In my last post, I wrote a long commentary on a recent PNAS article by Lieberman & Eisenberger claiming to find evidence that the dorsal anterior cingulate cortex is “selective for pain” using my Neurosynth framework for large-scale fMRI meta-analysis. I argued that nothing about Neurosynth supports any of L&E’s major conclusions, and that they made several major errors of inference and analysis. L&E have now responded in detail on Lieberman’s blog.Â If this is the first you’re hearing of this exchange, and you have a couple of hours to spare, I’d suggest proceeding in chronological order: read the original article first, then my commentary, then L&E’s response then this response to the response (if you really want to leave no stone unturned, you could also read Alex Shackman’s commentary, which focuses on anatomical issues).Â If you don’t have that kind of time on your hands,Â just read onÂ andÂ hope for the best, I guess.
Before I get to the substantive issues, let me say that I appreciate L&E taking the time to reply to my comments in detail. I recognize that they have other things they could be doing (as do I), and I think their willingness to engage in this format sets an excellent example as the scientific community continuesÂ to move rapidly towards more open, rapid, and interactive online scientific discussion. I would encourage readers to weigh in on the debate themselves or raise any questions they feel haven’t been addressed (either here on on Lieberman’s blog).
With that said, I have to confess that I don’t think my view is any closer to L&E’s than it previously was. I disagree with L&E’s suggestions that we actually agree on more than I thought in my original post; if anything, I think the opposite is true. However, I did find L&E’s response helpful inasmuch as it helped me better understand where their misunderstandings of Neurosynth lie.
In what follows, I provided a detailed rebuttal to L&E’s response. I’ll warn you right now that this will be a very long and fairly detail-oriented post. In a (probably fruitless) effort to minimize reader boredom, I’ve divided my response into two sections, much as L&E did. In the first section, I summarize what I see as the two most important points of disagreement. In the second part, I quote L&E’s entire response and insert my own comments in-line (essentially responding email-style). I recognize that this is a rather unusual thing to do, and it makes for a decidedly long read (the post clocks in at over 20,000 words, though much of that is quotes fromÂ L&E’s response). but I did it this way because, frankly, I think L&E badly misrepresented much of what I said in my last post. I want to make sure the context is very clear to readers, so I’m going to quote the entirety of each of L&E’s points before I respond to them, so that at the very least I can’t be accused of quoting them out of context.
The big issues: reverse inference and selectivity
With preliminaries out of the way, let me summarize what I see as the two biggest problems with L&E’s argument (though, if you make it to the second halfÂ of this post, you’ll seeÂ that there are many other statistical and interpretational issues that are pretty serious in their own right). The first concerns their fundamental misunderstanding of the statistical framework underpinning Neurosynth, and its relation to reverse inference. The second concerns their use of a definition of selectivity that violates common sense and can’t possibly support their claim that “the dACC is selective for pain”.
Misunderstandings about the statistics of reverse inference
I don’t think there’s any charitable way to say this, so I’ll just be blunt: I don’t think L&E understand the statistics behind the images Neurosynth produces. In particular, I don’t think they understand the foundational role that the notion of probability plays in reverse inference. In their reply, L&E repeatedly say that my concerns about their lack of attention to effect sizes (i.e., conditional probabilities) are irrelevant, because theyÂ aren’t trying to make an argument about effect sizes. For example:
TY suggests that we made a major error by comparing the Z-scores associated with different terms and should have used posterior probabilities instead. If our goal had been to compare effect sizes this might have made sense, but comparing effect sizes was not our goal. Our goal was to see whether there was accumulated evidence across studies in the Neurosynth database to support reverse inference claims from the dACC.
ThisÂ captures perhaps the crux of L&E’s misunderstanding about both Neurosynth and reverse inference. Their argument here is basically that they don’t care about the actual probability of a term being used conditional on a particular pattern of activation; they just want to know that there’s “support for the reverse inference”. Unfortunately, it doesn’t work that way. The z-scores produced by Neurosynth (which are just transformations of p-values) don’t provide a direct index of the support for a reverse inference. What they measure is what p-values always measure: the probability of observing a result as extreme as the one observed under the assumption that the null of no effect is true. Conceptually, we can interpret this as a claim about the population-level association between a region and a term. Roughly, we can say that as z-scores increase, we can be more confident that there’s a non-zero (positive) relationship between a term and a brain region (though some Bayesians might want to take issue with even this narrow assertion). So, if all L&E wanted to say was, “there’s good evidence that there’s a non-zero association between pain and dACC activation across the population of published fMRI studies”, they would be in good shape. But what they’re arguing for is much stronger: they want to show that the dACC is selective for pain. And z-scores are of no use here. Knowing that there’s a non-zero association between dACC activation and pain tells us nothing about the level of specificity or selectivity of that association in comparison to other terms. If the z-score for the association between dACC activation and ‘pain’ occurrence is 12.4 (hugely statistically significant!), does that mean that the probability of pain conditional on dACC activation is closer to 95%, or to 25%? Does it tell us that dACC activation is a better marker of pain than conflict, vision, or memory? We don’t know. We literally have no way to tell, unless we’re actually willing to talk about probabilities within a Bayesian framework.
To demonstrate that this isn’t just a pedantic point about what could in theoryÂ happen, and that the issue is in fact completely fundamental to understanding what Neurosynth can and can’t support,Â here are three different flavors of the NeurosynthÂ maps for the “pain” map:
The top row is the reverse inference z-score map available on the website. The values here are z-scores, and what they tell us (being simply transformations of p-values) is nothing more than what the probability would be of observing an association at least as extreme as the one we observe under the null hypothesis of no effect. The second and third maps are both posterior probability maps. They display the probability of a study using the term ‘pain’ when activation is observed at each voxel in the brain. These maps aren’t available on the website (for reasons I won’t get into here, though the crux of it is that they’re extremely easy to misinterpret, for reasons that may become clear below)—though you can easily generate them with the Neurosynth core tools if you’re so inclined.
The main feature of these two probability maps that should immediately jump out at you is how strikingly different their numbers are. In the first map (i.e., middle row), the probabilities of “pain” max out around 20%; in the second map (bottom row), they range from around 70% – 90%. And yet, here I am telling you that these are both posterior probability maps that tell us the probability of a study using the term “pain” conditional on that study observing activity at each voxel. How could this be? How could the two maps be so different, if they’re supposed to be estimates of the same thing?
The answer lies in the prior. In the natural order of things, different terms occur with wildly varying frequencies in the literature (remember that Neurosynth is based on extraction of words from abstracts, not direct measurement of anyone’s mental state!). “Pain” occurs in only about 3.5% of Neurosynth studies. By contrast, the term “memory” occurs in about 16% of studies. One implication of this is that, if we know nothing at all about the pattern of brain activity reported in a given study, weÂ should already expect that study to be about five times more likely to involve memory than pain.Â Of course, knowing something about the pattern of brain activity should change our estimate. In Bayesian terminology, we can say that ourÂ priorÂ belief about the likelihood of different termsÂ gets updatedÂ by the activity pattern weÂ observe, producing somewhat more informedÂ posteriorÂ estimates.Â For example, if the hippocampus and left inferior frontal gyrus are active, that should presumably increase our estimate of “memory” somewhat; conversely, if the periaqueductal gray, posterior insula, and dACC are all active, that should instead increase our estimate of “pain”.
In practice, the degree to which the data modulate our Neurosynth-based beliefs is not nearly as extreme as you might expect. In theÂ first posterior probability map above (labeled “empirical prior”), what you can see are the posterior estimates for “pain” under the assumption that pain occurs in about 3.5% of all studies—which is the actual empirical frequency observed in the Neurosynth database. Notice thatÂ the very largest probabilities we ever see—located, incidentally, in the posterior insula, and not in the dACC—max out around 15 – 20%. This is not to be scoffed at; it means that observing activation in the posterior insula implies approximately a 5-fold increase in the likelihood of “pain” being present (relative to our empirical prior of 3.5%). Yet, in absolute terms, the probability of “pain” is still very low. Based on these data, no one in their right mind should, upon observing posterior insula activation (let alone dACC, where most voxels show a probability no higher than 10%), draw the reverse inference that pain is likely to be present.
To make it even clearerÂ why this inference would beÂ unsupportable, here are posterior probabilities for the same voxels as above, but nowÂ plottedÂ for several other terms, in addition to pain:
Notice how, in the bottom map (for ‘motor’, which occurs inÂ aboutÂ 18% of all studies in Neurosynth), the posterior probabilities in all of dACC are substantially higher for than for ‘pain’, even though z-scores in most of dACC show the opposite pattern. For ‘working memory’ and ‘reward’, the posterior probabilities are in the same ballpark as for pain (mostly around 8 – 12%).Â And forÂ ‘fear’, there are no voxels with posterior probabilities above 5% anywhere, because the empirical prior is so low (only 2% of Neurosynth studies).
What this means is that, if you observe activation in dACC—a region which shows large z-scores for “pain” and much lower ones for “motor”—your single best guess as to whatÂ process might be involved (of the five candidates in the above figure) should be ‘motor’ by a landslide. You could also guess ‘reward’ or ‘working memory’ with about the same probability as ‘pain’. Of course, the more general message you should take away from thisÂ is that it’s probably aÂ bad idea to infer any particular process on the basis of observed activity, given how low the posterior probability estimates for most terms are going to be.Â Put simply, it’s a giant leap to go from theseÂ results—which clearly don’t license anyone to conclude that the dACC is a marker of any single process—to concluding that “the dACC is selective for pain” and that pain represents the best psychological characterization of dACC function.
As if this isn’t bad enough, we now need to add aÂ further complication to the picture. The analysis above assumes we have a good prior for terms like “pain” and “memory”. In reality, we have no reason to think that the empirical estimates of term frequency we get out of Neurosynth are actually good reflections of the real world. For all we know, it could be that pain processing is actually 10 times as common as it appears to be in Neurosynth (i.e., that pain is severely underrepresented in fMRI studies relative to its occurrence in real-world human brains). If we use the empirical estimates from Neurosynth as our priors—with all of their massive between-term variation—then, as you saw above, the priors will tend to overwhelm our posteriors. In other words, no amount of activation in pain-related regions would ever lead us to conclude that a study is about a low-frequency term like pain rather than a high-frequency term like memory or vision.
For this reason, when I first built Neurosynth, my colleagues and I made the deliberate decisionÂ to impose a uniform (i.e., 50/50) prior on all terms displayed on the Neurosynth website. This approach greatly facilitatesÂ qualitative comparisonÂ of different terms; but it necessarily does so by artificially masking the enormous between-term variability in base rates.Â What this means is that when you see a posterior probability like 85% for pain in the dACC in the thirdÂ row of the pain figure above, the right interpretation of this is “if you pretend that the prior likelihood of a study using the term pain is exactly 50%, then your posterior estimate after observing dACC activation should now be 85%”. Is this a faithful representation of reality? No. It most certainly isn’t. And in all likelihood, neither is the empirical prior of 3.5%. But the problem is, we have to do something; Bayes’ rule has to have priors to work with; it can’t just conjure into existence aÂ conditional probability for a term (i.e., P(Term|Activation)) without knowing anything about itsÂ marginal probabilityÂ Â (i.e., P(Term)). Unfortunately, as you can see in the above figure, the variation in the posterior that’s attributable to the choice of prior will tend to swampÂ the variation that’s due to observed differences in brain activity.
The upshot is, if you come into aÂ study thinking that ‘pain’ is 90% likely to be occurring, then Neurosynth is probably not going to give you much reason to reviseÂ that belief. Conversely, if your task involves strictly visual stimuli, and you know that there’s no sensory stimulation at all—so maybe youÂ feel comfortable settingÂ the prior on pain at 1%—then no pattern of activity you could possibly see is going to lead you to conclude that there’s a high probability of pain. This may not be very satisfying, but hey, that’s life.
The interesting thing about all this is that, no matter what prior you choose for any given term, the Neurosynth z-score will never change. That’s because the z-score is a frequentist measure of statistical association between term occurrence and voxel activation.Â All it tells us is that, if the null of no effect were true, the data we observeÂ would be very unlikely. This may or may not be interesting (I would argue that it’s not, but that’s for a different post), but it certainly doesn’t license a reverse inference like “dACC activation suggests that pain is present”. To draw the latter claim, you have to use a Bayesian framework and pick some sensibleÂ priors. No priors, no reverse inference.
Now, as I noted in my last post, it’s important to maintain a pragmatic perspective. I’m obviously not suggesting that the z-score maps on Neurosynth are worthless. If one’s goal is just to draw weak qualitative inferences about brain-cognition relationships, I think it’s reasonable to use NeurosynthÂ reverse inference z-score maps for that purpose. For better or worse, the vast majority of claims researchers make in cognitive neuroscience are not sufficiently quantitative that it makes much differenceÂ whether the probability of a particular term occurring given some observed pattern of activation is 24% or 58%. Personally, I would argue that this is to the detriment of the field; but regardless,Â the fact remains that if one’s goal is simply to say something like “we thinkÂ that the temporoparietal junction is associated with biological motion and theory of mind,” or “evidence suggests that the parahippocampal cortex is associated with spatial navigation,” I don’t see anything wrong with basing that claim on Neurosynth z-score maps. In marked contrast, however,Â Neurosynth provides noÂ license for sayingÂ much stronger things likeÂ “the dACC is selective for pain” or suggesting that one can make concreteÂ reverse inferences about mental processes on the basis of observed patterns of brain activity. If the question we’re asking is what are we entitled to conclude about the presence of pain when we observed significant activation in the dACC in a particular study?, the simple answer is: almost nothing.
Let’s now reconsider L&E’s statement—and by extension, their entire argument for selectivity—in this light. L&E say that their goal is not to compare effect sizes for different terms, but rather “to see whether there [is] accumulated evidence across studies in the Neurosynth database to support reverse inference claims from the dACC.” But what could this claim possibly mean, if not something like “we want to know whether it’s safe to infer the presence of pain given the presence of dACC activation?” How could this possibly be anything other than a statement about probability? Are L&E really saying that, given a sufficiently high z-score for dACC/pain, it would make no difference to them at all if the probability of pain given dACC activation was only 5%, even if there were plenty of other terms with much higher conditional probabilities? Do they expect us toÂ believe that, in their 2003 social pain paper—where they drew a strong reverse inference that social pain shares mechanisms with physical pain based purely on observation of dACC activation (which, ironically, wasn’t even in pain-related areas of dACC—it would have made no difference to their conclusion even if they’d known conclusivelyÂ that dACC activation actually only reflects pain processing 5% of the time? Such aÂ claim is absurd on its face.
Let me summarize this section by making the following points about Neurosynth. First, it’s possible to obtain almost any posterior probability for any term given activation in any voxel, simply by adjusting the prior probability of term occurrence. Second, a choice about the prior must be made; there is no “default” setting (well, there is on the website, but that’s only because I’ve already made the choice for you). Third, the choice of prior will tend to dominate the posterior—which is to say, if you’re convinced that there’s a high (or low) prior probability that your study involves pain, then observing different patterns of brain activity will generally not do nearly as much as you might expect to change your conclusions. Fourth, this is not a Neurosynth problem, it’s a reality problem. The fundamental fact of the matter is that we simply do not know with any reasonable certainty, in any given context, what the prior probability of a particular process occuring in our subjects’ head is. Yet, without that, we have little basis for drawing any kind of reverse inference when we observe brain activity in a given study.
