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!

poll_responses.jpg

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.

Footnotes:
[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.

Read More »

Some thoughts on writing ‘Bayes Glaze’ theoretical papers.

[This was a twitter navel-gazing thread someone ‘unrolled’. I was really surprised that it read basically like a blog post, so I thought why not post it here directly! I’ve made a few edits for readability. So consider this an experiment in micro-blogging ….]

In the past few years, I’ve started and stopped a paper on metacognition, self-inference, and expected precision about a dozen times. I just feel conflicted about the nature of these papers and want to make a very circumspect argument without too much hype. As many of you frequently note, we have way too many ‘Bayes glaze’ review papers in glam mags making a bunch of claims for which there is no clear relationship to data or actual computational mechanisms.

It has gotten so bad, I sometimes see papers or talks where it feels like they took totally unrelated concepts and plastered “prediction” or “prediction error” in random places. This is unfortunate, and it’s largely driven by the fact that these shallow reviews generate a bonkers amount of citations. It is a land rush to publish the same story over and over again just changing the topic labels, planting a flag in an area and then publishing some quasi-related empirical stuff. I know people are excited about predictive processing, and I totally share that. And there is really excellent theoretical work being done, and I guess flag planting in some cases is not totally indefensible for early career researchers. But there is also a lot of cynical stuff, and I worry that this speaks so much more loudly than the good, careful stuff. The danger here is that we’re going to cause a blowback and be ultimately seen as ‘cargo cult computationalists’, which will drag all of our research down both good and otherwise.

In the past my theoretical papers in this area have been super dense and frankly a bit confusing in some aspects. I just wanted to try and really, really do due-diligence and not overstate my case. But I do have some very specific theoretical proposals that I think are unique. I’m not sure why i’m sharing all this, but I think because it is always useful to remind people that we feel imposter syndrome and conflict at all career levels. And I want to try and be more transparent in my own thinking – I feel that the earlier I get feedback the better. And these papers have been living in my head like demons, simultaneously too ashamed to be written and jealous at everyone else getting on with their sexy high impact review papers.

Specifically, I have some fairly straightforward ideas about how interoception and neural gain (precision) inter-relate, and also have a model i’ve been working on for years about how metacognition relates to expected precision. If you’ve seen any of my recent talks, you get the gist of these ideas.

Now, I’m *really* going to force myself to finally write these. I don’t really care where they are published, it doesn’t need to be a glamour review journal (as many have suggested I should aim for). Although at my career stage, I guess that is the thing to do. I think I will probably preprint them on my blog, or at least muse openly about them here, although i’m not sure if this is a great idea for theoretical work.

Further, I will try and hold to three key promises:

  1. Keep it simple. One key hypothesis/proposal per paper. Nothing grandiose.
  2. Specific, falsifiable predictions about behavioral & neurophysiological phenomenon, with no (minimal?) hand-waving
  3. Consider alternative models/views – it really gets my goat when someone slaps ‘prediction error’ on their otherwise straightforward story and then acts like it’s the only game in town. ‘Predictive processing’ tells you almost *nothing* about specific computational architectures, neurobiological mechanisms, or general process theories. I’ve said this until i’m blue in the face: there can be many, many competing models of any phenomenon, all of which utilize prediction errors.

These papers *won’t* be explicitly computational – although we have that work under preparation as well – but will just try to make a single key point that I want to build on. If I achieve my other three aims, it should be reasonably straight-forward to build computational models from these papers.

That is the idea. Now I need to go lock myself in a cabin-in-the-woods for a few weeks and finally get these papers off my plate. Otherwise these Bayesian demons are just gonna keep screaming.

So, where to submit? Don’t say Frontiers…

For whom the bell tolls? A potential death-knell for the heartbeat counting task.

Interoception – the perception of signals arising from the visceral body – is a hot topic in cognitive neuroscience and psychology. And rightly so; a growing body of evidence suggests that brain-body interaction is closely linked to mood1, memory2, and mental health3. In terms of basic science, many theorists argue that the integration of bodily and exteroceptive (i.e., visual) signals underlies the genesis of a subjective, embodied point of view4–6.  However, noninvasively measuring (and even better, manipulating) interoception is inherently difficult. Unlike visual or tactile awareness, where an experimenter can carefully control stimulus strength and detection difficulty, interoceptive signals are inherently spontaneous, uncontrolled processes. As such, prevailing methods for measuring interoception typically involve subjects attending to their heartbeats and reporting how many heartbeats they counted in a given interval. This is known as the heartbeat counting task (or Schandry task, named after its creator)7. Now a new study has cast extreme doubt on what this task actually measures.

The study, published by Zamariola et al in Biological Psychology8, begins by detailing what we already largely know: the heartbeat counting task is inherently problematic. For example, the task is easily confounded by prior knowledge or beliefs about one’s average heart rate. Zamariola et al write:

“Since the original task instruction requires participants to estimate the number of heartbeats, individuals may provide an answer based on beliefs without actually attempting to perceive their heartbeats. Consistent with this view, one study (Windmann, Schonecke, Fröhlig, & Maldener, 1999) showed that changing the heart rate in patients with cardiac pacemaker, setting them to low (50 beats per minute, bpm), medium (75 bpm), or high (110 bpm) heart rate, did not influence their reported number of heartbeats. This suggests that these patients performed the task by relying on previous knowledge instead of perception of their bodily states.”

