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Developmental stability of task-rest neural efficiency in youth using a threat and cognitive control task.

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  1. [1] § Methods › fMRI data preprocessing ↔ code/b03.Reliability.R, lines 113–157 · score 0.62 · retest reliability, global signal, partial correlation, scans, Neural efficiency, regressed

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R · 204 lines · 9.7 KB · CC0-1.0 · 1 match

  1. #Script that tests the retest reliability of the neural efficiency
  2. #########################################################
  3. ### (A) Installing and loading required packages
  4. #########################################################
  5. if (!require("dplyr")) {
  6. install.packages("dplyr", dependencies = TRUE)
  7. library(dplyr)
  8. }
  9. if (!require("tidyverse")) {
  10. install.packages("tidyverse", dependencies = TRUE)
  11. library(tidyverse)
  12. }
  13. if (!require("lme4")) {
  14. install.packages("lme4", dependencies = TRUE)
  15. library(lme4)
  16. }
  17. if (!require("lmerTest")) {
  18. install.packages("lmerTest", dependencies = TRUE)
  19. library(lmerTest)
  20. }
  21. if (!require("ggplot2")) {
  22. install.packages("ggplot2", dependencies = TRUE)
  23. library(ggplot2)
  24. }
  25. if (!require("writexl")) {
  26. install.packages("writexl", dependencies = TRUE)
  27. library(writexl)
  28. }
  29. #########################################################
  30. ### (B) Set paths
  31. #########################################################
  32. if(file.exists("/MyWorkingDirectory/derivatives")){
  33. datadir1 <-"MyWorkingDirectory/derivatives/stats"
  34. datadir2 <-"/Volumes/NIHDATA/TAU/Data/derivatives/Cohort1/Efficiency"
  35. datadir3 <-"/Volumes/NIHDATA/TAU/Data/derivatives/Cohort2/Efficiency"
  36. figuredir <-"MyWorkingDirectory/derivatives/stats/Figures/Figure_2"
  37. }
  38. #########################################################
  39. ### (C) Load the respective files
  40. #########################################################
  41. setwd(datadir1)
  42. TAU<-read.csv("Phenotype.csv")
  44. setwd(datadir2)
  45. Eff11f<-read.csv("Efficiency_TAU1_Ses1_out.norm.nonparametric_full.csv")
  46. Eff11f$Results<-as.numeric(Eff11f$Results)
  47. Eff11p<-read.csv("Efficiency_TAU1_Ses1_out.norm.nonparametric_partial.csv")
  48. Eff11p$Results<-as.numeric(Eff11p$Results)
  49. Eff12f<-read.csv("Efficiency_TAU1_Ses2_out.norm.nonparametric_full.csv")
  50. Eff12f$Results<-as.numeric(Eff12f$Results)
  51. Eff12p<-read.csv("Efficiency_TAU1_Ses2_out.norm.nonparametric_partial.csv")
  52. Eff12p$Results<-as.numeric(Eff12p$Results)
  54. setwd(datadir3)
  55. Eff21f<-read.csv("Efficiency_TAU2_Ses1_out.norm.nonparametric_full.csv")
  56. Eff21f$Results<-as.numeric(Eff21f$Results)
  57. Eff21p<-read.csv("Efficiency_TAU2_Ses1_out.norm.nonparametric_partial.csv")
  58. Eff21p$Results<-as.numeric(Eff21p$Results)
  59. Eff22f<-read.csv("Efficiency_TAU2_Ses2_out.norm.nonparametric_full.csv")
  60. Eff22f$Results<-as.numeric(Eff22f$Results)
  61. Eff22p<-read.csv("Efficiency_TAU2_Ses2_out.norm.nonparametric_partial.csv")
  62. Eff22p$Results<-as.numeric(Eff22p$Results)
  64. data11<-full_join(Eff11f, Eff11p, by = "ID")