If all this makes you think, “oh, this seems like it would make it almost impossible in practice to draw meaningful reverse inferences in individual studies,” well, you’re not wrong.
L&E’s PNAS paper, and their reply to my last post, suggests that they don’t appreciate any of these points. The fact of the matter is that it’s impossible to draw any reverse inference about an individual study unless one is willing to talk about probabilities. L&E don’t seem to understand this, because ifÂ they did, they wouldn’t feel comfortable saying that they don’t care about effect sizes, and that z-scores provide adequate support for reverse inference claims. In fact, they wouldn’t feel comfortable making any claim about the dACC’s selectivity for pain relative to other terms on the basis of Neurosynth data.
I want to be clear that I don’t think L&E’s confusion about these issues is unusual. The reality is that many of these core statistical concepts—both frequentist and Bayesian—are easy to misunderstand, even for researchers who rely onÂ them on a day-to-day basis. By no means am IÂ excluding myself from this analysis; I still occasionally catch myself making similar slips when explaining what the z-scores and conditionalÂ probabilities in Neurosynth mean—and I’ve been thinking about these exact ideas in this exact context for a pretty long time! So I’m not criticizing L&E for failing toÂ correctly understand reverse inference and its relation to Neurosynth. What I’m criticizing L&E for is writing anÂ entire paper making extremelyÂ strong claims about functional selectivity based entirely on Neurosynth results, without ensuring that they understandÂ the statistical underpinnings of the framework, and withoutÂ soliciting feedback from anyone who might be in a position to correct their misconceptions. Personally, if I were in their position, I would move to retract the paper. But I have no control over that. All I can say is thatÂ it’s my informed opinion—asÂ the creator of the software framework underlying all of L&E’s analyses—that theÂ conclusions they draw in their paper are not remotely supported by any data that I’ve ever seen come out of Neurosynth.
On ‘strong’ vs. ‘weak’ selectivity
The other major problem with L&E’s paper, from my perspective, lies in their misuse of the term ‘selective’. In their response, L&E take issue with my criticism of their claim that they’ve shown the dACC to be selective for pain. They write:
Regarding the term selective, I suppose we could say there’s a strong form and a weak form of the word, with the strong form entailing further constraints on what constitutes an effect being selective. TY writes in his blog: “it’s one thing to use Neurosynth to support a loose claim like “some parts “¨of the dACC are preferentially associated with pain“, and quite another to claim that the dACC is selective for pain, that virtually nothing else activates dACC“. The last part there gets at what TY thinks we mean by selective and what we would call the strong form of selectivity.
L&E respectively define these strong and weak forms of selectivity as follows:
Selectivitystrong: The dACC is selective for pain, if pain and only pain activates the dACC.
Selectivityweak: The dACC is selective for pain, if pain is a more reliable source of dACC activation than the other terms of interest (executive, conflict, salience).
They suggest that I accused them of claiming ‘strong’ selectivity when they were really just making the much weaker claim that dACC activation is more strongly associated with dACC activation than with other terms. I disagree with this characterization. I’ll come back to what I meant by ‘selective’ in a bit (I certainly didn’t assume anything like L&E’s strong definition). But first, let’s talk about L&E’s ‘weak’ notion of selectivity, which in my view is at odds with any common-sense understanding of what ‘selective’ means, and would have an enormously destructive effect on the field if it were to become widely used.
The fundamental problem with the suggestion that we can say dACC is pain-selective if “it’s a more reliable source of dACC activation than the other terms of interest” is that this definition provides a free pass for researchers to make selectivity claims about an extremely large class of associations, simply by deciding what is or isn’t of interest in any given instance. L&E claim to be “interested” in executive control, conflict, and salience. This seems reasonable enough; after all, these are certainly candidate functions that people have discussed at length in the literature. The problem lies with all the functions L&E don’t seem to be interested in: e.g., fear, autonomic control, or reward—three other processes that many researchers have argued the dACC is crucially involved in, and that demonstrably show robust effects in dACC in Neurosynth. If we take L&E’s definition of weak selectivity at face value, we find ourselves in the rather odd position of saying that one can use Neurosynth to claim that a region is “selective” for a particular function just as long as it’s differentiable from some other very restricted set of functions. Worse still,Â one apparently does not have to justify the choice of comparison functions! In their PNAS paper, L&E never explain why they chose to focus only on three particular ACC accounts that don’t show robust activation in dACC in Neurosynth, and ignored several other common accounts that do show robust activation.
If you think this is a reasonable way to define selectivity, I have some very good news for you. I’ve come up with a list of other papers that someone could easily write (and, apparently, publish in a high-profile journal) based entirely on results you can obtain from the Neurosynth websites. The titles of these papers (and you could no doubt come up with many more)Â include:
- “The TPJ is selective for theory of mind”
- “The TPJ is selective for biological motion”
- “The anterior insula is selective for inhibition”
- “The anterior insula is selective for orthography”
- “The VMPFC is selective for autobiographical memory”
- “The VMPFC is selective for valuation”
- “The VMPFC is selective for autonomic control”
- “The dACC is selective for fear”
- “The dACC is selective for autonomic control”
- “The dACC is selective for reward”
These are all interesting-sounding articles that I’m sure would drum up considerable interest and controversy. And the great thing is, as long as you’re careful about what you find “interesting” (and you don’t have to explicitly explain yourself in the paper!), Neurosynth will happily support all of these conclusions. You just need to make sure not to include any comparison terms that don’t fit with your story. So, if you’re writing a paper about the VMPFC and valuation, make sure you don’t include autobiographical memory as a control. And if you’re writing aboutÂ theory of mindÂ in the TPJ, it’s probably best to not find biological motion interesting.
Now, you might find yourself thinking, “how could it make sense to have multiple people write different papers using Neurosynth, each one claiming that a given region is ‘selective’ for a variety of different processes? Wouldn’t that sort of contradict any common-sense understanding of what the term ‘selective’ means?” My own answer would be “yes, yes it would”. But L&E’s definition of “weak selectivity”—and the procedures they use in their paper—allow for multiple such papers to co-exist without any problem. Since what counts as an “interesting” comparison condition is subjective—and, if we take L&E’s PNAS example as a model, one doesn’t even need to explicitly justify the choices one makes—there’s really nothing stopping anyone from writing any of the papers I suggested above. Following L&E’s logic, a researcher who favored a fear-based account of dACC could simply select two or three alternative processes as comparison conditions—say, sustained attention and salience—do all of the same analyses L&E did (pretending for the moment that those analyses are valid, which they aren’t), and conclude that the dACC is selective for fear. It really is that easy.
In reality, I imagine that if L&E came across an article claiming that Neurosynth shows that the dACC is selective for fear, I doubt they’d say “well, I guess the dACC is selective for fear. Good to know.” I suspect they would (quite reasonably) take umbrage at the fear paper’s failure to include pain as a comparison condition in the analysis. Yet, by their own standards, they’d have no real basis for any complaint. The fear paper’s author could simply, say, “pain’s not interesting to me,” and that would be that. No further explanation necessary.
Perhaps out of recognition that there’s something a bit odd about their definition of selectivity, L&E try to prime our intuition that their usage is consistent with the rest of the field. They point out that, in most experimental fMRI studies claiming evidence for selectivity, researchers only ever compare the target stimulus or process to a small number of candidates. For example, they cite a Haxby commentary on a paper that studied category specificity in visual cortex:
From Haxby (2006): “numerous small spots of cortex were found that respond with very high selectivity to faces. However, these spots were intermixed with spots that responded with equally high selectivity to the other three categories.“
Their point is that nobody expects ‘selective’ here to mean that the voxel in question responds to only that visual category and no other stimulus that could conceivably have been presented. In practice, people take ‘selective’ to mean “showed a greater response to the target category than to other categories that were tested”.
I agree with L&E that Haxby’s usage of the term ‘selective’ here is completely uncontroversial. The problem is, the study in question is a lousy analogy for L&E’s PNAS paper. A much better analogy would be a study that presented 10 visual categories to participants, but then made a selectivity claim in the paper’s title on the basis of a comparison between the target category and only 2 other categories, with no explanation given for excluding the other 7 categories, even though (a) some of those 7 categories were well known to also be associated with the same brain region, and (b) strong activation in response to some of those excluded categories was clearly visible in a supplementary figure. I don’t know about L&E, but I’m pretty sure that, presented with such a paper, the vast majority of cognitive neuroscientists would want to say something like, “how can you seriously be arguing that this part of visual cortex responds selectively to spheres, when you only compared spheres with faces and houses in the main text, and your supplemental figure clearly shows that the same region responds strongly to cubes and pyramids as well? Shouldn’t you maybe be arguing that this is a region specialized for geometric objects, if anything?” And I doubt anyone would be very impressed if the authors’ response to this critique was “well, it doesn’t matter what else we’re not focusing on in the paper. We said this region is sphere-selective, which just means it’s more selective than a couple of other stimulus categories people have talked about. Pyramids and cubes are basically interchangeable with spheres, right? What more do you want from us?”
I think it’s clear that there’s no basis for making a claim like “the dACC is selective for pain” when one knows full well that at least half a dozen other candidate functions all reliably activate the dACC. As I noted in my originalÂ post, the claim is particularly egregious in this case, because it’s utterly trivial to generate a ranked list of associations for over 3,000 different terms in Neurosynth. So it’s not even as if one needs to think very carefully about which conditions to include in one’s experiment, or to spend a lot of time running computationally intensive analyses. L&E were clearly aware that a bunch of other terms also activated dACC; they briefly noted as much in the Discussion of their paper. What they didn’t explain is why this observation didn’t lead them to seriously revise their framing. Given what they knew, there were at least two alternative articles they could have written that wouldn’t have violated common sense understanding of what the term ‘selective’ means. One might have been titled something like “Heterogeneous aspects of dACC are preferentially associated with pain, autonomic control, fear, reward, negative affect, and conflict monitoring”. The other might have been titled “the dACC is preferentially associatedÂ withÂ X-related processes”—where “X” is some higher-order characterization that explains why all of these particular processes (and not others) are activated in dACC. I have no idea whether either of these papers would have made it through peer review at PNAS (or any other journal), but at the very least they wouldn’t have been flatly contradicted by Neurosynth results.
To be fair to L&E, while they didn’t justify their exlcusion of terms like fear and autonomic control in the PNAS paper, they did provide some explanation in their reply to my last post. Here’s what they say:
TY criticizes us several times for not focusing on other accounts of the dACC including fear, emotion, and autonomic processes. We agree with TY that these kind of processes are relevant to dACC function. Indeed, we were writing about the affective functions of dACC (Eisenberger & Lieberman, 2004) when the rest of the field was saying that the dACC was purely for cognitive processes (Bush, Luu, & Posner, 2000). We have long posited that one of the functions of the dACC was to sound an alarm when certain kinds of conflict arise. We think the dACC is evoked by a variety of distress-related processes including pain, fear, and anxiety. As Eisenberger (2015) wrote: “Interestingly, the consistency with which the dACC is linked with fear and anxiety is not at odds with a role for this region in physical and social pain, as threats of physical and social pain are key elicitors of fear and anxiety.“ And the outputs of this alarm process are partially autonomic in nature. Thus, we don’t think of fear and autonomic accounts as in opposition to the pain account, but rather in the same family of explanations. We think this class of dACC explanations stands in contrast to the cognitive explanations that we did compare to (executive, conflict, salience). Most of this, and what is said below, is discussed in Naomi Eisenberger’s (2015) Annual Review chapter.
Essentially, their response is: “it didn’t make sense for us to include fear or autonomic control, because these functions are compatible with the underlying role we think the dACC is playing in pain”. This is not compelling, for three reasons. First, it’s a bait-and-switch. L&E’s paper isn’t titled “the dACC is selective for a family of distress-related processes”, it’s titled “the dACC is selective for pain“. One cannot publish a paper purporting to show that the dACC is selective for pain, and arguing that pain is the single best psychological characterization of its role in cognition, and then, in a section of their Discussion that they admit is the “most speculative” part of the paper, essentially say, “just kidding–we don’t think it’s really doing pain per se, we think it’s a much more general set of functions. But we don’t have any real evidence for that.”
Second, it’s highly uncharitable for L&E to spontaneously lump alternative accounts of dACC function like fear/avoidance, autonomic control, and bodily orientation in with their general “distress-related” account, because proponents of many alternative views of dACC function have been very explicit in saying that they don’t view these functions as fundamentally affective (e.g., Vogt and colleagues view posterior dACC as a premotor region). While L&E may themselves believe that pain, fear, and autonomic control in dACC all reflect some common function, that’s an extremely strong claim that requires independent evidence, and is not something that they’re entitled to simply assume. A perfectly sensible alternative is that these are actually dissociable functions with only partially overlapping spatial representations in dACC. Since the terms themselves are distinct in Neurosynth, that should be L&E’s operating assumption until they provide evidence for their stronger claim that there’s some underlying commonality. Nothing about this conclusion simply falls out of the data in advance.
Third, let me reiterate the point I made above about L&E’s notion of ‘weak selectivity’: if we take at face value L&E’s claim that fear and autonomic control don’t need to be explicitly considered because they could be interpreted alongside pain under a common account, then they’re effectively conceding that it would have made just as much sense to publish a paper titled “the dACC is selective for fear” or “the dACC is selective for autonomic control” that relegated the analysis of the term “pain” to a supplementary figure. In the paper’s body, you would find repeated assertions that the authorsÂ Â have shown that autonomic control is the “best general psychological account of dACC function”. When pressed as to whether this was a reasonable conclusion, the authors would presumablyÂ defend their decision to ignore pain as a viable candidate by saying things like, “well, sure pain also activates the dACC; everyone knows that. But that’s totally consistent with our autonomic control account, because pain produces autonomic outputs! So we don’t need to consider that explicitly.”
I confess to some skepticismÂ that L&E would simply accept such a conclusion without any objection.
Before moving on, let me come full circle and offer a definition of selectivity that I think is much more workable than either of the ones L&E propose, and is actually compatible with the way people use the term ‘selective’ more broadly in the field:
Selectivityrealistic: A brain region can be said to be ‘selective’ for a particular function if it (i) shows a robust association with that function, (ii) shows a negligible association with all other readily available alternatives, and (iii) the authors have done due diligence in ensuring that the major candidate functions proposed in the literature are well represented in their analysis.
Personally, I’m not in love with this definition. I think it still allows researchers to make claims that are far too strong in many cases. And it still allows for a fair amount of subjectivity in determining what gets to count as a suitable control—at least in experimental studies where researchers necessarily have to choose what kinds of conditions to include. But I think this definition is more or less in line with the way most cognitive neuroscientists expect each other to use the term.Â It captures the fact that most people would feel justifiably annoyedÂ if someone reported a “selective” effect in one condition while failing to acknowledge that 4 other unreported conditions showed the same effect. And it also captures the notion that researchers should be charitable to each other: if I publish a paper claiming that the so-called fusiform ‘face’ area is actually selective for houses, based on a study that completely failed to include a face condition, no one is going to take my claim of house selectivity seriously. Instead, they’re going to conclude that I wasn’t legitimately engaging with other people’s views.
