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Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.

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The authors' code

R · 234 lines · 7.4 KB · no license

  1. # ******************ADEBIYI SOBITAN, PH.D.*****************
  2. # March. 10, 2025
  3. # This script performs Linear Mixed Model for Edges in the Left Hemisphere
  4. # generate violin plots for All and significant Edges
  6. setwd("C:/Users/sobitanab/Documents/Ade/Edges_2025")
  8. chooseCRANmirror(ind=1)
  10. # Install and access necessary packages
  11. install.packages(c("tidyverse", "factoextra", "ggplot2", "readxl","car","reshape2","psych","lme4","FactoMineR"))
  12. install.packages(c("lmerTest","sjPlot", "lattice", "dplyr","factoextra", "stringr", "ggpubr", "ggcorrplot"))
  14. library(sjPlot)
  15. library(lattice)
  16. library(tidyverse)
  17. library(readxl)
  18. library(car)
  19. library(reshape2)
  20. library(psych)
  21. library(lme4)
  22. library(FactoMineR)
  23. library(ggplot2)
  24. library(dplyr)
  25. library(factoextra)
  26. library(stringr)
  27. library(ggpubr)
  28. library(ggcorrplot)
  29. library(lmerTest)
  31. data_dir<- "C:/Users/sobitanab/Documents/Ade/Edges_2025"
  33. subject_folders<- list.dirs(data_dir, full.names=TRUE, recursive=FALSE)
  35. gender_mapping<- read_csv("C:/Users/sobitanab/Documents/Ade/Edges_2025/gender_mapfile2.csv")
  36. #head(gender_mapping)
  38. all_data<- list()
  40. for (folder in subject_folders) {
  41. # Construct each subject path
  43. subject_id<- basename(folder)
  45. # modify line below for different hemisphere
  46. file_pattern<- paste0(subject_id,"_jackknife_sigConn_aparc35_node2vec/",subject_id,"_jackknife_sigConn_aparc35_LH_analysis_results.xlsx")
  49. file_path<- file.path(folder, pattern = file_pattern)
  51. # check if excel file exist
  52. if (length(file_path)==0) {
  53. cat("File does not exist for:", subject_id, "\n")
  54. next
  55. }
  57. # Read sheet data
  58. sheet2_data <- read_excel(file_path, sheet = "Edges and Weights")
  60. # Combine
  61. patient_data<- sheet2_data %>%
  62. mutate(
  63. PatientID = subject_id,
  64. NodeID = rep(1:nrow(sheet2_data))
  65. )
  67. # Append to the list
  68. all_data[[subject_id]] <- patient_data
  69. }
  71. # combine into a single data frame
  72. if (length(all_data) > 0) {
  73. combined_data_LH <- bind_rows(all_data)
  74. } else {
  75. stop("No data was loaded. Please check file paths.")
  76. }
  78. str(combined_data_LH)
  80. combined_data_LH<- combined_data_LH %>%
  81. mutate(PatientID = as.character(PatientID))
  83. gender_mapping<- gender_mapping %>%
  84. mutate(PatientID = as.character(PatientID))
  85. # Filter
  86. filtered_gender_mapping<- gender_mapping %>%
  87. filter(as.character(PatientID) %in% combined_data_LH$PatientID)
  89. # Merge with gender information
  90. combined_data_LH <- combined_data_LH %>%
  91. left_join(filtered_gender_mapping, by = "PatientID")
  94. # Check if gender information was successfully added
  95. if(any(is.na(combined_data_LH$Gender))){
  96. stop("Some patients are missing gender information.")
  98. # Check if all rows have been merged successfully
  99. if(any(is.na(combined_data))){
  100. cat("Some rows are missing data after merging. Please check the input files.\n")
  101. }
  102. }
  104. combined_data_LH$SSAGA_Educ.2<- as.character(combined_data_LH$SSAGA_Educ.2)
  106. head(combined_data_LH)
  108. # merge Source and Target columns into one
  109. new_data_LH <- combined_data_LH %>%
  110. mutate(Edges = paste(Source, Target, sep = "-"))
  112. head(new_data_LH)
  113. write.csv(new_data_LH, file="combined_LH_2.csv")
  115. # LMMs for each Node
  117. # Fit models for each node
  118. results_LH <- new_data_LH %>%
  119. group_by(Edges) %>%
  120. summarise(
  121. Model = list(
  122. tryCatch(
  123. lmer(Weight ~ Gender + Age + Handedness + SSAGA_Educ.2 +(1 | PatientID), data = cur_data(),
  124. control=lmerControl(optimizer="bobyqa", optCtrl=list(maxfun=10000))),
  125. error = function(e)NULL
