OSCR

Replicability of Functional Brain Networks: A Study Through the Lens of Seven Resting-State Networks.

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 · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and Methods › ABIDE I Dataset › Brain Parcellation ↔ Data Preprocessing/Atlas Parcellation/parcellation.R, lines 120–158 · score 0.77 · FEF_R, SMG_R, centroid, Manhattan, Minkowski, Euclidean
  2. [2] § Materials and Methods › Analysis of Intra‐Subject Network Variability ↔ Analysis/Intra-Subject Variability/norm_pdiv_baseline_bootstrap.R, lines 1–65 · score 0.64 · parametric bootstrapping, portrait divergence, atlas combination, baseline, norm, Intra
  3. [3] § Materials and Methods › LMM and ComBat Harmonization for Controlling Site Effects ↔ Analysis/ComBat/combat_adjusted_analysis.R, lines 1–70 · score 0.63 · neuroCombat, ComBat adjusted, harmonization, LMM, model
  4. [4] § Materials and Methods › ABIDE I Dataset › Brain Parcellation ↔ Data Preprocessing/Atlas Parcellation/parcellation.R, lines 1–55 · score 0.63 · AAL atlas, CONN networks, CONN atlas, reslice, atlases, Parcellation
  5. [5] § Results › Functional Network Block Structure by Pipeline and Parcellation ↔ Analysis/Functional Network Block Structure/within_vs_between_network_corr.R, lines 43–98 · score 0.63 · network block structure, standard errors, correlation, filtered, pipeline, atlas
  6. [6] § Materials and Methods › ABIDE I Dataset › Band‐Pass Filtering ↔ Analysis/Exploratory Analysis/minor_revision_checks.R, lines 13–28 · score 0.58 · power spectra, 0.01–0.1 Hz, 0.01 Hz
  7. [7] § Results › Edgewise Relative Effects of Processing Choices Using a Linear Mixed Effect Model ↔ Analysis/LMM/pipeline_atlas_interaction_dmn.R, the whole file · a weak match · score 0.56 · pipeline atlas interaction, PCC, MPFC, fitted, subtracting, DMN
  8. [8] § Materials and Methods › Analysis of Intra‐Subject Network Variability ↔ Analysis/Intra-Subject Variability/norm_pdiv_data_calculation.R, lines 1–31 · score 0.54 · intra subject variability, portrait divergence, Frobenius norm, FC networks, pipeline
  9. [9] § Materials and Methods › Analysis of Intra‐Subject Network Variability ↔ Analysis/Intra-Subject Variability/norm_pdiv_baseline_bootstrap.R, lines 1–65 · score 0.53 · bootstrap replicates, portrait divergence, baseline, norm, Intra, Variability
  10. [10] § Materials and Methods › LMM and ComBat Harmonization for Controlling Site Effects ↔ Analysis/ComBat/combat_adjusted_analysis.R, lines 1–70 · score 0.51 · ComBat adjusted, batch, harmonization, LMM, model
  11. [11] § Materials and Methods › Analysis of Intra‐Subject Network Variability ↔ Analysis/Intra-Subject Variability/frobenius_norm_bootstrap_comparison.R, lines 40–80 · score 0.51 · Frobenius norm, density curves, bootstrapped, baseline, Intra, Variability
  12. [12] § Materials and Methods › Analysis of Intra‐Subject Network Variability ↔ Analysis/Intra-Subject Variability/norm_pdiv_baseline_bootstrap.R, lines 133–164 · score 0.50 · intra subject variability, portrait divergence, FC networks, norm, pipeline