In the context of Neurosynth—where one has 3,000 individual terms or several hundred latent topics at their disposal—this definition makes it very clear that researchers who want to say that a region is selective for something have an obligation to examine the database comprehensively, and not just to cherry-pick a couple of terms for analysis. That is what I meant when I said that L&E need to show that “virtually nothing else activates dACC”. I wasn’t saying that they have to show that no other conceivable process reliably activates the dACC (which would be impossible, as they observe), but simply that they need to show that no non-synonymous terms in the Neurosynth database do. I stand by this assertion. I see no reason why anyone should accept a claim of selectivity based on Neurosynth data if just a minute or two of browsing the Neurosynth website provides clear-cut evidence that plenty of other terms also reliably activate the same region.
To sum up, nothing L&E say in their paper gives usÂ any reason to think that the dACC is selective for pain (even ifÂ we were to ignore all the problems withÂ their understanding of reverse inference and allow them to claim selectivity based on inappropriate statisticalÂ tests). I submitÂ that no definition of ‘selective’ that respects common sense usage of the term, and isÂ appropriately charitable to other researchers, could possibly have allowed L&E to conclude that dACC activity isÂ “selective” for pain when they knew full well that fear, autonomic control, and reward all also reliably activated the dACC in Neurosynth.
Having focused on what I view as the two overarching issues raised by L&E’s reply, I now turn to comprehensively addressing each of their specific claims. As I noted at the outset, I recognize this is goingÂ to make for slow reading. But I want to make sure I address L&E’s points clearly and comprehensively, as I feel that they blatantly mischaracterized what I said in my original post in many cases. I don’t actuallyÂ recommend that anyone read this entire section linearly. I’m writing it primarily as a reference—so that if you think there were some good points L&E made in their reply to my original post, you can find those points by searching for the quote, and my response will be directly below.
Okay, let’s begin.
Tal Yarkoni (hereafter, TY), the creator of Neurosynth, has now posted a blog (here (link is external)) suggesting that pretty much all of our claims are either false, trivial, or already well-known. While this response was not unexpected, it’s disappointing because we love Neurosynth and think it’s a powerful tool for drawing exactly the kinds of conclusions we’ve drawn.
I’m surprised to hear that my response was not unexpected. This would seem to imply that L&E had some reason to worry that I wouldn’t approve of the way they were using Neurosynth, which leads me to wonder why they didn’t solicit my input ahead of time.
While TY is the creator of Neurosynth, we don’t think that means he has the last word when it comes to what is possible to do with it (nor does he make this claim). In the end, we think there may actually be a fair bit of agreement between us and TY. We do think that TY has misunderstood some of our claims (section 1 below) and failed to appreciate the significance and novelty of our actual claims (sections 2 and 4). TY also thinks we should have used different statistical analyses than we did, but his critique assumes we had a different question than the one we really had (section 5).
I agree that I don’t have the last word, and I encourage readers to consider both L&E’s arguments and mine dispassionately. I don’t, however, think that there’s a fair bit of agreement between us. Nor do I think I misunderstood L&E’s claim or failed to appreciate their significance or novelty. And, as I discuss at length both above and below, the problem is not that L&E are asking a different question than I think, it’s that they don’t understand that theÂ methods they’re using simply can’t speak to the question they say they’re asking.
1. Misunderstandings (where we sort of probably agree)
We think a lot of the heat in TY’s blog comes from two main misunderstandings of what we were trying to accomplish. The good news (and we really hope it is good news) is that ultimately, we may actually mostly agree on both of these points once we get clear on what we mean. The two issues have to do with the use of the term “selective“ and then why we chose to focus on the four categories we did (pain, executive, conflict, salience) and not others like fear and autonomic.
Misunderstanding #1: Selectivity. Regarding the term selective, I suppose we could say there’s a strong form and a weak form of the word…
I’ve already addressed this in detail at the beginning of this post, so I’ll skip the next few paragraphs and pick up here:
We mean this in the same way that Haxby and lots of others do. We never give a technical definition of selectivity in our paper, though in the abstract we do characterize our results as follows:
“Results clearly indicated that the best psychological description of dACC function was related to pain processing—not executive, conflict, or salience processing.“
Thus, the context of what comparisons our selectivity refers to is given in the same sentence, right up front in the abstract. In the end, we would have been just as happy if “selectivity“ in the title was replaced with “preferentially activated“. We think this is what the weak form of selectivity entails and it is really what we meant. We stress again, we are not familiar with researchers who use the strong form of selectivity. TY’s blog is the first time we have encountered this and was not what we meant in the paper.
I strongly dispute L&E’s suggestion that the average reader will conclude from the above sentence that they’re clearly analyzing only 4 terms. Here’s the sentence in their abstract that directly precedes the one they quote:
Using Neurosynth, an automated brainmapping database [of over 10,000 functional MRI (fMRI) studies], we performed quantitative reverse inference analyses to explore the best general psychological account of the dACC function P(Î¨ processjdACC activity).
It seems quite clear to me that the vast majority of readers are going to parse the title and abstract of L&E’s paper as implying a comprehensive analysis to find the best general psychological account of dACC function, and not “the best general psychological account if you only consider these 4 very specific candidates”. Indeed, I have trouble making any sense of the use of the terms “best” and “general” in this context, if what L&E meant was “a very restricted set of possibilities”. I’ll also note that in five minutes of searching the literature, I couldn’t find any other papers with titles or abstracts that make nearly as strong a claim about anterior cingulate function as L&E’s present claims about pain. So I reject the idea that their usage is par for the course. Still, I’m happy to give them the benefit of the doubt and accept that they truly didn’t realize that their wording might lead others to misinterpret their claims. I guess the good news is that, now that they’re aware of the potential confusion claims like this can cause, they will surely be much more circumspect in the titles and abstracts of their future papers.
Before moving on, we want to note that in TY’11 (i.e. the Yarkoni et al., 2011 paper announcing Neurosynth), the weak form of selectivity is used multiple times. In the caption for Figure 2, the authors refer to “regions in c were selectively associated with the term“ when as far as we can tell, they are talking only about the comparison of three terms (working memory, emotion, pain). Similarly on p. 667 the authors write “However, the reverse inference map instead implicated the anterior prefrontal cortex and posterior parietal cortex as the regions that were most selectively activated by working memory tasks.“ Here again, the comparison is to emotion and pain, and the authors are not claiming selectivity relative to all other psychological processes in the Neurosynth database. If it is fair for Haxby, Botvinick, and the eminent coauthors of TY’11 to use selectivity in this manner, we think it was fine for us as well.
I reject the implication of equivalence here. I think the scope of the selectivity claim I made in the figure caption in question is abundantly clear from the immediate context, and provides essentially no room for ambiguity. Who would expect, in a figure with 3 different maps, the term ‘selective’ to mean anything other than ‘for this one and not those two’? I mean, if L&E had titled their paper “pain preferentially activates the dACC relative to conflict, salience, or executive control”, and avoided saying that they were proposing the “best general account” of psychological function in dACC, I wouldn’t have taken issue with their use of the term ‘selective’ in their manuscript either, because the scope would have been equally clear. Conversely, if I had titled my 2011 paper “the dACC shows no selectivity for any cognitive process”, and said, in the abstract, something like “we show that there is no best general psychological function of the dACC–not pain, working memory, or emotion”, I would have fully expected to receive scorn from others.
That said, I’m willing to put my money where my mouth is. If a few people (say 5) write in to say (in the comments below, on twitter, or by email) that they took the caption in Figure 2 of my 2011 paper to mean anything other than “of these 3 terms, only this one showed an effect”, I’ll happily send the journal a correction. And perhaps, L&E could respond in kind by commiting to changing the title of their manuscript to something like “the dACC is preferentially active for pain relative to conflict, salience or executive control” if 5 people write in to say that they interpreted L&E’s claims as being much more global than L&E suggest they are. I encourage readers to use the comments below to clarify how they understood both of these selectivity claims.
We would also point readers to the fullest characterization of the implication of our results on p. 15253 of the article:
“The conclusion from the Neurosynth reverse inference maps is unequivocal: The dACC is involved in pain processing. When only forward inference data were available, it was reasonable to make the claim that perhaps dACC was not involved in pain per se, but that pain processing could be reduced to the dACC’s “real“ function, such as executive processes, conflict detection, or salience responses to painful stimuli. The reverse inference maps do not support any of these accounts that attempt to reduce pain to more generic cognitive processes.“
We think this claim is fully defensible and nothing in TY’s blog contradicts this. Indeed, he might even agree with it.
This claim does indeed seem to me largely unobjectionable. However, I’m at a loss to understand how the reader is supposed to know that this one very modest sentence represents “the fullest characterization” of the results in a paper replete with much stronger assertions. Is the reader supposed to, upon reading this sentence, retroactively ignore all of the other claims—e.g., the title itself, and L&E’s repeated claim throughout the paper that “the best psychological interpretation of dACC activity is in terms of pain processes”?
*Misunderstanding #2: We did not focus on fear, emotion, and autonomic accounts*. TY criticizes us several times for not focusing on other accounts of the dACC including fear, emotion, and autonomic processes. We agree with TY that these kind of processes are relevant to dACC function. Indeed, we were writing about the affective functions of dACC (Eisenberger & Lieberman, 2004) when the rest of the field was saying that the dACC was purely for cognitive processes (Bush, Luu, & Posner, 2000). We have long posited that one of the functions of the dACC was to sound an alarm when certain kinds of conflict arise. We think the dACC is evoked by a variety of distress-related processes including pain, fear, and anxiety. As Eisenberger (2015) wrote: “Interestingly, the consistency with which the dACC is linked with fear and anxiety is not at odds with a role for this region in physical and social pain, as threats of physical and social pain are key elicitors of fear and anxiety.“ And the outputs of this alarm process are partially autonomic in nature. Thus, we don’t think of fear and autonomic accounts as in opposition to the pain account, but rather in the same family of explanations. We think this class of dACC explanations stands in contrast to the cognitive explanations that we did compare to (executive, conflict, salience). Most of this, and what is said below, is discussed in Naomi Eisenberger’s (2015) Annual Review chapter.
I addressed this in detailÂ above, in the section on “selectivity”.
We speak to some but not all of this in the paper. On p. 15254, we revisit our neural alarm account and write “Distress-related emotions (“negative affect“ “distress“ “fear“) were each linked to a dACC cluster, albeit much smaller than the one associated with “pain“.“ While we could have said more explicitly that pain is in this distress-related category, we have written about this several times before and assumed this would be understood by readers.
There is absolutely no justification for assuming this. The community of people who might find a paper titled “the dorsal anterior cingulate cortex is selective for pain” interesting is surely at least an order of magnitude larger than the community of people who are familiar with L&E’s previous work on distress-related emotions.
So why did we focus on executive, conflict, and salience? Like most researchers, we are the products of our early (academic) environment. When we were first publishing on social pain, we were confused by the standard account of dACC function. A half century of lesion data and a decade of fMRI studies of pain pointed towards more evidence of the dACC’s involvement in distress-related emotions (pain & anxiety), yet every new paper about the dACC’s function described it in cognitive terms. These cognitive papers either ignored all of the pain and distress findings for dACC or they would redescribe pain findings as reducible to or just an instance of something more cognitive.
When we published our first social pain paper, the first rebuttal paper suggested our effects were really just due to “expectancy violation“ (Somerville et al., 2006), an account that was later invalidated (Kawamoto 2012). Many other cognitive accounts have also taken this approach to physical pain (Price 2000; Vogt, Derbyshire, & Jones, 2006).
Thus for us, the alternative to pain accounts of dACC all these years were conflict detection and cognitive control explanations. This led to the focus on the executive and conflict-related terms. In more recent years, several papers have attempted to explain away pain responses in the dACC as nothing more than salience processes (e.g Iannetti’s group) that have nothing to do with pain, and so salience became a natural comparison as well. We haven’t been besieged with papers saying that pain responses in the dACC are “nothing but“ fear or “nothing but“ autonomic processes, so those weren’t the focus of our analyses.
This is a informative explanation of L&E’s worldview and motivations. But it doesn’t justify ignoring numerous alternative accounts whose proponents very clearly don’t agree with L&E that their views can be explained away as “distress-related”. If L&E had written a paper titled “salience is not a good explanation of dACC function,” I would have happily agreed with their conclusion here. But they didn’t. They wrote a paper explicitly asserting that pain is the best psychological characterization of the dACC. They’re not entitled to conclude this unless they compare pain properly with a comprehensive set of other possible candidates—not just the ones that make pain look favorable.
We want to comment further on fear specifically. We think one of the main reasons that fear shows up in the dACC is because so many studies of fear use pain manipulations (i.e. shock administration) in the process of conditioning fear responses. This is yet another reason that we were not interested in contrasting pain and fear maps. That said, if we do compare the Z-scores in the same eight locations we used in the PNAS paper, the pain effect has more accumulated evidence than fear in all seven locations where there is any evidence for pain at all.
This is a completelyÂ speculative account, andÂ no evidence is provided for it. Worse,Â it’s completely invertible: one could just as easily say that pain shows up in the dACC because it invariably produces fear, or because it invariably elicits autonomic changes (frankly, it seems more plausible to me that pain almost always generates fear than that fear is almost always elicited by pain). There’s no basis for ruling out these other candidate functions a priori as being more causally important. This is simply question-begging.
Its interesting to us that TY does not in principle seem to like us trying to generate some kind of unitary account of dACC writing “There’s no reason why nature should respect our human desire for simple, interpretable models of brain function.“ Yet, TY then goes on to offer a unitary account more to his liking. He highlights Vogt’s “four-region“ model of the cingulate writing “I’m especially partial to the work of Brent Vogt“¦“. In Vogt’s model, the aMCC appears to be largely the same region as what we are calling dACC. Although the figure shown by TY doesn’t provide anatomical precision, in other images, Vogt shows the regions with anatomical boundaries. Rotge et al. (2015) used such an image from Vogt (2009) to estimate the boundaries of aMCC as spanning 4.5 â‰¤ y â‰¤ 30 which is very similar to our dACC anterior/posterior boundaries of 0 â‰¤ y â‰¤ 30) (see Figure below). Vogt ascribes the function of avoidance behavior to this region – a pretty unitary description of the region that TY thinks we should avoid unitary descriptions of.
There is no charitable way to put it: this is nothing short of a gross misrepresentation of what I said about the Vogt account. As a reminder, here’s what I actually wrote in my post:
I’m especially partial to the work of Brent Vogt and colleagues (e.g., Vogt (2005); Vogt & Sikes, 2009), who have suggested a division within the anterior mid-cingulate cortex (aMCC; a region roughly co-extensive with the dACC in L&E’s nomenclature) between a posterior region involved in bodily orienting, and an anterior region associated with fear and avoidance behavior (though the two functions overlap in space to a considerable degree) … the Vogt characterization of dACC/aMCC … fits almost seamlessly with the Neurosynth results displayed above (e.g., we find MCC activation associated with pain, fear, autonomic, and sensorimotor processes, with pain and fear overlapping closely in aMCC). Perhaps most importantly, Vogt and colleagues freely acknowledge that their model—despite having a very rich neuroanatomical elaboration—is only an approximation. They don’t attempt to ascribe a unitary role to aMCC or dACC, and they explicitly recognize that there are distinct populations of neurons involved in reward processing, response selection, value learning, and other aspects of emotion and cognition all closely interdigitated with populations involved in aspects of pain, touch, and fear. Other systems-level neuroanatomical models of cingulate function share this respect for the complexity of the underlying circuitry—complexity that cannot be adequately approximated by labeling the dACC simply as a pain region (or, for that matter, a “survival-relevance“ region).