  126. )
  127. ),
  129. GenderEffect = if(!is.null(Model[[1]])){
  130. coef_summary<- summary(Model[[1]])$coefficients
  131. if ("GenderMale" %in% rownames(coef_summary)){
  132. coef_summary["GenderMale", "Estimate"]
  133. } else {
  134. NA
  135. }
  136. } else {
  137. NA
  138. },
  139. PValue = if(!is.null(Model[[1]])){
  140. coef_summary<- summary(Model[[1]])$coefficients
  141. if ("GenderMale" %in% rownames(coef_summary)){
  142. coef_summary["GenderMale", "Pr(>|t|)"]
  143. } else {
  144. NA
  145. }
  146. } else {
  147. NA
  148. }
  149. )
  150. #print("Summary of Results for Each Node:")
  151. print(results_LH)
  152. results_LH<- results_LH[, !sapply(results_LH, is.list)]
  153. write.csv(results_LH, file="LH_LMM_Edges.csv")
  155. plot_LH_Edges_data <- new_data_LH %>%
  156. group_by(Edges, Gender) %>%
  157. summarise(
  158. Mean = mean(Weight, na.rm = TRUE),
  159. SE = sd(Weight, na.rm = TRUE) / sqrt(n()),
  160. .groups = "drop"
  161. )
  163. #ggplot(plot_RH_Edges_data, aes(x = Edges, y = Mean, fill = Gender)) +
  164. ggplot(new_data_LH, aes(x = Edges, y = Weight, fill = Gender)) +
  165. geom_violin(alpha=0.6, trim = FALSE, position = position_dodge(0.8)) +
  166. #geom_boxplot(width=0.2, position=position_dodge(0.8), outlier.shape=NA, alpha=0.8)+
  167. #geom_bar(stat = "identity", position = position_dodge(0.8), width = 0.7) +
  168. #geom_errorbar(data=plot_LH_Edges_data, aes(x= Edges,ymin = Mean - SE, ymax = Mean + SE, color=Gender, group=Gender),
  169. # position = position_dodge(0.8), width = 0.25) +
  170. scale_fill_manual(values=c("Male" = "blue", "Female" = "red")) +
  171. scale_color_manual(values=c("Male" = "blue", "Female" = "red")) +
  172. labs(
  173. title = "Gender Differences for Brain Connections in the Left Hemisphere",
  174. x = "Brain Connections in the Left Hemisphere",
  175. y = "Mean ± SE"
  176. ) +
  177. theme_minimal() +
  178. theme(
  179. axis.text.x=element_text(angle=90, hjust=1),
  180. legend.title = element_blank(),
  181. panel.grid = element_blank()
  182. ) +
  183. geom_text(data = results_LH, aes(x = Edges, y = max(new_data_LH$Weight) + 0.2,
  184. label = ifelse(PValue < 0.05, "*", "")),
  185. inherit.aes = FALSE)
  186. #inherit.aes = FALSE, size = 5, color = "black")
  188. ggsave(file = "RH_Edges31325.jpg", dpi = 300)
  190. # Filter
  191. sig_LH_Edges<- results_LH %>% filter(PValue<0.05) %>% pull (Edges)
  193. #Subset
  194. LH_edges_filtered<- new_data_LH %>% filter(Edges %in% sig_LH_Edges)
  196. # stats for filtered dat
  197. filter_LH_Edges <- LH_edges_filtered %>%
  198. group_by(Edges, Gender) %>%
  199. summarise(
  200. Mean = mean(Weight, na.rm = TRUE),
  201. SE = sd(Weight, na.rm = TRUE) / sqrt(n()),
  202. .groups = "drop"
  203. )
  205. gender_colors<- c("Male" = "blue"
  206. ggplot(LH_edges_filtered, aes(x = Edges, y = Weight, fill = Gender)) +
  207. geom_violin(alpha=0.6, trim = FALSE, position = position_dodge(0.8)) +
  208. geom_boxplot(width=0.2, position=position_dodge(0.8), outlier.shape=NA, alpha=0.8)+
  209. #geom_bar(stat = "identity", position = position_dodge(0.8), width = 0.7) +
  210. #geom_errorbar(data=plot_RH_Edges_data, aes(x= Edges,ymin = Mean - SE, ymax = Mean + SE, color=Gender, group=Gender),
  211. # position = position_dodge(0.8), width = 0.25) +
  212. scale_fill_manual(values=c("Male" = "blue", "Female" = "red")) +
  213. scale_color_manual(values=c("Male" = "blue", "Female" = "red")) +
  214. labs(
  215. title = "Gender Differences in the Left Hemisphere",
  216. x = "Brain Connections in the Left Hemisphere",
  217. y = "Weight (Correlation strength)"
  218. ) +
  219. theme_minimal() +
  220. theme(
  221. axis.text.x=element_text(size=7,angle=90, hjust=1),
  222. legend.title = element_blank(),
  223. panel.grid = element_blank()
  224. )
  225. +
  226. geom_text(data = sig_Edges, aes(x = Edges, y = max(edges_filtered$Weight) + 0.2,
  227. label = ifelse(PValue < 0.05, "*", "")),
  228. inherit.aes = FALSE)
  231. ggsave(file = "sig_LH_Edges.jpg", dpi = 300)

