fMRIPrep

fMRIPrep preprocesses functional magnetic resonance imaging (fMRI) data to produce standardized, analysis-ready outputs for task-based and resting-state neuroimaging studies.

Key Features:

• BIDS-based standardization: Standardizes input (scanner MRI data) and output datasets using the Brain Imaging Data Structure (BIDS).
• Preprocessing steps: Performs coregistration, spatial normalization, unwarping, segmentation, and skull stripping of fMRI and anatomical data.
• Noise component extraction: Extracts noise components from fMRI time series for subsequent confound handling.
• Analysis-ready outputs: Produces outputs prepared for statistical modeling and group-level analyses, including files suitable for both surface and volume-based statistics.
• fMRI modalities supported: Prepares data for task-based and resting-state fMRI analyses.

Scientific Applications:

• Task-based fMRI analysis: Provides preprocessed data for voxel- and surface-based statistical analysis of task-evoked activity.
• Resting-state fMRI analysis: Supplies cleaned, standardized time series suitable for functional connectivity analyses.
• Group-level statistics: Enables aggregation of preprocessed datasets for group comparisons and population-level inference.
• Surface and volume-based statistics: Supports analyses performed in cortical surface space and volumetric space.
• Graph theory and network analysis: Delivers data appropriate for graph theoretic measures of brain networks.

Methodology:

Computational steps explicitly include BIDS-based input/output standardization and preprocessing operations: coregistration, normalization, unwarping, noise component extraction, segmentation, and skull stripping.