Commentary on a comment

If you want to see a masterclass in dissecting a paper, go read Tal Yarkoni’s discussion of “The dACC is selective for pain“:

That conclusion rests almost entirely on inspection of meta-analytic results produced by Neurosynth, an automated framework for large-scale synthesis of results from thousands of published fMRI studies. And while I’ll be the first to admit that I know very little about the anterior cingulate cortex, I am probably the world’s foremost expert on Neurosynth*—because I created it.

…The basic argument L&E make is simple, and largely hangs on the following observation about Neurosynth data: when we look for activation in the dorsal ACC (dACC) in various “reverse inference” brain maps on Neurosynth, the dominant associate is the term “pain”…The blue outline in panel A is the anatomical boundary of dACC; the colorful stuff in B is the Neurosynth map for ‘dACC’…As you can see, the two don’t converge all that closely. Much of the Neurosynth map sits squarely inside preSMA territory rather than in dACC proper…That said, L&E should also have known better, because they were among the first authors to ascribe a strong functional role to a region of dorsal ACC that wasn’t really dACC at all… Much of the ongoing debate over what the putative role of dACC is traces back directly to this paper.

…Localization issues aside, L&E clearly do have a point when they note that there appears to be a relatively strong association between the posterior dACC and pain. Of course, it’s not a novel point…Of course, L&E go beyond the claims made in Yarkoni et al (2011)—and what the Neurosynth page for pain reveals—in that they claim not only that pain is preferentially associated with dACC, but that “the clearest account of dACC function is that it is selectively involved in pain-related processes.”…Perhaps the most obvious problem with the claim is that it’s largely based on comparison of pain with just three other groups of terms, reflecting executive function, cognitive conflict, and salience**. This is, on its face, puzzling evidence for the claim that the dACC is pain-selective.

etc. etc. Traditionally, this type of critique would slowly be drafted as a short rebuttal to PNAS. But isn’t this better? Look how deep the critique is, look how well everything is defined and explained. And what is stopping the authors from directly interacting with the author of the critique to really get at the problem? The only thing left is some way for pubmed or Google Scholar to link these directly to the paper.

Go read the whole thing and be learned.

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Sticking electrodes in humans: the need to proceed

“But, right now, do you think I made you stronger or weaker?”

“I felt the flash and as we talked through it… it made me stronger.”

I can trace my interest in neuroscience quite directly to when I was nine and my teacher showed us a video of the famous Penfield Experiments (see above, I’m pretty sure this is exactly the clip that I saw.) By directly stimulating the brain and asking patients what they saw or felt, Penfield was able to learn about the brain in ways that studying animals could not. And much more charismatically, too.

Although it can be tough to convince an ethics panel that you should be allowed to open up someone’s brain to prod it with an electrode, people with epilepsy often have electrodes implanted as a way to localize the source of the seizures. And while the surgeons are in there implanting the electrode, why not spend a little time zapping things?

When such a procedure was used to stimulate a region in the anterior cingulate cortex – roughly, an area just behind the front and center portion of your brain – they found an area that they describe as inducing the ‘will to perservere’. I can’t embed it in this page, but go and watch the video of the patient describing what it feels like.

“Can you give us some examples of how this could happen in your daily life? Let’s say you are driving …”

(Laughs) “I don’t get to drive.”

“I know, but let’s say you were driving when you were 30 … what should happen on the road that would give you this feeling?”

“You mean what would happen when I would start to feel like that before? Something like that would only be triggered by a major accident, you know, cause anything small in life you have to be able to handle. ‘Cause there are so many millions of small things that happen to you daily that, you have to handle them, you have to deal with them. It’s the major things that if you give up on, you’re in trouble. You can’t give up.”

So cool.

