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MEPrep: A robust pipeline for multi-echo fMRI denoising and preprocessing.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Methods › Pre-denoising multi-echo fMRI data with probabilistic ICA ↔ MEPrep_Source/MEPrep_ICA-AROMA/ICA_AROMA_functions_preICA.py, lines 217–287 · score 0.92 · shifted versions, realignment parameter, maximum absolute, maxRPcorr, maximum correlations, motion parameters
  2. [2] § Methods › Pre-denoising multi-echo fMRI data with probabilistic ICA ↔ MEPrep_Source/MEPrep_ICA-AROMA/ICA_AROMA_functions_pureMotion.py, lines 213–283 · score 0.92 · shifted versions, realignment parameter, maximum absolute, maxRPcorr, maximum correlations, motion parameters
  3. [3] § Methods › Pre-denoising multi-echo fMRI data with probabilistic ICA ↔ MEPrep_Source/MEPrep_ICA-AROMA/ICA_AROMA_functions_preICA.py, lines 461–535 · score 0.68 · maxRPcorr, CSF fractions, ICA AROMA, HFC, score, edge
  4. [4] § Methods › Pre-denoising multi-echo fMRI data with probabilistic ICA ↔ MEPrep_Source/MEPrep_ICA-AROMA/ICA_AROMA_functions_pureMotion.py, lines 457–533 · score 0.68 · maxRPcorr, CSF fractions, ICA AROMA, HFC, score, edge
  5. [5] § Methods › The framework and implementation of MEPrep ↔ MEPrep_Source/fmriprep/workflows/bold/confounds.py, lines 626–769 · score 0.51 · CIFTI formats, alignment, FD, confound, WM, NIfTI
  6. [6] § Methods › The framework and implementation of MEPrep ↔ MEPrep_Source/meprep/src/workflows/bold/confounds.py, lines 670–745 · score 0.50 · CIFTI formats, pre denoising, alignment, confound, WM, multiecho

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 591 lines · 27 KB · no license · 2 matches

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It can be read at the source: MEPrep_Source/MEPrep_ICA-AROMA/ICA_AROMA_functions_preICA.py.

Overview

Authors: Zhishun Wang1,2, Feng Liu1,2, Rachel Marsh1,2, Gaurav H. Patel1,2, Jack Grinband1,2
ORCID iDs: Zhishun Wang, Jack Grinband
  1. The Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
  2. New York State Psychiatric Institute, New York, NY, United States
Institutions: New York State Psychiatric Institute; Columbia University (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1198
Dates: received 7 October 2025; accepted 8 March 2026; published online 6 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1198 · PMID 41952841 · PMCID PMC13055012 · OpenAlex W7136943186
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), methods / tools (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Graphs, fMRI & imaging
Keywords: multi-echo functional MRI (ME-fMRI), functional connectome, fMRIPrep, tedana, independent component analysis (ICA), Nipype
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Multi-echo fMRI has emerged as a powerful strategy to mitigate head motion-related noise and minimize susceptibility-related signal loss in BOLD data. Multi-echo independent component analysis (ME-ICA) effectively distinguishes between BOLD-related (TE-dependent) signals and non-BOLD (TE-independent) noise, yielding substantial enhancements in performance compared to traditional echo-combination methods. We introduce a novel ICA-based denoising step, preICA, applied to raw multi-echo data before optimal T2*-weighted echo combination. This approach, combined with ME-ICA, yields substantial gains in data denoising. Our results show that preICA significantly enhances the efficacy of optimal echo combination and ME-ICA to reduce noise. To facilitate the reliable processing of multi-echo fMRI data, we integrated preICA and ME-ICA into fMRIPrep, resulting in the creation of a robust multi-echo processing pipeline, called MEPrep, offering flexibility in preprocessing options (with or without preICA and/or ME-ICA) beyond the echo combination approach offered by fMRIPrep. We validated MEPrep on an open resting-state multi-echo fMRI dataset, demonstrating that incorporating the preICA step leads to statistically significant improvements in denoising efficacy, as evidenced by (1) enhanced T2* exponential model fitting accuracy; (2) reduced motion-related BOLD fluctuations; (3) increased temporal signal-to-noise ratio; (4) improved spatial and temporal reliability of functional connectivity; and (5) increased Shannon entropy. MEPrep outperforms existing pipelines by synergistically integrating preICA and ME-ICA, achieving superior noise suppression while preserving the neurobiological complexity of denoised BOLD signals. By automating multi-echo preprocessing within a robust pipeline, MEPrep provides a scalable solution for high-quality multi-echo fMRI data preprocessing. The pipeline is openly available, ensuring reproducibility and accessibility for the neuroimaging community.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

