A whole pile of “this is how your brain looks like” fMRI-based science has been potentially invalidated because someone finally got around to checking the data. The problem is simple: to get from a high-resolution magnetic resonance imaging scan of the brain to a scientific conclusion, the brain is divided into tiny “voxels”. Software, rather than humans, then scans the voxels looking for clusters. When you see a claim that “scientists know when you're about to move an arm: these images prove it”, they're interpreting what they're told by the statistical software. Now, boffins from Sweden and the UK have cast doubt on the quality of the science, because of problems with the statistical software: it produces way too many false positives. In this paper at PNAS, they write: “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.” For example, a bug that's been sitting in a package called 3dClustSim for 15 years, fixed in May 2015, produced bad results (3dClustSim is part of the AFNI suite; the others are SPM and FSL). That's not a gentle nudge that some results might be overstated: it's more like making a bonfire of thousands of scientific papers. Further: “Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape”. The researchers used published fMRI results, and along the way they swipe the fMRI community for their “lamentable archiving and data-sharing practices” that prevent most of the discipline's body of work being re-analysed. ® Sponsored: Practical tips for Office 365 tenant-to-tenant migration
Scientists from Germany and the UK have cast doubt on the science behind high - resolution magnetic resonance imaging brain scans after they found the software interpreting it had a false positive rate of up to 50 % . Their findings call into question around 40,000 MRI studies and " may have a large impact on the interpretation of neuroimaging data " . The experts also criticised " lamentable archiving and data - sharing practices " .