This raises the question of what exactly the task is measuring. The essence of heartbeat counting tasks is that one must silently count the number of perceived heartbeats over multiple temporal intervals. From this, an “interoceptive accuracy score” (IAcc) is computed using the formula:

1/3 ∑ (1–(|actual heartbeats – reported heartbeats|)/actual heartbeats)

This formula is meant to render over-counting (counting heartbeats that don’t occur) and under-counting (missing actual heartbeats) equivalent, in a score bounded by 0-1. Zamariola et al argue that these scores lack fundamental construct validity on the basis of four core arguments. I summarize each argument below; see the full article for the detailed explanation:

  1. [interoceptive] abilities involved in not missing true heartbeats may differ from abilities involved in not over-interpreting heartbeats-unrelated signals. [this assumption] would be questioned by evidence showing that IAcc scores largely depend on one error type only.
  2. IAcc scores should validly distinguish between respondents. If IAcc scores reflect people’s ability to accurately perceive their inner states, a correlation between actual and reported heartbeats should be observed, and this correlation should linearly increase with higher IAcc scores (i.e., better IAcc scorers should better map actual and reported heartbeats).
  3. a valid measure of interoception accuracy should not be structurally tied to heart condition. This is because heart condition (i.e. actual heartbeats) is not inherent to the definition of the interoceptive accuracy construct. In other words, it is essential for construct validity that people’s accuracy at perceiving their inner life is not structurally bound to their cardiac condition.”
  4. The counting interval [i.e., 10, 15, 30 seconds] should not impact IAcc; a wide range of scores are in fact used and these should be independent of the resultant measure.

Zamariola et al then go on to show that in a sample of 572 healthy individuals (386 female), each of these assumptions are strongly violated; IAcc scores depend largely on under-reporting heartbeats (Fig. 1), that the correlation of actual and perceived heartbeats is extremely low and higher at average than higher IAcc levels (Fig. 2), that IAcc is systematically increased as slower heart rates (Fig. 3), and that longer time intervals lead to substantially worse IAccc (not shown):

Fig. 1

  1. iACC scores are mainly driven by under-reporting; “less than 5% of participants showed overestimation… Hence, IAcc scores essentially inform us of how (un)willing participants are to report they perceived a heartbeat”

Fig. 2

2. Low overall correlation (grey-dashed line) of heartbeats counted and actual heartbeats (r = 0.16, 2.56% shared variance). Further, the correlation varied non-linearly across bins of iACC scores, which in the author’s words demonstrates that “IAcc scores fail to validly differentiate individuals in their ability to accurately perceive their inner states within the top 60% IAcc scorers.”

Fig. 3

3. iACC scores depend negatively on the number of actual heartbeats, suggesting that individuals with lower overall heart-rate will be erroneously characterized as ‘good interoceptive accuracy’.

Overall, the authors draw the conclusion the heartbeat counting task is nigh-useless, lacking both face and construct validity. What should we measure instead? The authors offer that, if one can have very many trials, than the mere correlation of counted and actual heartbeats may be a (slightly) better measure. However, given the massive bias present in under-reporting heartbeats, they suggest that the task measures only the willingness to report a heartbeat at all. As such, they highlight the need for true psychophysical tasks which can distinguish participant reporting bias (i.e., criterion) from the true sensitivity to heart beats. A potentially robust alternative may be the multi-interval heartbeat discrimination task9, in which a method of constant stimuli is used to compare heartbeats to multiple intervals of temporal stimuli. However, this task is substantially more difficult to administer; it requires some knowledge of psychophysics and as much as 45 minutes to complete. As many (myself included) are interesting in measuring interoception in sensitive patient populations, it’s not a given that this task will be widely adopted.

I’m curious what my readers think. For me, this paper proffers a final nail in the coffin of heartbeat counting tasks. Nearly every interoception researcher I’ve spoken to has expressed concerns about what the task actually measures. Worse, large intrasubject variance and the fact that many subjects perform incredibly poorly on the task seems to undermine the idea that it is anything like a measure of cardiac perception. At best, it seems to be a measure of interoceptive attention and report-bias. The study by Zamariola and colleagues is well-powered, sensibly conducted, and seems to provide unambiguous evidence against the task’s basic validity. Heart-beart counting; the bell tolls for thee.

References

  1. Foster, J. A. & McVey Neufeld, K.-A. Gut–brain axis: how the microbiome influences anxiety and depression. Trends Neurosci. 36, 305–312 (2013).
  2. Zelano, C. et al. Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function. J. Neurosci. 36, 12448–12467 (2016).
  3. Khalsa, S. S. et al. Interoception and Mental Health: a Roadmap. Biol. Psychiatry Cogn. Neurosci. Neuroimaging (2017). doi:10.1016/j.bpsc.2017.12.004
  4. Park, H.-D. & Tallon-Baudry, C. The neural subjective frame: from bodily signals to perceptual consciousness. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130208 (2014).
  5. Seth, A. K. Interoceptive inference, emotion, and the embodied self. Trends Cogn. Sci. 17, 565–573 (2013).
  6. Barrett, L. F. & Simmons, W. K. Interoceptive predictions in the brain. Nat. Rev. Neurosci. 16, 419–429 (2015).
  7. Schandry, R., Sparrer, B. & Weitkunat, R. From the heart to the brain: A study of heartbeat contingent scalp potentials. Int. J. Neurosci. 30, 261–275 (1986).
  8. Zamariola, G., Maurage, P., Luminet, O. & Corneille, O. Interoceptive Accuracy Scores from the Heartbeat Counting Task are Problematic: Evidence from Simple Bivariate Correlations. Biol. Psychol. doi:10.1016/j.biopsycho.2018.06.006
  9. Brener, J. & Ring, C. Towards a psychophysics of interoceptive processes: the measurement of heartbeat detection. Philos. Trans. R. Soc. B Biol. Sci. 371, 20160015 (2016).