Since the original invention of functional Magnetic Resonance Imaging (fMRI) in the early 1990s, the field--and the public--have become nearly obsessed with the technique. The result has been literally tens of thousands of functional brain imaging studies, investigating the neural underpinnings of everything from addition to addiction. Indeed, the beautiful pictures that fMRI produces have become so ubiquitous in both the public and the scientific imagination that we might fairly call the last 20 years of cognitive neuroscience "the age of the image".
That was then, this is now. While you shouldn't expect to see any less brain imaging in the coming years, we are undergoing a sea change in the way those images are being interpreted. Instead of reading these images individually, researchers over the past few years have begun pioneering efforts to interpret these images collectively. Since no human being can hope to assimilate and make visual sense out of thousands of brain images, investigating all manner of psychological phenomena, these researchers use various computational methods to analyze and find hidden patterns in all of that data.
This movement--recently dubbed "Cognitive Neuroscience 2.0" by Tal Yarkoni and colleagues--promises to radically alter our understanding both of the brain, and of brain science. In this post I'll mention one of the ways it is changing our understanding of brain organization, and in Part II, I'll talk about its promise to change the way we pursue psychology.
For a simple example of the sort of surprising insight that collective data interpretation can produce, consider the principle of selectivity. A guiding ideal of brain science for at least 50 years, selectivity is the notion that individual neurons, as well as larger networks, respond to only a narrow class of stimuli-straight lines but not curved ones; faces but not houses; nouns but not verbs. The principle of selectivity is what is behind the popular understanding of a brain composed of neural specialists clustered together like ethnic neighborhoods in New York-vision downtown, language over on the west side, executive control somewhere in midtown Manhattan. Certainly individual fMRI studies can reinforce this impression; for any given investigation, the brain will "light up" in only a few places, apparently highlighting the specialists responsible for the task under investigation.
But when you look at brain activity across many, many such individual studies, things don't really look that way. One early study investigated 135 experiments in four different cognitive domains: language, vision, attention and memory, and color coded the regions that were activated by tasks in each using standard 4-color printing techniques. Instead of seeing large regions of the brain painted in simple primary colors, indicating dedication to tasks in a single domain, each brain region took on its own mixed hue, reflecting its contribution to many different tasks across the four domains. This finding was recently confirmed by a much larger study involving over 1,100 experiments across 11 different cognitive domains. Bottom line: the neighborhoods of the brain are highly integrated and functionally diverse.
The picture below represents those brain demographics on a cool-to-hot scale, in a way designed to emphasize the differences in diversity between regions. It uses a scale of zero (black) to one (bright red), where zero means all activity restricted to a single domain, and one means a region that is equally likely to be active in all 11 domains, like a perfectly integrated neighborhood. It turns out that the 78 standard anatomical regions of the brain have an average diversity of 0.70! In fact, a typical brain region is active in tasks across nine of the eleven domains.
Map of regional brain diversity
This probably comes as a surprise to many readers. But when you think about it from an evolutionary perspective, it makes some sense. If the brain were a collection of regionally segregated specialists, then that would mean that new cognitive abilities would emerge only via the development of new dedicated brain tissue, like adding a new specialized organ or appendage. That's one possible pathway, of course, but evolution is also known to repurpose existing resources to meet emerging challenges. If that's the way the brain evolved-and it does seem to be a more efficient use of metabolically expensive brain matter-then what we would expect to see is what we indeed see: regions used and reused for a variety of purposes in different circumstances.
One important upshot of this is that there typically isn't a "brain area for X". The brain doesn't operate by differentially activating one or a few dedicated local regions to achieve some task. Instead, the brain dynamically assembles different coalitions of partners. Achieving a task is not a matter of finding a single neural specialist, but is rather about putting together the right neural team for the job.
[Image prepared by Josh Kinnison and Srikanth Padmala, Indiana University.]