LH_Edges_violin (1).R at commit 359a1b8, no license · at the source

Overview

Authors: Jinqing Liang1, Divesh Thaploo1, Adebiyi Sobitan1, Kristen Wingert1, Atsuko Kurosu1, Stein Acker1, Ahmad Shafiei1, Ninet Sinaii2, Nadia M Biassou1,3
ORCID iDs: Divesh Thaploo, Stein Acker, Nadia M Biassou
  1. The Integrative Neuroscience of Communication Research Unit, National Institute on Deafness and Other Communication Disorders, Bethesda, MD, USA
  2. Biostatistics and Clinical Epidemiology Service, National Institutes of Health Clinical Center, Bethesda, MD, USA
  3. Division of Neuroradiology, National Institutes of Health, Clinical Center Department of Radiology and Imaging Sciences, Bethesda, MD, USA
Institutions: National Institute on Deafness and Other Communication Disorders (United States); National Institutes of Health (United States); National Institutes of Health Clinical Center (United States)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 2, pages 267-280
Dates: received 8 July 2025; accepted 1 December 2025; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.536 · PMID 42039102 · PMCID PMC13108337 · OpenAlex W7111081105
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: Task-based fMRI, Static functional connectivity (traditional), Common connection, Jackknife analysis, Spearman, Pearson, Bonferroni correction
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute on Deafness and Other Communication Disorders (Intramural Research Program Funding); NIH Clinical Center (ZIA CL090077)
Citations: not cited yet (Europe PMC); 38 references in the paper

