The research described here was supported in part by the National Institute of Mental Health Intramural Research Program. R.M. Birn is currently supported in part by NIH grant RC1MH090912 as well as the HealthEmotions Research Institute. References Anderson, J.S., Druzgal, T.J., Lopez-Larson, M., Jeong, E.K., Desai, K., Yurgelun-Todd, D., 2011. Network anticorrelations, global regression, and phase-shifted soft tissue correction. Hum. Brain Mapp. 32, 919–934. Balota, D.A., Yap, M.J., Cortese, M.J., Hutchison, K.A., Kessler, B., Loftis, B., Neely, J.H., Nelson, D.L., Simpson, G.B., Treiman, R., 2007. The English Lexicon Project. Behav. Res. Meth. 39, 445–459. Beall, E.B., Lowe, M.J., 2007. Isolating physiologic noise sources with independently determined spatial measures. NeuroImage 37, 1286–1300. Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M., 2005. Investigations into restingstate connectivity using independent component analysis. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 1001–1013. Birn, R.M., Diamond, J.B., Smith, M.A., Bandettini, P.A., 2006. Separating respiratoryvariation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage 31, 1536–1548. Birn, R.M., Murphy, K., Handwerker, D.A., Bandettini, P.A., 2009. fMRI in the presence of task-correlated breathing variations. NeuroImage 47, 1092–1104. Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541. Biswal, B., DeYoe, A.E., Hyde, J.S., 1996. Reduction of physiological fluctuations in fMRI using digital filters. Magn. Reson. Med. 35, 107–113. Biswal, B., Hudetz, A.G., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1997. Hypercapnia reversibly suppresses low-frequency fluctuations in the human motor cortex during rest using echo-planar MRI. J. Cereb. Blood Flow Metab. 17, 301–308. Brosch, J.R., Talavage, T.M., Ulmer, J.L., Nyenhuis, J.A., 2002. Simulation of human respiration in fMRI with a mechanical model. IEEE Trans. Biomed. Eng. 49, 700–707. Chang, C., Glover, G.H., 2009. Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI. NeuroImage 47, 1381–1393. Chang, C., Cunningham, J.P., Glover, G.H., 2009. Influence of heart rate on the BOLD signal: the cardiac response function. NeuroImage 44, 857–869. Chuang, K.H., Chen, J.H., 2001. IMPACT: image-based physiological artifacts estimation and correction technique for functional MRI. Magn. Reson. Med. 46, 344–353. Cordes, D., Haughton, V.M., Arfanakis, K., Carew, J.D., Turski, P.A., Moritz, C.H., Quigley, M.A., Meyerand, M.E., 2001. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR Am. J. Neuroradiol. 22, 1326–1333. Dagli, M.S., Ingeholm, J.E., Haxby, J.V., 1999. Localization of cardiac-induced signal change in fMRI. NeuroImage 9, 407–415. Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M., Beckmann, C.F., 2006. Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. U. S. A. 103, 13848–13853. Davis, T.L., Kwong, K.K., Weisskoff, R.M., Rosen, B.R., 1998. Calibrated functional MRI: mapping the dynamics of oxidative metabolism. Proc. Natl. Acad. Sci. U. S. A. 95, 1834–1839.
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