What’s really striking about this study is that it’s arguably the best example to date (or at least, the best example that I know of–I don’t follow this literature closely) of the power that new structural MRI techniques provide to assess in vivo brain connectivity in humans. In this case, the authors used diffusion spectrum imaging, a technique that lets the researcher construct whole-brain images of white matter fiber density and then (using some sophisticated post-processing) plot the trajectories of those tracts. The authors defined a connection between regions as the presence of at least one fiber with end-points in both regions (the more terminating fibers, the stronger the connection). Given an N x N matrix (where N = 998 different brain regions in this case!) of connectivity strengths between regions, they could then apply the suite of network topology metrics to produce those pretty, pretty figures.

Lest you think this all sounds like black magic (as I suspect a reviewer or two did), Hagmann et al. provide evidence that these structure-based connectivity maps (a) are reliable across hemispheres and scanning sessions; (b) degrade gracefully in the presence of noise; (c) conform nicely to connectivity data obtained from more conventional anatomical tract tracing techniques in monkeys; and (d) are quantitatively very similar to maps obtained using functional resting-state data in the same participants.  The sheer breadth of analysis in this paper is really quite striking, and you’d have to nit-pick to find faults with the methodology.

That said, there’s one critical question that these results don’t really address, and that’s what the findings mean from a functional standpoint. it’s easy to make the general argument that a small-world network structure is A Good Thing ™ for the brain to have; but the (arguably) more interesting question is why the hubs are located in these particular brain regions. The fact that a majority of the hubs (including posterior cingulate, precuneus, lateral parietal cortex, and superior temporal sulcus) are components of the brain’s “default” or task-negative network is clearly no coincidence. So what functional purpose does this pattern of connectivity serve? Why do those regions that are maximally activated at rest have the broadest pattern of connectivity with the rest of the cortex? Or is it perhaps the other way around, so that these regions develop their default status precisely because they receive inputs from multiple sources, and are ideally situated to mediate transitions between different task sets? Clearly, many questions remain to be addressed (warning: a horribly cliched ending to this post is imminent), but the Hagmann et al. paper will probably turn out to be a pretty important piece of the puzzle (see, I warned you).

Hat-tip: Neurophilosophy.

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).

The “cognitive neuroscience of religion” phrase is used early on in the piece:

“This is a new science that’s emerging,” says Patrick McNamara, an assistant professor of neurology at Boston University School of Medicine. “You might call it the cognitive neuroscience of religion. This is definitely a new discipline, and it’s poised to make some major new discoveries.”

The article itself alternates haphazardly between covering genuinely interesting developments in the neuroscience of belief and spirituality (e.g., the recent focus on Buddhist meditation) and focusing on the beliefs of neuroscientists who happen to harbor religious or mystic worldviews themselves. The author, Richard Monastersky, doesn’t acknowledge anywhere in the piece that there’s a big difference between trying to understand the neural bases of religious belief and trying to justify one’s beliefs via neuroscience. Plenty of people are interested in the former; relatively few are interested in the latter. Yet Monastersky allocates the majority of time to the latter group. Some choice quotes:

Mr. Price also questions the reigning materialist concept of the mind, asking, “Why say that consciousness exists only inside a body?” Regarding subjective experience, he wonders, “Are we talking about some organ inside our skull, or are we talking about our connection with something outside ourselves? That connection outside ourselves can include a spiritual connection.”

…

Other scientists are asking similarly heretical questions. Jeffrey M. Schwartz, a research professor of psychiatry at the University of California at Los Angeles, has been treating people with obsessive-compulsive disorders to counter their urges through focused attention of the mind. Scans of his patients’ brains reveal that such mental therapy can alter the behavior of their brains, something that could not happen if the mind emerged entirely from the brain, he says.

“It is a tragedy of history that materialism became the regnant paradigm,” says Dr. Schwartz, who rails against the contemporary norms that divide science and religion (see related story, Page A18). There are a growing number of scientists, he says, who “believe that this separation of science from religion is a cultural artifice.”

In Monastersky’s defense, he does give some airtime to opposing (and sensible) views from some of the field’s luminaries:

Stephen F. Heinemann, president of the Society for Neuroscience and a professor in the molecular-neurobiology lab at the Salk Institute for Biological Studies, in La Jolla, Calif., echoed many scientists’ reactions when he said in an e-mail message, “I think the concept of the mind outside the brain is absurd.”

