The practical impact of numerical variability on structural MRI measures of Parkinson’s disease

Abstract

Abstract Numerical variability is rarely quantified in neuroimaging despite many measures relying on subtle morphometric differences across individuals. We instrumented FreeSurfer 7.3.1, a widely used neuroimaging pipeline, to simulate numerical differences across computational environments, and used it to measure numerical variability in MRI analyses of Parkinson’s disease patients and controls. In multiple cortical and subcortical regions, numerical variation reached nearly one-third of the population variability, altering statistical conclusions about group differences and clinical associations. To assess the impact of numerical noise in existing studies, we developed a practical tool that estimates the Numerical-Population Variability Ratio (NPVR) in a study, and propagates the resulting numerical variability to common statistics and associated p-values. By applying this framework to thirteen previously published studies reporting MRI measures in Parkinson’s disease, we quantified the probability of numerically induced false positives and false negatives in the literature, highlighting a substantial impact of numerical variability on MRI measures of Parkinson’s disease with an average significance-flip probability of 5% for cross-sectional studies and 10% for longitudinal studies. These results underscore the importance of systematically evaluating numerical stability in neuroimaging and provide a practical framework to do so.