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PLoS releases metrics for all papers

This is pretty cool: the Public Library of Science (PLoS), which publishes a number of open-access top-tier journals (plus PLoS ONE, a not-so-top-tier journal), has just put metrics for all of its articles online. You can now see the number of citations, blog mentions, article views and PDF downloads for every PLoS article, so if you’ve published in one of the PLoS journals, you’re free to go find out just how many times your article’s been viewed compared to everyone else’s. And then you of course you’re free to make up all sorts of convenient excuses for the fact that your stats suck and your paper’s only been viewed seventeen times in the last three years.

What’s even cooler is the PLoS editors have collated all of that information and released it as one monstrous Excel spreadsheet. So you can now run off and do a quick regression analysis to determine whether or not having longer paper titles leads to more citations, if you’re so inclined.

One can only hope that the for-profit publishers follow the PLoS lead and start putting some of this information on-line. Given the widespread availability of things like download counts, bibliometricians (is that a word?) could develop novel measures of impact that go beyond simple citation counts (or derivative measures such as the H-index). You can imagine, for example, a metric that quantifies the “reach” of an article, which could take into account the number of views and downloads but not necessarily the citation count. Or, as some have suggested, one could simply use the download data as a more readily available proxy for citation rates, since it turns out that download counts within the first few months of publication are a strong predictor of citation rates several years later.

Of course, skepticism is probably warranted given the for-profit publishers’ track record of not exactly being eager to do what’s in the best interest of the scientific community. But perhaps other open-access providers (e.g., the Frontiers series) will follow suit…

http://journalofvision.org/9/4/i/

Posted in academics, open access.


why base rates matter

Here are three recent scientific findings you may or may not have heard about:

1. The use of stimulant medications commonly prescribed for ADHD is associated with a nearly 8-fold increase in the likelihood of dying suddenly among children aged 7 – 19.

2. Gum disease increases the risk of head and neck cancer quite dramatically: for every millimeter of alveolar bone loss (i.e., loss of the bone that surrounds the roots of your teeth), there is a 400% increase in the risk of cancer (note: article requires paid access).

3. People who talk on a cell phone while driving are 1.3 times more likely to have an accident than people who drive without any distractions.

At a cursory glance, all three of these stories seem like pretty bad news. And they are. But one of them is actually much worse than the others. Your job is to decide which one; take a moment to think about it, then read on.

If you’re like most people, you probably picked either the first or the second story. After all, it’s pretty terrible to think of children dying suddenly, or of getting cancer of the head and neck. Sudden death implies death for certain, and cancer implies death with a high probability. Most of us generally don’t see death as a good thing, so we want to avoid those outcomes. Car accidents aren’t anyone’s idea of a good time, of course; but at least most car accidents aren’t fatal. And then there’s the matter of the differing odds to consider: in the first story, the negative outcome is 8 times as likely, and in the second, it’s 4 times as likely, but in the third story, it’s only 1.3 times as likely. Surely then, it’s more important to avoid taking stimulant drugs and to brush and floss regularly than to worry about talking on a cell phone!

Well, as you might have guessed from the fact that I started the previous paragraph with “if you’re like most people…”, the truth is actually somewhat counterintuitive. The fact of the matter is that, even if the above stories are completely true (and as far as I know, they are, pending further research), turning off your cell phone when you drive is probably a much, much better way to minimize your chance of dying early than swearing off stimulants or practicing great oral hygiene (though the latter is still important!). The reason is that the information I gave you in the three stories above neglects what’s probably the most important piece of of all to consider: the base rate (or frequency) of each event occurring.

