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

Code ↔ Paper

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

  1. # Author: Jean-Philippe Fortin, [email hidden]
  2. # This is a modification of the ComBat function code from the sva package that
  3. # can be found at https://bioconductor.org/packages/release/bioc/html/sva.html
  4. # The original and present code is under the Artistic License 2.0.
  5. # If using this code, make sure you agree and accept this license.
  6. # Code optimization improved by Richard Beare
  8. # Modified by Andrew Chen for covbat.R
  9. # Added functionality to use only training data as input and to have
  10. # residualized observations as output
  12. #' Combatting batch effects when combining batches of gene expression microarray
  13. #' data
  14. #'
  15. #' \code{combat} is a modified version of the ComBat code written by
  16. #' Jean-Philippe Fortin available at
  17. #' \url{https://github.com/Jfortin1/ComBatHarmonization/}. The function
  18. #' harmonizes the mean and variance of observations across sites under an
  19. #' empirical Bayes framework. \code{combat} additionally includes options
  20. #' to output residualized observations, estimate coefficients using a training
  21. #' subset, and regress out unwanted confounders.
  22. #'
  23. #' @param dat A \emph{p x n} matrix (or object coercible by \link[base]{as.matrix}
  24. #' to a numeric matrix) of observations where \emph{p} is the number of
  25. #' features and \emph{n} is the number of subjects.
  26. #' @param batch Factor (or object coercible by \link[base]{as.factor} to a
  27. #' factor) designating batch IDs.
  28. #' @param mod An optional design matrix to preserve, usually output of
  29. #' \link[stats]{model.matrix}.
  30. #' @param nuisance.mod An optional design matrix to regress out, usually output
  31. #' of \link[stats]{model.matrix} without intercept.
  32. #' @param train Optional logical vector specifying subset of observations used
  33. #' to estimate coefficients.
  34. #' @param resid Whether to leave intercept and covariates regressed out.
  35. #' @param eb If \code{TRUE}, uses ComBat model with empirical Bayes.
  36. #' @param parametric If \code{TRUE}, uses parametric updates.
  37. #' @param mean.only If \code{TRUE}, ComBat step does not harmonize variance.
  38. #' @param verbose Whether additional details are printed to console.
  39. #'
  40. #' @return
  41. #' @export
  42. #'
  43. #' @examples
  44. #'
  45. #' @seealso Modification to ComBat to regress out unwanted confounders
  46. #' proposed by Wachinger et al. (2020), \url{https://arxiv.org/abs/2002.05049}
  47. combat <- function(dat, batch, mod = NULL, nuisance.mod = NULL,
  48. train = NULL, resid = FALSE, eb = TRUE,
  49. parametric = TRUE, mean.only = FALSE, verbose = FALSE)
  50. {
  51. dat <- as.matrix(dat)
  53. .checkConstantRows <- function(dat){
  54. sds <- rowSds(dat)
  55. ns <- sum(sds==0)
  56. if (ns>0){
  57. message <- paste0(ns, " rows (features) were found to be constant across samples. Please remove these rows before running ComBat.")
  58. stop(message)
  59. }
  60. }
  61. .checkConstantRows(dat)
  62. if (eb){
  63. if (verbose) cat("[combat] Performing ComBat with empirical Bayes\n")
  64. } else {
  65. if (verbose) cat("[combat] Performing ComBat without empirical Bayes (L/S model)\n")
  66. }
  67. # make batch a factor and make a set of indicators for batch
  68. batch <- as.factor(batch)
  69. batchmod <- model.matrix(~-1+batch)
  70. if (verbose) cat("[combat] Found",nlevels(batch),'batches\n')
  72. # add nuisance mod to mod, if specified
  73. if (!is.null(nuisance.mod)) {
  74. mod <- cbind(mod, nuisance.mod)
  75. }
  77. # A few other characteristics on the batches
  78. n.batch <- nlevels(batch)
  79. batches <- lapply(levels(batch), function(x)which(batch==x))
  80. n.batches <- sapply(batches, length)
  81. n.array <- sum(n.batches)
  82. #combine batch variable and covariates
  83. design <- cbind(batchmod,mod)
  84. # check for intercept in covariates, and drop if present
  85. check <- apply(design, 2, function(x) all(x == 1))
  86. design <- as.matrix(design[,!check])
  88. # Number of covariates or covariate levels
  89. if (verbose) cat("[combat] Adjusting for",ncol(design)-ncol(batchmod),'covariate(s) or covariate level(s)\n')
  91. # Check if the design is confounded
  92. if(qr(design)$rank<ncol(design)){
  93. if(ncol(design)==(n.batch+1)){
  94. stop("[combat] The covariate is confounded with batch. Remove the covariate and rerun ComBat.")
  95. }
  96. if(ncol(design)>(n.batch+1)){
  97. if((qr(design[,-c(1:n.batch)])$rank<ncol(design[,-c(1:n.batch)]))){
  98. stop('The covariates are confounded. Please remove one or more of the covariates so the design is not confounded.')
  99. } else {
  100. stop("At least one covariate is confounded with batch. Please remove confounded covariates and rerun ComBat.")
  101. }
  102. }
  103. }
  105. ## Standardize Data across features
  106. if (verbose) cat('[combat] Standardizing Data across features\n')
  108. # Estimate coefficients using training set if specified, otherwise use full data
