The limitations of fMRI are not related to physics or poor engineering, and are unlikely to be resolved by increasing the sophistication and power of the scanners; they are instead due to the circuitry and functional organization of the brain, as well as to inappropriate experimental protocols that ignore this organization.

That’s not to say that all is lost, of course. On the whole, Logothetis is pretty optimistic about the value of fMRI, even going so far as to suggest that “MRI is currently the best tool we have for gaining insights into brain function and formulating interesting and eventually testable hypotheses”; it’s just that it’s not perfect by a long shot.  But anyway, there’s much more to the review than I can convey coherently in my current sleepy state, so if you have access to Nature, it’s definitely worth reading.

The Science piece (“Growing Pains for fMRI”) is a much lighter news article by Greg Miller, and it focuses mostly on a controversy that played out in the pages of the New York Times last year. The thumbnail sketch is  that one group of fMRI researchers did some very shoddy “research” on the way people view the different election candidates, and another (larger) group of researchers called them on it.  The exchange then led to a period of widespread soul-searching amongst cognitive neuroscientists, until ultimately, in March 2008, the Cognitive Neuroscience Society imposed a moratorium on publication of all fMRI data until a common set of guidelines for rigorous and ethical research conduct was agreed upon.  Ok, that last part is completely made up. But the point is that the article is a good read, and you should check it out if you can.  It’s not often you hear one scientist say that another scientist’s study was “really closer to astrology than it was to real science” (for the record, I agree with that assessment in this case).

That’s the number of citations in PubMed containing the terms ‘fMRI’ and ‘language’ in the abstract or title, plotted by year of publication. Figures like this purport to show that interest in a topic is increasing dramatically. Just look at that increase! In 1996, there were only 13 hits; by 2005, there were 99! It’s as clear as daylight that interest in the neural bases of language is increasing!

Of course, the poorly-kept secret is that fMRI didn’t exist twenty years ago, and wasn’t really widely adopted until the last few years. So it’s natural to see an increase in publications that study language using neuroimaging methods. You’d expect a similar increase for almost every other area of research. The more pertinent question is whether interest in a particular topic has increased disproportionately relative to the general increase in the use of fMRI over the last few years. Instead of plotting absolute numbers, what we want is something like this:

In the above figure, the pink line represents the number of papers with the terms ‘fMRI’ and ‘language’ in the title (the blue line in the first figure has now turned pink–sorry about the color confusion!). But now the additional (blue) line shows the number of papers that have just the term ‘fMRI’ in the abstract. The increase in language papers starts to look suspect, since it’s clear the increase in fMRI papers on language is essentially paralleled by the increase in fMRI papers in general. Here’s an even better representation:

That’s the proportion of PubMed studies with the terms ‘fMRI’ and ‘language’ in the title or abstract over the last few years relative to the total number of studies with just the term “fMRI”. As you can see, it’s a very different picture. It’s a small sample size, but there’s not much reason to think people are any more interested in studying language in 2006 than in 1998—at least, relative to interest in other topics that can be studied with fMRI.

So what to make of claims that research interest is increasing in topics X, Y, and Z? Well, in a sense those claims are true, since the total number of neuroimaging publications continues to rise fairly dramatically. But in the sense that researchers probably care about more—namely, the “if I have a magnet and I want to do a study, what’s a hot topic right now?” sense—most research topics can’t be on the rise, by definition (just like most people can’t be of above average intelligence). Moreover, the number of academic publications in general has increased pretty dramatically over the last few years, so it’s not even clear from the above just how much of the increase in the number of fMRI papers on language is due to greater adoption of fMRI as opposed to a more global increase in scientific research output.

Now, the point of this post isn’t just to malign a ubiquitous research tactic. One can’t really fault people for wanting to think their own research is more interesting than other people’s. I’ll be the first to confess I’ve inserted some rather disingenuous comments about how oh-so-fascinating my results are and how much they (should) mean to other researchers in my papers. It’s hard to motivate a paper without doing that to some degree, or even to get motivated to do the research in the first place. What the second graph above does point up though, is that the question as to what topics are ‘hot’ is an empirical one—and fortunately, one that can be relatively easily (though imprecisely) tested.

