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An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies.

Code ↔ Paper

12 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 12 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § METHODS › Data Processing › 8P, GMSR, and spike regression (SR). ↔ Step2.ResampleProbabilityMaps/ResampleT1TissueMapstoNative.sh, the whole file · a weak match · score 0.79 · partial volume, CSF mask, WM mask, tissue, erosion, fMRIPrep
  2. [2] § METHODS › Data Processing › T1-weighted MP2RAGE processing. ↔ Step1.XNAT_BIDS_FS_PREP/Step2.Presurfer_Freesurfer.sh, lines 57–107 · score 0.70 · MP2RAGE, FreeSurfer, presurfer, recon, segmentation, UNI
  3. [3] § METHODS › Data Processing › FC estimation. ↔ Step2.ResampleProbabilityMaps/ResampleT1TissueMapstoNative.sh, the whole file · a weak match · score 0.68 · probability map, partial volume, preprocessed BOLD, fMRIPrep, voxels, space
  4. [4] § METHODS › Data Processing › ICA-AROMA. ↔ Step3.SingleEchoICARegressorsDenoising/Step.Initial_1_Melodic.sh, the whole file · a weak match · score 0.64 · skull extracted, Brain Extraction, brain mask, fMRIPrep, FSL, MELODIC
  5. [5] § METHODS › Data Processing › 8P, GMSR, and spike regression (SR). ↔ Step3b.MultiEchoRegressors/Step3a.Multiecho.Step_fmri_3_ME_ICA_NO_AROMA.ConfoundRegression.sh, the whole file · a weak match · score 0.59 · CSF mask, WM mask, eroded, confound, Probabilistic, derivatives
  6. [6] § METHODS › Data Processing › T1-weighted MP2RAGE processing. ↔ Step1.XNAT_BIDS_FS_PREP/Step1.download_xnat.sh, the whole file · a weak match · score 0.58 · UNI image, fMRI, MP2RAGE, INV2, brain
  7. [7] § METHODS › Data Processing › Removing ICA-derived noise signals. ↔ Step3a.MultiEchoICADenoising/Step_fmri_2.tedana_melodic.sh, lines 104–181 · score 0.57 · rejected components, fsl_regfilt, noise, ICA, AROMA, denoising
  8. [8] § METHODS › Data Processing › Functional data processing—minimal preprocessing (MP). ↔ Step1.XNAT_BIDS_FS_PREP/Step3.fMRIPrep.sh, lines 53–128 · score 0.57 · FreeSurfer, reverse, sequences, fMRIPrep, maps, echo
  9. [9] § RESULTS › Pipeline Rankings ↔ Step6.CombiningIQMSAcrossStreams/CombineFigs.py, lines 449–509 · score 0.56 · percentage decrement, behavioral predictions, denoising efficacy, score, ranked, KRR
  10. [10] § METHODS › Behavioral Measures and Predictions ↔ Step5.KRRs/Step1KRR_Run.m, the whole file · a weak match · score 0.53 · Kernel Ridge Regression, variables, KRR, behavioral, predictive
  11. [11] § METHODS › Behavioral Measures and Predictions ↔ Step5.KRRs/Step2.KRR_Plot.py, lines 64–108 · score 0.53 · Agreeableness, Conscientiousness, Extraversion, Neuroticism, Openness, WASI
  12. [12] § METHODS › Data Processing › ICA-AROMA. ↔ Step3.SingleEchoICARegressorsDenoising/Step2a.AROMA.sh, the whole file · a weak match · score 0.51 · fMRIPrep, ICA AROMA, brain mask, FSL, MELODIC, preprocessed