I have no idea how L&E read this and concluded that I was arguing that we should simply replace the label “pain” with “fear”. I don’t feel the need to belabor the point further, because I think what I wrote is quite clear.
In the end though, if TY prefers a fear story to our pain story, we think there is some evidence for both of these (a point we make in our PNAS paper). We think they are in a class of processes that overlap both conceptually (i.e. distress-related emotions) and methodologically (i.e. many fear studies use pain manipulations to condition fear).
No, I don’t prefer a fear story. My view (which should be abundantly clear from the above quote) is that both a fear story and a pain story would be gross oversimplifications that shed more heat than light. I will, however, reiterate my earlier point (which L&E never responded to), which is that their PNAS paper provides no reason at all to think that the dACC is involved in distress-related emotion (indeed, they explicitly said that this was the most speculative part of the paper). If anything, the absence of robust dACC activation for terms like ‘disgust’, ’emotion’, and ‘social’ would seem to me like pretty strong evidence against a simplistic model of this kind. I’m not sure why L&E are so resistant to the idea that maybe, just maybe, the dACC is just too big a region to attach a single simple label to. As far as I can tell, they provide no defense of this assumption in either their paper or their reply.
After focusing on potential misunderstandings we want to turn to our first disagreement with TY. Near the end of his blog, TY surprised us by writing that the following conclusions can be reasonably drawn from Neurosynth analyses:
* “There are parts of dACC (particularly the more posterior aspects) that are preferentially activated in studies involving painful stimulation.“
* “It’s likely that parts of dACC play a greater role in some aspect of pain processing than in many other candidate processes that at various times have been attributed to dACC (e.g., monitoring for cognitive conflict)“
Our first response was “˜Wow. After pages and pages of criticizing our paper, TY pretty much agrees with what we take to be the major claims of our paper. Yes, his version is slightly watered down from what we were claiming, but these are definitely in the ballpark of what we believe.’
L&E omitted my third bullet point here, which was that “Many of the same regions of dACC that preferentially activate during pain are also preferentially activated by other processes or tasks—e.g., fear conditioning, autonomic arousal, etc.” I’m not sure why they left it out; they could hardly disagree with it either, if they want to stand by their definition of “weak selectivity”.
I’ll leave it to you to decide whether or not my conclusions are really just “watered down” versions “in the ballpark” of the major claims L&E make in their paper.
But then TY’s next statement surprised us in a different sort of way. He wrote
“I think these are all interesting and potentially important observations. They’re hardly novel“¦“.
We’ve been studying the dACC for more than a decade and wondered what he might have meant by this. We can think of two alternatives for what he might have meant:
* That L&E and a small handful of others have made this claim for over a decade (but clearly not with the kind of evidence that Neurosynth provides).
* That TY already used Neurosynth in 2011 to show this. In the blog, he refers to this paper writing “We explicitly noted that there is preferential activation for pain in dACC“.
I’m not sure what was confusing about what I wrote. Let’s walk through the three bullet points. The first one is clearly not novel. We’ve known for many years that many parts of dACC are preferentially active when people experience painful stimulation. As I noted in my last post, L&E explicitly appealed to this literature over a decade ago in their 2003 social pain paper. The second one is also clearly not novel. For example, Vogt and colleagues (among others) have been arguing for at least two decades now that the posterior aspects of dACC support pain processing in virtue of their involvement in processes (e.g., bodily orientation) that clearly preclude most higher cognitive accounts of dACC. The third claim isn’t novel either, as there has been ample evidence for at least a decade now that virtually every part of dACC that responds to painful stimulation also systematically responds to other non-nociceptive stimuli (e.g., the posterior dACC responds to non-painful touch, the anterior to reward, etc.). I pointed to articles and textbooks comprehensively reviewing this literature in my last post. So I don’t understand L&E’s surprise. Which of these three claims do they think is actually novel to their paper?
In either case, “they’re hardly novel“ implies this is old news and that everyone knows and believes this, as if we’re claiming to have discovered that most people have two eyes, a nose, and a mouth. But this implication could not be further from the truth.
No, that’s not what “hardly novel” implies. I think it’s fair to say that the claim that social pain is represented in the dACC in virtue of representations shared with physical pain is also hardly novel at this point, yet few people appear to know and believe it. I take ‘hardly novel’ to mean “it’s been said before multiple times in the published literature.”
There is a 20+ year history of researchers ignoring or explaining away the role of pain processing in dACC.
I’ll address the “explained away” part of this claim below, but it’s completely absurd to suggest that researchers have ignored the role of pain processing in dACC for 20 years. I don’t think I can do any better than link to Google Scholar, where the reader is invited to browse literally hundreds of articles that all take it as an established finding that the dACC is important for pain processing (and many of which have hundreds of citations from other articles).
When pain effects are mentioned in most papers about the function of dACC, it is usually to say something along the lines of “˜Pain effects in the dACC are just one manifestation of the broader cognitive function of conflict detection (or salience or executive processes)’. This long history is indisputable. Here are just a few examples (and these are all reasonable accounts of dACC function in the absence of reverse inference data):
* Executive account: Price’s 2000 Science paper on the neural mechanisms of pain assigns to the dACC the roles of “directing attention and assigning response priorities“
* Executive account: Vogt et al. (1996) says the dACC “is not a “˜pain centre’“ and “is involved in response selection“ and “response inhibition or visual guidance of responses“
* Conflict account: Botvinick et al. (2004) wrote that “the ACC might serve to detect events or internal states indicating a need to shift the focus of attention or strengthen top-down control (, see also ), an idea consistent, for example, with the fact that the ACC responds to pain “ (Botvinick et al. 2004)
* Salience account: Iannetti suggests the “˜pain matrix’ is a myth and in Legrain et al. (2011) suggests that the dACC’s responses to pain “could mainly reflect brain processes that are not directly related to the emergence of pain and that can be engaged by sensory inputs that do not originate from the activation of nociceptors.“
I’m not really sure what to make of this argument either. All of these examples clearly show that even proponents of other theories of dACC function are well aware of the association with pain, and don’t dispute it in any way. So L&E’s objection can’t be that other people just don’t believe that the dACC supports pain processing. Instead, L&E seem to dislike the idea that other theorists have tried to “explain away” the role of dACC in pain by appealing to other mechanisms. Frankly, I’m not sure what the alternative to such an approach could possibly be. Unless L&E are arguing that dACC is the neural basis of an integrated, holistic pain experience (whatever such a thing might mean), there presumably must be some specific computational operations going on within dACC that can be ascribed a sensible mechanistic function. I mean, even L&E themselves don’t take the dACC to be just about, well, pain. Their whole “distress-related emotion” story is itself intended to explain what it is that dACC actually does in relation to pain (since pretty much everyone accepts that the sensory aspects of pain aren’t coded in dACC).
The only way I can make sense of this “explained away” concern is if what L&E are actually objecting to is the fact that other researchers have disagreed or ignored their particular story about what the dACC does in pain—i.e., L&E’s view that the dACC role in pain is derived from distress-related emotion. As best I can tell, what bothers them is that other researchers fundamentally disagree with–and hence, don’t cite–their “distress-related emotion” account. Now, maybe this irritation is justified, and there’s actually an enormous amount of evidence out there in favor of the distress account that other researchers are willfully ignoring. I’m not qualified to speak to that (though I’m skeptical). What I do feel qualified to say is that none of the Neurosynth results L&E present in their paper make any kind of case for an affective account of pain processing in dACC. The most straightforward piece of evidence for that claim would be if there were a strong overlap between pain and negative affect activations in dACC. But we just don’t see this in Neurosynth. As L&E themselves acknowledge, the peak sectors of pain-related activation in dACC are in mid-to-posterior dACC, and affect-related terms only seem to reliably activate the most anterior aspects.
To be charitable to L&E, I do want to acknowledge one valuable point that they contribute here, which is that it’s clear that dACC function cannot be comprehensively explained by, say, a salience account or a conflict monitoring account. I think that’s a nice point (though I gather that some people who know much more about anatomy than I do are in the process of writing rebuttals to L&E that argue it’s not as nice as I think it is). The problem is, this argument can be run both ways. Meaning, much as L&E do a nice job showing that conflict monitoring almost certainly can’t explain activations in posterior dACC, the very maps they show make it clear that pain can’t explain all the other activations in anterior dACC (for reward, emotion, etc.). Personally, I think the sensible conclusion one ought to take away from all this is “it’s really complicated, and we’re not going to be able to neatly explain away all of dACC function with a single tidy label like ‘pain’.” L&E draw a different conclusion.
But perhaps this approach to dACC function has changed in light of TY’11 findings (i.e. Yarkoni et al. 2011). There he wrote “For pain, the regions of maximal pain-related activation in the insula and DACC shifted from anterior foci in the forward analysis to posterior ones in the reverse analysis.“ This hardly sounds like a resounding call for a different understanding of dACC that involves an appreciation of its preferential involvement in pain.
Right. It wasn’t a resounding call for a different understanding of dACC, because it wasn’t a paper about the dACC—a brain region I lack any deep interest in or knowledge of—it was a paper about Neurosynth and reverse inference.
Here are quotes from other papers showing how they view the dACC in light of TY’11:
* Poldrack (2012) “The striking insight to come from analyses of this database (Yarkoni et al., in press) is that some regions (e.g., anterior cingulate) can show high degrees of activation in forward inference maps, yet be of almost no use for reverse inference due to their very high base rates of activation across studies“
* Chang, Yarkoni et al. (2012) “the ACC tends to show substantially higher rates of activation than other regions in neuroimaging studies (Duncan and Owen 2000; Nelson et al. 2010; Yarkoni et al. 2011), which has lead some to conclude that the network is processing goal-directed cognition (Yarkoni et al. 2009)“
* Atlas & Wager (2012) “In fact, the regions that are reliably modulated (insula, cingulate, and thalamus) are actually not specific to pain perception, as they are activated by a number of processes such as interoception, conflict, negative affect, and response inhibition“
I won’t speak for papers I’m not an author on, but with respect to the quote from the Chang et al paper, I’m not sure what L&E’s point actually is. In Yarkoni et al. (2009), I argued that “effort” might be a reasonable generic way to characterize the ubiquitous role of the frontoparietal “task-positive” network in cognition. I mistakenly called the region in question ‘dACC’ when I should have said ‘preSMA’. I already gave L&E deserved credit in my last post for correcting my poor knowledge of anatomy. But I would think that, if anything, the fact that I was routinely confusing these terms circa 2011 should lead L&E to conclude that maybe I don’t know or care very much about the dACC, and not that I’m a proud advocate for a strong theory of dACC function that many other researchers also subscribe to. I think L&E give me far too much credit if they think that my understanding of the dACC in 2011 (or, for that matter, now) is somehow representative of the opinions of experts who study that region.
Perhaps the reason why people who cite TY’11 in their discussion of dACC didn’t pay much attention to the above quote from TY’11 (““For pain, the regions of maximal pain-related“¦“) was because they read and endorsed the following more direct conclusion that followed ““¦because the dACC is activated consistently in all of these states [cognitive control, pain, emotion], its activation may not be diagnostic of any one of them“ (bracketed text added). If this last quote is taken as TY’11’s global statement regarding dACC function, then it strikes us still as quite novel to assert that the dACC is more consistently associated with one category of processes (pain) than others (executive, conflict, and salience processes).
I don’t think TY’11 makes any ‘global statement regarding dACC function’, because TY’11 was a methodological paper about the nature of reverse inference, not a paper about grand models of dACC function. As for the quote L&E reproduce, here’s the full context:
These results showed that without the ability to distinguish consistency from selectivity, neuroimaging data can produce misleading inferences. For instance, neglecting the high base rate of DACC activity might lead researchers in the areas of cognitive control, pain and emotion to conclude that the DACC has a key role in each domain. Instead, because the DACC is activated consistently in all of these states, its activation may not be diagnostic of any one of them and conversely, might even predict their absence. The NeuroSynth framework can potentially address this problem by enabling researchers to conduct quantitative reverse inference on a large scale.
I stand by everything I said here, and I’m not sure what L&E object to. It’s demonstrably true if you look at Figure 2 in TY’11 that pain, emotion, and cognitive control all robustly activate the dACC in the forward inference map, but not in the reverse inference maps. The only sense I can make of L&E’s comment is if they’re once again conflating z-scores with probabilities, and assuming that the presence of significant activation for pain means that dACC is in fact diagnostic for pain. But, as I showed much earlier in this post, that would betray very deep misunderstanding of what the reverse inference maps generated by Neurosynth mean. There is absolutely no basis for concluding, in any individual study, that people are likely to be perceiving pain just because the dACC is active.
In the article, we showed forward and reverse inference maps for 21 terms and then another 9 in the supplemental materials. These are already crowded busy figures and so we didn’t have room to show multiple slices for each term. Fortunately, since Neurosynth is easily accessible (go check it out now at neurosynth.org ““ its awesome!) you can look at anything we didn’t show you in the paper. Tal takes us to task for this.
He then shows a bunch of maps from x=-8 to x=+8 on a variety of terms. Many of these terms weren’t the focus of our paper because we think they are in the same class of processes as pain (as noted above). So it’s no surprise to us that terms such as “˜fear,’ “˜empathy,’ and “˜autonomic’ produce dACC reverse inference effects. In the paper, we reported that “˜reward’ does indeed produce reverse inference effects in the anterior portion of the dACC (and show the figure in the supplemental materials), so no surprise there either. Then at the bottom he shows cognitive control, conflict, and inhibition which all show very modest footprints in dACC proper, as we report in the paper.
Once again: L&E are not entitled to exclude a large group of viable candidate functions from their analysis simply because they believe that they’re “in the same class of [distress-related affect] processes” (a claim that many people, including me, would dispute). If proponents of the salience monitoring view wrote a Neurosynth-based paper neglecting to compare salience with pain because “pain is always salient, so it’s in the same class of salience-related processes”, I expect that L&E would not be very happy about it. They should show others the same charity they themselves would expect.
But in any case, if it’s not surprising to L&E that reward, fear, and autonomic control all activate the dACC, then I’m at a loss to understand why they didn’t title the paper something like “the dACC is selectively involved in pain, reward, fear, and autonomic control”. That would have much more accurately represented the results they report, and would be fully consistent with their notion of “weak selectivity”.
There are two things that make the comparison of what he shows and what we reported in the paper not a fair comparison. First, his maps are thresholded at p<.001 and yet all the maps that we report use Neurosynth’s standard, more conservative, FDR criterion of p<.01 (a standard TY literally set). Here, TY is making a biased, apples-to-oranges comparison by juxtaposing the maps at a much more liberal threshold than what we did. Given that each of the terms we were interested in (pain, executive, conflict, salience) had more than 200 studies in the database its not clear why TY moved from FDR to uncorrected maps here.
The reason I used a threshold of p < .001 for this analysis is because it’s what L&E themselves used:
In addition, we used a threshold of Z > 3.1, P < 0.001 as our threshold for indicating significance. This threshold was chosen instead of Neurosynth’s more strict false discovery rate (FDR) correction to maximize the opportunity for multiple psychological terms to “claim“ the dACC.