Let’s add some context to each of the three stories. Take the first one. It’s true (at least based on one preliminary study) that kids who take stimulant medications are much more likely to die suddenly than kids who don’t. But the critical thing to consider is the base rate of sudden death. You probably won’t be surprised to hear that the odds of dying suddenly are incredibly low when you’re 7 – 19 years old. It’s unclear exactly how low they are, but consider that the study that reported this finding scoured state databases between the years of 1985 and 1996 and still only came up with 564 cases of sudden death. That’s a tiny, tiny, tiny fraction of the number of kids who make it past 19 years of age in good health. Suppose we say that the probability of sudden death for a kid in this age range is 0.0001% per year. An eightfold increase would mean that the average kid goes from a one in a million chance to just under a one in a hundred thousand chance of dying per year. And of course, it’s not average kids for whom stimulant medications are prescribed; usually, there’s a condition (e.g., ADHD) that the drugs are intended to alleviate. When you weigh the increase in the negligible likelihood of sudden death against the very sizeable benefits conferred by stimulant medications, it’s clear that this finding isn’t really cause for alarm. As John Grohol notes, “The finding is of greater interest in trying to understand why it’s occurring at all, not for anyone to make a treatment decision based upon it.”

What about the second story? Well, you can probably already see where this is going. Head and neck cancer is quite rare, accounting for fewer than 50,000 new cases per year in the United States. In other words, approximately one in every 6000 people will develop head and neck cancer. This of course includes both people who have good oral hygiene and people who don’t, so the reality is that, even if you have terrible oral hygiene and rampant gum disease, you’re very unlikely to ever develop head and neck cancer. Conversely, there are other factors that present even greater risk factors for head and neck cancer than gum disease (e.g., smoking). This isn’t to say that you shouldn’t brush your teeth, of course; there are plenty of other good reasons to take good care of your gums. It’s just to say that you shouldn’t lose any sleep over the prospect of developing head and neck cancer because of your gums. In the grand scheme of things, there are any number of other things you should be much more concerned about.

One of the things you should be much more concerned about, actually, is your risk of having a car accident while talking on your cell phone. Unlike sudden death in children and head and neck cancers in adults, the odds of dying in a car accident are not very small. Worldwide, approximately 2% of deaths every year are caused by road accidents. And that’s to say nothing about serious injuries sustained in non-fatal accidents. Put simply, a 1.3-fold increase in the likelihood of enduring car accidents is not trivial. If we do a back-of-the-envelope calculation and assume that the odds of dying in a car accident increase by the same proportion (i.e., that drivers on cell phones don’t have more serious accidents than drivers off cellphones–which is debatable), it turns out that you can reduce your overall odds of dying in any given year by about 0.6% just by not talking on your cell phone while driving. Admittedly, that’s a very loose estimate that’s based on questionable data and many simplifying assumptions. And it’s not like it’s a dramatic reduction by any stretch (which only goes to further illustrate the importance of considering base rates). But the point is, there are probably relatively few lifestyle change you could make this year that would require so little effort for such a large benefit. So take your ADHD meds, brush your teeth regularly, and don’t talk on your cell phone while driving.

For a nice overview of empirical data on the base rate fallacy, see this article in BBS. For more blogospheric bloviation on base rates, see here, here, and here.

Posted in statistics, tutorials.

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Poldrack on the limits of fMRI

Cerebrum, the Dana Foundation’s neuroscience magazine, has a really nice article on the limits of fMRI by Russ Poldrack. You should go read it, but to summarize some of Poldrack’s main points:

  • fMRI studies tell us what’s true of brain activation on the average, but we’re nowhere near the point where fMRI scans have diagnostic value at the level of individual subjects. Put another way, you may be able to tell that people with ADHD have somewhat different neural responses than people without ADHD on average, but you can’t tell whether someone has ADHD or not by looking at their individual brain activation.
  • The functional implications of specific differences in brain activation are not always clear. If schizophrenics show more activation in frontal brain regions than healthy controls, is that a good or bad thing? Are the increases reflective of the underlying disorder, or do they represent the brain’s attempt to compensate for the underlying deficit? We just don’t know at this point.
  • Scientists and journalists alike are too quick to draw what Poldrack has called the “reverse inference“, arguing that a specific cognitive function must be involved in a certain task on the basis of the spatial location of activation. For example, many researchers who observe increased amygdala activation in response to a particular stimulus are quick to suggest that people are engaging in emotional processing, when in fact the amygdala may be activated by many other types of processes. There just isn’t enough evidence to support most reverse inferences.
  • For all of the above reasons (and others), the people selling various fMRI-related services–e.g., “neuromarketing” firms that purport to tell major companies how people “really” feel about their brands–are basically selling snake oil.