Paper

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The authors' code

R · 283 lines · 10 KB · GPL-3.0 · 3 matches

  1. ############# Baseline by non-parametric bootstrap #########
  2. # Import portrait divergence functions from github: https://github.com/bagrow/network-portrait-divergence
  4. # Packages
  5. library(abind)
  6. library(tidyverse)
  7. library(foreach)
  8. library(doParallel)
  9. library(reticulate)
  11. # Working directory
  12. #setwd("~/FC-Network-Replicability-Effects")
  14. # Load in df
  15. load("Data/processed_data_network_baseline.RData")
  16. within_network <- df_baseline
  18. load("Data/processed_data_between_network_baseline.RData")
  19. between_network <- df_baseline
  21. # Merge df
  22. df <- merge(within_network, between_network, by = c("pipeline","filter","atlas","site","ID"))
  24. # Disregard combinations that use no filtering - not important enough effect, drop site (not used)
  25. df <- df %>% filter(filter == "filt") %>% select(-c(site,filter))
  27. # Make new variable for treatment - pipeline + atlas combination
  28. df$combo <- paste0(df$pipeline,"_",df$atlas)
  30. # Drop pipeline and atlas and reorder combo
  31. df <- df %>% select(-c(pipeline,atlas)) %>% relocate(combo)
  33. # Pivot df and reform into array to make structure easier to bootstrap
  34. df <- df %>% pivot_longer(cols = -c(1:2), names_to = "edge", values_to = "response") # pivot longer to undo edge
  35. df <- df %>% pivot_wider(names_from = combo, values_from = response) # pivot wider to make columns by combo
  37. df_array <- abind(split(df, df$edge), along = 3) # reform into array
  38. dim(df_array) # final dimensions of array are (subject, combination, network edge)
  40. # Clear environment except for df_array
  41. rm(list = setdiff(ls(), "df_array"))
  43. # Load upper triangle labels to reconstruct correlation matrix
  44. load("Analysis/Intra-Subject Variability/upper_triangle_labels.RData")
  46. # Get number of cores in machine for parallel computing
  47. n_cores <- detectCores()
  49. # Register cluster
  50. cluster <- makeCluster(n_cores - 1)
  51. registerDoParallel(cluster)
  53. # Set seed
  54. set.seed(1234)
  56. # Setting sample size
  57. n <- dim(df_array)[1]
  59. # Setting treatment size
  60. p <- dim(df_array)[2] - 2
  62. # Setting number of bootstrap replicates
  63. B <- 100
  65. ########### Functions #######################
  67. # Function to calculate difference in Frobenius norms for a given pair of combinations
  68. calc_frobenius <- function(col, subject_data){
  69. combo_pair <- strsplit(col, "__")[[1]]
  70. idx1 <- unlist(strsplit(combo_pair[1], "\\."))
  71. idx2 <- unlist(strsplit(combo_pair[2], "\\."))
  73. data1 <- subject_data %>%
  74. filter(pipeline == idx1[1] & atlas == idx1[2]) %>%
  75. select(-c(1:3))
  77. data2 <- subject_data %>%
  78. filter(pipeline == idx2[1] & atlas == idx2[2]) %>%
  79. select(-c(1:3))
  81. if (nrow(data1) == 1 && nrow(data2) == 1) {
  82. A <- as.matrix(data1)
  83. B <- as.matrix(data2)
  84. output <- norm(A - B, type = "F")
  85. }
  86. return(output)
  87. }
  89. # Function to apply calc_frobenius to all pairwise combinations for a particular subject
  90. sub_frobenius <- function(x){
  91. subject <- x[["ID"]]
  92. subject_data <- subset(df, ID == subject)
  94. output <- results %>% filter(ID == subject) %>%
  95. mutate(across(-ID,
  96. .fns = ~calc_frobenius(
  97. col = cur_column(),
  98. subject_data = subject_data)))
  100. return(output)
