Is the resting BOLD signal physiological noise? What about resting EEG?

Over the past 5 years, resting-state fMRI (rsfMRI) has exploded in popularity. Literally dozens of papers are published each day examining slow (< .1 hz) or “low frequency” fluctuations in the BOLD signal. When I first moved to Europe I was caught up in the somewhat North American frenzy of resting state networks. I couldn’t understand why my Danish colleagues, who specialize in modelling physiological noise in fMRI, simply did not take the literature seriously. The problem is essentially that the low frequencies examined in these studies are the same as those that dominate physiological rhythms. Respiration and cardiac pulsation can make up a massive amount of variability in the BOLD signal. Before resting state fMRI came along, nearly every fMRI study discarded any data frequencies lower than one oscillation every 120 seconds (e.g. 1/120 Hz high pass filtering). Simple things like breath holding and pulsatile motion in vasculature can cause huge effects in BOLD data, and it just so happens that these artifacts (which are non-neural in origin) tend to pool around some of our favorite “default” areas: medial prefrontal cortex, insula, and other large gyri near draining veins.

Naturally this leads us to ask if the “resting state networks” (RSNs) observed in such studies are actually neural in origin, or if they are simply the result of variations in breath pattern or the like. Obviously we can’t answer this question with fMRI alone. We can apply something like independent component analysis (ICA) and hope that it removes most of the noise- but we’ll never really be 100% sure we’ve gotten it all that way. We can measure the noise directly (e.g. “nuisance covariance regression”) and include it in our GLM- but much of the noise is likely to be highly correlated with the signal we want to observe. What we need are cross-modality validations that low-frequency oscillations do exist, that they drive observed BOLD fluctuations, and that these relationships hold even when controlling for non-neural signals. Some of this is already established- for example direct intracranial recordings do find slow oscillations in animal models. In MEG and EEG, it is well established that slow fluctuations exist and have a functional role.

So far so good. But what about in fMRI? Can we measure meaningful signal while controlling for these factors? This is currently a topic of intense research interest. Marcus Raichle, the ‘father’ of the default mode network, highlights fascinating multi-modal work from a Finnish group showing that slow fluctuations in behavior and EEG signal coincide (Raichle and Snyder 2007; Monto, Palva et al. 2008). However, we should still be cautious- I recently spoke to a post-doc from the Helsinki group about the original paper, and he stressed that slow EEG is just as contaminated by physiological artifacts as fMRI. Except that the problem is even worse, because in EEG the artifacts may be several orders of magnitude larger than the signal of interest[i].

Understandably I was interested to see a paper entitled “Correlated slow fluctuations in respiration, EEG, and BOLD fMRI” appear in Neuroimage today (Yuan, Zotev et al. 2013). The authors simultaneously collected EEG, respiration, pulse, and resting fMRI data in 9 subjects, and then perform cross-correlation and GLM analyses on the relationship of these variables, during both eyes closed and eyes open rest. They calculate Respiratory Volume per Time (RVT), a measure developed by Rasmus Birn, to assign a respiratory phase to each TR (Birn, Diamond et al. 2006). One key finding is that the global variations in EEG power are strongly predicted by RVT during eyes closed rest, with a maximum peak correlation coefficient of .40. Here are the two time series:


You can clearly see that there is a strong relationship between global alpha (GFP) and respiration (RVT). The authors state that “GFP appears to lead RVT” though I am not so sure. Regardless, there is a clear relationship between eyes closed ‘alpha’ and respiration. Interestingly they find that correlations between RVT and GFP with eyes open were not significantly different from chance, and that pulse did not correlate with GFP. They then conduct GLM analyses with RVT and GFP as BOLD regressors. Here is what their example subject looked like during eyes-closed rest:


Notice any familiar “RSNs” in the RVT map? I see anti-correlated executive deactivation and default mode activation! Very canonical.  Too bad they are breath related. This is why noise regression experts tend to dislike rsfMRI, particularly when you don’t measure the noise. We also shouldn’t be too surprised that the GFP-BOLD and RVT-BOLD maps look similar, considering that GFP and RVT are highly correlated. After looking at these correlations separately, Yuan et al perform RETROICOR physiological noise correction and then reexamine the contrasts. Here are the group maps:


Things look a bit less default-mode-like in the group RVT map, but the RVT and GFP maps are still clearly quite similar. In panel D you can see that physiological noise correction has a large global impact on GFP-BOLD correlations, suggesting that quite a bit of this co-variance is driven by physiological noise. Put simply, respiration is explaining a large degree of alpha-BOLD correlation; any experiment not modelling this covariance is likely to produce strongly contaminated results. Yuan et al go on to examine eyes-open rest and show that, similar to their RVT-GFP cross-correlation analysis, not nearly as much seems to be happening in eyes open compared to closed:


The authors conclude that “In particular, this correlation between alpha EEG and respiration is much stronger in eyes-closed resting than in eyes-open resting” and that “[the] results also suggest that eyes-open resting may be a more favorable condition to conduct brain resting state fMRI and for functional connectivity analysis because of the suppressed correlation between low-frequency respiratory fluctuation and global alpha EEG power, therefore the low-frequency physiological noise predominantly of non-neuronal origin can be more safely removed.” Fair enough- one conclusion is certainly that eyes closed rest seems much more correlated with respiration than eyes open. This is a decent and useful result of the study. But then they go on to make this really strange statement, which appears in the abstract, introduction, and discussion:

“In addition, similar spatial patterns were observed between the correlation maps of BOLD with global alpha EEG power and respiration. Removal of respiration related physiological noise in the BOLD signal reduces the correlation between alpha EEG power and spontaneous BOLD signals measured at eyes-closed resting. These results suggest a mutual link of neuronal origin between the alpha EEG power, respiration, and BOLD signals”’ (emphasis added)

That’s one way to put it! The logic here is that since alpha = neural activity, and respiration correlates with alpha, then alpha must be the neural correlate of respiration. I’m sorry guys, you did a decent experiment, but I’m afraid you’ve gotten this one wrong. There is absolutely nothing that implies alpha power cannot also be contaminated by respiration-related physiological noise. In fact it is exactly the opposite- in the low frequencies observed by Yuan et al the EEG data is particularly likely to be contaminated by physiological artifacts! And that is precisely what the paper shows – in the author’s own words: “impressively strong correlations between global alpha and respiration”. This is further corroborated by the strong similarity between the RVT-BOLD and alpha-BOLD maps, and the fact that removing respiratory and pulse variance drastically alters the alpha-BOLD correlations!

So what should we take away from this study? It is of course inconclusive- there are several aspects of the methodology that are puzzling to me, and sadly the study is rather under-powered at n = 9. I found it quite curious that in each of the BOLD-alpha maps there seemed to be a significant artifact in the lateral and posterior ventricles, even after physiological noise correction (check out figure 2b, an almost perfect ventricle map). If their global alpha signal is specific to a neural origin, why does this artifact remain even after physiological noise correction? I can’t quite put my finger on it, but it seems likely to me that some source of noise remained even after correction- perhaps a reader with more experience in EEG-fMRI methods can comment. For one thing their EEG motion correction seems a bit suspect, as they simply drop outlier timepoints. One way or another, I believe we should take one clear message away from this study – low frequency signals are not easily untangled from physiological noise, even in electrophysiology. This isn’t a damnation of all resting state research- rather it is a clear sign that we need be to measuring these signals to retain a degree of control over our data, particularly when we have the least control at all.


Birn, R. M., J. B. Diamond, et al. (2006). “Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI.” Neuroimage 31(4): 1536-1548.

Monto, S., S. Palva, et al. (2008). “Very slow EEG fluctuations predict the dynamics of stimulus detection and oscillation amplitudes in humans.” The Journal of Neuroscience 28(33): 8268-8272.

Raichle, M. E. and A. Z. Snyder (2007). “A default mode of brain function: a brief history of an evolving idea.” Neuroimage 37(4): 1083-1090.

Yuan, H., V. Zotev, et al. (2013). “Correlated Slow Fluctuations in Respiration, EEG, and BOLD fMRI.” NeuroImage pp. 1053-8119.


[i] Note that this is not meant to be in anyway a comprehensive review. A quick literature search suggests that there are quite a few recent papers on resting BOLD EEG. I recall a well done paper by a group at the Max Planck Institute that did include noise regressors, and found unique slow BOLD-EEG relations. I cannot seem to find it at the moment however!


18 thoughts on “Is the resting BOLD signal physiological noise? What about resting EEG?

  1. Very interesting stuff Micah. The email copy of this article has an older version of the text: “every 120 seconds (e.g. 120 Hz high pass filtering).”