Anyway, it’s a good piece, so give it a read. Poldrack is widely respected in the field of cognitive neuroscience for his thoughtfulness and methodological rigor, so it’s always worth paying attention to what he has to say on such matters.

[hat-tip: MindHacks]

Posted in general.

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the evil that shall not be named …looks pretty awesome.

Wolfram Alpha makes its debut on May 18th. I have to confess that after the pomposity with which Stephen Wolfram published his much-uncelebrated book “A New Kind of Science” seven year ago (an anniversary he celebrated rather pompously again today), I was sort of hoping Wolfram Alpha would fail to live up to the hype. But so far it’s looking pretty good. Here’s a screencast showing what Wolfram Alpha can do right now.

While I’m sure the engine isn’t quite as flexible or powerful as the demo makes it seem (presumably Wolfram only asks questions he knows Wolfram Alpha has a good answer to), there’s clearly a lot of functionality already built in. What really blows me away in this demo is the ability to instantaneously plot the relation between arbitrary variables–in Wolfram’s example, the correlation between national GDP and railway length for European countries. Wolfram Alpha is built on Mathematica, so assuming that some of Mathematica’s statistical functions make it in (e.g., linear regression), it’ll make for a pretty awesome toy.

Of course, all this looks like it’ll require a good deal more computing power to serve up than your average Google query, so we’ll see how well Wolfram Alpha’s servers survive the onslaught that’s sure to accompany its arrival next week.

Posted in general.


Python + fMRI = love

I was completely unaware of this until someone pointed it out to me the other day, but there’s a really nice effort underway to develop a Neuroimaging package for Python:

The neuroimaging in python (NIPY) project is an environment for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI).

This strikes me as a great project for a number of reasons (see this page for more):

 

  • The existing free software packages for fMRI analysis (or at least the two I’m moderately familiar with) have limitations that are pretty hard to live with. SPM is about as close to an industry standard as there is, but has a hideously clunky GUI, depends on expensive proprietary software (MATLAB), and lacks integration with other environments/languages. FSL is very powerful, but also lacks interoperability, and in practice, I’ve found it hard to build complex models with FSL.
  • Speed. NIPY is built on SciPy/NumPy, an increasingly popular set of Python libraries for scientific computing. Much of the SciPy/NumPy code is just a wrapper for C++/Fortran libraries that do the heavy lifting. So in theory, NIPY could be very fast (though Matlab is comparable for many operations. For a nice comparison of different numerical analysis packages, see this page).
  • Open source / total interoperability. In theory, SPM and FSL are both “open” to varying degrees. But as the NIPY developers note, in practice, relatively few people actually make substantial contributions to the SPM or FSL codebase. Moving to a high-level language that’s easier to learn and develop in could do a lot to increase the level of community support for any package.
  • The language. I can’t see myself ever contributing much to the SPM codebase precisely because I find programming in Matlab to be about as pleasant as pulling teeth. That’s not because I’m a terrible programmer; I have a fair amount of experience with a number of other languages. It’s because Matlab isn’t really a programming language. There’s limited or non-existent support for any number of operations that are a single call away in Python or R. And if the functionality you want doesn’t exist, you’ll probably have to write it yourself. Whereas Python has freely available packages for just about everything. And the language just makes sense. If you’re going to build a new package for fMRI analysis, it’s not a bad idea to build it in a language that’s actually fun to program in.
  • Great support. NIPy has great developers and institutional support (the project is maintained by the Brain Imaging Center at Berkeley), and seems likely to stay funded for the foreseeable future.

 

So what’s the downside? Well, the software clearly isn’t ready for prime-time yet. The developers themselves counsel you not to use it for any serious data analysis. But there’s already a reasonable amount of functionality, and it’s generally well-documented. Give it another year or two and NIPy should start to siphon users away from SPM and FSL. I’ll certainly be happy to make the switch.

Posted in general.

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When is peer review not peer view? (hint: when Merck pays Elsevier)

This one’s straight out of the twilight zone: for (at least) the past 5 years, Merck (and possibly other drug companies) has been paying academic publishing giant Elsevier to publish fake journals promoting Merck products. From The Scientist (free registration required):

Merck paid an undisclosed sum to Elsevier to produce several volumes of a publication that had the look of a peer-reviewed medical journal, but contained only reprinted or summarized articles–most of which presented data favorable to Merck products–that appeared to act solely as marketing tools with no disclosure of company sponsorship.

