Systematic fMRI signal differences across cohorts alter lifespan trajectories of functional brain networks.

Code ↔ Paper

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The 2 matches

[1] § Methods › Data harmonization ↔ R/R/combat.R, lines 12–70 · score 0.52 · batch factor, ComBat, variance, harmonization, covariates, model
[2] § Methods › Data harmonization ↔ R/R/covbat.R, lines 11–80 · score 0.51 · CovBat, ComBat, variance, harmonization, covariates, model

Paper

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The authors' code

R · 220 lines · 8.4 KB · no license · 1 match

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Overview

Authors: Micaela Y Chan¹, Liang Han¹, Gagan S Wig^1,2

ORCID iDs: Micaela Y Chan, Liang Han, Gagan S Wig

Institutions: The University of Texas at Dallas (United States); The University of Texas Southwestern Medical Center (United States)

Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1338

Dates: received 15 January 2026; accepted 15 July 2026; published online 24 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1162/imag.a.1338 · PMID 42643755 · PMCID PMC13504956 · OpenAlex W7171803933

Open access: diamond, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism), systems (subfield)

Methods: Spectral & time-frequency, Preprocessing, Connectivity, Graphs, fMRI & imaging

Keywords: resting-state functional correlation, brain network, lifespan neuroimaging, data harmonization, human connectome project

MeSH: Aging*, Brain*, Connectome*, Magnetic Resonance Imaging*, Nerve Net*, Adult, Aged, Aged, 80 and over, Cohort Studies, Female, Humans, Longevity, Male, Middle Aged, Signal-To-Noise Ratio, Young Adult (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: NIA NIH HHS (U01 AG052564, R01 AG092219, R01 AG063930); NIMH NIH HHS (U01 MH109589, U54 MH091657)

Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Large-scale lifespan neuroimaging studies increasingly integrate data across distinct cohorts to characterize trajectories of brain development and aging. However, systematic differences in acquisition protocols and hardware across cohorts can alter signal characteristics in ways that bias downstream analyses. Here, we examine three cohorts from the Human Connectome Project (HCP), spanning development (HCP-D), young adulthood (HCP-YA) and aging (HCP-A), to illustrate this issue and evaluate existing strategies to mitigate it. HCP has set standards for open, deeply phenotyped, high-resolution human neuroimaging, which are frequently used as high-quality reference datasets in tool validation, replication studies, and cross-cohort meta-analyses. However, neuroimaging acquisitions have differed across HCP cohorts because of changes in scanner hardware and acquisition sequences across study phases. Because of HCP’s widespread usage, even modest protocol differences between cohorts—and their downstream effects—can have outsized impacts on the field of neuroscience research. Our analysis reveals that the HCP-YA cohort exhibits systematically weaker temporal signal-to-noise ratio (tSNR) relative to HCP-D/A. These signal quality discrepancies propagate to downstream analyses, leading to differences in overall resting-state functional correlations and whole-brain and node-level measures of resting-state network organization (e.g., system segregation, modularity, participation coefficient). Consistent with protocol-driven signal differences, resting-state network measures derived from HCP-YA depart from expected lifespan trajectories, as confirmed by examination of two other lifespan datasets. Harmonization approaches accounting for protocol and scanner-model differences substantially lessen these artifactual differences in brain network measures. Our findings underscore that signal differences do not merely introduce noise, but can qualitatively alter estimated lifespan trajectories of functional network organization, including partially inverting expected lifespan patterns. Without appropriate harmonization, analyses that combine HCP cohorts can therefore result in biologically misleading inferences about brain development and aging. We demonstrate how small acquisition differences bias resting-state-derived network metrics, and how these effects can be mitigated. This work advances best practices for valid inference in multi-cohort lifespan neuroscience research.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

andy1764/CovBat_Harmonization

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: cacf370af1ffc87485f50733f797b236c4f251a7, 13 March 2023

Languages: R (4), Python (3)

Size: 25 files, 7 scripts

Software Heritage: not archived

Found in: “Data and Code Availability”

Holds: README, environment (R/DESCRIPTION), documentation

Not found: license file, CITATION.cff, tests, continuous integration

Tools: NumPy (3 files), pandas (3 files), scikit-learn (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

8 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 cacf370, when its fingerprint is the one OSCR verified. How this works.