…

Michael S. Gazzaniga, a professor of psychology at the University of California at Santa Barbara and a leading neuroscientist who serves on President Bush’s Council on Bioethics, says that essentially all brain biologists accept the materialist view of the mind. “I would say that 98 or 99 percent of people in the business think that,” he says.

You’d think 98 or 99 percent would come across as a pretty big number, yet somehow the article still left me with the same uncomfortable sense of ambiguity found in much of the pro-ID literature (though I’m certainly not suggesting Monastersky, a widely-acclaimed science writer, is pro-ID): the sense that somehow, somewhere, there’s a genuine controversy, and researchers need to give equal time to both sides. Which of course they don’t, as the Gazzaniga quote makes abundantly clear.

One would expect that the brain sciences would be among the last places deeply religious people would venture. To my mind it seems it would be very difficult for most people to continue to hold non-materialist views of the mind after a year or two of staring at images of localized activation increases or dealing with patients with focal lesions and equally selective behavioral deficits. When I look around at my colleagues, I don’t see the “growing numbers of religious and nonreligious researchers who support” the view of a non-material mind. What I do see is a growing number of researchers who want to understand why it is that so many people around them maintain religious beliefs, and for the first time feel they have the tools to do it at the level of the brain.

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.

For example, in 2001, Professor Nikos Logothetis, of the Plank Institute in Germany, published a paper in Nature in which he simultaneously recorded the electrical signals of neurons and measured blood flow using fMRI. No one had ever done this before. Logothetis found that the increases in blood flow measured by fMRI do not necessarily parallel increased neural firing rates. In fact, increased blood flow can also parallel a constant, or even a decreasing neural firing rate.

The 2001 paper was indeed seminal—it’s already been cited a thousand times (in just 5 years!)—but not for the reasons Lehrer suggests. What Logothetis actually showed conclusively was that the BOLD signal correlates very strongly with neural activity. There’s some nuance involved (see below), but the basic message is pretty clear. Here’s a figure from the paper that captures the findings nicely:

There’s a lot going on in this figure, but focus first on panels b and d. These panels illustrate the timecourse of both the BOLD (i.e., fMRI) signal and neural activity in response to sensory stimulation. Panel b illustrates the effect of a sustained stimulus (on for 12 seconds, off the rest of the time); panel d shows what happens when you rapidly turn the stimulus on or off (the stimuli here were all rotating checkerboard patterns with varying degrees of contrast). The dark grey blocks are periods when the stimulus is present, and the light grey shows the neural response. The red and blue solid lines reflect the BOLD response. You’ll notice that both lines are delayed a few seconds relative to the stimulus. This is expected behavior: blood takes time to make its way to a particular brain region following increased activation. Since this delay is always explicitly modeled and we know exactly what shape it has, it’s not a problem. In fact, you can see it’s not a problem by looking at the correspondence between the red and blue lines. The blue line is the ‘predicted’ activation—in other words, it’s what you would predict the BOLD timecourse should look like if you assume the correlation between neuronal activity and the BOLD signal is perfect (i.e., if they measure exactly the same thing). The red line is the actual data Logothetis et al. measured. Notice how strong the correspondence is.

If you look at panel c, you can see the association between the two measures quantified. The panel shows the distribution of correlations between the two measures: the x-axis indicates the strength of the correlation (in bins) and the y-axis shows the number of trials that showed a correlation in that range. What you see clearly is that, across all of the samples, including all of the different stimuli lengths (ranging from 4 – 24 seconds), there’s a tight coupling. How strong is it? Well, the modal amount of overlap between BOLD and LFP (local field potential) is 0.67. That number is given in r², which is an index of how much variance two measures share. The 0.67 indicates that the two measures, share, on average, 67% of their variance. If that doesn’t seem very high, consider that in typical human samples, weight and height only share about 35% of their variance. Yet it’s perfectly obvious to just about anyone that, on average, taller people tend to also be heavier. Moreover, the 67% value is the mode for individual trials. Because individual trials contain substantial measurement error, the correlation is likely to be much higher when averaged over many trials (as fMRI studies do)–likely near unity. In sum, there’s simply no question that the BOLD signal reflects neuronal activity. Here’s what the authors had to say about it in the Discussion:

Our results show unequivocally that a spatially localized increase in the BOLD contrast directly and monotonically reflects an increase in neural activity.