  101. }
  103. # Function to take input data vector and transform back into weighted adjacency matrix
  104. get_cor_mat <- function(input_dat){
  105. # Extract single vector of z-transformed values corresponding to one network
  106. cor_vec <- data.frame(t(input_dat))
  107. colnames(cor_vec) <- "response"
  108. cor_vec$key <- rownames(cor_vec)
  109. rownames(cor_vec) <- NULL
  111. # Merge with labels
  112. cor_matrix_labs <- merge(upper_tri_labs,cor_vec, by = "key")
  114. # Fix ordering
  115. cor_matrix_labs <- cor_matrix_labs[order(cor_matrix_labs$original_order), ]
  116. rownames(cor_matrix_labs) <- NULL
  118. # Create an empty n x n matrix
  119. adj_mat <- matrix(0, nrow = 30, ncol = 30)
  121. # Fill the lower triangle with the vector values
  122. adj_mat[upper.tri(adj_mat)] <- cor_matrix_labs$response
  124. # Make the matrix symmetric by adding its transpose
  125. adj_mat <- adj_mat + t(adj_mat)
  127. # Make any negative values equal to 0 (very few of these) - network alg can't handle negative weights
  128. adj_mat <- pmax(adj_mat, 0)
  130. return(adj_mat)
  131. }
  133. # Function to calculate portrait divergence for a given pair of combinations
  134. calc_pdiv <- function(col, subject_data){
  135. combo_pair <- strsplit(col, "__")[[1]]
  136. idx1 <- unlist(strsplit(combo_pair[1], "\\."))
  137. idx2 <- unlist(strsplit(combo_pair[2], "\\."))
  139. # Import for use within parallel process - CHANGE PATHS AS NEEDED
  140. nx <- reticulate::import("networkx")
  141. pdiv <- reticulate::import_from_path("portrait_divergence",
  142. path = "~/FC-Network-Replicability-Effects/Analysis/Intra-Subject Variability")
  144. data1 <- subject_data %>%
  145. filter(pipeline == idx1[1] & atlas == idx1[2]) %>%
  146. select(-c(1:3))
  148. data2 <- subject_data %>%
  149. filter(pipeline == idx2[1] & atlas == idx2[2]) %>%
  150. select(-c(1:3))
  152. if (nrow(data1) == 1 && nrow(data2) == 1) {
  153. adj_mat1 <- get_cor_mat(data1)
  154. adj_mat2 <- get_cor_mat(data2)
  156. g1 <- nx$from_numpy_array(adj_mat1)
  157. g2 <- nx$from_numpy_array(adj_mat2)
  159. output <- pdiv$portrait_divergence_weighted(g1, g2)
  160. } else {
  161. output <- NA
  162. }
  163. return(output)
  164. }
  166. # Function to apply calc_pdiv to all pairwise combinations for a particular subject
  167. sub_pdiv <- function(x){
  168. subject <- x[["ID"]]
  169. subject_data <- subset(df, ID == subject)
  171. output <- results %>% filter(ID == subject) %>%
  172. mutate(across(-ID,
  173. .fns = ~calc_pdiv(
  174. col = cur_column(),
  175. subject_data = subject_data)))
  177. return(output)
  178. }
  180. #############################################
  182. # Begin bootstrapping process
  183. for (b in 1:B){
  184. subjects_b <- sample(1:n, size = n, replace = T)
  185. df_array_boot <- df_array[subjects_b,,]
  187. for (i in 1:n){
  188. treatments_ib <- sample(3:(p+2), size = p, replace = T) # resample non-id columns
  189. df_array_boot[i,,] <- df_array_boot[i,c(1:2,treatments_ib),] # create bootstrapped array
  190. }
  192. # Reassign old unique ID to bootstrapped df to have a unique ID and replicate structure
  193. df_array_boot[,1,] <- df_array[,1,]
  195. # Transform array back into original df structure
  196. df_boot <- apply(df_array_boot, 3, as.data.frame)
  197. df_boot <- do.call(rbind, df_boot)
  199. # Pivot df back to original structure
  200. df_boot <- df_boot %>% pivot_longer(cols = -c(1:2), names_to = "combo", values_to = "response") # pivot longer to undo combo