This is a sensible thing to do here, because L&E are trying to accept the null of no effect (or at least, it’s more sensible than applying a standard, conservative correction). Accepting the null hypothesis because an effect fails to achieve significance is the cardinal sin of null hypothesis significance testing, so there’s no real justification for doing what L&E are trying to do. But if you are going to accept the null, it at least behooves you to use a very liberal threshold for your analysis. I’m not sure why it’s okay for L&E to use a threshold of p < .001 but not for me to do the same (and for what it’s worth, I think p < .001 is still an absurdly conservative cut-off given the context).
Second, the Neurosynth database has been updated since we did our analyses. The number of studies in the database has only increased by about 5% (from 10,903 to 11,406 studies) and yet there are some curious changes. For instance, fear shows more robust dACC now than it did a few months ago even though it only increased from 272 studies to 298 studies.
Although the number of studies has nominally increased by only 5%, this actually reflects the removal of around 1,000 studies as a result of newer quality control heuristics, and the addition of around 1,500 new studies. So it should not be surprising if there are meaningful differences between the two. In any case, it seems odd for L&E to use the discrepancy between old and new versions of the database as a defense of their findings, given that the newer results are bound to be more accurate. If L&E accept that there’s a discrepancy, perhaps what they should be saying is “okay, since we used poorer data for our analyses than what Neurosynth currently contains, we should probably re-run our analyses and revise our conclusions accordingly”.
We were more surprised to discover that the term “˜rejection’ has been removed from the Neurosynth database altogether such that it can no longer be used as a term to generate forward and reverse inference maps (even though it was in the database prior to the latest update).
This claim is both incorrect and mildly insulting. It’s incorrect because the term “rejection” hasn’t been in the online Neurosynth database for nearly two years, and was actually removed three updates ago. And it’s mildly insulting, because all L&E had to do to verify the date at which rejection was removed, as well as understand why, was visit the Neurosynth data repository and inspect the different data releases. Failing that, they could have simply asked me for an explanation, instead of intimating that there are “curious” changes. So let me take this opportunity to remind L&E and other readers that the data displayed on the Neurosynth website are always archived on GitHub. If you don’t like what’s on the website at any given moment, you can always reconstruct the database based on an earlier snapshot. This can be done in just a few lines of Python code, as the IPython notebook I linked to last time illustrates.
As to why the term “rejection” disappeared: in April 2014, I switched from a manually curated set of 525 terms (which I had basically picked entirely subjectively) to the more comprehensive and principled approach of including all terms that passed a minimum frequency threshold (i.e., showing up in at least 60 unique article abstracts). The term “rejection” was not frequent enough to survive. I don’t make decisions about individual terms on a case-by-case basis (well, not since April 2014, anyway), and I certainly hope L&E weren’t implying that I pulled the ‘rejection’ term in response to their paper or any of their other work, because, frankly, they would be giving themselves entirely too much credit.
Anyway, since L&E seem concerned with the removal of ‘rejection’ from Neurosynth, I’m happy to rectify that for them. Here are two maps for the term “rejection” (both thresholded at voxel-wise p < .001, uncorrected):
The first map is from the last public release (March 2013) that included “rejection” as a feature, and is probably what L&E remember seeing on the website (though, again, it hasn’t been online since 2014). It’s based on 33 studies. The second map is the current version of the map, based on 52 studies. The main conclusion I personally would take away from both of these maps is that there’s not enough data here to say anything meaningful, because they’re both quite noisy and based on a small number of studies. This is exactly why I impose a frequency cut-off for all terms I put online.
That said, if L&E would like to treat these “rejection” analyses as admissible evidence, I think it’s pretty clear that these maps actually weigh directly against their argument. In both cases, we see activation in pain-related areas of dACC for the forward inference analysis but not for the reverse. Interestingly, we do see activation in the most anterior part of dACC in both cases. This seems to me entirely consistent with the argument many people have made that subjective representations of emotion (including social pain) are to be found primarily in anterior medial frontal cortex, and that posterior dACC activations for pain have much more to do with motor control, response selection, and fear than with anything affective.
Given that Neurosynth is practically a public utility and federally funded, it would be valuable to know more about the specific procedures that determine which journals and articles are added to the database and on what schedule. Also, what are the conditions that can lead to terms being removed from the database and what are the set of terms that were once included that have now been removed.
I appreciate L&E’s vote of confidence (indeed, I wish that I believed Neurosynth could do half of what they claim it can do). As I’ve repeatedly said in this post and the last one, I’m happy to answer any questions L&E have about Neurosynth methods (preferably on the mailing list, which is publicly archived and searchable). But to date, they haven’t asked me any. I’ll also reiterate that it would behoove L&E to check the data repository on GitHub (which is linked to from the neurosynth.org portal) before they conclude that the information they want isn’t already publicly accessible (because most of it is).
In any event, we did not cherry pick data. We used the data that was available to us as of June 2015 when we wrote the paper. For the four topics of interest, below we provide more representative views of the dACC, thresholded as typical Neurosynth maps are, at FDR p<.01. We’ve made the maps nice and big so you can see the details and have marked in green the dACC region on the different slices (the coronal slice are at y=14 and y=22). When you look at these, we think they tell the same story we told in the paper.
I’m not sure what the point here is. I was not suggesting that L&E were lying; I was arguing that (a) visual inspection of a few slices is no way to make a strong argument about selectivity; (b) the kinds of analyses L&E report are a statistically invalid way to draw the conclusion they are trying to draw, and (c) even if we (inappropriately) use L&E’s criteria, analyses done with more current data clearly demonstrate the presence of plenty of effects for terms other than pain. L&E dispute the first two points (which we’ll come back to), but they don’t seem to contest the last. This seems to me like it should lead L&E to the logical conclusion that they should change their conclusions, since newer and better data are now available that clearly produce different results given the same assumptions.
(I do want to be clear again that I don’t condone L&E’s analyses, which I show above and below in detail simply don’t support their conclusions. I was simply pointing out that even by their own criteria, Neurosynth results don’t support their claims.)
4. Surprising lack of appreciation for what the reverse inference maps show in pretty straightforward manner.
Let’s start with pain and salience. Iannetti and his colleagues have made quite a bit of hay the last few years saying that the dACC is not involved in pain, but rather codes for salience. One of us has critiqued the methods of this work elsewhere (Eisenberger, 2015, Annual Review). The reverse inference maps above show widespread robust reverse inference effects throughout the dACC for pain and not a single voxel for salience. When we ran this initially for the paper, there were 222 studies tagged for the term salience and now that number is up to 269 and the effects are the same.
Should our tentative conclusion be that we should hold off judgment until there is more evidence? TY thinks so: “If some terms have too few studies in Neurosynth to support reliable comparisons with pain, the appropriate thing to do is to withhold judgment until more data is available.“ This would be reasonable if we were talking about topics with 10 or 15 studies in the database. But, there are 269 studies for the term salience and yet there is nothing in the dACC reverse inference maps. I can’t think of anyone who has ever run a meta-analysis of anything with 250 studies, found no accumulated evidence for an effect and then said “we should withhold judgment until more data is available“.
This is another gross misrepresentation ofÂ what I said in my commentary. So let me quote what Â I actually said. Here’s the context:
While it’s true that terms with fewer associated studies will have more variable (i.e., extreme) posterior probability estimates, this is an unavoidable problem that isn’t in any way remedied by focusing on z-scores instead of posterior probabilities. If some terms have too few studies in Neurosynth to support reliable comparisons with pain, the appropriate thing to do is to withhold judgment until more data is available. One cannot solve the problem of data insufficiency by pretending that p-values or z-scores are measures of effect size.
This is pretty close to the textbook definition of “quoting out of context”. It should be abundantly clear that I was not saying that L&E shouldn’t interpret results from a Neurosynth meta-analysis of 250 studies (which would be absurd). The point of the above quote was that if L&E don’t like the result they get when they conduct meta-analytic comparisons properly with Neurosynth, they’re not entitled to replace the analysis with a statistically invalid procedure that does give results they like.
TY and his collaborators have criticized researchers in major media outlets (e.g. New York Times) for poor reverse inference ““ for drawing invalid reverse inference conclusions from forward inference data. The analyses we presented suggest that claims about salience and the dACC are also based on unfounded reverse inference claims. One would assume that TY and his collaborators are readying a statement to criticize the salience researchers in the same way they have previously.
This is another absurd, and frankly insulting, comparison. My colleagues and I have criticized people for saying that insula activation is evidence that people are in love with their iPhones. I certainly hope that this is in a completely different league from inferring that people must be experiencing pain if the dACC is activated (because if not, some of L&E’s previous work would appear to be absurd on its face). For what it’s worth, I agree with L&E that nobody should interpret dACC activation in a study as strong evidence of “salience”—and, for that matter, also of “pain”. As for why I’m not readying a statement to criticize the salience researchers, the answer is that it’s not my job to police the ACC literature. My interest is in making sure Neurosynth is used appropriately. L&E can rest assured that if someone published an article based entirely on Neurosynth results in which their primary claim was that the dACC is selective for salience, I would have written precisely the same kind of critique. Though it should perhaps concern them that, of the hundreds of published uses of Neurosynth to date, theirs is the first and only one that has moved me to write a critical commentary.
But no. Nowhere in the blog does TY comment on this finding that directly contradicts a major current account of the dACC. Not so much as a “Geez, isn’t it crazy that so many folks these days think the dACC and AI can be best described in terms of salience detection and yet there is no reverse inference evidence at all for this claim.“
Once again: I didn’t comment on this because I’m not interested in the dACC; I’m interested in making sure Neurosynth is used appropriately. If L&E had asked me, “hey, do you think Neurosynth supports saying that dACC activation is a good marker of ‘salience’?”, I would have said “no, of course not.” But L&E didn’t write a paper titled “dACC activity should not be interpreted as a marker of salience”. They wrote a paper titled “the dACC is selective for pain”, in which they argue that pain is the best psychological characterization of dACC—a claim that Neurosynth simply does not support.
For the terms executive and conflict, our Figure 3 in the PNAS paper shows a tiny bit of dACC. We think the more comprehensive figures we’ve included here continue to tell the same story. If someone wants to tell the conflict story of why pain activates the dACC, we think there should be evidence of widespread robust reverse inference mappings from the dACC to conflict. But the evidence for such a claim just isn’t there. Whatever else you think about the rest of our statistics and claims, this should give a lot of folks pause, because this is not what almost any of us would have expected to see in these reverse inference maps (including us).
No objections here.
If you generally buy into Neurosynth as a useful tool (and you should), then when you look at the four maps above, it should be reasonable to conclude, at least among these four processes, that the dACC is much more involved in that first one (i.e. pain). Let’s test this intuition in a new thought experiment.
Imagine you were given the three reverse inference maps below and you were interested in the function of the occipital cortex area marked off with the green outline. You’d probably feel comfortable saying the region seems to have a lot more to do with Term A than Terms B or C. And if you know much about neuroanatomy, you’d probably be surprised, and possibly even angered, when I tell you that Term A is “˜motor’, Term B is “˜engaged’, and Term C is “˜visual’. How is this possible since we all know this region is primarily involved in visual processes? Well it isn’t possible because I lied. Term A is actually “˜visual’ and Term C is “˜motor’. And now the world makes sense again because these maps do indeed tell us that this region is widely and robustly associated with vision and only modestly associated with engagement and motor processes. The surprise you felt, if you believed momentarily that Term A was motor was because you have the same intuition we do that these reverse inference maps tell us that Term A is the likely function of this region, not Term B or Term C ““ and we’d like that reverse inference to be what we always thought this region was associated with ““ vision. It’s important to note that while a few voxels appear in this region for Terms B and C, it still feels totally fine to say this region’s psychological function can best be described as vision-related. It is the widespread robust nature of the effect in Term A, relative to the weak and limited effects of Terms B and C, that makes this a compelling explanation of the region.
I’m happy to grant L&E that it may “feel totally fine” to some people to make a claim like this. But this is purely an appeal to intuition, and has zero bearing on the claim’s actual validity. I hope L&E aren’t seriously arguing that cognitive neuroscientists should base the way we do statistical inference on our intuitions about what “feels totally fine”. I suspect it felt totally fine to L&E to conclude in 2003 that people were experiencing physical pain because the dACC was active, even though there was no evidential basis for such a claim (and there still isn’t). Recall that, in surveys of practicing researchers, a majority of respondents routinely endorse the idea that a p-value of .05 means that that there’s at least a 95% probability that the alternative hypothesis is correct (it most certainly doesn’t mean this). Should we allow people to draw clearly invalid conclusions in their publications on the grounds that it “feels right” to them? Indeed, as I show below, L&E’s arguments for selectivity rest in part on an invalid acceptance of the null hypothesis. Should they be given a free pass onÂ what is probably the cardinal sin of NHST, on the grounds that it probably “felt right” to them to equate non-significance with evidence of absence?
The point of Neurosynth is that it provides a probabilistic framework for understanding the relationship between psychological function and brain activity. The framework has many very serious limitations that, in practice, make it virtually impossible to draw any meaningful reverse inference from observed patterns of brain activity in any individual study. If L&E don’t like this, they’re welcome to build their own framework that overcomes the limitations of Neurosynth (or, they could even help me improve Neurosynth!). But they don’t get to violate basic statistical tenets in favor of what “feels totally fine” to them.
Another point of this thought experiment is that if Term A is what we expect it to be (i.e. vision) then we can keep assuming that Neurosynth reverse inference maps tell us something valuable about the function of this region. But if Term A violates our expectation of what this region does, then we are likely to think about the ways in which Neurosynth’s results are not conclusive on this point.
We suspect if the dACC results had come out differently, say with conflict showing wide and robust reverse inference effects throughout the dACC, and pain showing little to nothing in dACC, that most of our colleagues would have said “Makes sense. The reverse inference map confirms what we thought ““ that dACC serves a general cognitive function of detecting conflicts.“ We think it is because of the content of the results rather than our approach that is likely to draw ire from many.
I can’t speak for L&E’s colleagues, but my own response to their paper was indeed driven entirely by their approach. If someone had published a paper using Neurosynth to argue that the dACC is selective for conflict, using the same kinds of arguments L&E make, I would have written exactly the same kind of critique I wrote in response to L&E’s paper. I don’t know how I can make it any clearer that I have zero attachment to any particular view of the dACC; my primary concern is with L&E’s misuse of Neurosynth, not what they or anyone else thinks about dACC function. I’ve already made it clear several times that I endorse their conclusion that conflict, salience, and cognitive control are not adequate explanations for dACC function. What they don’t seem to accept is that pain isn’t an adequate explanation either, as the data from Neurosynth readily demonstrate.
5. L&E did the wrong analyses
TY suggests that we made a major error by comparing the Z-scores associated with different terms and should have used posterior probabilities instead. If our goal had been to compare effect sizes this might have made sense, but comparing effect sizes was not our goal. Our goal was to see whether there was accumulated evidence across studies in the Neurosynth database to support reverse inference claims from the dACC.
I’ve already addressed the overarching problem with L&E’s statistical analyses in the first part of this post. Below I’ll just walk through each of L&E’s assertions in detail and point out all of the specific issues in detail. I’ll warn you right now that this is not likely to make for very exciting reading.