Abstract

Functional connectivity (FC) analyses of task-based fMRI (tbfMRI) often rely on static correlation methods that average signal relationships over time. While widely used, these methods may miss transient but meaningful neural interactions. In this study, we investigated whether jackknife resampling—a technique that systematically omits one time point at a time—enhances sensitivity in detecting language-related FC networks during an auditory comprehension task. We analyzed surface-based FC networks in 172 healthy young adults from the Human Connectome Project. FC matrices were computed across 68 cortical regions of interest, and statistically significant edges were identified using Bonferroni correction. We compared FC networks derived from a traditional static correlation approach with those obtained using jackknife resampling, applying an edge consistency threshold to retain only the most stable connections across time points. The static method identified 75 significant language-related FCs. Jackknife-based analyses recovered all of these and revealed 24 additional connections or edges (eight left-hemispheric, five right-hemispheric, 11 interhemispheric; p < 0.001), including well-established language regions such as the middle temporal gyrus and posterior cingulate cortex. Jackknife resampling enhances detection of robust, task-relevant FCs, offering a promising alternative for modeling language networks and improving neurocomputational representations in both research and clinical settings.

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

Repository

Its files are read in the Code ↔ Paper reader above.

dthaploo/thinc_labs

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 359a1b879d78b6c8b353187717b5e9bc33fc3384, 29 August 2025
Languages: R (6)
Size: 16 files, 6 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), car (5 files), ggplot2 (5 files), ggpubr (5 files), lme4 (5 files), lmerTest (5 files), psych (5 files), reshape2 (5 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
7 files
At the source: github.com/dthaploo/THINC_LABS/tree/master/NN_paper

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;
  • 6 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 and Code Availability

The data used in this paper are freely available from https://db.humanconnectome.org/ (requires free registration). All code for this paper is available at Github: THINC_LABS/NN_paper at master dthaploo/THINC_LABS (https://github.com/dthaploo/THINC_LABS/tree/master/NN_paper).

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 7 keywords, 2 funders, 38 references.

Cite

This paper

Liang, J., Thaploo, D., Sobitan, A., Wingert, K., Kurosu, A., Acker, S., Shafiei, A., Sinaii, N., & Biassou, N. M. (2026). Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses. Network neuroscience (Cambridge, Mass.), 10(2), 267-280. https://doi.org/10.1162/netn.a.536

BibTeX

@article{liang2026increased,
author = {Liang, Jinqing and Thaploo, Divesh and Sobitan, Adebiyi and Wingert, Kristen and Kurosu, Atsuko and Acker, Stein and Shafiei, Ahmad and Sinaii, Ninet and Biassou, Nadia M},
title = {{Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {10},
number = {2},
pages = {267--280},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.536},
url = {https://doi.org/10.1162/netn.a.536},
pmid = {42039102},
pmcid = {PMC13108337}
}

RIS

TY - JOUR
AU - Liang, Jinqing
AU - Thaploo, Divesh
AU - Sobitan, Adebiyi
AU - Wingert, Kristen
AU - Kurosu, Atsuko
AU - Acker, Stein
AU - Shafiei, Ahmad
AU - Sinaii, Ninet
AU - Biassou, Nadia M
TI - Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/04/22
VL - 10
IS - 2
SP - 267
EP - 280
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.536
UR - https://doi.org/10.1162/netn.a.536
LA - en
ER -

CSL-JSON

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"container-title": "Network neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Liang",
"given": "Jinqing"
},
{
"family": "Thaploo",
"given": "Divesh"
},
{
"family": "Sobitan",
"given": "Adebiyi"
},
{
"family": "Wingert",
"given": "Kristen"
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{
"family": "Kurosu",
"given": "Atsuko"
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{
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"given": "Stein"
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"given": "Ahmad"
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"DOI": "10.1162/netn.a.536",
"PMID": "42039102",
"PMCID": "PMC13108337",
"ISSN": "2472-1751",
"publisher": "MIT Press",
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"language": "en",
"issued": {
"date-parts": [
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22
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]
}
}

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