To generate the above graphs, I used data from PubMed. One of the many nice things about PubMed is that it has an API that allows you to access the database programmatically (in contrast to Google Scholar, which is inaccessible via API due to agreements between Google and the major publishers to keep it that way). So, in the interest of doing some trendspotting, I wrote a small Visual Basic program to quantify the emergence (or lack thereof) of real ‘trends’ in research. I used the search string “fMRI [tiab]” as the control—i.e., all articles containing the string “fMRI” in the title or abstract. This is a conservative approach since the standard PubMed search also searches article contents, resulting in a difference of an order of magnitude in hits (7000 vs. 160000). But the more conservative approach is likely more accurate, since any study that includes the term in its title or abstract is much more likely to report original fMRI data than studies that just mention the terms in passing.

This reference number (broken down by year) was then compared with the results of a series of more specific searches. Basically, for a variety of topics, I added a single search term like “language” or “emotion” to the basic search. Again, the stipulation was that only titles and abstracts be searched. The ratio between the specific and the general term was then plotted for each year in order to highlight potential trends.

What do the results look like? Here are the ‘trends’ in neuroimaging for four major areas of research, broken down for the years 1996-2006:

What can we infer from the above figure? Well, just by eyeballing it, it looks like there’s a general trend toward relative increases in the number of papers on emotion, working memory, and attention, and no change for language. Statistical tests reveal that the three positive trends are significant (p < .05 for all three). So there’s at least some evidence that there are in fact trends in neuroimaging research (assuming there isn’t some alternative explanation, e.g., abstracts just getting longer and consequently mentioning more terms). The key point is that this kind of information can’t be gleaned just by looking at the first figure presented in this post. Absolute increases in publication count aren’t particularly informative. In contrast, when you use a control condition—though in this case, an admittedly crude one—you can feel a little more confident about the conclusions you’re able to draw. Naturally, this is a small sample size, and as I mentioned, the search is highly conservative (obviously, more than 46 fMRI articles on emotion were published in 2006!). But it’s likely that the results are a good representation of what’s out there, and that we can safely generalize to the many papers that use fMRI to study these topics but didn’t use the exact term in the abstract.

What about other ways of carving up the literature? Here’s the breakdown by sensory modality:

Doesn’t look like much is going on, and indeed none of the regression slopes are statistically significant. But at least this analysis is somewhat reassuring given the increases seen above for working memory, attention, and emotion: it’s clearly not as though all search terms are being mentioned more frequently in more recent fMRI abstracts.

Here’s one last figure (this could obviously go on for a very long time) plotting the trajectory of publication count in a few less-studied domains:

The trends for ‘social’, ‘reward’, and ‘decision making’ are significant here, but the trendline for pain isn’t. Social neuroscience research in particular appears to be emerging as a prominent domain of fMRI research, more than doubling its relative share of the literature between 2005 and 2006, though it’s still a relatively small field.

In evaluating the figures above, there are several caveats to keep in mind. One major limitation of this trendspotting approach is that it’s not well-suited to quantifying trends in more fine-grained areas of research, because there may only be a handful of studies per year, resulting in a pretty unreliable measure. Then again, claims that one small niche of research within the broader field of cognitive neuroscience is on the rise probably aren’t that interesting to begin with. If a particular topic was studied by 2 people in 2000 and 6 in 2005 (instead of a projection of, say, 4), you might want to wait a while before hopping on the bandwagon.

Another obvious limitation is that the procedure I used to generate these graphs was extremely simplistic. One can easily imagine more sophisticated approaches that control much more tightly for potential confounds (e.g., tier of journal, mean abstract length, etc.) and use better quantitative measures than the simple ratio I used above. That’s ok though; the point I want to make isn’t that this particular set of graphs provides a particularly accurate insight into the state of the field of neuromaging. Rather, the point is that scientific trends can be studied empirically just like anything else, and there’s a massive amount of data freely available for mining. Entire journals are devoted to tracking and discussing current research fads (see the ‘Trends in…’ series), but it’s unclear whether the editors at such outlets make their decisions on the basis of quantitative information. Conversely, from an author’s perspective, knowing what’s hot isn’t just a matter of curiosity—careful attention to trends could conceivably increase the rate of acceptance of one’s publications.

