Researchers are facing up to methodological flaws that plague functional magnetic resonance imaging, but the interpretative problems might be harder to solve

Back in 2009, researchers at the University of California, Santa Barbara performed a curious experiment. In many ways, it was routine — they placed a subject in the brain scanner, displayed some images, and monitored how the subject’s brain responded. The measured brain activity showed up on the scans as red hot spots, like many other neuroimaging studies.



Except that this time, the subject was an Atlantic salmon, and it was dead.

Dead fish do not normally exhibit any kind of brain activity, of course. The study was a tongue-in-cheek reminder of the problems with brain scanning studies. Those colorful images of the human brain found in virtually all news media may have captivated the imagination of the public, but they have also been subject of controversy among scientists over the past decade or so. In fact, neuro-imagers are now debating how reliable brain scanning studies actually are, and are still mostly in the dark about exactly what it means when they see some part of the brain “light up.”

Brain scans decode dream content | Mo Costandi Read more

That’s the introduction to my latest feature, BOLD Assumptions: Why Brain Scans Are Not Always What They Seem, which is online now at a brand new neuroscience website called Brain Decoder.

The article focuses on the problems plaguing functional neuroimaging research - small sample sizes, low statistical power, and lack of replicability - and how researchers in the field are facing up to them. It also touches on problems with how the results from neuroimaging experiments are interpreted. These are arguably deeper issues, because they arise from gaps in our understanding.

Neuroscientists know that the method detects changes in blood flow around the brain, and therefore only measures brain activity indirectly. And while most of them are confident in the notion of neurovascular coupling - that local increases in blood flow are closely associated with increased cellular activity - they can’t be certain that this is the case.

Nor do they know exactly what it means when they see changes in blood flow, and a study published earlier this week re-emphasises this point.

Danielle Bassett of the University of Pennsylvania and her colleagues scanned participants’ brains repeatedly while they learned and mastered a simple motor task over a six-week period, examining how activity in a distributed network of key ‘hub’ regions changed during that time.

Typically, smaller signals are taken to mean reduced neuronal activity, and this is often associated with poorer performance on the task at hand. They found that faster learners exhibit decreased network activity, however, indicating reduced communication between the hubs within it. This seems counterintuitive, but one possible explanation is that faster learners have brains that process information more efficiently.