Exploring the impact of analysis software on task fMRI results.

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Data Science

Background:

• Functional magnetic resonance imaging (fMRI) research relies on diverse analysis tools for understanding cognitive function.
• Variability in fMRI analysis pipelines, software, and versions can lead to inconsistent scientific results.
• Reproducibility and data sharing are increasingly critical in neuroimaging research.

Purpose of the Study:

• To investigate the impact of different neuroimaging software packages on fMRI analysis outcomes.
• To quantify and qualify the variability introduced by using AFNI, FSL, and SPM for fMRI data analysis.

Main Methods:

• Reanalyzed three publicly available task fMRI datasets using AFNI, FSL, and SPM.
• Employed parametric and nonparametric inference methods for analysis.
• Compared results quantitatively (Dice similarity coefficients) and qualitatively, referencing original publications for processing details.

Main Results:

• Qualitative similarities were observed across software packages, supported by Neurosynth association analyses.
• Significant quantitative differences were found, with Dice similarity coefficients for thresholded maps ranging from 0.000 to 0.684.
• Challenges in reanalyzing published studies highlight the complexities of ensuring reproducibility.

Conclusions:

• The choice of neuroimaging software package demonstrably influences fMRI analysis results.
• While some qualitative consistencies exist, quantitative differences necessitate careful consideration of software in fMRI research.
• This study underscores the importance of methodological transparency and reproducible research practices in cognitive neuroscience.