Tom Nichols and colleagues have published a paper on the software used to analyze fMRI data:
Functional MRI (fMRI) is 25 years old, yet surprisingly its most common statistical methods have not been validated using real data. Here, we used resting-state fMRI data from 499 healthy controls to conduct 3 million task group analyses. Using this null data with different experimental designs, we estimate the incidence of significant results. 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%. These results question the validity of some 40,000 fMRI studies and may have a large impact on the interpretation of neuroimaging results.
The ongoing discussion over the use of prediction algorithms in the criminal justice system reminds me a bit of the introduction of DNA evidence decades ago. Ultimately, there is a technology that few people truly understand and there are questions as to whether the information they provide is fair or accurate.
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