The journals in question–at least 14 of which go by the “Australasian journal of…” moniker–look and read like peer-reviewed journals, but aren’t. They’re apparently just bound collections of ads for drugs like Merck’s Fosamax.

The Scientist article is really worth a read. It’s like something out of The Onion, except the funny drains out of it when you realize that literally thousands of physicians have received copies of “The Australasian Journal of Bone and Joint Medicine” or its other Australasian cousins over the past few years.

Elsevier, of course, has responsible and contrite things to say about the episode:

A spokesperson for Elsevier, however, told The Scientist, “I wish there was greater disclosure that it was a sponsored journal.” Disclosure of Merck’s funding of the journal was not mentioned anywhere in the copies of issues obtained by The Scientist.

The Elsevier spokesperson said the company wasn’t aware of how many copies of the Australasian Journal of Bone and Joint Medicine were produced or how the publication was distributed in Australia, but noted that “the common practice for sponsored journals is that doctors receive them complimentary.” The spokesperson added that Elsevier had no plans to look further into the matter.

The bitter irony is that Elsevier, along with the other major academic publishers, have spent the last few years ceaselessly lobbying against the open access movement, on the grounds that open access journals can’t be trusted to maintain the high quality of peer review that the  commercial publishers provide. Any guesses as to whether Elsevier will rethink that stance following this fiasco?

Much more on this story over at Peter Suber’s open access blog…

Posted in general.

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A tale of two regions (and their convoluted relationship)

Hare and colleagues published an fMRI paper in Science last week on the neural mechanisms of self-control. There are pretty thorough procedural descriptions of the study elsewhere, so I’ll just summarize the key points here.

In a nutshell, the authors scanned a group of dieting participants as they viewed and rated a series of foods that varied in their taste and health value. The dieters were classified into “self-control” (SC) and “no self-control” (NSC) groups based on their behavior during the task. The SC participants rejected foods that were unhealthy even if they liked those foods, whereas the NSC participants based their decisions to accept or reject foods based almost entirely on their taste value.

The imaging results tell a very similar story to the one Rangel’s group at Caltech has been promoting for a while now. To summarize:

  • Ventromedial PFC (VMPFC) activation shows stronger responses to foods participants accept than foods they reject. In Hare et al’s terminology, it represents the “goal value” of the food.
  • VMPFC likes tasty foods in all participants, but only likes healthy foods in SC participants.
  • When trials that required self-control (i.e., those that offered tasty but unhealthy foods) were binned based on whether participants successfully exercised self-control (rejecting the item) or failed to exercise self-control (accepting the item), both groups of dieters showed activation in dorsolateral prefrontal cortex (DLPFC). But the SC dieters showed more activation.

So far so good. These are nice results, and while I don’t know that they’re Science-worthy, they’re at the very least consistent with the idea that (to put it simplistically) VMPFC tells us what we like, and DLPFC tells us whether or not we’re going to choose what we like. But of course, that interpretation (which shows up in the title of the paper) implies a stronger claim, namely, that DLPFC actually modulates the VMPFC representation. And here’s where things get a bit wobbly.

The first analysis Hare et al present to try and make the case that DLPFC and VMPFC are actually talking to each other is a simple correlational analysis demonstrating that DLPFC and VMPFC are inversely related across SC participants. Meaning, self-controllers who show relatively more DLPFC activation also show relatively less VMPFC activation.

While this first result is interesting, it’s not very convincing, for a couple of reasons. One is that there often are very large individual differences in global activation in fMRI studies, meaning that some people just systematically show more activation than others in regions that are task-positive, and more deactivation in regions that are task-negative. VMPFC and DLPFC fit the bill here, in that the former was strongly deactivated and the latter was strongly activated. So one would like to see some evidence of specificity here. Is this association selective to the DLPFC-VMPFC circuit, or are similar inverse correlations found between very diffuse brain networks? And similarly, is the correlation the authors report specific to self-control trials, or does it also show up for non self-control trials? The authors only report the former, and one would like to know that the latter didn’t hold (i.e., you shouldn’t see a correlation that’s supposedly related to self-control in a condition where there’s no need for self-control!).