  65. colnames(data11) <- c("ID", "S1_Eff_full_glob", "S1_Eff_part_glob")
  66. data12<-full_join(Eff12f, Eff12p, by = "ID")
  67. colnames(data12) <- c("ID", "S2_Eff_full_glob", "S2_Eff_part_glob")
  68. data21<-full_join(Eff21f, Eff21p, by = "ID")
  69. colnames(data21) <- c("ID", "S1_Eff_full_glob", "S1_Eff_part_glob")
  70. data22<-full_join(Eff22f, Eff22p, by = "ID")
  71. colnames(data22) <- c("ID", "S2_Eff_full_glob", "S2_Eff_part_glob")
  73. data1<-left_join(data11,data12, by = "ID")
  74. data2<-left_join(data21,data22, by = "ID")
  76. newdata<-rbind(data1,data2)
  77. newdata$ID <- gsub("sub-s", "", newdata$ID)
  79. TAU$ID <- as.character(TAU$ID)
  80. TAU<-left_join(newdata,TAU,by="ID")
  81. rm(newdata, data1, data11, data12, data2, data22, data21, Eff11f, Eff11p, Eff12f, Eff12p, Eff21f, Eff21p, Eff22f, Eff22p)
  83. TAU1 <- TAU %>% filter(Cohort == 1)
  84. TAU2 <- TAU %>% filter(Cohort == -1)
  86. Results <- data.frame(
  87. Model = character(), # Empty character column for model names
  88. ICC = numeric(), # Empty numeric column for ICC values
  89. CI_low = numeric(), # Empty numeric column for lower confidence interval
  90. CI_high = numeric() # Empty numeric column for upper confidence interval
  91. )
  93. #####################################################################################
  94. # Calculate Reliability of Neural Efficiency
  95. #####################################################################################
  96. # (1) Total Sample
  97. #####################################################################################
  98. # (1.1) For Efficiency derived from the full correlations
  99. #create dataframe that has the correct format and contains only the relevant variables
  100. TAUshortfull<-TAU[,c("ID","S1_Eff_full_glob","S2_Eff_full_glob","Days.Btw.Scans","DX","Cohort","Scanner")]
  101. TAUshortfull <- TAUshortfull[complete.cases(TAUshortfull[,3]),]
  102. TAUshortfull <- TAUshortfull[(TAUshortfull[,5]) == "HV",]
  103. cor.test(TAUshortfull$S1_Eff_full_glob,TAUshortfull$S2_Eff_full_glob,use = "complete.obs",method=c("spearman"))
  104. TAUshortfulllong<-gather(TAUshortfull,Session,Efficiency,S1_Eff_full_glob,S2_Eff_full_glob)
  105. TAUshortfulllong<-TAUshortfulllong[order(TAUshortfulllong$ID),]
  106. TAUshortfullmodel <- lmer(Efficiency ~ Session + Days.Btw.Scans + (1 | ID) , data = TAUshortfulllong)
  107. icc_model<-performance::icc(TAUshortfullmodel, ci=TRUE, ci_level = 0.95)
  108. Results[2,1]<-"Total Sample Full Correlation, with GR"
  109. Results[2,2]<-round(icc_model[1,1],3)
  110. Results[2,3]<-round(icc_model[2,1],3)
  111. Results[2,4]<-round(icc_model[3,1],3)
  113. #####################################################################################
  114. # (1.2) For Efficiency derived from the partial correlations
  115. #create dataframe that has the correct format and contains only the relevant variables
  116. TAUshortpart<-TAU[,c("ID","S1_Eff_part_glob","S2_Eff_part_glob","Days.Btw.Scans","DX","Cohort","Scanner")]
  117. TAUshortpart <- TAUshortpart[complete.cases(TAUshortpart[,3]),]
  118. TAUshortpart <- TAUshortpart[(TAUshortpart[,5]) == "HV",]
  119. cor.test(TAUshortpart$S1_Eff_part_glob,TAUshortpart$S2_Eff_part_glob,use = "complete.obs",method=c("spearman"))