While we think the maps for each term speak volumes just from visual inspection, we thought it was also critical to run the comparisons across terms directly. We all know the statistical error of showing that A is significant, while B is not and then assuming, but not testing A > B, directly. TY has a section called “A>B does not imply ~B“ (where ~B means “˜not B’). Indeed it does not, but all the reverse inference maps for the executive, conflict, and salience terms already established ~B. We were just doing due diligence by showing that the difference between A and B was indeed significant.
I apologize for implying that L&E weren’t aware that A > B doesn’t entail ~B. I drew that conclusion because the only other way I could see their claim of selectivity making any sense is if they were interpreting a failure to detect a significant effect for B as positive evidence of no effect. I took that to be much more unlikely, because it’s essentially the cardinal sin of NHST. But their statement here explicitly affirms that this is, in fact, exactly what they were arguing—which leads me to conclude that they don’t understand the null hypothesis statistical testing (NHST) framework they’re using. The whole point of this section of my post was that L&E cannot conclude that there’s no activity in dACC for terms like conflict or salience, because accepting the null is an invalid move under NHST. Perhaps I wasn’t sufficiently clear about this in my last post, so let me reiterate: the reverse inference maps do not establish ~B, and cannot establish ~B. The (invalid) comparison tests of A > B do not establish ~B, and cannot cannot establish ~B. In fact, no analysis, figure, or number L&E report anywhere in their paper establishes ~B for any of the terms they compare with pain. Under NHST, the only possible result of any of L&E’s analyses that would allow them to conclude that a term is not positively associated with dACC activation would be a significant result in the negative direction (i.e., if dACC activation implied a decrease in likelihood of a term). But that’s clearly not true of any of the terms they examine.
Note that this isn’t a fundamental limitation of statistical inference in general; it’s specifically an NHST problem. A Bayesian model comparison approach would have allowed L&E to make a claim about the evidence for the null in comparison to the alternative (though specifying the appropriate priors here might not be very straightforward). Absent such an analysis, L&E are not in any position to make claims about conflict or salience not activating the dACC—and hence, per their own criteria for selectivity, they have no basis for arguing that pain is selective.
Now, in my last post, I went well beyond this logical objection and argued that, if you analyze the data using L&E’s own criteria, there’s plenty of evidence for significant effects of other terms in dACC. I now regret including those analyses. Not because they were wrong; I stand by my earlier conclusion (which should be apparent to anyone who spends five minutes browsing maps on Neurosynth.org), and this alone should have prevented L&E from making claims about pain selectivity. But the broader point is that I don’t want to give the impression that this debate is over what the appropriate statistical threshold for analysis is—i.e., that maybe if we use p < 0.05, I’m right, and if we use FDR = 0.1, L&E are right. The entire question of which terms do or don’t show a significant effect is actually completely beside the point given that L&E’s goal is to establish that only pain activates the dACC, and that terms like conflict or salience don’t. To accomplish that, L&E would need to use an entirely different statistical framework that allows them them to accept the null (relative to some alternative).
If it’s reasonable to use the Z-scores from Neurosynth to say “How much evidence is there for process A being a reliable reverse inference target for region X“ then it has to be reasonable to compare Z-scores from two analyses to ask “How much MORE evidence is there for process A than process B being a reliable reverse inference target for region X“. This is all we did when we compared the Z-scores for different terms to each other (using a standard formula from a meta-analysis textbook) and we think this is the question many people are asking when they look at the Neurosynth maps for any two competing accounts of a neural region.
I addressed this in the earlier part of this post, where I explained why one cannot obtain support for a reverse inference using z-scores or p-values. Reverse inference is inherently a Bayesian notion, and makes sense only if you’re willing to talk about prior and posterior probabilities. So L&E’s first premise here—i.e., that it’s reasonable to use z-scores from Neurosynth to quantify “evidence for process A being a reliable reverse inference target for region X” is already false.
For what it’s worth, the second premise is also independently false, because it’s grossly inappropriate to use meta-analytic z-score comparison test in this situation. For one thing, there’s absolutely no reason to compare z-scores given that the distributional information is readily available. Rosenthal (the author of the meta-analysis textbook L&E cite) himself explicitly notes that such a test is inferior to effect size-based tests, and is essentially a last-ditchÂ approach. Moreover, the intended use of the test in meta-analysis is to determine whether or not there’s heterogeneity in p-values as a precursor to combining them in an analysis (whichÂ is a concern that makes no sense in the context of Neurosynth data). At best, what L&E would be able to say with this test is something like “it looks like these two z-scores may be coming from different underlying distributions”. I don’t know why L&E think this is at all an interesting question here, because we already know with certainty that there can be no meaningful heterogeneity of this sort in these z-scores given that they’re all generated using exactly the same set of studies.
In fact, the problems with the z-score comparison test L&E are using run so deep that I can’t help point out just one truly stupefying implication of the approach: it’s possible, under a wide range of scenarios, to end up concluding that there’s evidence that one term is “preferentially” activated relative to another term even when the point estimate is (significantly) larger for the latter term. For example, consider a situation in which we have a probability of 0.65 for one term with n = 1000 studies, and a probability of 0.8 for a second term with n = 100 studies. The one-sample proportion test for these two samples, versus a null of 0.5, gives z-scores of 9.5 and 5.9, respectively–so both tests are highly significant, as one would expect. But the Rosenthal z-score test favored by L&E tells us that the z-score for the first sample is significantly larger than the z-score for the second. It isn’t just wrong to interpret this as evidence that the first term has a more selective effect; it’s dangerously wrong. A two-sample test for the difference in proportions correctly reveals a significant effect in the expected direction (i.e., the 0.8 probablity in the smaller sample is in fact significantly greater than the 0.65 probability in the much larger sample). Put simply, L&E’s test is broken. It’s not clear that it tests anything meaningful in this context, let alone allowing us to conclude anything useful about functional selectivity in dACC.
As for what people are asking when they look at the Neurosynth maps for any two competing accounts of a neural region: I really don’t know, and I don’t see how that would have any bearing on whether the methods L&E are using are valid or not. What I do know that I’ve never seen anyone else compare Neurosynth z-scores using a meta-analytic procedureÂ intended to test for heterogeneity of effects—and I certainly wouldn’t recommend it.
TY then raises two quite reasonable issues with the Z-score comparisons, one of which we already directly addressed in our paper. First, TY raises the issue that Z-scores increase with accumulating evidence, so terms with more studies in the database will tend to have larger Z-scores. This suggests that terms with the most studies in the database (e.g. motor with 2081 studies) should have significant Z-scores everywhere in the brain. But terms with the most studies don’t look like this. Indeed, the reverse inference map for “functional magnetic“ with 4990 studies is a blank brain with no significant Z-scores.
Not quite. It’s true that for any fixed effect size, z-scores will rise (in absolute value) as sample size increases. But if the true effect size is very small, one will still obtain a negligible z-score even in a very large sample. So while terms with more studies will indeed tend to have larger absolute z-scores, it’s categorically false that “terms with the most studies in the database should have significant z-scores everywhere in the brain”.
However, TY has a point. If two terms have similar true underlying effects in dACC, then the one with the larger number of studies will have a larger Z-score, all else being equal. We addressed this point in the limitations section of our paper writing “It is possible that terms that occur more frequently, like “pain,“ might naturally produce stronger reverse inference effects than less frequent terms. This concern is addressed in two ways. First, the current analyses included a variety of terms that included both more or fewer studies than the term “pain“ and no frequency-based gradient of dACC effects is observable.“ So while pain (410 studies) is better represented in the Neurosynth database than conflict (246 studies), effort (137 studies), or Stroop (162 studies), several terms are better represented than pain including auditory (1004 studies), cognitive control (2474 studies), control (2781 studies), detection (485 studies), executive (531 studies), inhibition (432 studies), motor (1910 studies), and working memory (815). All of these, regardless of whether they are better or worse represented in the Neurosynth database show minimal presence in the dACC reverse inference maps. It’s also worth noting that painful and noxious, with only 158 and 85 studies respectively, both show broader coverage within the dACC than any of the cognitive or salience terms considered in our paper.
L&E don’t seem to appreciateÂ that the relationship between the point estimate of a parameter and the uncertainty around that estimate is not like the relationship between two predictors in a regression, where one can (perhaps) reason logically about what would or should be true if one covariate was having an influence on another. One cannot “rule out” the possibility that sample size is a problem by pointing to some large-N terms with small effects or some small-N terms with large effects. Sampling error is necessarily larger in smaller samples. The appropriate way to handle between-term variation in sample size is to properly build that differential uncertainty into one’s inferential test. Rosenthal’s z-score comparison doesn’t do this. The direct meta-analytic contrast one can perform with Neurosynth does do this, but of course, being much more conservative than the Rosenthal test (appropriately so!), L&E don’t seem to like the results it produces. (And note that the direct meta-analytic contrast would still require one to make strong assumptions about priors if the goal was to make quantitative reverse inferences, as opposed to detecting a mean difference in probability of activation.)
TY’s second point is also reasonable, but is also not a problem for our findings. TY points out that some effects may be easier to produce in the scanner than others and thus may be biased towards larger effect sizes. We are definitely sympathetic to this point in general, but TY goes on to focus on how this is a problem for comparing pain studies to emotion studies because pain is easy to generate in the scanner and emotion is hard. If we were writing a paper comparing effect sizes of pain and emotion effects this would be a problem but (a) we were not primarily interested in comparing effect sizes and (b) we definitely weren’t comparing pain and emotion because we think the aspect of pain that the dACC is involved in is the affective component of pain as we’ve written in many other papers dating back to 2003 (Eisenberger & Lieberman, 2004; Eisenberger, 2012; Eisenberger, 2015).
It certainly is a problem for L&E’s findings. Z-scores are related one-to-one with effect size for any fixed sample size, so if the effect size is artificially increased in one condition, so too is the z-score that L&E stake their (invalid) analysis on. Any bias in the point estimate will necessarily distort the z-value as well. This is not a matter of philosophical debate or empirical conjecture, it’s a mathematical necessity.
Is TY’s point relevant to our actual terms of comparison: executive, conflict, and salience processes? We think not. Conflict tasks are easy and reliable ways to produce conflict processes. In multiple ways, we think pain is actually at a disadvantage in the comparison to conflict. First, pain effects are so variable from one person to the next that most pain researchers begin by calibrating the objective pain stimuli delivered, to each participant’s subjective responses to pain. As a result, each participant may actually be receiving different objective inputs and this might limit the reliability or interpretability of certain observed effects. Second, unlike conflict, pain can only be studied at the low end of its natural range. Due to ethical considerations, we do not come close to studying the full spectrum of pain phenomena. Both of these issues may limit the observation of robust pain effects relative to our actual comparisons of interest (executive, conflict, and salience processes.
Perhaps I wasn’t sufficiently clear, but I gave the pain-emotion contrast as an example. The point is that meta-analytic comparisons of the kind L&E are trying to make are a very dangerous proposition unless one has reason to think that two classes of manipulations are equally “strong”. It’s entirely possible that L&E are right that executive control manipulations are generally stronger than pain manipulations, but that case needs to be made on the basis of data, and cannot be taken for granted.
6. About those effect size comparison maps
After criticizing us for not comparing effect sizes, rather than Z-scores, TY goes on to produce his own maps comparing the effect sizes of different terms and claiming that these represent evidence that the dACC is not selective for pain. A lot of our objections to these analyses as evidence against our claims repeats what’s already been said so we’ll start with what’s new and then only briefly reiterate the earlier points.
a) We don’t think it makes much sense to compare effect sizes for terms in voxels for which there is no evidence that it is a valid reverse inference target. For instance, the posterior probability at 0 26 26 for pain is .80 and for conflict is .61 (with .50 representing a null effect). Are these significantly different from one another? I don’t think it matters much because the Z-score associated with conflict at this spot is 1.37, which is far from significant (or at least it was when we ran our analyses last summer. Strangely, now, any non-significant Z-scores seem to come back with a value of 0, whereas they used to give the exact non-significant Z-score).
I’m not sure why L&E think that statistical significance makes a term a “valid target” for reverse inference (or conversely, that non-significant terms cannot be valid targets). If they care to justify this assertion, I’ll be happy to respond to it. It is, in any case, a moot point, since many of the examples I gave were statistically significant, and L&E don’t provide any explanation as to why those terms aren’t worth worrying about either.
As for the disappearance of non-significant z-scores, that’s a known bug introduced by the last major update to Neurosynth, and it’ll be fixed in the next major update (when the entire database is re-generated).
If I flip a coin twice I might end up with a probability estimate of 100% heads, but this estimate is completely unreliable. Comparing this estimate to those from a coin flipped 10,000 times which comes up 51% heads makes little sense. Would the first coin having a higher probability estimate than the second tell us anything useful? No, because we wouldn’t trust the probability estimate to be meaningful. Similarly, if a high posterior probability is associated with a non-significant Z-score, we shouldn’t take this posterior probability as a particularly reliable estimate.
L&E are correct that it wouldn’t make much sense to compare an estimate from 2 coin flips to an estimate from 10,000 coin flips. But the error is in thinking that comparing p-values somehow addresses this problem. As noted above, the p-value comparison they use is a meta-analytic test that only tells one if a set of z-scores are heterogenous, and is not helpful for comparing proportions when one has actual distributional information available. It would be impossible to answer the question of whether one coin is biased relative to another using this test—and it’s equally impossible to use it to determine whether one term is more important than another for dACC function.
b) TY’s approach for these analyses is to compare the effect sizes for any two processes A & B by finding studies in the database tagged for A but not B and others tagged for B but not A and to compare these two sets. In some cases this might be fine, but in others it leaves us with a clean but totally unrealistic comparison. To give the most extreme example, imagine we did this for the terms pain and painful. It’s possible there are some studies tagged for painful but not pain, but how representative would these studies be of “painful“ as a general term or construct? It’s much like the clinical problem of comparing depression to anxiety by comparing those with depression (but not anxiety) to those with anxiety (but not depression). These folks are actually pretty rare because depression and anxiety are so highly comorbid, so the comparison is hardly a valid test of depression vs. anxiety. Given that we think pain, fear, emotion, and autonomic are actually all in the same class of explanations, we think comparisons within this family are likely to suffer from this issue.
There’s nothing “unrealistic” about this comparison. It’s not the inferential test’s job to make sure that the analyst is doing something sensible, it’s the analyst’s job. Nothing compels L&E to run a comparison between ‘pain’ and ‘painful’, and I fully agree that this would be a dumb thing to do (and it would be an equally dumb thing to do using any other statistical test). One the other hand, comparing the terms ‘pain’ and ’emotion’ is presumably not a dumb thing to do, so it behooves us to make sure that we use an inferential test that doesn’t grossly violate common sense and basic statistical assumptions.
Now, if L&E would like to suggest an alternative statistical test that doesn’t exclude the intersection of the two terms and still (i) produces interpretable results, (ii) weights all studies equally, (iii) appropriately accounts for the partial dependency structure of the data, and (iv) is sufficiently computationally efficient to apply to thousands of terms in a reasonable amount of time (which rules out most permutation-based tests), then I’d be delighted to consider their suggestions. The relevant code can be found here, and L&E are welcome to open a GitHub issue to discuss this further. But unless they have concrete suggestions, it’s not clear what I’m supposed to do with their assertion that doing meta-analytic comparison properly sometimes “leaves us with a clean but totally unrealistic comparison”. If they don’t like the reality, they’re welcome to help me improve the reality. Otherwise they’re simply engaging in wishful thinking. Nobody owes L&E a statistical test that’s both valid and gives them results they like.
c) TY compared topics (i.e., a cluster of related terms), not terms. This is fine, but it is one more way that what TY did is not comparable to what we did (i.e. one more way his maps can’t be compared to those we presented).
