HALFpipe

HALFpipe provides a standardized, containerized workflow for preprocessing, quality assessment, feature extraction, and statistical analysis of task-based and resting-state functional magnetic resonance imaging (fMRI) data to support reproducible neuroimaging research.

Key Features:

• Preprocessing: Utilizes fMRIPrep for preprocessing and extends it with spatial smoothing, grand mean scaling, temporal filtering, confound regression, and eliminates the need for conversion to Brain Imaging Data Structure (BIDS) format.
• Quality Assessment: Performs quality assessment of key preprocessing outputs and raw data to evaluate data integrity.
• Post-Processing Functions: Provides task-based activation analysis, seed-based connectivity, network-template (dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF).
• Combinatorial Feature Evaluation: Enables evaluation of multiple features or preprocessing settings within a single run.
• Group-Level Analysis: Implements flexible factorial models for mixed-effects regression at the group level and applies multiple-comparison corrections.
• Harmonized Analysis: Applies harmonized analysis principles to promote consistency and reproducibility across studies.

Scientific Applications:

• Task-based fMRI analysis: Supports detection and group-level comparison of task-evoked activation patterns in task-based fMRI studies.
• Resting-state connectivity and intrinsic activity: Enables seed-based connectivity, network-template/dual regression, atlas-based connectivity matrices, ReHo, and fALFF extraction from resting-state fMRI.
• Consortium and multi-site studies (ENIGMA): Facilitates standardized, reproducible preprocessing and analysis workflows suitable for consortium-scale projects such as ENIGMA.

Methodology:

Computational steps explicitly include fMRIPrep preprocessing, spatial smoothing, grand mean scaling, temporal filtering, confound regression, extraction of task activation, seed-based connectivity, network-template/dual regression, atlas-based functional connectivity matrices, ReHo and fALFF computation, combinatorial evaluation of preprocessing options, and group-level flexible factorial mixed-effects regression with multiple-comparison correction.