Diffusion magnetic resonance imaging (dMRI) is one of the most prevalent methods to investigate the micro- and macrostructure of the human brain in vivo. Prior to any group analysis, dMRI data are generally processed to alleviate adverse effects of known artefacts such as signal drift, data noise and outliers, subject motion, and geometric distortions. These dMRI data processing steps are often combined in automated pipelines, such as the one of the Human Connectome Project (HCP). While improving the performance of processing tools has clearly shown its benefits at each individual step along the pipeline, it remains unclear whether, and to what degree, choices for specific user-defined parameter settings can affect the final outcome of group analyses. In this work, we demonstrate how making such a choice for a particular processing step of the pipeline drives the final outcome of a group study. More specifically, we performed a dMRI group analysis on gender using HCP data sets and compared the results obtained with two diffusion tensor imaging estimation methods: the widely used ordinary linear least squares (OLLS) and the more reliable iterative weighted linear least squares (IWLLS). Our results show that the effect sizes for group analyses are significantly smaller with IWLLS than with OLLS. While previous literature has demonstrated higher estimation reliability with IWLLS than with OLLS using simulations, this work now also shows how OLLS can produce a larger number of false positives than IWLLS in a typical group study. We therefore highly recommend using the IWLLS method. By raising awareness of how the choice of estimator can artificially inflate effect size and thus alter the final outcome, this work may contribute to improvement of the reliability and validity of dMRI group studies.
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