As a side note, if anyone wants to suggest possible searches for trends they’d like to see quantified, feel free to leave a comment below or to email me. I may release the VB program at some point, but it’s in no shape to see the light of day at the moment. Of course, you can always head over to PubMed and enter search terms manually.

In brief, Papassotiropoulos and colleagues conducted a whole-genome scan on 500,000 distinct single nucleotide polymorphism (SNPs) in a large Swiss sample, and identified a correlation with episodic memory performance in two genes. They subsequently replicated the association between one of the genes (Kibra) and memory performance in two independent samples. What’s striking isn’t just the presence of two successful replications (almost unheard of in a paper that’s first to identify a gene-behavior relationship—many effects of this sort fail to replicate at all in subsequent studies), but also the size of the effect. In the initial sample, T allele non-carriers (i.e., subjects who had 2 C alleles of the Kibra SNP) performed 24% better on a free recall task after a 5 minute delay. You often hear people write off the molecular genetic approach to studying cognitive differences on the grounds that individual genes account for only a fraction of the variance and it’d take dozens or hundreds of genes to form a meaningful account. What the Kibra effect and other similar studies suggest is that, at least for some traits, a handful of genes may actually account for a considerable portion of the variance.

While the discovery and replication of the Kibra-episodic memory association alone would be a high-impact finding, Papassotiropoulos et al. didn’t stop there. They then went on to conduct brain imaging analyses in both humans and mice, demonstrating that a truncated version of Kibra is densely expressed in the medial temporal lobe (a region heavily implicated in episodic memory formation), but not in other areas such as the frontal lobes. They suggest that Kibra may exert its effect on memory via modulation of hippocampal function, though the precise locus and mechanism of effect is currently unknown.

But wait! There’s more! The authors then went on to conduct an fMRI study, in which they imaged 15 T allele carriers and 15 non-carriers during performance of an encoding task (a face-profession association task). They observed selective increases in the medial temporal lobes in non-carriers (the group with poorer performance in the genetic samples) relative to carriers, and no regions showing the converse effect. Because the two groups were matched for delayed memory performance, the relative increase in the group with poorer performance likely reflects less efficient processing, requiring greater activation to achieve the same level of memory performance.

As if all this wasn’t enough, Papassotiropolous et al. also conducted structural imaging analyses using both automated whole-brain and manual tracing approaches. These analyses didn’t turn up any significant findings, but given the amount of effort and breadth of expertise required for all of these analyses, one can only applaud them for trying.

On the whole, I can’t say enough good things about this paper. Regardless of the implications of the substantive finding itself (which, if replicated by other groups, should have important implications both theoretically and practically), it’s remarkable to see such a diversity of approaches and sources of expertise brought to bear on a single problem. Lots of people pay lip service to the notion that inter-disciplinary science is a good thing, but to date there are relatively few demonstrations of the idea on a large scale. The combination of molecular genetics and cognitive neuroimaging seems like a particularly profitable approach, yet few people have applied it thus far (with several notable exceptions, e.g., centers at the NIH and Pittsburgh). If this is the shape of things to come, it’ll be fun to watch over the next few years as this sort of research takes off…

On the other side of the ball, I think there are also a number of reasonable criticisms of fMRI that haven’t been highlighted yet. Some of these highlight important problems with current practices (e.g., limitations on power, overly liberal statistical analyses, or rampant overinterpretation) or interpretative (drawing rather speculative conclusions from thin results). These are the types of criticism non-cognitive neuroscientists tend to focus on, and they’re certainly worth paying attention to. Still, I think the largest and most important class of criticisms of fMRI are essentially constructive ones that take the form of “well, why not do it this way?” There’s just so much you can do with fMRI that relatively few people are doing because of a lack of awareness or an absence of consensus procedures and corresponding tools.