I almost always use topics rather than terms in my own analyses, for a variety of reasons (they have better construct validity, are in theory more reliable, reduce the number of comparisons, etc.). I didn’t try out the analyses I ran with any of the term-based features, but I encourage L&E to do so if they like, and I’d be surprised if the results differ appreciably (they should, in general, simply be slightly less robust all around). In any case, I deliberately made my code available so that L&E (or anyone else) could easily reproduce and modify my analyses. (And of course, nothing at all hangs on the results in any case, because the whole premise that this is a suitable way to demonstrate selectivity is unfounded.)
d) Finally and most importantly, our question would not have led us to comparing effect sizes. We were interested in whether there was greater accumulated evidence for one term (i.e. pain) being a reverse inference target for dACC activations than for another term (e.g. conflict). Using the Z-scores as we did is a perfectly reasonable way to do this.
See above. Using the z-scores the way L&E did is not reasonable and doesn’t tell us anything anyone would want to know about functional selectivity.
7. Biases all around
Towards the end of his blog, TY says what we think many cognitive folks believe:
“I don’t think it’s plausible to think that much of the brain really prizes pain representation above all else.“
We think this is very telling because it suggests that the findings such as those in our PNAS paper are likely to be unacceptable regardless of what the data shows.
AnotherÂ misrepresentation of what I actually said, which was:
One way to see this is to note that when we meta-analytically compare pain with almost any other term in Neurosynth (see the figure above), there are typically a lot of brain regions (extending well outside of dACC and other putative pain regions) that show greater activation for pain than for the comparison condition, and very few brain regions that show the converse pattern. I don’t think it’s plausible to think that much of the brain really prizes pain representation above all else. A more sensible interpretation is that the Neurosynth posterior probability estimates for pain are inflated to some degree by the relative ease of inducing pain experimentally.
The context makes it abundantly clear that I was not making a general statement about the importance of pain in some grand evolutionary sense, but simply pointing out the implausibility of supposing that Neurosynth reverse inference maps provide unbiased windows into the neural substrates of cognition. In the case of pain, there’s tentative evidence to believe that effect sizes are overestimated.
In contrast, we can’t think of too many things that the brain would prize above pain (and distress) representations. People who don’t feel pain (i.e. congenital insensitivity to pain) invariably die an early death ““ it is literally a death sentence to not feel pain. What could be more important for survival? Blind and deaf people survive and thrive, but those without the ability to feel pain are pretty much doomed.
I’m not sure what this observation is supposed to tell us. One could make the same kind of argument about plenty of other functions. People who suffer from a variety of autonomic or motor problems are also likely to suffer horrible early deaths; it’s unclear to me how this would justify a claim like “the brain prizes little above autonomic control”, or what possibly implications such a claim would have for understanding dACC function.
Similar (but not identical) to TY’s conclusions that we opened this blog with, we think the following conclusions are supported by the Neurosynth evidence in our PNAS paper:
I’ll take these one at a time.
* There is more widespread and robust reverse inference evidence for the role of pain throughout the dACC than for executive, conflict, and salience-related processes.
I’m not sure what is meant here by “robust reverse inference evidence”. Neurosynth certainly provides essentially no basis for drawing reverse inferences about the presence of pain in individual studies. (Let me remind L&E once again: at best, the posterior probability for ‘pain’ in dACC is around 80%–but that’s given an assumed based rate of 50%, not the more realistic real-world rate of around 3%). If what they mean is something like “on average, taking the average of all voxels in dACC, there’s more evidence of a statistical association between pain and dACC than pain and conflict monitoring”, then I’m fine with that.
* There is little to no evidence from the Neurosynth database that executive, conflict, and salience-related processes are reasonable reverse inference targets for dACC activity.
Again, this depends on what L&E mean. If they mean that one shouldn’t, upon observing activation in dACC, proclaim that conflict must be present, then they’re absolutely right. But again, the same is true for pain. On the other hand, if they mean that there’s no evidence in Neurosynth for a reverse inference association between these terms and dACC activity, where the criterion is surviving FDR-correction, then that’s clearly not true: for example, the conflict map clearly includes voxels within the dACC. Alternatively, if L&E’s point is that the dACC/preSMA region centrally associated with conflict monitoring or executive control is more dorsal than many (though not all) people have assumed, then I agree with them without qualification.
* Pain processes, particularly the affective or distressing part of pain, are in the same family with other distress-related processes including terms like distress, fear, and negative affect.
I have absolutely no idea what evidence this conclusion is based on. Nothing I can see in Neurosynth seems to support this—let alone anything in the PNAS paper. As I’ve noted several times now, most distress-related terms do not seem to overlap meaningfully with pain-related activations in dACC. To the extent that one thinks spatial overlap is a good criterion for determining family membership (and for what it’s worth, I don’t think it is), the evidence does not seem particularly suggestive of any such relationship (and L&E don’t test it formally in any way).
Postscript. *L&E should have used reverse inference, not forward inference, when examining the anatomical boundaries of dACC.*
We saved this one for the postscript because this has little bearing on the major claims of our paper. In our paper, we observed that when one does a forward inference analysis of the term “˜dACC’ the strongest effect occurs outside the dACC in what is actually SMA. This suggested to us that people might be getting activations outside the dACC and calling them dACC (much as many activations clearly not in the amygdala have been called amygdala because it fits a particular narrative). TY admits having been guilty of this in TY’11 and points out that we made this mistake in our 2003 Science paper on social pain. A couple of thoughts on this.
a) In 2003, we did indeed call an activation outside of dACC (-6 8 45) by the term “dACC“. TY notes that if this is entered into a Neurosynth analysis the first anatomical term that appears is SMA. Fair enough. It was our first fMRI paper ever and we identified that activation incorrectly. What TY doesn’t mention is that there are two other activations from the same paper (-8 20 40; -6 21 41) where the top named anatomical term in Neurosynth is anterior cingulate. And if you read this in TY’s blog and thought “I guess social pain effects aren’t even in the dACC“, we would point you to the recent meta-analysis of social pain by Rotge et al. (2015) where they observed the strongest effect for social pain in the dACC (8 24 24; Z=22.2 PFDR<.001). So while we made a mistake, no real harm was done.
I mentioned the preSMA activation because it was the critical data point L&E leaned on to argue that the dACC was specifically associated with the affective component of pain. Here’s the relevant excerpt from the 2003 social pain paper:
As predicted, group analysis of the fMRI data indicated that dorsal ACC (Fig. 1A) (x ““ 8, y 20, z 40) was more active during ESE than during inclusion (t 3.36, r 0.71, P < 0.005) (23, 24). Self-reported distress was positively correlated with ACC activity in this contrast (Fig. 2A) (x ““ 6, y 8, z 45, r 0.88, P < 0.005; x ““ 4, y 31, z 41, r 0.75, P < 0.005), suggesting that dorsal ACC activation during ESE was associated with emotional distress paralleling previous studies of physical pain (7, 8). The anterior insula (x 42, y 16, z 1) was also active in this comparison (t 4.07, r 0.78, P < 0.005); however, it was not associated with self-reported distress.
Note that both the dACC and anterior insula were activated by the exclusion vs. inclusion contrast, but L&E concluded that it was specifically the dACC that supports the “neural alarm” system, by virtue of being correlated with participants’ subjective reports (whereas the insula was not). Setting aside the fact that these results were observed in a sample size of 13 using very liberal statistical thresholds (so that the estimates are highly variable, spatial error is going to be very high, there’s a high risk of false positives, and accepting the null in the insula because of the absence of a significant effect is probably a bad idea), in focusing on the the preSMA activation in my critique, I was only doing what L&E themselves did in their paper:
Dorsal ACC activation during ESE could reflect enhanced attentional processing, previously associated with ACC activity (4, 5), rather than an underlying distress due to exclusion. Two pieces of evidence make this possibility unlikely. First, ACC activity was strongly correlated with perceived distress after exclusion, indicating that the ACC activity was associated with changes in participants’ self-reported feeling states.
By L&E’s own admission, without the subjective correlation, there would have been little basis for concluding that the effect they observed was attributable to distress rather than other confounds (attentional increases, expectancy violation, etc.). That’s why I focused on the preSMA activation: because they did too.
That said, since L&E bring up the other two activations, let’s consider those too, since they also have their problems. While it’s true that both of them are in the anterior cingulate, according to Neurosynth, neither of them is a “pain” voxel. The top functional associates for both locations are ‘inteference’, ‘task’, ‘verbal’, ‘verbal fluency’, ‘word’, ‘demands’, ‘words’, ‘reading’ … you get the idea. Pain is not significantly associated with these points in Neurosynth. So while L&E might be technically right that these other activations were in the anterior cingulate, if we take Neurosynth to be as reliable a guide to reverse inference as they think, then L&E never had any basis for attributing the social exclusion effect to pain to begin with—because, according to Neurosynth, literally none of the medial frontal cortex activations reported in the 2003 paper are associated with pain. I’ll leave it to others to decide whether “no harm was done” by their claim that the dACC is involved in social pain.
In contrast, TY’11’s mistake is probably of greater significance. Many have taken Figure 3 of TY’11 as strong evidence that the dACC activity can’t be reliably associated with working memory, emotion, or pain. If TY had tested instead (2 8 40), a point directly below his that is actually in dACC (rather than 2 8 50 which TY now acknowledges is in SMA), he would have found that pain produces robust reverse inference effects, while neither working memory or emotion do. This would have led to a very different conclusion than the one most have taken from TY’11 about the dACC.
Nowhere in TY’11 is it claimed that dACC activity isn’t reliably associated with working memory, emotion or pain (and, as I already noted in my last post, I explicitly said that the posterior aspects of dACC are preferentially associated with pain). What I did say is that dACC activation may not be diagnostic of any of these processes. That’s entirely accurate. As I’ve explained at great length above, there is simply no basis for drawing any strong reverse inference on the basis of dACC activation.
That said, if it’s true that many people have misinterpreted what I said in my paper, that would indeed be potentially damaging to the field. I would appreciate feedback from other people on this issue, because if there’s a consensus that my paper has in fact led people to think that dACC plays no specific role in cognition, then I’m happy to submit an erratum to the journal. But absent such feedback, I’m not convinced that my paper has had nearly as much influence on people’s views as L&E seem to think.
b) TY suggested that we should have looked for “dACC“ in the reverse inference map rather than the forward inference map writing “All the forward inference map tells you is where studies that use the term “dACC“ tend to report activation most often“. Yet this is exactly what we were interested in. If someone is talking about dACC in their paper, is that the region most likely to appear in their tables? The answer appears to be no.
No, it isn’t what L&E are interested in. Let’s push this argument to its logical extreme to illustrate the problem: imagine that every single fMRI paper in the literature reported activation in preSMA (plus other varying activations)—perhaps because it became standard practice to do a “task-positive localizer” of some kind. This is far-fetched, but certainly conceptually possible. In such a case, searching for every single region by name (“amygdala”, “V1”, you name it) would identify preSMA as the peak voxel in the forward inference map. But what would this tell us, other than that preSMA is activated with alarming frequency? Nothing. What L&E want to know is what brain regions have the biggest impact on the likelihood that an author says “hey, that’s dACC!”. That’s a matter of reverse inference.
c) But again, this is not one of the central claims of the paper. We just thought it was noteworthy so we noted it. Nothing else in the paper depends on these results.
I agree with this. I guess it’s nice to end on a positive note.
20 thoughts on “Still not selective: comment on comment on comment on Lieberman & Eisenberger (2015)”
A great exposition on posterior probabilities, their dependence on prior probabilities, and problem of choosing the prior probabilities… in the service of reverse inference. But you seem to dismiss the value of the default 50/50 prior.
> Unfortunately, as you can see in the above figure, the variation in the posterior thatâ€™s attributable to the choice of prior will tend to swamp the variation thatâ€™s due to observed differences in brain activity.
But by using a fixed 50/50 value puts all the terms on a level playing field, no? And then makes for a useful comparison between terms, right?
Thanks for the comment, Tom! I definitely didn’t mean to give the impression that there’s no point in computing posterior probabilities under any prior (if I did, I wouldn’t put that data on the website). Let me know if you have suggestions for edits to make clear that this wasn’t my intent. I actually really like the 50/50 prior for the reason you mention. If the goal is to make simple qualitative comparisons between terms, that’s probably your best bet. So, for instance, I’m very comfortable with someone looks at a voxel in Neurosynth, observing that many of the highest probabilities seem to be language-related, and saying something like “it looks like this region is preferentially associated with language-related functions”. As a tool for drawing tentative conclusions about where different functions are represented in the brain, I think Neurosynth is great. But the translation from those kinds of observations to stronger selectivity claims, or meaningful application to individual studies, is very difficult. (Not difficult in the technical sense, just in the sense that I’ve seen very few cases where the separation between the posteriors of different groups of terms is sufficiently large that I’d feel comfortable arguing a region is ‘selective’ for anything.) Maybe we should chat a bit at some point about the possibility of building in sensible Bayesian tests for these kinds of comparisons…
Oh, and I forgot to add: if we ever want to apply the knowledge we gain from inspecting maps derived with a 50/50 prior to actual fMRI studies in the real world, we still have to believe in that prior to some reasonable degree… I.e., the idealization that all terms are equal is nice in theory, but if we ultimately believe that “pain” is actually a much rarer mental process or state than “language”, then we should presumably select very different priors for the two terms when doing our formal analysis. In which case we land right back in “the prior swamps the posterior” territory.
As I discussed in my modestly related blog post on this issue, L&E’s definition of selectivity certainly does not match that which is typically used in the neurophysiology literature. To claim that something is selective, you need to have an idea of the stimulus space to which the ROI is tuned. The sitmulus space can be derived by a transformation and/or be somewhat artificial – but you should at least have one. A set of arbitrary categories without any conceptual ordering is not sufficient to measure selectivity.
If a ROI responds only to apples but not to pears or oranges, it is responsive to applies. You might even claim it is somewhat selective for types of fruit (although you can’t really say just how selective). It definitely is not selective for apples.
Thanks Sam. While I agree with you that the neurophysiology usage is different, I think it’s okay from cognitive neuroscientists to deviate from that as long as the field has its own consistent standard. My concern is more that I don’t think L&E’s usage follows that standard.
I understand that although as far as I can tell the cognitive neuroscience field doesn’t have a consistent standard and therein lies the problem. I also don’t actually think the usage is (or should be) much different. In my view, it is important that we actually have terminology to disentangle stimulus preference from selectivity. They are different things that can actually have different consequences for processing and behaviour. L&E are talking about preference and as you have nicely argued even that claim is built on very muddy ground. The way I see it they can’t even begin to talk about selectivity.
Hey Tal, as well as Matthew and Naomi,
While probably generating some levels of discomfort not only for you guys but, in a vicarious sense also for some readers, I think it’s awesome (and for me awe inspiring) that you’re opening this discussion to the public. And I hope that, in the end, we can all move towards a better understanding of the possibilities and limitations of the tools we have in hand at this time–and maybe find ways to improve them.
