Isolating the sources of pipeline-variability in group-level task-fMRI results.

Alexander Bowring,Thomas E Nichols,Camille Maumet

doi:10.1002/hbm.25713

Abstract

Task‐fMRI researchers have great flexibility as to how they analyze their data, with multiple methodological options to choose from at each stage of the analysis workflow. While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group‐level results of a task‐fMRI investigation (Bowring et al., 2019, BMN). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in BMN, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages (AFNI, FSL, and SPM) across the remaining steps of the pipeline. We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets (e.g., choice of first‐level signal model had the most impact for the balloon analog risk task dataset, while first‐level noise model and group‐level model were more influential for the false belief and antisaccade task datasets, respectively). We also observe areas of the analysis workflow where changing the software package causes minimal differences in the final results, finding that the group‐level results were largely unaffected by which software package was used to model the low‐frequency fMRI drifts.

Highlights

Public speaking could be the most nerve-jangling aspect of working as a scientist
The preprocessing of each subject’s data for all three studies was assessed using the summary reports provided as part of the fMRIPrep workflow
445 Comparisons of the statistical maps obtained from the collection of pipelines applied to the three datasets have shown both the robustness and fragility of group-level task-functional magnetic resonance imaging (fMRI) results in response to variation of the software package at di↵erent stages of the analysis workflow

Summary

Introduction

Public speaking could be the most nerve-jangling aspect of working as a scientist. While presenting research findings is, in theory, a perfect opportunity to showcase the fruits of our labor to fellow experts in our subject area, a big talk can often be an anxiety-ridden experience 5 characterized by sweaty palms, a dry mouth, and butterflies in the stomach. A famous 10 investigation carried out in the 1980’s found that our own facial expressions can influence our psychological state of mind (Strack et al, 1988). For public speaking, this arms us with a plan of attack: by hiding any initial feelings of dread with a big smile and a ‘Wonder Woman’-like posture, academics can exert the genuine confidence and joy they wish for in their presentations! Various attempts to replicate the e↵ects of power-posing have been unsuccessful (Ranehill et al, 2015, Gar rison et al, 2016, Smith and Apicella, 2017), prompting a statement from one of the original study authors to retract their findings (Carney, 2017). For academics, there may not be any shortcuts to mastering an oral presentation after all

Methods

Results

Conclusion