The more quantitatively sophisticated groups (e.g., the folks at UCL, who’ve pioneered most of the statistical approaches currently in use) regularly publish all sorts of papers detailing clever approaches to data that everyone concludes sounds really neat in principle but that most people (myself included) don’t really understand. Collectively, neuroimaging researchers are only just starting to talk about things like functional connectivity and parametric analyses; there are all sorts of places people want to go, but the methods haven’t caught up to the ideas yet.

Part of the problem is that functional neuroimaging datasets are much more flexible than behavioral datasets. A single subject’s brain might contain 100,000 voxels (each an interesting measurement in its own right) per image, with several hundred images per scan. And these observations aren’t independent of one another; what one part of your brain is doing at a given point in time depends not just on the experimental manipulation, but on interactions with other regions (David Dobbs provided an excellent overview of he problem in his Scientific American article). Given that level of complexity, it’s not surprising that people have generally stuck with familiar methods imported from other areas of behavioral research. T-tests on subtractive contrasts aren’t necessarily the most natural way to explore brain activity, but they’re what’s closest to standard analyses of experimental treatments in other fields. As time goes on, we’ll probably see a shift toward more sophisticated multivariate modeling. Hopefully, such studies will be less susceptible to many of the common criticisms.

V1 is the primary visual cortex–essentially, the first cortical stop for incoming visual information. The traditional view used to be that, because of their position at the bottom of the information-processing hierarchy, neurons in V1 respond only to very simple configurations of light patterns (e.g., specific spatial orientations or frequencies). That view is increasingly being challenged by anatomical data indicating that V1 receives projections from a variety of other cortical areas and functional data showing that V1 neurons respond to a variety of seemingly high-level properties .

For example, Shuler & Bear recently demonstrated that neurons in rat visual cortex anticipate the onset of reward, showing a ‘learning’ profile much like that of dopamine neurons in much more anterior regions (i.e., closer to the front of the brain). In humans, neuroimaging studies reliably observe a top-down effect of spatial attention on visual cortex activation: when you expect a stimulus to appear in a particular location, activation in much of visual cortex (including V1) increases. What’s striking about this effect is that it’s endogenous (i.e., it’s produced by the brain and not by sensory information). The amplification occurs irrespective of whether a stimulus actually appears on-screen or not; what matters is whether people are visually attending to a spatial location. Moreover, the effect is spatially specific. V1 is highly retinotopic, meaning that it preserves the spatial structure of light hitting the retina quite well. By ‘stretching’ V1 out and visualizing it as a flat surface (normally it wraps around the banks of the occipital sulcus), researchers can actually pinpoint the location of stimuli in the environment based on the neural response. It turns out that the top-down modulation of V1 is strongest in the area of cortex that corresponds to the attended location in space.

The fact that attention is capable of modulating even the earliest reaches of visual cortex is pretty remarkable. Still, it’s at least intuitive that paying attention to a particular location would facilitate visual processing there (much of the early neuroimaging research on spatial attention was motivated by Posner and colleagues’ behavioral work demonstrating the existence of large facilitation effects). And it’s not exclusive to V1 by any means: the higher up in the visual hierarchy one goes, the stronger the facilitation effect gets.

The finding Jack et al. report in Neuron is just as intriguing, but much less intuitive. Essentially, they’ve discovered a signal in V1 that’s tied specifically to what they refer to as ‘task structure’. That is, the signal is produced whenever a task-relevant event occurs–e.g., a motor response. How did they identify the signal? Here’s what their first experiment looked like:

As you can see, there are two conditions in the task: immediate response and delayed response. In both cases, a checkerboard pattern is presented in 50% of trials and no stimulus s presented in the other 50%. However, in the immediate response condition, participants respond almost as soon as the stimulus period is over (their task is simply to judge whether the checkerboard was present or not), whereas in the delayed response condition there’s an 8 second delay before the response. That separation allowed the authors to compare the timecourse of visual cortex activation across the two conditions to detect any changes in signal. Here’s what they found:

The solid lines in this figure represent trials when the stimulus was actually presented; the dashed lines represent trials where there was no change in the visual display. Clearly, none of the effects depend on the actual presence of the stimuli, so I won’t say anything else about that. (Ignore the line colors for the moment; I’ll get to that in a second).

