Functional magnetic resonance imaging (fMRI) offers a way to gain important information about brain function and is commonly used by researchers to understand neurological activity. But a recent Stanford study shows that conventional methods of analyzing fMRI scans may "systematically skew which regions of the brain appear to be activating, potentially [biasing] hundreds of papers that use the technique."
The research was conducted in the lab of Brian Knutson, PhD, associate professor of psychology, who studies reward processing in a small area of the brain known as the nucleus accumbens. A recent Stanford Report article offers more details about the researchers' findings:
... [F]MRI has only been in use since the mid-1990s. Many of the most common analyses in use today are holdovers from older, lower-resolution types of imaging and seem to have some undesired effects on the finer-grained signals fMRI can provide.
Knutson and [Matthew Sacchet, a PhD student in the meurosciences program at the School of Medicine] found that when researchers process fMRI data with a traditional "smoothing kernel" of 8mm, they end up averaging their images over too large an area. Activity in smaller brain structures can then be overlooked, or even shifted to areas that receive more blood flow and where the blood oxygenation level-dependent signal is stronger.
"It might seem strange that a systematic bias like that could bias the whole field," Knutson said. "But if half the people use 8mm and half use 4mm, you might end up with very different results, and it could add up."
Sacchet, who provided the above image from the study, explains that the yellow, orange and red areas highlight "regions that are likely to be involved in reward anticipation when applying relatively large spatial smoothing. Hotter colors indicate higher probability of association."