A second and more serious concern is that the presence of a between-subject correlation between two regions really says almost nothing about whether those two regions are talking to one another in a meaningful way. What Hare et al want to argue is that, on a trial-by-trial basis, when DLPFC kicks in, VMPFC is downregulated, resulting in a behavioral change. In other words, changes in DLPFC activation cause changes in VMPFC activation. But demonstrating that people who show more activation in DLPFC on average also show less activation in VMPFC on average isn’t the same thing. There are any number of ways you could get a negative between-subject correlation between two regions without any within-subject correlation whatsoever. One trivial example I alluded to above is that some people might just be more engaged in the task than others. DLPFC and VMPFC tend to show a negative correlation in all kinds of tasks; one plausible interpretation for this is that people who pay more attention to the task will show more activation in “task-positive” regions and more deactivation in “task-negative” regions. So you could very well get this effect for free, no self-regulation required.

In any case, Hare et al go on to report a second “functional connectivity” analysis. Unlike the above correlational analysis (which, unfortunately, some people also refer to as a functional connectivity analysis), the functional connectivity analysis was conducted over time rather than over participants. In other words, Hare et al were looking for regions in which activation tended to covary with a “seed” region of interest (in this case, DLPFC). The rationale for this type of analysis is that if two regions tend to coactivate, it’s reasonable to suppose that there might be a causal relationship between them (though articulating that relationship is not so easy).

The big problem with functional connectivity analysis is that it’s very difficult to test whether the correlation between two regions differs reliably across conditions. Unfortunately, that’s often exactly what we want to know. In this case, what Hare et al want to show is that the correlation between DLPFC and VMPFC exists specifically during trials that require self-control (i.e., unhealthy foods), and isn’t always there (which would argue against a self-control-specific explanation). So they perform what’s called a psychophysiological interaction (PPI) analysis. The basic idea here is that you add a term into your model that codes for the interaction between the experimental variables you care about (e.g., healthy vs. unhealthy trials) and the activation in the seed region. (Statistically, this is just the product of the two variables, after controlling for their main effects.) You can then interpret any regions you identify through this analysis roughly as “regions that show a stronger correlation with the seed region in one condition than another”. In this case, Hare et al identify a number of regions that show functional connectivity with DLPFC.

Sounds good, right? If it’s this easy, why doesn’t everyone use PPI analysis? Unfortunately, there’s a big technical problem, which is that the BOLD response (the signal that fMRI detects) is kind of slow, and lags behind neuronal activation for several seconds. Without getting too deep into the details, what this basically means is that if you run a standard PPI analysis, as implemented in some fMRI packages, you actually aren’t identifying regions that covary with neural activation in your seed region. Instead, you’re identifying regions in which activation correlates with the delayed hemodynamic response in your seed region. Which, to put it bluntly, makes it very difficult to have any idea what you’re really looking at.

The solution some people have adopted to this problem is to use a very complicated deconvolution approach to essentially try and figure out what neural activity must have been like several seconds before the response you observed. Once you’ve done that, you then use that estimate of neural activity in your PPI analysis rather than the observed signal itself. After that, you can interpret the results in the relatively straightforwad way I suggest above, i.e., you can identify regions that show changes in functional connectivity with your seed region as a function of condition.

If this all sounds like black magic to you, you’re not alone. While opinions certainly vary, mine, for what it’s worth, is that PPI analyses are close to worthless. You have to make so many convoluted (no pun intended) assumptions about what activation must have been like to produce the observed signal, and what activation will probably be like if we just multiply this indicator variable by this time-series and pretend that everything remains perfectly stationary when we reconvolve it, and what the shape of the hemodynamic response is in all the other regions we’re correlating with the seed ROI, that it’s virtually impossible to come away feeling confident that you know what your results reflect. So, on purely methodological grounds, I think it’s reasonable to express a good deal of skepticism about any paper that bases its strongest claim on a very convoluted technique that really hasn’t been adequately validated.

