There are probably lots of criteria you could use to determine the relative importance of different scientific disciplines, but the one I like best is the Largest Number of Authors on a Paper. Physicists have long had their hundred-authored papers (see for example this individual here; be sure to click on the “show all authors/affiliations” link), and with the initial sequencing and analysis of the human genome, which involved contributions from 452 different persons, molecular geneticists also joined the ranks of Officially Big Science. Meanwhile, us cognitive neuroscientists have long had to content ourselves with silly little papers that have only four to seven authors (maybe a dozen on a really good day). Which means, despite the pretty pictures we get to put in our papers, we’ve long had this inferiority complex about our work, and a nagging suspicion that it doesn’t really qualify as big science (full disclosure: so when I say “we”, I probably just mean “I”).
Thanks to the efforts of Bharat Biswal and 53 collaborators (yes, I counted) reported in a recent paper in PNAS, fMRI is now officially Big, Big Science. Granted, 54 authors is still small potatoes in physics-and-biology-land. And for all I know, there could be other fMRI papers with even larger author lists out there that I’ve missed. BUT THAT’S NOT THE POINT. The point is, people like me now get to run around and say we do something important.
You might think I’m being insincere here, and that I’m really poking fun at ridiculously long author lists that couldn’t possibly reflect meaningful contributions from that many people. Well, I’m not. While I’m not seriously suggesting that the mark of good science is how many authors are on the paper, I really do think that the prevalence of long author lists in a discipline are an important sign of a discipline’s maturity, and that the fact that you can get several dozen contributors to a single paper means you’re seeing a level of collaboration across different labs that previously didn’t exist.
The importance of large-scale collaboration is one of the central elements of the new PNAS article, which is appropriately entitled Toward discovery science of human brain function. What Biswal et al have done is compile the largest publicly-accessible fMRI dataset on the planet, consisting of over 1,400 scans from 35 different centers. All of the data, along with some tools for analysis, are freely available for download from NITRC. Be warned though: you’re probably going to need a couple of terabytes of free space if you want to download the entire dataset.
You might be wondering why no one’s assembled an fMRI dataset of this scope until now; after all, fMRI isn’t that new a technique, having been around for about 20 years now. The answer (or at least, one answer) is that it’s not so easy–and often flatly impossible–to combine raw fMRI datasets in any straightforward way. The problem is that the results of any given fMRI study only really make sense in the context of a particular experimental design. Functional MRI typically measures the change in signal associated with some particular task, which means that you can’t really go about combining the results of studies of phonological processing with those of thermal pain and obtain anything meaningful (actually, this isn’t entirely true; there’s a movement afoot to create image-based centralized databases that will afford meta-analyses on an even more massive scale, but that’s a post for another time). You need to ensure that the tasks people performed across different sites are at least roughly in the same ballpark.
What allowed Biswal et al to consolidate datasets to such a degree is that they focused exclusively on one particular kind of cognitive task. Or rather, they focused on a non-task: all 1400+ scans in the 1000 Functional Connectomes Project (as they’re calling it) are from participants being scanned during the “resting state”. The resting state is just what it sounds like: participants are scanned while they’re just resting; usually they’re given no specific instructions other than to lie still, relax, and not fall asleep. The typical finding is that, when you contrast this resting state with activation during virtually any kind of goal-directed processing, you get widespread activation increases in a network that’s come to be referred to as the “default” or “task-negative” network (in reference to the fact that it’s maximally active when people are in their “default” state).
One of the main (and increasingly important) applications of resting state fMRI data is in functional connectivity analyses, which aim to identify patterns of coactivation across different regions rather than mean-level changes associated with some task. The fundamental idea is that you can get a lot of traction on how the brain operates by studying how different brain regions interact with one another spontaneously over time, without having to impose an external task set. The newly released data is ideal for this kind of exploration, since you have a simply massive dataset that includes participants from all over the world scanned in a range of different settings using different scanners. So if you want to explore the functional architecture of the human brain during the resting state, this should really be your one-stop shop. (In fact, I’m tempted to say that there’s going to be much less incentive for people to collect resting-state data from now on, since there really isn’t much you’re going to learn from one sample of 20 – 30 people that you can’t learn from 1,400 people from 35+ combined samples).
Aside from introducing the dataset to the literature, Biswal et al also report a number of new findings. One neat finding is that functional parcellation of the brain using seed-based connectivity (i.e., identifying brain regions that coactivate with a particular “seed” or target region) shows marked consistency across different sites, revealing what Biswal et al call a “universal architecture”. This type of approach by itself isn’t particularly novel, as similar techniques have been used before. Bt no one’s done it on anything approaching this scale. Here’s what the results look like:
You can see that different seeds produce difference functional parcellations across the brain (the brighter areas denote ostensive boundaries).
Another interesting finding is the presence of gender and age differences in functional connectivity:
What this image shows is differences in functional connectivity with specific seed regions (the black dots) as a function of age (left) or gender (right). (The three rows reflect different techniques for producing the maps, with the upshot being that the results are very similar regardless of exactly how you do the analysis.) It isn’t often you get to see scatterplots with 1,400+ points in cognitive neuroscience, so this is a welcome sight. Although it’s also worth pointing out the inevitable downside of having huge sample sizes, which is that even tiny effects attain statistical significance. Which is to say, while the above findings are undoubtedly more representative of gender and age differences in functional connectivity than anything else you’re going to see for a long time, notice that they’re they’re very small effects (e.g., in the right panels, you can see that the differences between men and women are only a fraction of a standard deviation in size, despite the fact that these regions are probably selected because they show some of the “strongest” effects). That’s not meant as a criticism; it’s actually a very good thing, in that these modest effects are probably much closer to the truth than what previous studies have reported. Such findings should serve as an important reminder that most of the effects identified by fMRI studies are almost certainly massively inflated by small sample size (as I’ve discussed before here and in this paper).
Anyway, the bottom line is that if you’ve ever thought to yourself, “gee, I wish I could do cutting-edge fMRI research, but I really don’t want to leave my house to get a PhD; it’s almost lunchtime,” this is your big chance. You can download the data, rejoice in the magic that is the resting state, and bathe yourself freely in functional connectivity. The Biswal et al paper bills itself as “a watershed event in functional imaging,” and it’s hard to argue otherwise. Researchers now have a definitive data set to use for analyses of functional connectivity and the resting state, as well as a model for what other similar data sets might look like in the future.
More importantly, with 54 authors on the paper, fMRI is now officially big science. Prepare to suck it, Human Genome Project!
Biswal, B., Mennes, M., Zuo, X., Gohel, S., Kelly, C., Smith, S., Beckmann, C., Adelstein, J., Buckner, R., Colcombe, S., Dogonowski, A., Ernst, M., Fair, D., Hampson, M., Hoptman, M., Hyde, J., Kiviniemi, V., Kotter, R., Li, S., Lin, C., Lowe, M., Mackay, C., Madden, D., Madsen, K., Margulies, D., Mayberg, H., McMahon, K., Monk, C., Mostofsky, S., Nagel, B., Pekar, J., Peltier, S., Petersen, S., Riedl, V., Rombouts, S., Rypma, B., Schlaggar, B., Schmidt, S., Seidler, R., Siegle, G., Sorg, C., Teng, G., Veijola, J., Villringer, A., Walter, M., Wang, L., Weng, X., Whitfield-Gabrieli, S., Williamson, P., Windischberger, C., Zang, Y., Zhang, H., Castellanos, F., & Milham, M. (2010). Toward discovery science of human brain function Proceedings of the National Academy of Sciences, 107 (10), 4734-4739 DOI: 10.1073/pnas.0911855107