And here’s an even stronger sentiment from another Logothetis paper Lehrer links to:

The combination of this technique with electrophysiology has fully confirmed the longstanding assumption that the regional activations measured in MR neuroimaging do indeed reflect local increases in neural activity.

Now, I mentioned that there were nuances involved. Lehrer alludes to this when he observes that blood flow “can also parallel a constant or even a decreasing neural firing rate.” The basic issue here is that ‘neural activity’ is an ambiguous construct. You can measure activation levels in individual neurons, or in a few neurons, or you can measure the local field potential, which is essentially an average of a whole bunch of neurons in relative proximity carrying out a whole bunch of functions. From the perspective of fMRI researchers, it’s really only the last of these that matters. The reason is that the response to stimulation in individual neurons tends to habituate very rapidly. Meaning that if you present a stimulus for 20 seconds, most individual neurons don’t fire at the same increased rate for the entire 20 seconds; they’ll fire rapidly at first and then quickly tail off. Given that fMRI has a temporal resolution on the order of seconds, and that researchers often want to use relatively long-lasting trials, it would actually be disastrous if the BOLD signal was perfectly correlated with the firing of individual neurons, because then fMRI could only provide meaningful data for very brief presentation durations. Fortunately, the BOLD signal correlates most strongly with the LFP, which typically sustains throughout the duration of a stimulus presentation.

All this is basically to say that, based on these results, you can think of the BOLD signal as reflecting the aggregate activity of a whole bunch of neurons located in a particular part of the brain. And that’s exactly what researchers have been assuming all along. (Actually this is still an oversimplification, since LFP reflects not just local processing but also inputs and outputs from other regions. But in subsequent work, Logothetis and colleagues have shown that all of these are highly correlated with the BOLD signal, with inputs and outputs being marginally more influential.)

What about the other problems? Lehrer notes that:

In 2004, Logothetis’ lab found something even stranger . Neurons that had been chemically silenced – they could no longer become active – could still generate an fMRI signal that appeared active.

The irony here is that this finding was actually used by Logothetis et al. to support their argument that the BOLD signal reflects the LFP. What they showed was that applying serotonin to the target brain region (monkey visual cortex) completely abolished the firing of individual neurons, but barely affected either the LFP or the BOLD signal, which remained closely coupled. They concluded:

The response to the stimuli was unaltered, indicating once again the possibility of a total dissociation between spiking activity and hemodynamic responses. On the basis of all of these dissociations, we conclude that the LFP signal is the key variable for the BOLD response.

Again, this isn’t at all problematic—it’s great. It’s exactly what fMRI researchers want out of the BOLD signal. We want to know that greater blood flow means greater general involvement of a region in a cognitive task; we don’t care what individual neurons X, Y, and Z are doing. Now it’s certainly pretty interesting that you can get a dissociation between individual neuron spikes and the local field potential at all; but that’s not an issue that concerns fMRI researchers, since it’s pretty clear that it’s a lower-level phenomenon. Put differently, if the dissociation between individual spikes and the LFP is a reason to question imaging results, then a lot of other areas of neuroscience are in trouble, because local field potentials are used all over the place.

The last three of Lehrer’s points I think I can mostly agree with, but they’re hardly damning criticisms:

It gets worse. A 2002 study by Robert Harrison at the University of Toronto showed that fMRI signals “emanated only from areas endowed with a rich vascular network, and [that] no signals were obtained from adjacent regions in which the vasculature was less dense.”

I wasn’t familiar with this article, so I’m grateful for the pointer. Not having read it thoroughly, I’ll restrict my comments to the following. First, the authors note that capillary density likely develops as a result of a regions demands:

Our working hypothesis is that the capillary density of any brain area develops in direct relationship to the metabolic demand of local neurons.

If this is true, it’s not a huge concern, in that it just means brain regions that do more work than others are going to be easier to detect with fMRI. Second,the study used chinchillas, so it’s unclear to what extent the findings generalize to humans (though the basic organizing principle certainly seems plausible). And third, supposing it’s true that there are some regions in which signal is impossible to detect, such regions are clearly in the minority. Virtually every part of the brain has been demonstrated to show reliable activation in one literature or another. I’ve personally had the (frequent) experience of being frustrated at the fact that too much of the brain is activated by a task; I suspect other researchers have too. So empirically, this doesn’t seem like a major concern, though it’s probably worth keeping in mind.

