Numerical Instabilities in Analytical Pipelines Lead to Large and Meaningful Variability in Brain Networks

Abstract

The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions1-7. We explored the instability of results by instrumenting a connectome estimation pipeline with Monte Carlo Arithmetic8,9 to introduce random noise throughout. We evaluated the reliability of the connectomes, their features10,11, and the impact on analysis12,13. The stability of results was found to range from perfectly stable to highly unstable. This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks alongside increasing the robustness of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational science. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also leads to an increase in the reliability of datasets.