Paper

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

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The authors' code

Shell · 68 lines · 3.1 KB · Apache-2.0 · 2 matches

  1. #!/bin/env bash
  2. #Here, I resample the T1 probability maps to NATIVE space, and use associated masks to extract timeseries
  4. subject_list=$(cat /home/tconstab1/kg98_scratch/Toby/WHOLEMBBP/workspace/subjlist.txt)
  5. for s in $subject_list; do
  6. echo 'sub-${s}'
  7. module load fsl
  8. module load ants
  10. #https://neurostars.org/t/moving-images-from-mni-to-bold-epi-native-space/3833/8
  11. output_dir=/home/tconstab1/kg98_scratch/Toby/WHOLEMBBP/workspace/PipelineComparisons/ResampleT1TisseuMaskstoNative
  12. fmriprep=/home/tconstab1/kg98_scratch/Toby/WHOLEMBBP/workspace/derivatives/fmriPREP_pepolar/rest
  14. cd $output_dir
  16. if [ ! -d ${output_dir}/sub-${s}/sub-${s}/*best*CSF* ]; then
  17. mkdir sub-${s}
  18. #Take first volume
  19. fslroi ${fmriprep}/sub-${s}/func/sub-${s}_task-rest_dir-RL_echo-2_desc-preproc_bold.nii.gz sub-${s}/sub-${s}_firstvolume_task-rest_dir-RL_echo-2_desc-preproc_bold.nii.gz 0 1
  20. for TISSUE in CSF WM GM
  21. do
  22. #Resample to first vol
  23. antsApplyTransforms -d 3 -i ${fmriprep}/sub-${s}/anat/sub-${s}_label-${TISSUE}_probseg.nii.gz -r $output_dir/sub-${s}/sub-${s}_firstvolume_task-rest_dir-RL_echo-2_desc-preproc_bold.nii.gz -o sub-${s}/sub-${s}_label-${TISSUE}_probseg_Native.nii.gz -n Linear -t ${fmriprep}/sub-${s}/func/sub-${s}_task-rest_dir-RL_from-T1w_to-scanner_mode-image_xfm.txt
  24. done
  25. fi
  27. #Erosion to avoid partial voluming effects
  28. output_dir=/home/tconstab1/kg98_scratch/Toby/WHOLEMBBP/workspace/PipelineComparisons/ResampleT1TisseuMaskstoNative/sub-${s}
  29. cd $output_dir
  30. wm_probability_map=sub-${s}_label-WM_probseg_Native.nii.gz
  31. csf_probability_map=sub-${s}_label-CSF_probseg_Native.nii.gz
  32. gm_probability_map=sub-${s}_label-GM_probseg_Native.nii.gz
  34. if [ ! -f ${output_dir}/*best*CSF* ]; then
  35. #Erode CSF mask once
  36. fslmaths ${csf_probability_map} -ero -kernel 3D sub-${s}_space-NATIVE_label-CSF_probseg_eroded_1_times.nii.gz
  37. #Erode wm mask 5x
  38. input_mask=${wm_probability_map}
  39. for ((i = 1; i < 6; i++)); do
  40. output_mask="sub-${s}_space-NATIVE_label-WM_probseg_eroded_${i}_times.nii.gz"
  41. fslmaths "${input_mask}" -ero -kernel 3D "${output_mask}"
  42. input_mask="${output_mask}"
  43. done
  44. # Find the file with most erosions and at least 5 voxels
  45. selected_wm_mask=""
  46. for ((i = 5; i >= 1; i--)); do
  47. mask="sub-${s}_space-NATIVE_label-WM_probseg_eroded_${i}_times.nii.gz"
  48. voxel_count=$(fslstats "${mask}" -V | awk '{print $1}')
  50. if [ "${voxel_count}" -ge 5 ]; then
  51. selected_wm_mask="${mask}"
  52. break
  53. fi
  54. done
  56. # Define filenames for the best eroded WM mask and eroded CSF mask
  57. mv ${selected_wm_mask} best_erosion_${selected_wm_mask}
  59. #The CSF erosion sometimes deletes ALL voxels. If so, use original mask
  60. selected_eroded_csf_mask=sub-${s}_space-NATIVE_label-CSF_probseg_eroded_1_times.nii.gz
  61. voxel_count_CSF=$(fslstats "sub-${s}_space-NATIVE_label-CSF_probseg_eroded_1_times.nii.gz" -V | awk '{print $1}')
  62. if [ "${voxel_count_CSF}" -le 5 ]; then
  63. selected_eroded_csf_mask=${csf_probability_map}
  64. fi
  65. mv ${selected_eroded_csf_mask} best_erosion_${selected_eroded_csf_mask}
  66. fi
  68. done

ResampleT1TissueMapstoNative.sh at commit 210b1a6, under Apache-2.0 · at the source

Overview

Authors: Toby Constable1, Jeggan Tiego1, Kane Pavlovich1, Arshiya Sangchooli1, Priscilla Thalenberg Levi1, Bree Hartshorn1, Jessica Kwee1, Kate Fortune1, Kate Thompson1, Sam Brown1, James McLauchlan1, Nancy Ong Tran1, Rebecca O’Neill1, Mark A. Bellgrove1, Alex Fornito1
ORCID iDs: Toby Constable, Jeggan Tiego, Kane Pavlovich, Bree Hartshorn
  1. School of Psychological Sciences, Turner Institute for Brain and Mental Health, and Monash Biomedical Imaging, Monash University, Melbourne, Australia
Institutions: Monash University (Australia)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 2, pages 444-474
Dates: received 17 September 2025; accepted 11 January 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.547 · PMID 42039094 · PMCID PMC13108510 · OpenAlex W7124441218
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing, fMRI & imaging
Keywords: fMRI, Preprocessing, Multi-echo, Kernel ridge regression, Methods, Motion
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 103 references in the paper