Furthermore, blood also moves slower than the electricity in our neurons, so it’s always difficult for fMRI to decipher what thought process the blood flow actually correlates with.

This is true, but isn’t really a ‘problem’ per se. It’s an acknowledged limitation of the technology, and every neuroimaging researcher is well aware of the lag between neural activity and the hemodynamic response. So long as it’s modeled correctly (and it isn’t always, but that’s a literature unto itself), there’s no problem in attributing activation to the appropriate timeframe (subject to the general temporal resolution limitation, of course).

Finally, whole parts of our brain remain invisible to fMRI machines. The base of our frontal lobe – a brain area crucial for consciousness – is too close to our nasal ducts to be visualized. The magnetism of air interferes.

They’re not really invisible; they’re just shy. You can get at the orbitofrontal cortex and surrounding areas, but it requires extra catering to if you want to get a really good signal. This isn’t a dirty secret, though; there’s a small literature discussing methods for improving signal-to-noise ratio in different parts of the brain, with people proposing using different pulse sequences, brain orientations, field strengths, and so on. So if you want to do a study you think will specifically activate the OFC (e.g., decision-making or emotion studies often do), you can go the extra step. But the OFC is certainly not invisible, and you do frequently see activation in deeper brain regions even without doing anything different procedurally.

The bottom line is that while fMRI has limitations, just like every other method, it didn’t become the workhorse of cognitive neuroscience just by taking pretty pictures (though it certainly does that). Other methods can in principle take equally pretty pictures of the brain: PET and EEG, for example. But those methods are limited in critical ways (PET is invasive, and EEG has terrible spatial resolution). The reason most people like fMRI is because it optimizes a bunch of trade-offs in a way that previous methods haven’t been able to do. And while Lehrer’s right inasmuch as the question of the mapping from BOLD signal onto neural activity was once a major issue, that concern has mostly gone away now. Which isn’t to say that the BOLD signal always measures exactly what we think it does; there are always going to be circumstantial factors we don’t know about and can’t anticipate. But that’s not a problem with fMRI—it’s a problem with doing research!

The crux of Bloom’s argument isn’t really that there’s anything wrong with imaging itself (though he does seem rather unimpressed with much of what it’s produced), just that the excessive focus on it detracts from other areas of cognitive science and psychology:

… [Imaging] is more than just phrenology. But it is not so dazzling that it should usurp other areas of research.

I’m not sure Bloom’s advocating for anything like an actual reallocation of the resources imaging receives, but for the sake of argument, let’s suppose he is. His characterization of the current state of affairs is, I think, completely accurate: it’s true that the media disproportionately focuses on fMRI studies, that fMRI research gets more funding than cognitive psychology research, and that people ooh and aah over pictures of brains lighting up in ways they don’t over eloquent verbal descriptions of Methods and Results sections. But I don’t think this is much of a problem–not for the media, not for the public, and not even for cognitive psychology. Here’s why:

• It’s far from clear that the exaggerated media focus on fMRI research takes anything away from the rest of psychology. While it’s certainly true that the number of stories covering imaging research has gone up in recent years, I don’t see any reason to suppose relative coverage of other areas of psychology has gone down (can anyone furnish empirical data?). It’s not as though in the absence of fMRI, reporters would be climbing over each other to report on new tweaks to the ACT-R model, or publishing expositions on brave new approaches to transformations of RT distributions. And there certainly are areas of (mostly applied) psychological research that continually pique the media’s interest. Cell phones and driving performance are one example; parental influence on childrens’ behavior are another perennial favorite (and some of the reporting on parent-child relationships makes fMRI coverage look positively Pulitzer prize winning!)
• A large number of neuroimaging researchers (likely the largest contingent) are trained as psychologists, and have at least a cursory familiarity with other branches of the field (most commonly cognitive). As such, a good deal of imaging work aims to address issues first posed by cognitive psychologists, or is informed by cognitive psychological models. My guess is that if anything, public awareness of major issues in psychological research has increased as a result of the focus on neuroimaging. Naturally, the sophistication of the cognitive theories presented in imaging papers doesn’t rival that of full-length cogpsych articles. But it shouldn’t be expected to. Criticizing cognitive neuroscientist for not getting the psychology perfectly right is on a par with the frequent charge leveled by cognitive neuroscientists that many of the explanatory models psychologists propose aren’t neurally plausible and seem at odds with current knowledge of biology. It’s true enough, but isn’t particularly bothersome, because biological implementation usually isn’t a major concern among cognitive psychologists. Conversely, the level of abstraction cognitive psychologists strive for isn’t necessarily an objective cognitive neuroscientists share.
• Neuroimaging is a rapidly growing field. While it may reach saturation in the next few years (it’s hard to say right now), it’s worth remembering that PET and fMRI have only been around 20-30 years, and fMRI has only been widely deployed for about 10 of those. Setting aside the question of whether imaging offers better information than traditional cognitive psychology, it pretty clearly offers different information. Given that we’re only scratching the surface so far in terms of what we can expect to accomplish with fMRI, it’s not unreasonable to increase imaging funding until, say, every major university in the U.S. has a research scanner (probably still a decade or so away). That’s a level of support that’s pretty much on par with other branches of expensive science. Moreover, neuroscience is a much larger field than academic psychology (SFN draws annual crowds of 30,000; APA around a third of that), and it’s not unreasonable to expect cognitive neuroscience to eventually end up somewhere intermediate to the two disciplines in terms of size and resource allocation.
• There are several other areas of science that attract as much or more media coverage as imaging research. Cosmology and population genetics come to mind right away. And once again, developmental psychology (a field Bloom himself has made important contributions to) isn’t that far behind. So it’s probably unfair to focus attention solely on neuroimaging. Sure, people are wowed by any mention of the brain. And as Bloom observes (and has been instrumental in demonstrating), biological explanations have a stronger allure for most people than purely psychological ones do. But genetic explanations are pretty pervasive and compelling too these days, and that’s no reason to stop doing genetic research. What’s important is whether the results advance our understanding of the world, not whether lay people find them interesting or not.
• Bloom argues that the study of reaction times in psychology has told us much more about the mind than the collective field of neuroimaging. Well, maybe and maybe not; there’s really no good way to operationalize that statement. I certainly know several people who would lean in the opposite direction, but simply asserting as much doesn’t make it that way. More to the point though, it would hardly be surprising if the study of RT has produced more knowledge; after all, people have been studying RTs for a hundred years and taking pictures of the brain for only a couple of decades. If anything, the fact that the comparison between the two is even worth making could be taken as evidence that cognitive neuroscience has come a long way very quickly.
• Aesthetics, aesthetics, aesthetics. Having partaken in an embarrassingly large number of “my discipline’s better than yours!” arguments over the years (I don’t mean to suggest this is what Bloom’s saying. I don’t think he is), I’ve come to the conclusion that much of what drives people to choose once scientific discipline over another is just an aesthetic preference for some levels of explanation over others. There’s rarely any objective way to arbitrate between the utility of different scientific disciplines (of course, one can always appeal to practical application–but in that case virtually all of psychology fares poorly compared to most of the natural and biomedical sciences). Instead, what one generally finds is a two-way street where people working at a higher level have predictable criticisms of those working at a lower level, and the converse. Philosophers of mind think psychologists are obsessed with details and overlook the really important problems; psychologists think much the same of neuroscientists, with the added concern that neuroscientists worry too much about physical implementation and lack sufficient regard for theoretical models; cognitive neuroscientists think all of the above of molecular or systems neuroscientists, and so on. When pushed in the opposite direction, the criticism tends to focus on how nebulous the constructs are at the higher level and how they lack any realistic implementation in biological structures. Well, who’s right? Everyone and no one, as far as I can tell. But I’d love to hear a good argument in favor of disciplinary superiority, if anyone knows one. (Note that I don’t extend this relativistic stance outside of science; I think there are plenty of good reasons for privileging the scientific approach over, say, literary theory).

Having said all this, I’m well aware of the irony implicit in writing a long piece in defense of a discipline that’s already hogging the limelight. And I’ve certainly experienced my fair share of frustration at the poor quality of media coverage of neuroimaging studies. Still, I think the focus on neuroimaging is justified, for the most part, and I don’t see how it detracts substantively from the rest of psychology (though it may bruise a few egos).