Swedish Study Highlights Potential Flaws in fMRI Findings
Swedish researchers have warned that the software packages used to
analyze the results of functional magnetic resonance imaging (fMRI)
contain flaws that increase the chance of a false positive by as much as 70
percent. Anders Eklund, “Cluster failure: Why fMRI inferences for spatial
extent have inflated false-positive rates,” PNAS, June 2016.
For more than 15 years, scientists have used fMRI analyses to explore the
food addiction framework and the effect of food advertising on the brain,
among other things.
The Swedish study explains that the majority of fMRI studies rely on
SPM, FSL or AFNI software packages based on “parametric statistical
methods that depend on a variety of assumptions,” even though these
methods have only been validated with simulated—as opposed to real—
data. As a result, the researchers questioned whether these methods
could potentially show brain activity in its absence, raising the issue of
false positives.
Using resting-state data from 499 healthy controls to conduct 3 million
task-group analyses, the study’s authors apparently estimated the
incidence of significant results and concluded that “the parametric levels
can give a very high degree of false positives” for clusterwise inference.
“In theory, we should find 5% false positives (for a significance threshold
of 5%), but instead we found that the most common software packages
for fMRI analysis (SPM, FSL, AFNI) can result in false-positive rates of
up to 70%,” explain the researchers. “These results question the validity
of some 40,000 fMRI studies and may have a large impact on the
interpretation of neuroimaging results.”
Highlighting new graphics cards with increased processing power, the
study offers another statistical method “in which few assumptions are
made and significantly more calculations—a thousand times more—are
done, which yields a significantly more certain result,” according to a
June 28, 2016, Linköping University press release.
“Our results suggest that the principal cause of the invalid cluster
inferences is spatial autocorrelation functions that do not follow the
assumed Gaussian shape,” concludes the study. “It is not feasible to
redo 40,000 fMRI studies, and lamentable archiving and data-sharing
practices mean most could not be reanalyzed either. Considering that it
is now possible to evaluate common statistical methods using real fMRI
data, the fMRI community should, in our opinion, focus on validation of
existing methods.”
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