  109. if (!is.null(train)) {
  110. design_tr <- design[train,]
  112. B.hat1 <- solve(crossprod(design_tr))
  113. B.hat1 <- tcrossprod(B.hat1, design_tr)
  114. B.hat <- tcrossprod(B.hat1, dat[,train])
  115. } else {
  116. B.hat1 <- solve(crossprod(design))
  117. B.hat1 <- tcrossprod(B.hat1, design)
  118. B.hat <- tcrossprod(B.hat1, dat)
  119. }
  121. # Standardization Model
  122. grand.mean <- crossprod(n.batches/n.array, B.hat[1:n.batch,])
  123. var.pooled <- ((dat-t(design%*%B.hat))^2)%*%rep(1/n.array,n.array)
  124. stand.mean <- crossprod(grand.mean, t(rep(1,n.array)))
  126. if(!is.null(design)){
  127. tmp <- design;tmp[,c(1:n.batch)] <- 0
  128. stand.mean <- stand.mean+t(tmp%*%B.hat)
  129. }
  130. s.data <- (dat-stand.mean)/(tcrossprod(sqrt(var.pooled), rep(1,n.array)))
  132. ## Get regression batch effect parameters
  133. if (eb){
  134. if (verbose) cat("[combat] Fitting L/S model and finding priors\n")
  135. } else {
  136. if (verbose) cat("[combat] Fitting L/S model\n")
  137. }
  138. batch.design <- design[,1:n.batch]
  139. gamma.hat <- tcrossprod(solve(crossprod(batch.design, batch.design)), batch.design)
  140. gamma.hat <- tcrossprod(gamma.hat, s.data)
  141. delta.hat <- NULL
  142. for (i in batches){
  143. delta.hat <- rbind(delta.hat,rowVars(s.data[,i], na.rm=TRUE)) # fixed error
  144. }
  146. # Empirical Bayes correction:
  147. gamma.star <- delta.star <- NULL
  148. gamma.bar <- t2 <- a.prior <- b.prior <- NULL
  149. if (eb){
  150. ##Find Priors
  151. #gamma.bar <- apply(gamma.hat, 1, mean)
  152. #t2 <- apply(gamma.hat, 1, var)
  153. gamma.bar <- rowMeans(gamma.hat)
  154. t2 <- rowVars(gamma.hat)
  155. a.prior <- apriorMat(delta.hat)
  156. b.prior <- bpriorMat(delta.hat)
  158. ##Find EB batch adjustments
  159. if (parametric){
  160. if (verbose) cat("[combat] Finding parametric adjustments\n")
  161. for (i in 1:n.batch){
  162. temp <- it.sol(s.data[,batches[[i]]],gamma.hat[i,],delta.hat[i,],gamma.bar[i],t2[i],a.prior[i],b.prior[i])
  163. gamma.star <- rbind(gamma.star,temp[1,])
  164. delta.star <- rbind(delta.star,temp[2,])
  165. }
  166. } else {
  167. if (verbose) cat("[combat] Finding non-parametric adjustments\n")
  168. for (i in 1:n.batch){
  169. temp <- int.eprior(as.matrix(s.data[, batches[[i]]]),gamma.hat[i,], delta.hat[i,])
  170. gamma.star <- rbind(gamma.star,temp[1,])
  171. delta.star <- rbind(delta.star,temp[2,])
  172. }
  173. }
  175. }
  177. if (mean.only) {
  178. delta.star <- array(1, dim = dim(delta.star))
  179. }
  181. ### Normalize the Data ###
  182. if (verbose) cat("[combat] Adjusting the Data\n")
  183. bayesdata <- s.data
  184. j <- 1
  185. for (i in batches){
  186. if (eb){
  187. bayesdata[,i] <- (bayesdata[,i]-t(batch.design[i,]%*%gamma.star))/tcrossprod(sqrt(delta.star[j,]), rep(1,n.batches[j]))
  188. } else {
  189. bayesdata[,i] <- (bayesdata[,i]-t(batch.design[i,]%*%gamma.hat))/tcrossprod(sqrt(delta.hat[j,]), rep(1,n.batches[j]))
  190. }
  191. j <- j+1
  192. }
  194. # Reintroduce wanted covariates
  195. all.mean <- crossprod(grand.mean, t(rep(1,n.array)))
  196. if (!is.null(nuisance.mod)) {
  197. tmp <- design;tmp[,c(1:n.batch)] <- 0
  198. tmp[,(n.batch+dim(mod)[2]-dim(nuisance.mod)[2]):(dim(design)[2])] <- 0
  199. wanted.mean <- all.mean+t(tmp%*%B.hat)
  200. }
  202. if (!is.null(nuisance.mod)) {
  203. bayesdata <- (bayesdata*(tcrossprod(sqrt(var.pooled), rep(1,n.array)))) +
  204. wanted.mean
  205. } else if (resid == FALSE) {
  206. bayesdata <- (bayesdata*(tcrossprod(sqrt(var.pooled), rep(1,n.array)))) +
  207. stand.mean
  208. } else {
  209. bayesdata <- bayesdata*(tcrossprod(sqrt(var.pooled), rep(1,n.array)))
  210. }
  212. return(list(dat.combat=bayesdata,
  213. s.data=s.data,
  214. gamma.hat=gamma.hat, delta.hat=delta.hat,
  215. gamma.star=gamma.star, delta.star=delta.star,
  216. gamma.bar=gamma.bar, t2=t2, a.prior=a.prior, b.prior=b.prior,
  217. batch=batch, mod=mod,
  218. stand.mean=stand.mean, stand.sd=sqrt(var.pooled)[,1],
  219. B.hat = B.hat))
  220. }

combat.R at commit cacf370, no license · at the source

Overview

Authors: Micaela Y Chan1, Liang Han1, Gagan S Wig1,2
ORCID iDs: Micaela Y Chan, Liang Han, Gagan S Wig
  1. Center for Vital Longevity, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, United States
  2. Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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
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
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

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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

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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"author": [
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"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1338",
"DOI": "10.1162/imag.a.1338",
"PMID": "42643755",
"PMCID": "PMC13504956",
"ISSN": "2837-6056",
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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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