Python/R-test.R — R, 34 lines, shown from its source
Python/combat_brent.py — Python, 229 lines, shown from its source
Python/covbat.py — Python, 349 lines, shown from its source
Python/test.py — Python, 43 lines, shown from its source
R/R/combat.R — R, 220 lines, 1 match, shown from its source
R/R/covbat.R — R, 252 lines, 1 match, shown from its source
R/R/utils.R — R, 308 lines, shown from its source
README.md — Text, 52 lines, shown from its source

At the source: github.com/andy1764/CovBat_Harmonization

mychan24/system-segregation-and-graph-tools

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: e7c30345b8ebf29e3a9ab44593d95e41cd0b16ed, 15 October 2024

Languages: MATLAB (11), R (6)

Size: 22 files, 17 scripts

Software Heritage: archived

Found in: “Data and Code Availability”

Holds: README, license file, environment (DESCRIPTION), tests, continuous integration

Not found: CITATION.cff, documentation

Tools: GIfTI library for MATLAB (3 files), cifti-matlab (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

19 files

MATLAB/export/colormap_roi.m — MATLAB, 208 lines
MATLAB/export/colormap_roi_meta.m — MATLAB, 240 lines
MATLAB/fsLR2roizmat.m — MATLAB, 98 lines
MATLAB/fsLR2roizmat_ciftidat_ciftinode.m — MATLAB, 62 lines
MATLAB/fsLR2roizmat_ciftidat_giftinode.m — MATLAB, 81 lines
MATLAB/map_to_gii.m — MATLAB, 44 lines
MATLAB/mat2cytoscape.m — MATLAB, 42 lines
MATLAB/segregation.m — MATLAB, 71 lines
MATLAB/segregation_by_type_eqcont.m — MATLAB, 155 lines
MATLAB/segregation_by_type_prcont.m — MATLAB, 164 lines
R/segregation.R — R, 63 lines
R/segregation_by_type_eqcont.R — R, 100 lines
R/segregation_by_type_prcont.R — R, 105 lines
R/segregation_individual_systems.R — R, 55 lines
test/b_other_example.m — MATLAB, 27 lines
test/testR.R — R, 63 lines
test/test_segtype_proc.R — R, 42 lines
LICENSE — License, 21 lines
README.md — Text, 20 lines

At the source: github.com/mychan24/system-segregation-and-graph-tools

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
24 scripts, each with its path and the digest of its content;
2 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

humanconnectome.org/study/hcp-lifespan-aging — at Human Connectome Project; found in the references
humanconnectome.org/study/hcp-young-adult — at Human Connectome Project; found in the references
openneuro:ds004856 — at OpenNeuro; found in “Data and Code Availability”

Data and Code Availability

HCP-YA S1200 release data (downloaded March 2023) are available via the Human Connectome Project website (https://www.humanconnectome.org). HCP-D and HCP-A release 2.0 data (downloaded August 2022) are available via the NIMH Data Archive (NDA; https://nda.nih.gov). NKI processed data were downloaded from the NKI-RS repository (S3 bucket). DLBS data are available on OpenNeuro (https://openneuro.org/datasets/ds004856). CovBat was applied using R code available on https://github.com/andy1764/CovBat_Harmonization. RSFC matrix extraction and brain system segregation calculation were completed using code available on https://github.com/mychan24/system-segregation-and-graph-tools. Modularity, participation coefficient, and clustering coefficient were calculated using the Brain Connectivity Toolbox (BCT; https://sites.google.com/site/bctnet).

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 5 keywords, 16 MeSH terms, 2 funders, 60 references.

Cite

This paper

Chan, M. Y., Han, L., & Wig, G. S. (2026). Systematic fMRI signal differences across cohorts alter lifespan trajectories of functional brain networks. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1338. https://doi.org/10.1162/imag.a.1338

BibTeX

@article{chan2026systematic, author = {Chan, Micaela Y and Han, Liang and Wig, Gagan S}, title = {{Systematic fMRI signal differences across cohorts alter lifespan trajectories of functional brain networks}}, journal = {Imaging neuroscience (Cambridge, Mass.)}, year = {2026}, month = aug, volume = {4}, pages = {IMAG.a.1338}, publisher = {MIT Press}, issn = {2837-6056}, doi = {10.1162/imag.a.1338}, url = {https://doi.org/10.1162/imag.a.1338}, pmid = {42643755}, pmcid = {PMC13504956} }

RIS

TY - JOUR AU - Chan, Micaela Y AU - Han, Liang AU - Wig, Gagan S TI - Systematic fMRI signal differences across cohorts alter lifespan trajectories of functional brain networks T2 - Imaging neuroscience (Cambridge, Mass.) J2 - Imaging Neurosci (Camb) PY - 2026 DA - 2026/08/24 VL - 4 SP - IMAG.a.1338 SN - 2837-6056 PB - MIT Press DO - 10.1162/imag.a.1338 UR - https://doi.org/10.1162/imag.a.1338 LA - en ER -

CSL-JSON

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