fMRIPrep: a robust preprocessing pipeline for functional MRI.

Area of Science:

• Neuroimaging
• Computational Neuroscience

Background:

• Functional magnetic resonance imaging (fMRI) preprocessing is complex, with researchers often creating custom workflows.
• The increasing complexity of fMRI acquisition and processing necessitates standardized preprocessing methods.

Purpose of the Study:

• To introduce fMRIPrep, an analysis-agnostic tool for robust and reproducible fMRI data preprocessing.
• To address the challenges posed by complex and variable fMRI datasets.

Main Methods:

• fMRIPrep automatically adapts a best-in-breed preprocessing workflow to individual datasets.
• An iterative integration framework with visual assessment checkpoints was used for software testing.

Main Results:

• fMRIPrep demonstrated robust, high-quality preprocessing results across a diverse fMRI data collection.
• The tool introduces less uncontrolled spatial smoothness compared to common preprocessing tools.

Conclusions:

• fMRIPrep provides neuroscientists with an easy-to-use and transparent preprocessing workflow.
• This tool enhances the validity of inference and interpretability of fMRI results.