A gut, heart, and breath check: what matters most for cognition?

Last week I asked twitter a question that comes up frequently in our lab: what visceral rhythm exerts the most impact on cognition [1]? Now, this is a question which is deliberately vague in nature. The goal is to force a ‘gut check’ on which visceral systems that we, as neuroscientists, might reasonably expect to bias cognition. What do I mean by cognition? Literally any aspect of information processing. Perception, memory, learning, emotion, pain, you name it. Some of you jokingly pointed out that if any of these rhythms cease entirely (e.g., in death), cognition will surely be impacted. So to get a bit closer to an experimental design which might build on these intuitions, I offered the following guidelines:

I.e., what I largely had in mind was the kinds of psychophysiology experiments that are currently in vogue – presenting stimuli during different phases of a particular visceral cycle, and then interpreting differences in reaction time, accuracy, subjective response, or whatever as evidence of ‘brain-body interaction’. Of course, these are far from the only ways in which we can measure the influence of the body on the brain, and I intentionally left the question as open as possible. I wanted to know: what are your ‘gut feelings’, about gut feelings? And the twitter neuroscience community answered the call!


Here you can see that overall, respiration was a clear winner, and was also my own choice. Surprisingly, gastric rhythms just beat out cardiac, at about 29 vs 27.5%. More on this later. Roughly 380/1099 respondent’s felt that, all else being equal, respiration was likely to produce the most influence on cognition. And I do agree; although the literature is heavily biased in terms of numbers of papers towards the cardiac domain, intuitively respiration feels like a better candidate for the title of heavy-weight visceral rhythm champion.

Why is that? At least a few reasons spring to mind. For one thing, the depth and frequency of respiration directly modulates heart-rate variability, through basic physiological reflexes such as the respiratory sinus arrhythmia. At a more basic level still, respiration is of course responsible for gas exchange and pH regulation, conditioning the blood whose transport around the body depends upon the heart. That is to say; the heart is ultimately the chauffeur for the homeostatic function of the lungs, always second fiddle.

In the central nervous system both systems matter in a big way of course, and are closely tied to one another. A  lesion to the brain-stem that results in cardiac or respiratory arrest is equally deadly, and the basic homeostatic clocks that control these rhythms are tightly interwoven for good reason.

But here, one can reasonably argue that these low-level phenomenon don’t really speak to the heart of the question, which is about (‘higher-order’) cognition. What can we say about that? Neuroviscerally speaking, in my opinion the respiratory rhythm has the potential to influence a much broader swath of brain areas. Respiration reaches the brain through multiple pathways: bypassing the limbic system altogether to target the prefrontal cortex via the innervation of the nasal septum, through basic somatosensory entrainment via the mechanical action of the lungs and chest wall, and through the same vagally mediated pathways as those carrying baroreceptive information from the heart. In fact, the low level influence of respiration on the heart means that the brain can likely read-out or predict heart-rate at least partially from respiration alone, independently of any afferent baro-receptor information (that is of course, speculation on my part). I think Sophie Betka’s response captures this intuition beautifully:

All of which is to say, that respiration affords many potential avenues by which to bias, influence, or modulate cognition, broadly speaking. Some of you asked whether my question was more aimed at “the largest possible effect size” or the “most generalized effect size”. This is a really important question, which again, I simply intended to collapse across in my poll, whose main purpose was to generate thought and discussion. An it really is a critical issue for future research; we might predict that cardiac or gastric signals would modulate very strong effects in very specific domains (e.g., fear or hunger), but that respiration might effect weak to moderate effects in a wide variety of domains. Delineating this difference will be crucial for future basic neuroscience, and even more so if these kinds of effects are to be of clinical significance.