The figure pretty much tells the story. The left panel shows the V1 response when the subject’s motor response immediately follows the expected stimulus. As you can see, there’s only one peak, probably because the two events (stimulus and response) are so close together than fMRI isn’t able to tease them apart. Presumably, this lack of temporal resolution is also why (amazingly!) no one else had detected this effect before (people don’t t normally use 10 second trials just for the hell of it).

In contrast, if you look at the right panel, you can clearly see two distinct peaks of activation. The first is locked to the stimulus, the second to the motor response. The remarkable aspect of this finding is that there’s no change in visual display when the motor response occurs. The response period is cued by an auditory tone, so the second peak doesn’t reflect anything that’s happening on screen.

The fun doesn’t stop there. In addition to the temporal dissociation between the two V1 signals, Jack et al. also found two more ways in which the signals differed. One is apparent if you look at the colors in the right panel. The blue lines represent foveated stimuli (i.e., stimuli that are presented right in the center of the display, where vision is most acute) and the red lines represent stimuli presented in the periphery. What you can see is that the attention-induced V1 response (the first peak) is, not surprisingly, largest in the location where the subject expects the stimulus to appear. In contrast, the response-locked second peak is stronger in the more eccentric regions of V1 corresponding to peripheral spatial locations. In subsequent experiments, Jack et al. show that these effects are consistent: the stimulus-locked signal occurs wherever subjects expect the stimulus to appear, and the response-locked signal is always stronger in the periphery.

A second dissociation isn’t apparent in the figure above, because I removed the other panels (showing the timecourses in other areas of visual cortex) for simplicity’s sake. But the general finding is that, whereas the stimulus-locked signal is stronger in higher visual regions, the response-locked effect occurs almost exclusively in V1. That’s a pretty dramatic result in and of itself, since it suggests the latter signal is driven by bottom-up influences, and isn’t attentional.

The rest of the paper (including 5 other experiments) basically consists of a very meticulous attempt to rule out alternative sources of the response-locked V1 signal. I won’t go into detail, but here’s a short list of what Jack et al. show: (a) the signal isn’t just locked to overt motor responses, since it occurs even on ‘no-go’ trials when subjects aren’t supposed to respond; (b) the signal isn’t due to the auditory tone signaling the response phase (there are known projections from auditory cortex to V1, so this is a potential confound), because it occurs even when subjects count silently to themselves for 8 seconds before making self-generated responses; and (c) the signal isn’t due to blinks or eye movements, because it holds even after controlling for these factors. Suffice it to say that the effect appears to be highly reliable, doesn’t depend on obvious (and some not so obvious) confounds, and is independent of the previously identified top-down signal throughout visual cortex. In short, methodologically, the paper seems impeccable.

Of course, to many people, the question is: what does this signal mean? Here, Jack et al. don’t have very much to say. But then, it’s hard to blame them: as they observe themselves, what they’ve identified is, paradoxically, a signal in the earliest and most topographically organized part of visual cortex that’s nonperceptual and spatially diffuse. One possibility the authors reject is the notion that the signal might reflect a generic process such as arousal. They note that regions outside of visual cortex don’t show the same effect, and that the signal’s intensity doesn’t seem to track arousal-related variables such as task difficulty. Instead, Jack et al.’s major positive suggestion is that the novel V1 signal could potentially demarcate event boundaries, acting as a relatively general gating signal that helps the cognitive system transition between discrete states. It’s a pretty speculative idea, but the dynamics of the signal are so counter-intuitive that it’s hard to think of plausible alternatives. Given the impact that the study will probably have, odds are it won’t be long before a flurry of follow-up articles come along to hopefully provide more insight.