zswang-brainimaging/MEPrep

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: c16de3143eff7ed5e475a810adc5ca7a3a541ff5, 6 April 2026
Languages: Python (97), Shell (1)
Size: 651 files, 98 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, environment (MEPrep_Source/Dockerfile, MEPrep_Source/env.yml, MEPrep_Source/pyproject.toml, MEPrep_Source/requirements.txt, MEPrep_Source/MEPrep_ICA-AROMA/Dockerfile, MEPrep_Source/MEPrep_ICA-AROMA/requirements.txt), tests
Not found: license file, CITATION.cff, continuous integration, documentation
Tools: Nipype (50 files), NumPy (29 files), NiBabel (23 files), fMRIPrep (21 files), PyBIDS (19 files), FSL (14 files), TemplateFlow (9 files), FreeSurfer (8 files), pandas (4 files), SciPy (3 files), Matplotlib (2 files), seaborn (2 files), tedana (2 files), AFNI (1 file), BIDS Validator (1 file), h5py (1 file), Nilearn (1 file), scikit-image (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
99 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit c16de31, when its fingerprint is the one OSCR verified. How this works.

At the source: github.com/zswang-brainimaging/MEPrep

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 98 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data and Code Availability

Our MEPrep pipeline is publicly available on Docker Hub. Users can download and install MEPrep on a Docker-enabled system by running a command under Docker: “docker pull zswang2020/meprep_final:latest”. In addition to the Docker image, the full MEPrep source code and documentation have been made available in the GitHub repository at https://github.com/zswang-brainimaging/MEPrep to facilitate extension, community contributions, and integration into other neuroimaging workflows. A quick user guide on how to install and use MEPrep is available in the Supplementary Methods (https://doi.org/10.1162/IMAG.a.1198#supplementary-data). The readers can test this pipeline using the same dataset as we used, the Siemens dataset (Multi-echo Cambridge), which can be downloaded from https://openfmri.org/dataset/ds000258/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 72 references.

Cite

This paper

Wang, Z., Liu, F., Marsh, R., Patel, G. H., & Grinband, J. (2026). MEPrep: A robust pipeline for multi-echo fMRI denoising and preprocessing. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1198. https://doi.org/10.1162/imag.a.1198

BibTeX

@article{wang2026meprep,
author = {Wang, Zhishun and Liu, Feng and Marsh, Rachel and Patel, Gaurav H. and Grinband, Jack},
title = {{MEPrep: A robust pipeline for multi-echo fMRI denoising and preprocessing}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1198},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1198},
url = {https://doi.org/10.1162/imag.a.1198},
pmid = {41952841},
pmcid = {PMC13055012}
}

RIS

TY - JOUR
AU - Wang, Zhishun
AU - Liu, Feng
AU - Marsh, Rachel
AU - Patel, Gaurav H.
AU - Grinband, Jack
TI - MEPrep: A robust pipeline for multi-echo fMRI denoising and preprocessing
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/06
VL - 4
SP - IMAG.a.1198
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1198
UR - https://doi.org/10.1162/imag.a.1198
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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