Abstract

Resting-state functional magnetic resonance imaging (rsfMRI) is widely used to study brain-wide functional connectivity (FC). However, the resulting signals are highly noise sensitive, and the best strategies for mitigating this noise remains unclear. In 358 healthy individuals, we compared 60 multi-echo (ME) and 30 single-echo (SE) rsfMRI preprocessing pipelines across six measures of data quality and associated effect sizes in FC-based prediction models of personality and cognition (cross-validated kernel ridge regression). ME pipelines generally outperformed SE pipelines, but no single pipeline excelled at both denoising and behavioral prediction. Using a heuristic scheme to rank pipelines across benchmarks, ME optimum combination (OC) with ME independent component analysis (ICA), ICA-FMRIB’s ICA-based Xnoiseifier (FIX), and with head motion, cerebrospinal fluid, and white matter and gray matter signal regression, performed best when only considering denoising efficacy metrics. ME OC with ICA-FIX and head motion parameter regression performed best when only considering behavioral prediction results. ME OC with Automatic Removal of Motion Artifacts (AROMA) ICA, head motion parameter regression and Regressor Interpolation at Progressive Time Delays (RIPTiDe) performed best when aggregating across all evaluation metrics. These results favor ME acquisitions but show that no single denoising pipeline should be considered optimal for all purposes.

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 12 matches between paragraphs and lines of code.

cicadawing/Single-vs-Multi-Echo-fMRI-Denoising-Strategies

License: Apache-2.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 210b1a6826a5dc6682b819780816b8c5176de50f, 12 August 2025
Languages: Shell (35), Python (13), MATLAB (1)
Size: 71 files, 49 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (26 files), NumPy (13 files), pandas (13 files), NiBabel (11 files), SciPy (11 files), Nilearn (9 files), Matplotlib (8 files), scikit-learn (6 files), Nipype (4 files), seaborn (4 files), ANTs (3 files), FreeSurfer (3 files), dcm2niix (2 files), statsmodels (2 files), fMRIPrep (1 file), tedana (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
51 files
At the source: github.com/cicadawing/Single-vs-Multi-Echo-fMRI-Denoising-Strategies

Code Availability

Project code can be found here: https://github.com/cicadawing/Single-vs-Multi-Echo-fMRI-Denoising-Strategies.

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

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;
  • 49 scripts, each with its path and the digest of its content;
  • 12 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

No dataset and no data link were found in the paper.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 6 keywords, 101 references.

Cite

This paper

Constable, T., Tiego, J., Pavlovich, K., Sangchooli, A., Levi, P. T., Hartshorn, B., Kwee, J., Fortune, K., Thompson, K., Brown, S., McLauchlan, J., Tran, N. O., O’Neill, R., Bellgrove, M. A., & Fornito, A. (2026). An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies. Network neuroscience (Cambridge, Mass.), 10(2), 444-474. https://doi.org/10.1162/netn.a.547

BibTeX

@article{constable2026evaluation,
author = {Constable, Toby and Tiego, Jeggan and Pavlovich, Kane and Sangchooli, Arshiya and Levi, Priscilla Thalenberg and Hartshorn, Bree and Kwee, Jessica and Fortune, Kate and Thompson, Kate and Brown, Sam and McLauchlan, James and Tran, Nancy Ong and O’Neill, Rebecca and Bellgrove, Mark A. and Fornito, Alex},
title = {{An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {10},
number = {2},
pages = {444--474},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.547},
url = {https://doi.org/10.1162/netn.a.547},
pmid = {42039094},
pmcid = {PMC13108510}
}

RIS

TY - JOUR
AU - Constable, Toby
AU - Tiego, Jeggan
AU - Pavlovich, Kane
AU - Sangchooli, Arshiya
AU - Levi, Priscilla Thalenberg
AU - Hartshorn, Bree
AU - Kwee, Jessica
AU - Fortune, Kate
AU - Thompson, Kate
AU - Brown, Sam
AU - McLauchlan, James
AU - Tran, Nancy Ong
AU - O’Neill, Rebecca
AU - Bellgrove, Mark A.
AU - Fornito, Alex
TI - An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/04/22
VL - 10
IS - 2
SP - 444
EP - 474
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.547
UR - https://doi.org/10.1162/netn.a.547
LA - en
ER -

CSL-JSON

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