Suffice to say, I was pleased to see a clear majority agree that respiration is the wave of the future (my puns on the other hand, are likely growing tiresome). But I was surprised to see the strong showing of the gastric rhythm, relative to cardiac. My internal ranking was definitely leaning towards 1) respiration, 2) cardiac, 3) gastric. My thinking here was; sure, the brain may track the muscular contractions of the stomach and GI tract, but is this really that relevant for any cognitive domain other than eating behavior? To be fair, I think many respondents probably did not consider the more restricted case of, for example, presenting different trials or stimuli at gastric contraction vs expansion, but interpreted the question more liberally in terms of hormone excretion, digestion, and possibly even gut micobiome or enteric-nervous linked effects. And that is totally fair I think; taken as a whole, the rhythm of the stomach and gut is likely to exert a huge amount of primary and secondary effects on cognition. This issue was touched on quite nicely by my collaborator Paul Fletcher:

I think that is absolutely right; to a degree, how we answer the question depends exactly on which timescales and contexts we are interested in. It again raises the question of: what kind of effects are we most interested in? Really strong but specific, or weaker, more general effects? Intuitively, being hungry definitely modulates the gastric rhythm, and in turn we’ve all felt the grim specter of ‘hanger’ causing us to lash out at the nearest street food vendor.

Forgetting these speedy bodily ‘rabbits’ all together, what about those most slow of bodily rhythms [3]. Commenters Andrea Poli, Anil Seth, and others pointed out that at the very slowest timescales, hormonal and circadian rhythms can regulate all others, and the brain besides:

Indeed, if we view these rhythms as a temporal hierarchy (as some authors have argued), then it is reasonable to assume that causality should in general flow from the slowest, most general rhythms ‘upward’ to the fastest, most specific rhythms (i.e., cardiac, adrenergic, and neural). And there is definitely some truth to that; the circadian rhythm causes huge changes in baseline arousal, heart-rate variability, and even core bodily temperature. In the end, it’s probably best to view each of these smaller waves as inscribed within the deeper, slower waves; their individual shape may vary depending on context, but their global amplitude comes from the depths below. And of course, here the gloomy ghost of circular causality raises its incoherent head; because these faster rhythms can in turn regulate the slower, in a never ceasing allostatic push-me-pull-you affair.

All that considered, is is perhaps unsurprising then that in this totally unscientific poll at least, the gastric rhythm rose to challenge the all-mighty cardiac [2]. It seems clear that the preponderance of cardiac-brain studies is more an artifact of ease of study, rather than a deep seated engagement with their predominance. And ultimately, if we want to understand how the body shapes the mind, we will need to take precisely the multi-scale view espoused by many commenters.

A final thought on what kinds of effects might matter most: of all of these rhythms, only one is directly amenable to conscious control. That is of course, the breath. And it is intriguing also that across many cultural practices – elite sportsmanship, martial arts, meditation, and marksmanship for example – the regulation of the breath is taught as a core technique for altering awareness, attention, and mood. I think for this reason, respiration is among the most interesting of all possible rhythms. It sits at that rare precipice, teetering between fully automatic and fully conscious. Our ability to become conscious of the breath can be a curse and a gift; many of you may feel a slight anxiety as you read this article, becoming ever so slightly more aware of your own rising and falling breath [4]. From the point of view of neuropsychiatry, I can’t help but feel like whatever the effects of respiration are, this amenability to control, and the possibility to regulate all other rhythms in turn, makes understanding the breath an absolutely critical focus for clinical translation.

[1] Closely related to the question I am mostly commonly asked in talks: what effect size do you expect in general for cardiac/respiratory/gastric-brain interaction?

[2] I do apologize for the misleading usage of a poop emoji to signify the gastric rhythm. Although poop is certainly a causal product of the gastric rhythm, I did not mean to imply a stomach full of it.

[3] Regrettably, all of these rhythms would have been subsided in the general response category of ‘other’. This likely greatly suppressed their response rates, but I think we can all forgive this limitation of a deeply unscientific intuition pump poll.

[4] And that is something which seems to uniquely define the body in general; usually absent, potentially unpleasant (or very pleasant) when present. Phenomenologists call this the ‘transparency’ of the body-as-subject.

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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!