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

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.

Exploring the impact of analysis software on task fMRI results.

A wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this "methodological plurality" comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. In this work, our goal is to understand how choice of software package impacts on analysis results. We use publicly shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL, and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyse, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. Qualitatively we find similarities between packages, backed up by Neurosynth association analyses that correlate similar words and phrases to all three software package's unthresholded results for each of the studies we reanalyse. However, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 to 0.684 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our efforts to make this research reproducible.

Tricuspid annular plane systolic excursion in pulmonary hypertension-Moving beyond the sector plane.

Tricuspid annular plane systolic excursion (TAPSE) is usually measured with M-mode using sector line, however, this may not align with the anatomical shortening of the right ventricular (RV). In this study, we compared the different methods to measure TAPSE using three different reference lines (sector line, anatomical line, and apico-annular line). We included 148 patients diagnosed with pulmonary arterial hypertension (PAH) who underwent TTE and right heart catheterization within 2 weeks of each other. TAPSE was measured by M-mode (sector, anatomical), 2D (sector, anatomical), or as tricuspid apico-annular displacement (TAAD). Agreement between measures was assessed using coefficient of variation (COV), Spearman's correlation, and Bland-Altman analysis. Receiver-operating characteristics and Kaplan-Meier analysis were used to explore associations with the combined outcome of death or lung transplantation at 5 years. There was a good concordance between anatomical and sector M-mode with a COV of 15.5 ± 1.6% and a bias of -0.6 ± 3.2 mm. In contrast, anatomical M-mode TAPSE and TAAD differed significantly with the mean difference of 3.3 ± 3.8 mm (COV 30.5 ± 6.1%; p < 0.0001). Among the different 2D methods, anatomical 2D agreed well with anatomical M-mode TAPSE (COV of 11.8 ± 2.0%; r = 0.89; p < 0.0001). Among the five methods, TADD had the strongest association with the combined endpoint of death or transplantation at 5 years (C-statistic 0.64, 95% confidence interval [CI] 0.57-0.71). We concluded that different measures of TAPSE are not interchangeable.