Having said that, opinions do vary, and some people might feel perfectly comfortable with Hare et al’s analysis, had it produced the expected result–namely, that DLPFC activation correlations with VMPFC activation during evaluation of unhealthy foods, but not otherwise. But Hare et al don’t actually show that. What they find, instead, is that DLPFC is functionally connected to a number of other frontoparietal regions. You might think this to be a deal-break for the hypothesis; but the authors are persistent, and instead suggest that “DLPFC might modulate the vmPFC through its effect in a third region, such as IFG/BA46.”

It’s not immediately clear why this second hypothesis should be necessary; after all, the PPI analysis is acausal. It doesn’t tell you that DLPFC caused activation in VMPFC, it just tells you that the two are correlated. If there really was any sort of relation between DLFPC and VMPFC, however many nodes it was mediated by, you would still expect it to show up in the first analysis. If it doesn’t, you might want to conclude that any effect, however indirect, is relatively weak, and probably can’t account for the large differences in mean levels of activation in these regions.

In any case, to test their prediction, Hare et al then conduct a second PPI analysis, using the IFG region that was functionally connected with the original DLPFC seed as the new seed (minimal rationale for this choice is provided, and there were other available candidates, so it’s pretty clear this was a fishing expedition). This time they get lucky: the third region (IFG) was in fact functionally connected to the VMPFC. So Hare et al conclude that, indeed, “vmPFC was functionally connected to the left DLPFC through a two-node network”. Hypothesis confirmed!

In sum, the best evidence the authors have for their claim that “Self-Control in Decision-Making Involves Modulation of the vmPFC Valuation System” (the paper’s title) is that they were able to find a third region that appeared to be functionally connected to both DLPFC and VMPFC using some very methodologically convoluted analyses and a rather unprincipled approach to region selection. That’s my take on it, at least; I leave it to you to decide whether or not to believe the result.

Having said all that, I hasten to point out that I don’t think this is a bad study overall. Other than the connectivity analyses, the results are pretty compelling, and represent a nice addition to the growing literature (much of it from Rangel’s lab) highlighting the VMPFC as a central component of the brain’s valuation system. It’s just that Hare et al never really provide any convincing evidence for their central claim–a claim that, one suspects, is what got the paper accepted in Science (along with a credulous reviewer or two, perhaps).

Posted in general.

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Some things that fMRI can’t do

In no particular order:

  • Act like a cowboy
  • Play chess in the dark
  • Scrub its own back in the shower
  • Talk on a tin can telephone
  • Stuff a turkey
  • Break a pinata
  • Declare war on the neighborhood
  • Master R
  • Build a Ferrari out of jelly beans

Posted in general.

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bovine science, Google Earth style

Completely unrelated to brains, but quite possibly the neatest sentence I’ve seen in a journal article lately:

Body axes of cattle (Bos primigenius) of 308 evaluated herds/pastures (displayed on satellite images in Google Earth) showed a significant deviation from random distribution (Rayleigh test, P < 0.00001) with a preference for a rough N-S direction (mean vector: 5.4°/185.4° with geographic north as reference).

Translation: Begall et al. (note: PNAS online; restricted access) used Google Earth to show that cows like to face North/South, an observation that (as far as we know) none of the hundreds of thousands (millions?) of people who have good reason to interact with cows on a daily basis had ever noted before. The modern ability to conduct cutting-edge science from the comfort of one’s laptop (cf. The HapMap Project, fMRI data center, etc.) continues to amaze…

Posted in science.

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“The brain has hubs?”

If you read only one neuroimaging paper this week, make it this paper in PLoS Biology by Hagmann and colleagues. It’s a really remarkable combination of technical wizardry, creativity, and pretty, pretty pictures of the brain. What Hagmann et al have done is assemble rock-solid evidence that a network of brain regions located primarily in posterior midline cortex serves as the structural ‘core’ of the broader cortical connectivity map. Whereas most brain regions show sparse connectivity, typically talking to only a handful of other nearby regions , regions in the structural core are much more densely connected with one another and with other regions throughout the cortex. Hagmann et al. support this basic conclusion with five or six different analyses, each using a different network topology metric (herein lies the technical wizardry), but the bottom line is that they obtain much the same result no matter how they looked at the data.

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.

Posted in methodology, neuroimaging, research articles.

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