Reference
Parvizi J, Rangarajan V, Shirer W, Desai N, & Greicius MD (2013). The Will to Persevere Induced by Electrical Stimulation of the Human Cingulate Gyrus Neuron DOI: 10.1016/j.neuron.2013.10.057

Deciding about deciding

In the field of decision-making, a typical laboratory experiment goes something like this: give a subject an option between two choices, let them decide, force them to do it again.  Put a novel variation on the way the decision is made and BAM, you’ve got yourself a little paper!  Mostly the decisions are something akin to choosing between a picture of a cake and a picture of a moldy cheese.  But a more realistic decision process might involve choosing whether the cake and moldy cheese are the best one can get; maybe you should look for something better!  One (might) call this a foraging decision, something that has been studied extensively in other contexts.  Let’s look at how the brain represents this decision.

The setup of the first experiment is a bit tricky.  Subjects were shown a choice of two rewards that they could choose between, or a set of other rewards that could be selected from randomly.  In the initial ‘foraging’ round, they got to decide whether to keep the two rewards, or get two new (random) ones for a small price.  This was repeated until they were satisfied with the two options, at which point they moved to a ‘decision’ round where they chose between the two rewards.  It is a bit unsurprising that subjects required a higher expected value from the ‘foraging’ option in order to choose it.  The authors call this their ‘foraging readiness’ but it would be more accurate to call it their level of risk-aversion.  It has been known for a long time that people prefer more sure options than more risky options, even if the economically rational man would have no preference.  I guess that’s a less sexy phrase, though.

The authors zeroed in on the anterior cingulate cortex (ACC).  Like pretty much everything that comes out of fMRI and cognitive studies, there is a lot of controversy about what exactly the ACC is doing (this isn’t a ding on fMRI or cognitive studies, it’s just really hard).  Here, researchers find that activity in ACC was positively correlated with the expected value of the foraging and negatively correlated with the expected value of the binary decision.  The BOLD signal in ACC was able to predict the number of times a subject would repeatedly search, as well as how the subject weighted the expected value of the foraging option.  And that last point is important!  Even though the researchers knew that the two new options would be chosen with equal probability, the subjects did not know that.  Or, they at least did not know that they could trust that information from the researchers who are notoriously unreliable in what they tell their subjects.   So the signal probably represents some measure of what their posterior probability distribution was, as well as how much they valued risky gains and losses, all convolved with the expected reward of each option.

Another recent paper looked at a visual task in monkeys and skipped the whole fMRI step, just putting electrodes directly into the dorsal region of ACC (dACC).  Monkeys were allowed to saccade between patches that would give a continual reward that decreased with time.  They then faced a real foraging decision: when do you leave a depleted patch to find a new source of reward?  Neurons in dACC seemed to increase their firing rate when the monkeys were making this decision.  The speed with which the firing rate increased was related to the travel time to a new patch (the cost of going to that patch of reward).  This increase continued until it reached a threshold related to the relative value of leaving the patch.

The authors are clear that the dACC signal itself is not sufficient for a leaving-decision; an observer would have to get information from other regions to determine what the threshold for leaving is.  But the data strongly suggests that dACC is coding the value of relative value of leaving a patch.

So what do the two studies together tell us about how ACC helps us make a decision?  The first paper tells us that ACC is representing the predicted cost of finding new options.  This calculation probably includes the predicted probability distribution of all available options, and will also include how many times (how long) someone is willing to go searching for a better option.  The second paper is in broad agreement, and claims that dACC represents the relative expected value but is an insufficient signal to tell the brain when to make that decision; it just encodes that signal.  It does however represent the maximum cost the brain is currently willing to bear to find a new option, just like the fMRI study shows.

These two papers are great together as they really show how (1) fMRI can be useful and (2) the differences in how the same question is framed in different subfields of neuroscience.

References

Neural mechanisms of foraging.  Kolling, Behrens, Mars, Rushworth.  Science (2012).  DOI: 10.1126/science.1216930

Neuronal basis of sequential foraging decisions in a patchy environment.  Hayden, Pearson, Platt.  Nature Neuroscience (2011).  DOI: 10.1038/nn.2856

Picture from