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Related Experiment Video

Updated: Sep 29, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data.

Lea Waller¹, Susanne Erk¹, Elena Pozzi^2,3

¹Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences CCM, Berlin, Germany.

Human Brain Mapping

|March 19, 2022

Summary

This summary is machine-generated.

The Harmonized Analysis of Functional MRI pipeline (HALFpipe) addresses the neuroimaging reproducibility crisis by offering a user-friendly, open-source tool for standardized fMRI data analysis. It ensures reproducible results through comprehensive preprocessing, quality assessment, and advanced group-level statistics.

Keywords:

fMRI harmonization image analysis meta-analysis pipeline open source reproducibility

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Area of Science:

Neuroimaging
Computational Neuroscience
Data Science

Background:

The reproducibility crisis in neuroimaging necessitates standardized data processing workflows.
Existing tools may lack comprehensive features or require strict data formatting.
The ENIGMA consortium sought a solution to enhance fMRI analysis reproducibility.

Purpose of the Study:

To introduce HALFpipe, an open-source, containerized tool for reproducible task-based and resting-state fMRI analysis.
To extend fMRIPrep's capabilities with additional preprocessing and post-processing functions.
To provide an integrated solution for preprocessing, quality assessment, and group-level statistics.

Main Methods:

HALFpipe utilizes fMRIPrep for state-of-the-art preprocessing, adaptable to non-BIDS data.
Includes extended preprocessing: spatial smoothing, grand mean scaling, temporal filtering, and confound regression.
Features interactive quality assessment webpage, individual-level feature extraction (connectivity, ReHo, fALFF), and flexible group-level factorial models.

Main Results:

HALFpipe streamlines fMRI analysis by integrating preprocessing, quality assessment, and advanced statistical modeling.
It supports a wide range of analytical features, enabling evaluation of numerous preprocessing settings.
The pipeline facilitates reproducible neuroimaging research by standardizing data analysis procedures.

Conclusions:

HALFpipe represents a significant advancement in addressing the neuroimaging reproducibility crisis.
It provides a comprehensive, user-friendly workflow for reproducible fMRI data analysis.
The tool empowers the scientific community to conduct more reliable and robust neuroimaging studies.