  201. df_boot <- df_boot %>% pivot_wider(names_from = edge, values_from = response) # pivot wider to make columns by combo
  203. # Recreate pipeline and atlas columns
  204. df_boot <- df_boot %>% separate(col = combo,
  205. into = c("pipeline","atlas"),
  206. sep = "_")
  208. # Convert preprocessing effects as factors
  209. df_boot$pipeline <- factor(df_boot$pipeline, levels = c("cpac","dparsf","niak","ccs"))
  210. df_boot$atlas <- factor(df_boot$atlas, levels = c("cc200","cc400","dos160","ez","ho","tt","aal"))
  211. df_boot$ID <- factor(df_boot$ID)
  213. # Convert network columns to numeric
  214. df_boot[, 4:ncol(df_boot)] <- lapply(df_boot[, 4:ncol(df_boot)], as.numeric)
  216. # Export bootstrapped data
  217. save(df_boot, file = paste0("Analysis/Intra-Subject Variability/Bootstrap Data/processed_data_bootstrap",b,".RData"))
  219. # Rename
  220. df <- df_boot
  222. # Get unique subject IDs
  223. subjects <- unique(df$ID)
  225. # Create a data frame to store the results
  226. results <- data.frame(ID = subjects)
  228. # Get unique combinations of pipeline, and atlas
  229. combinations <- unique(df[, c("pipeline", "atlas")])
  231. # Generate unique pairwise combinations of the 28 combinations
  232. combo_indices <- combn(seq_len(nrow(combinations)), 2)
  234. combo_names <- apply(combo_indices, 2, function(idx) {
  235. combo1 <- combinations[idx[1], ]
  236. combo2 <- combinations[idx[2], ]
  237. paste(paste(combo1$pipeline, combo1$atlas, sep = "."),
  238. paste(combo2$pipeline, combo2$atlas, sep = "."),
  239. sep = "__")
  240. })
  242. # Initialize the results matrix with NA
  243. results_matrix <- matrix(NA, nrow = length(subjects), ncol = length(combo_names))
  244. colnames(results_matrix) <- combo_names
  246. # Add ID column to the results matrix and convert to df
  247. results <- data.frame(ID = subjects, results_matrix)
  249. # Split the dataframe into a list of smaller dataframes for parallel computing
  250. data_chunks <- split(results, cut(1:nrow(results), n_cores-1))
  252. # Compute the difference in Frobenius norm and pdiv for each pairwise combination across all subjects in parallel
  253. results <- foreach(chunk = data_chunks,
  254. .combine = c,
  255. .packages = 'dplyr',
  256. .export = c('calc_frobenius','calc_pdiv',
  257. 'get_cor_mat','upper_tri_labs')) %dopar% {
  258. result_norm <- apply(chunk, MARGIN = 1, FUN = sub_frobenius)
  259. result_pdiv <- apply(chunk, MARGIN = 1, FUN = sub_pdiv)
  261. list(result_norm = result_norm, result_pdiv = result_pdiv)
  262. }
  264. # Change format of results from list to df
  265. results_norm = results[seq(1, length(results), by = 2)]
  266. results_norm = lapply(results_norm, function(x) do.call(rbind, x))
  267. results_norm = do.call(rbind, results_norm)
  269. # Save the results to a CSV file
  270. write.csv(results_norm, file = paste0("Analysis/Intra-Subject Variability/Bootstrap Results/frobenius_norm_bootstrap",b,".csv"), row.names = FALSE)
  272. # Change format of results from list to df
  273. results_pdiv = results[seq(2, length(results), by = 2)]
  274. results_pdiv = lapply(results_pdiv, function(x) do.call(rbind, x))
  275. results_pdiv = do.call(rbind, results_pdiv)
  277. # Save the results to a CSV file
  278. write.csv(results_pdiv, file = paste0("Analysis/Intra-Subject Variability/Bootstrap Results/pdiv_bootstrap",b,".csv"), row.names = FALSE)
  280. }
  282. # Stop cluster after parallel computing is completed
  283. stopCluster(cl = cluster)