    Anyway the Yuan article seemed very interesting right up to the bit you point out. What a wacky chain of causation the authors derive! The changes in GFP of alpha are interesting especially when you factor in that alpha naturally increases when you close your eyes with an occipital regional focus if I remember correctly.

    I have not read the Yuan paper but did they do anything else to the scanner environment like switch off the helium pump or the internal cooling system? I know the latter tends to create artefacts in the Gamma range (cannot remember which part exactly) but I cannot remember what the helium pump spectra looks like.

  2. I believe that there is a trivially easy way to resolve this issue.

    Take meditating subjects who consistently report the pure consciousness state during TM practice, who often show extremely high levels of alpha power and alpha EEG coherence at the same time that they apparently stop breathing, and see what is what up with these periods as measured with fMRI:

    See figure 3, page 143 for a very dramatic example of this situation:

    Click to access 133.full.pdf

    • You will also need those practitioners who are able to slow their heart rate, in this case preferably to nil.

      • Why?

        Measuring fMRI during [apparent] breath suspension state would remove normal respiration artifacts from the equation, although research suggests that the person doesn’t actually stop breathing, but instead relaxes in a way that allows air to continue to circulate, primarily as a slow inhalation over the 60 second period, but possibly with secondary respiration due to the beating of the heart compressing the lungs slightly.

        Even so, it would give you a different data point involving a radically lower frequency for diaphragmatic breathing — 0.008hz approximately — for that minute while the EEG power and coherence of alpha EEG might be dramatically raised during that same period: (apologies for the facebook page -its the only permanent place I have to store images currently).

        • Pulsatile motion of the brain case and the blood vessels in the brain is the other major source of physiologically based movement noise.

          • I understand that, but you’ve eliminated, or at least changed radically, one source of the noise, while at the same time, according to EEG research, boosted the Alpha EEG power good bit, and the alpha EEG coherence, at least in some people, to its theoretical maximum.

            This gives you a radically different data set to work with than normal, and since the alpha EEG power and/or coherence of the person who shows this breath suspension state tends to fluctuate between normal levels, and these extreme levels in a singe meditation session, highly correlated with the change in breath8ing rate, you have a range of data available in a single session with a single subject.

            Surely this counts for something in attempting to resolve the issue…

          • It would indeed count for something and would as you indicate tend to eliminate one major source of noise. The pulsatile motion still remains but I could not say in what way it might effect such data. Holding your breath can though radically change the BOLD signal so the effects of breath holding may be beneficial on one hand but counter-productive on the other.

          • RE: breath holding…

            The respiratory suspension period isn’t breath-holding. It is actually a long, slow inhalation that starts with an exhale and ends with an exhale. My assumption is that the diaphragm is merely relaxing during this period. The current theory is that this is triggered by a slight change in CO2 sensitivity as a side-effect of how the thalamus is operating during this altered state.

            If that is true, then whatever artifacts that arise from it should be different than those which arise from breath-holding.

          • I really do not know enough about whatever breath suspension might be but any changes relative to “normal” breathing in CO2 concentration (and the resulting pH change) and/or O2 saturation is going to affect the BOLD signal perhaps detrimentally.

          • I should add a caveat of “unless you are doing something like calibrated BOLD.”

  3. I have not read the paper carefully but a number of things jump out in my quick scan of it that really make this study difficult to interpret. To name two: (1) The use of separate rigid body motion correction and slice timing correction steps (I never know what to think of data preprocessed like that) and (2) a TR of 2000 which of course can not capture the cardiac and respiration temporal dynamics.

    The eyes open vs closed result is sort of red flag isn’t they? Eyes open results in less correlation between BOLD and GFP or RVT. By what mechanism? Does eyes open result in less motion artifact? Above all I am confused by this work.

  4. The TR of 2000 is not “untypical” for cardiac and respiration dynamics I agree it is terrible but this is the kind of data most people will collect/see. At such slow sample rates you can expect some interesting aliasing effects to occur. The RETROICOR toolbox takes this into account though in that it produces aliased versions of the input ECG, pulse or respiration signals.

  5. Micah – thanks for a very interesting read! I have a quick (slightly tangental and naive) question though. You say here and in a previous post that pulsatile vasculature can lead to artefacts, citing ‘major draining veins’ as common culprits. I was, however, under the impression that only arteries had a pulse?

  6. By the way, does MEG have the same issues as EEG? I’ve seen meditation research using MEG that shows much the same pattern as the EEG, so does this mean that the MEG is contaminated in the same way? How would that happen?

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