Overall, my understanding of the statistical framework used strongly biases my judgment towards Tal’s argument; at the same time there remains a strong “intuition” that Neurosynth, as a tool, *ought* to be useful in situations where researchers would like to receive “guidance” as to which account involving a particular brain region seems, on the whole, most parsimonious (compared to other accounts that have been recently studied/sampled).
However, as far as I can tell, most of the discussion seems to hinge on two different “viewpoints” on this framework:
From a purely “inferential logic” point of view, I regard it as almost trivially obvious that Neurosynth wouldn’t allow certain conclusions to be drawn (with reasonable certainty). And my understanding is that you, Tal, would recommend users of Neurosynth to follow this logic through, and not take any “heuristic shortcuts” (for which human cognition, thinking fast instead of slow, is a bit infamous).
On the other hand, I fully appreciate that with the observed data, one strongly feels inclined to (possibly inappropriately so) use it as evidence for much stronger claims.
My own training in statistical theory (both frequentist as well as Bayesian) is most likely inadequate for me to come up with reasonable parametric statistics that would allow me to make any inferential statements, which is why I, in the end, would always try to manually construct a Null distribution–which would, instead of the classical approach of scrambling coordinate locations, scramble “term labels” across studies/peaks to find out what, given the evidence for a set of terms, the distribution of observed frequencies for a certain region mentioned under a specific term are and compare that to the point estimate.
If this approach sounds at least half-way plausible, maybe I can make a suggestion for Neurosynth? Would it be possible for researchers to enter a (small) set of terms (maybe up to 20) and then the website generates a “sliced-out table” with only the studies that contain at least one of the terms.
Next, and this is important, the researcher would be presented with ALL abstracts of the studies together with a grid of checkboxes to actually confirm the term is a POSITIVE predictor (i.e. manipulating the psychological/cognitive process of term X in the “up” direction leads to “additional” activity in region Y). Additionally, the grid could allow the researcher to make the opposite (which the current algorithmic scan of abstracts would not be able to resolve, I presume), as well as abstracts that make it clear the implied inference is actually reverse (i.e. abstracts mentioning a region Y that, according to the authors implies X).
Finally, the result would be downloadable (and preserved in the database for future reference and people who would like to confirm any claims by the researchers with a unique “search-id”), and some additional Python code could be written that would then allow the researchers to try to answer questions of the nature of does activity in region Y predict the presence of term (process) X against the other terms (processes) with reasonable evidential strength?
Tal, maybe you can also comment on this thought: in the absence of very strong *separate* evidence (from outside of fMRI studies), it would almost never be “permissible” (using the inferential framework) to conclude that “activity” in region Y predicts process X to be involved in the psychological state? To make my point, imagine that in the medical literature we would still be debating the evidence for different diseases to be “located” in different organs. And by now collected evidence for “terms” (diseases) such as “yellow fever” and “diarrhea” (and postmortems of people who presumably died from them) has revealed a “preferential association” with certain organs (regions).
At some point, the evidence would certainly be enough to form a THEORY that different processes are involved in those two “outcomes”, and that they are spatially localized. But without more substantial work into the mechanisms, one would probably (not even after hundreds of cases) *CONCLUDE* that those are necessarily different (as there would still be, for instance, the case of context: maybe people with different genetic vulnerability react to the same pathogen with different organ failures?)
That is to say, in the absence of, say, lesion studies, etc., neuroimaging data can, in the aggregate, be used to form theories (and possibly design tasks/experiments to TEST those), but the data cannot, in itself, be used to CONCLUDE anything, is that roughly what you’re saying?
In that case, maybe it would be good if you (as the author and maintainer of Neurosynth) could provide a guide as to what applications of Neurosynth are “supported” (i.e. you will, if applied this way, likely approve), and what applications are, from an inferential, not technical point of view, prohibited (i.e. you will, if applied this way, disapprove)?
Thanks for the thoughtful comments, Jochen! Thoughts below:
If this approach sounds at least half-way plausible, maybe I can make a suggestion for Neurosynth? Would it be possible for researchers to enter a (small) set of terms (maybe up to 20) and then the website generates a â€œsliced-out tableâ€ with only the studies that contain at least one of the terms.
This can actually be done in a single line of code with the Python tools (basically, something like study_ids = dataset.get_studies(expression=”pain|fear|emot*|reward*”)). There’s also some rudimentary capacity to do this on the website, under “custom analyses”, but I’ll warn you that it’s pretty buggy right now and not very well-documented. But this is definitely something I’d like to get working properly soon.
Next, and this is important, the researcher would be presented with ALL abstracts of the studies together with a grid of checkboxes to actually confirm the term is a POSITIVE predictor (i.e. manipulating the psychological/cognitive process of term X in the â€œupâ€ direction leads to â€œadditionalâ€ activity in region Y). Additionally, the grid could allow the researcher to make the opposite (which the current algorithmic scan of abstracts would not be able to resolve, I presume), as well as abstracts that make it clear the implied inference is actually reverse (i.e. abstracts mentioning a region Y that, according to the authors implies X).
Also a good idea! I don’t think the bandwidth exists to add this to the website in the near term (given other priorities, e.g., the one above), but implementing this in the Python code would be pretty straightforward (in essence, you already get back a list of PMIDs from the filtering query, so it would just need a small script to loop over IDs, show abstracts, and get user input). I like it! I’ve added an issue on GitHub for this (no promises on timeline though).
Finally, the result would be downloadable (and preserved in the database for future reference and people who would like to confirm any claims by the researchers with a unique â€œsearch-idâ€), and some additional Python code could be written that would then allow the researchers to try to answer questions of the nature of does activity in region Y predict the presence of term (process) X against the other terms (processes) with reasonable evidential strength?
The “preserved in the database” part has been on the roadmap for a long time, but is hard to get right, because it requires a fairly serious version control and crowdsourcing platform. Again, bandwidth is the issue. But I’d love to do it! But setting that aside, the comparison part can already be done with the Python tools (though I should probably implement a Bayesian test in addition to what right now just amounts to a standard t-test).
That is to say, in the absence of, say, lesion studies, etc., neuroimaging data can, in the aggregate, be used to form theories (and possibly design tasks/experiments to TEST those), but the data cannot, in itself, be used to CONCLUDE anything, is that roughly what youâ€™re saying?
I don’t think I’m saying anything that strong. To be clear, I don’t have a principled aversion to the idea that one might be able to draw strong reverse inferences about mental function from brain activity (as long as it’s done within an appropriate Bayesian framework—you can’t do it with p-values). What I’m saying is that, given the nature and quality of the data in Neurosynth, I don’t think you can do it in practice, because the posteriors just won’t support it (unless you assume unreasonably strong priors). But I would be absolutely delighted if at some point the data in Neurosynth improve to a point where we start to see some really clean separation between groups of terms. So I’m making a claim about Neurosynth as it currently is, not as it could be given the same framework but radically different source of input data.
In that case, maybe it would be good if you (as the author and maintainer of Neurosynth) could provide a guide as to what applications of Neurosynth are â€œsupportedâ€ (i.e. you will, if applied this way, likely approve), and what applications are, from an inferential, not technical point of view, prohibited (i.e. you will, if applied this way, disapprove)?
I don’t want to give the impression that there’s some set of officially sanctioned or prohibited uses of Neurosynth. People have used Neurosynth in lots of different ways in hundreds of projects now, and while I don’t necessarily agree with all them, this is the first instance that’s prompted me to write a response. I think the strong response to the paper’s initial publication on social media is pretty good evidence that most people who regularly use Neurosynth had an immediate sense that L&E’s conclusion went well beyond what the data support. So I don’t think people should feel the need to tread delicately here. I’ll also reiterate that there’s a mailing list people are welcome to use, where I (and others) are happy to answer questions about both methodology and interpretation.
That said, I definitely agree that I need to write much better documentation for Neurosynth; this has been a major weakness for a long time now. As with everything else, it’s just a matter of finding time. But as other people have suggested, I could probably start by repurposing much of what I’ve written in these blog posts. Also, if anyone is interested in helping out, please do contact me!
About the following: “Thatâ€™s because the z-score is a frequentist measure of statistical association between term occurrence and voxel activation. All it tells us is that, given all the data we have, itâ€™s very unlikely that thereâ€™s exactly zero association between a term and a region.” This seems backwards. “Given the zero association, it’s very unlikely that we’d get data as extreme as more extreme as we have”, right? Otherwise the authors will have more reason to claim that you agree with them! 😉
Woops, right you are; thanks for the catch! I’ve updated the wording. Reinforces my point that it’s very hard to avoid the common pitfalls when talking about these issues. 🙂
Thanks for a wonderful exposition of claims made in the PNAS paper. In fact, the life sciences are littered with simplistic models. I wish more science would be practiced like this. I would like to digress a bit from the posts to look at some more theoretical issues that go beyond statistical interpretations (which I am not at all qualified to comment on, but find fascinating to follow) and imaging studies to a more synthetic understanding, which is generally far less pervasive in the literature and which, thankfully you introduce in the blogs. I also touch on a philosophical perspective that may help understand underlying neural correlates of affective components to pain.
Accounting for discordant feelings towards the dorsal anterior cingulate cortex (dACC):
The current debate on the role of the dACC in pain, its proposed specificity and counter claim of a lack thereof is endemic concern in the fMRI literature. Step back a minute to really appreciate the significance of this debate. On the one hand Lieberman & Eisenberger find a quite restricted role in pain (processing?, experience?) and on the other hand, yourself, Tal Yarkoni suggests a non-specificity associated with practically every cognitive function ever described. For such a sophisticated debate with anointed expert(s) in the field, how may we explain this chasm in perspective and what the real role of dACC is.
Lieberman & Eisenberger using the visual cortex as support for well defined function in dACC, but this comparison is invalid because it is a unimodal sensory region and its function was established by neurophysiological and anatomical studies prior to fMRI. Conversely, the fact you can elicit pain so readily probably does suggest that the â€˜brain really prizes pain representationâ€™ and correcting for it would be an artificial constraint. This doesnâ€™t exclude that under appropriate circumstances emotional triggers canâ€™t generate similar aversive responses and pain associated with self mutilation (thought experiment) as a means to release cognitive or emotional distress might predict a correlation with an alternative distribution of activation representing an approach behaviour.
It is hard to escape the impression of arbitrary voxel-correlated boundaries generating phrenological interpretations of quite complex functions of the dACC that obviously involve widespread networks. As the authors admit there is a desire for simple rendering of function. There remains a modern bottom up empirically driven penchant in cognitive neuroscience for simplistic correlational evidence as a substitute for good theoretical discovery. Although, I hasten to add that a â€˜connectomeâ€™ wonâ€™t resolve some of these issues either.
That Neurosynth mapping of nociceptive terms results in inclusion of both dACC and preSMA areas might signify a functional connection. Plausibly, the highest z-score in preSMA associated with pain (if I understand you correctly) suggests some functional connection with dACC. The point that also comes across is that understanding the anatomy is critical to interpreting the images and fMRI etc are modest tools for understanding specific role for dACC or any other structure for that matter.
If we consider here a fine-grained analysis of dACC connections, then a common binding role is in sensorimotor transformations that accommodates salience, executive function, conflict or pain resolution where preparedness for output realized or not involves activation of premotor areas. This includes verbalizations, orientation and specific movements contingent on context. Even frontoparietal networks associated with focussing attention are probably enhanced by the output of the ACC and even extending back to the posterior cingulate. To be fair you address this possibility when referring to Vogt & Sikes (2009) paper and response selection has been proposed as a role for ACC for some time. DACC then, mediates selection by subcortical motivational centres in the hypothalamus, consistent with known pathways to dACC via the anterior thalamus (Vakalopoulos 2013). Augmented activation in pain could be related to level of neuromodulator release of dopamine and/or acetylcholine. This demonstrates a potential if not likely interaction of multiple neural elements manifest in fMRI findings. So there might be specificity in terms of a higher level procedural function as you allude to, that includes intentional or motivational outcomes to diverse cognitive appraisals of information in context.
Part of the essential problem is a scientific model of pain, but if we assume an operational definition like attempts at avoidance as part of this definition then the function of the dACC as postulated above is part of a theoretically defined neural correlate in addition to the obvious sensory areas described like the insula, (contrast approach and avoidance behaviours to pain discussed above), but enough of this philosophical perspective, this is about the data wars and great to see blogs like yours take the debate up to traditionally restrictive publication practices.
Vakalopoulos (2013). The Developmental Basis of Visuomotor Capabilities
and the Causal Nature of Motor Clumsiness to Cognitive and Empathic Dysfunction. Cerebellum. 2013 Apr;12(2):212-23. doi: 10.1007/s12311-012-0416-0.
I am only generally familiar with the fMRI/cognitive neuroscience literature, though I think I do understand conditional probabilities, posterior probabilities, p-values etc. Also, I have not read the L&E paper and have only guessed what Neurosynth does based on reading your initial blog post, the L&E response and this one. So apologies if I have missed something basic, and perhaps you can quickly guide me in the right direction. While I generally agree with the points you make, I am curious about the part where you seem to agree with L&E: a) â€œThe conclusion from the Neurosynth reverse inference maps is unequivocal: The dACC is involved in pain processing. When only forward inference data were available, it was reasonable to make the claim that perhaps dACC was not involved in pain per se, but that pain processing could be reduced to the dACCâ€™s â€œrealâ€ function, such as executive processes, conflict detection, or salience responses to painful stimuli. The reverse inference maps do not support any of these accounts that attempt to reduce pain to more generic cognitive processes.â€ and b) “To be charitable to L&E, I do want to acknowledge one valuable point that they contribute here, which is that itâ€™s clear that dACC function cannot be comprehensively explained by, say, a salience account or a conflict monitoring account.Â “.
Given that you used uniform priors, I am still trying to get how the reverse inference maps allow one to say that the effects of pain are not being mediated via salience/conflict/executive processes, in a way that the forward inference maps did not. Assuming that your agreement with the authors’ claim on this issue is based on the posterior-probability maps (and not the z-scores), isn’t this simply because the strength of activation (or strength of association) of dACC voxels by/with these other processes is not as strong as the one with pain ? Which would then mean, that the L&E claim is simply that these other processes are not activating the dACC but other nearby regions ? My second concern is related to one you also raised: perhaps studies manipulating conflict/salience do not manipulate them to the same extent as studies manipulating pain do, and therefore it is still entirely possible that the effects of pain are being mediated by these other processes ? Normally, to address this experimentally, I would imagine that you would try to carefully set up a situation where pain was being manipulated independent of these other factors and then compare the effects within the same study. Instead, here, across-study comparisons are being used to draw a conclusion about mediating factors: does this really work well ?
Thanks again for this discussion !!
UCLA’s Dr. Matthew Lieberman and Dr. Naomi Eisenberger have been running unethical Social Cognitive human research experiments on un-consenting human subjects to obtain scientific data they were able to manipulate and then use to jump ahead of their fields competition and fraudulently boost their careers and wrongfully obtain their government grants and awards..
UCLA’s Dr. Matthew Lieberman and Dr. Naomi Eisenberger’s research practices need to be thoroughly investigated and stopped. A Thorough investigation into their research practices will uncover that UCLA’S Dr. Matthew Lieberman and Dr. Naomi Eisenberger advanced their careers by obtained some of their scientific data by performing extremely unethical ,cruel ,painful and permanently damaging Social Cognitive Neuroscience Human Experiments on unwilling human subjects.