Unfortunately, small MRI samples frequently return strong associations as a matter of chance. For example, let’s say you want to see if there is a correlation between eye color and a preference for strawberries. If you look at enough groups of 25 random people, eventually you will happen upon a group in which blue-eyed people like strawberries much more than brown-eyed people do. But if five independent research groups run this study and only one of them finds this relationship between eye color and a love of berries, that will be the group most likely to publish its results – despite returning the least representative results. That’s because journals historically have preferred surprising correlations to findings of no correlation, a phenomenon known as publication bias. “The paradoxical effect is that the answer that’s the most wrong gets published if you use a small sample,” says Nico Dosenbach, an associate professor of neurology at Washington University and an author of the Nature study.
Scientists across disciplines have long known about this dynamic, but the Nature paper was able to pinpoint – at least in the case of brain-wide association studies – how many participants are needed to avoid it. Using MRI data from about 50,000 people, the authors searched for links between brain structure or activity and complex psychological traits in groups with different numbers of subjects. Subjects had to number in the thousands, on average, for studies to be replicated reliably.
The fact that so many associational studies are underpowered – and often untested in other groups of subjects before publication – has led to reports of myriad links between brain features and psychiatric disorders that are probably unreliable. These can be frightening and stigmatizing. “If you see a particular brain-activation pattern in a person with a psychiatric diagnosis, that does not mean it’s causing the disorder or symptoms,” Jernigan says. “It’s simply an association.”
But the conclusion of the Nature paper applies only to studies that compare MRIs from multiple people in order to identify differences among them relating to complex mental traits. Neuroimaging studies that show brain changes taking place within individuals, on the other hand, can be dependable even with very few participants. For instance, the first notable paper to demonstrate that most people’s brains work in roughly the same way appeared in Science in 2001 and included only six participants, says Russell A. Poldrack, a professor of psychology at Stanford University. That study’s researchers recorded each subject’s brain activity while viewing pictures of cats, faces, man-made objects and nonsense images. It did not matter that each brain was unique – the changes that took place in that brain could be assigned to seeing different types of pictures. The patterns were then tested and found to correctly predict, based on brain activity, what a participant was seeing. Those overall patterns, along with other evidence, Poldrack says, established that “when people engage in particular kinds of mental functions, particular brain areas become engaged.”
This realization that we tend to share brain patterns raises the tantalizing possibility that somewhere in the variations among them lies an explanation for why some people have a particular trait or collection of symptoms that others lack. But it’s extremely difficult to separate meaningful differences from the countless random differences that exist between all brains. One way to try to do so is to compare the MRIs of thousands of people and look for a variation – a certain pattern of neural connectivity, say – that is more common in those with a particular psychological condition. Recent advances in MRI technology, and in the ability to analyze vast amounts of data, have begun to make this sort of effort possible. For example, the Adolescent Brain Cognitive Development study has enrolled nearly 12,000 children in the United States between the ages of 9 and 10 whose brains will be scanned regularly into young adulthood. The study will also track socioeconomic variables, like parental income, and psychological attributes, like resilience, to see how they intertwine with brain development. “Without a study like this, you could never resolve these questions,” says Jernigan, director of the study’s coordinating center.