norm_pdiv_baseline_bootstrap.R at commit f950abf, under GPL-3.0 · at the source

Overview

Authors: Kaitlyn R Fales1, Xurui Zhi1, Hyebin Song1, Nicole A Lazar1,2
ORCID iDs: Kaitlyn R Fales, Xurui Zhi, Hyebin Song
  1. Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, USA
  2. Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA
Institutions: Pennsylvania State University (United States)
Journal: Human brain mapping, volume 47, issue 8, article e70559
Dates: received 1 December 2025; accepted 18 May 2026; published online 8 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70559 · PMID 42260753 · PMCID PMC13247136 · OpenAlex W7163991626
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Graphs, Statistics, fMRI & imaging, Preprocessing
Keywords: band‐pass filtering, brain parcellation, data preprocessing, functional connectivity, functional network, replicability, resting‐state fMRI
MeSH: Brain*, Connectome*, Default Mode Network*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Nerve Net*, Humans, Rest (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 75 references in the paper
Research resources: SPM RRID:SCR_007037

Abstract

The study of brain networks is essential for improving our understanding of how the human brain functions. Functional connectivity (FC) analysis is a widely used approach for studying co‐activating patterns among brain regions by estimating their temporal dependencies and constructing an undirected network. Data processing is critical before estimating a subject's functional network, but the absence of a standardized procedure serves as a source of heterogeneity in results, especially in multi‐site studies. Commonly studied functional networks include the default mode, sensorimotor, visual, salience, dorsal attention, frontoparietal, and language networks. These networks are stable and still exhibit intrinsic activation when an individual is at rest, making them ideal networks to focus on for studying how processing choices affect the replicability of functional connectivity networks. We use the aforementioned seven networks to assess the impact of various processing choices, including preprocessing pipeline, band‐pass filtering, and brain parcellation, on the replicability of functional connectivity estimates for multi‐site resting‐state fMRI (rs‐fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). Finally, we provide some practical recommendations for how researchers should proceed with processing choices in the face of these effects.

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 12 matches between paragraphs and lines of code.

kaitlyn-fales/FC-Network-Replicability-Effects

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f950abf38e051fb1f8e339e0d4a9203a4c2c8dc7, 12 May 2026
Languages: R (25)
Size: 138 files, 25 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (14 files), reshape2 (6 files), ggplot2 (4 files), ggpubr (2 files), lme4 (2 files), lmerTest (2 files), reticulate (2 files), UMAP (2 files), neuroCombat (1 file), RNifti (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
27 files
At the source: github.com/kaitlyn-fales/FC-Network-Replicability-Effects

preprocessed-connectomes-project.org/abide/index.html

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: preprocessed-connectomes-project.org/abide/index.html

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;
  • 25 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.

Data Availability Statement

The data that support the findings of this study are openly available from the Autism Brain Imaging Data Exchange at http://preprocessed‐connectomes‐project.org/abide/index.html (http://preprocessed-connectomes-project.org/abide/index.html). All code to reproduce the results can be accessed using our Github repository, https://github.com/kaitlyn‐fales/FC‐Network‐Replicability‐Effects (https://github.com/kaitlyn-fales/FC-Network-Replicability-Effects).

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, issue, pages, dates, 4 authors, 7 keywords, 8 MeSH terms, 67 references, 1 RRID.

Cite

This paper

Fales, K. R., Zhi, X., Song, H., & Lazar, N. A. (2026). Replicability of Functional Brain Networks: A Study Through the Lens of Seven Resting-State Networks. Human brain mapping, 47(8), e70559. https://doi.org/10.1002/hbm.70559

BibTeX

@article{fales2026replicability,
author = {Fales, Kaitlyn R and Zhi, Xurui and Song, Hyebin and Lazar, Nicole A},
title = {{Replicability of Functional Brain Networks: A Study Through the Lens of Seven Resting-State Networks}},
journal = {Human brain mapping},
year = {2026},
month = jun,
volume = {47},
number = {8},
pages = {e70559},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70559},
url = {https://doi.org/10.1002/hbm.70559},
pmid = {42260753},
pmcid = {PMC13247136}
}

RIS

TY - JOUR
AU - Fales, Kaitlyn R
AU - Zhi, Xurui
AU - Song, Hyebin
AU - Lazar, Nicole A
TI - Replicability of Functional Brain Networks: A Study Through the Lens of Seven Resting-State Networks
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/06/01
VL - 47
IS - 8
SP - e70559
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70559
UR - https://doi.org/10.1002/hbm.70559
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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