  120. TAUshortpartlong<-gather(TAUshortpart,Session,Efficiency,S1_Eff_part_glob,S2_Eff_part_glob)
  121. TAUshortpartlong<-TAUshortpartlong[order(TAUshortpartlong$ID),]
  122. TAUshortpartmodel <- lmer(Efficiency ~ Session + Days.Btw.Scans + (1 | ID) , data = TAUshortpartlong)
  123. icc_model <- performance::icc(TAUshortpartmodel, ci=TRUE, ci_level = 0.95)
  124. Results[6,1]<-"Total Sample Partial Correlation, with GR"
  125. Results[6,2]<-round(icc_model[1,1],3)
  126. Results[6,3]<-round(icc_model[2,1],3)
  127. Results[6,4]<-round(icc_model[3,1],3)
  129. #Make Figure 2A
  130. figure2A <- ggplot(data=TAUshortpart,aes(x=S1_Eff_part_glob,y=S2_Eff_part_glob,color=as.character(Cohort)))+
  131. geom_point(size = 6)+
  132. geom_smooth(method = lm, se = F, col = "black",size = 1,alpha = .8)+
  133. scale_color_manual(name = "Cohort",values = c("-1" = "snow3", "1" = "black"))+
  134. theme_classic()+
  135. theme(aspect.ratio=1)+
  136. ylim(.1,.25)+
  137. xlim(.1,.25)+
  138. theme(plot.title = element_text(size=24,face="bold"),
  139. axis.title.x = element_text(face="bold", size=20),
  140. axis.title.y = element_text(face="bold", size=20),
  141. axis.text.x = element_text(size=16), axis.text.y = element_text(size=16))+
  142. labs(x="Neural Efficiency | Timepoint 1", y="Neural Efficiency | Timepoint 2", title = "Retest Reliability, global signal regression")
  144. ################################################################################
  145. #(1.2.1) for the first cohort only
  146. TAU1shortpart<-TAU1[,c("ID","S1_Eff_part_glob","S2_Eff_part_glob","Days.Btw.Scans","DX","Cohort","Scanner")]
  147. TAU1shortpart <- TAU1shortpart[complete.cases(TAU1shortpart[,3]),]
  148. TAU1shortpart <- TAU1shortpart[(TAU1shortpart[,5]) == "HV",]
  149. cor.test(TAU1shortpart$S1_Eff_part_glob,TAU1shortpart$S2_Eff_part_glob,use = "complete.obs",method=c("spearman"))
  150. TAU1shortpartlong<-gather(TAU1shortpart,Session,Efficiency,S1_Eff_part_glob,S2_Eff_part_glob)
  151. TAU1shortpartlong<-TAU1shortpartlong[order(TAU1shortpartlong$ID),]
  152. TAU1shortpartmodel <- lmer(Efficiency ~ Session + Days.Btw.Scans + (1 | ID) , data = TAU1shortpartlong)
  153. icc_model <-performance::icc(TAU1shortpartmodel, ci=TRUE, ci_level = 0.95)
  154. Results[7,1]<-"Cohort 1 Partial Correlation, with GR"
  155. Results[7,2]<-round(icc_model[1,1],3)
  156. Results[7,3]<-round(icc_model[2,1],3)
  157. Results[7,4]<-round(icc_model[3,1],3)
  159. ################################################################################
  160. #(1.2.2) for the second cohort only
  161. TAU2shortpart<-TAU2[,c("ID","S1_Eff_part_glob","S2_Eff_part_glob","Days.Btw.Scans","DX","Cohort","Scanner")]
  162. TAU2shortpart <- TAU2shortpart[complete.cases(TAU2shortpart[,3]),]
  163. TAU2shortpart <- TAU2shortpart[(TAU2shortpart[,5]) == "HV",]
  164. cor.test(TAU2shortpart$S1_Eff_part_glob,TAU2shortpart$S2_Eff_part_glob,use = "complete.obs",method=c("spearman"))
  165. TAU2shortpartlong<-gather(TAU2shortpart,Session,Efficiency,S1_Eff_part_glob,S2_Eff_part_glob)
  166. TAU2shortpartlong<-TAU2shortpartlong[order(TAU2shortpartlong$ID),]
  167. TAU2shortpartmodel <- lmer(Efficiency ~ Session + Days.Btw.Scans + (1 | ID) , data = TAU2shortpartlong)
  168. icc_model<-performance::icc(TAU2shortpartmodel, ci=TRUE, ci_level = 0.95)
  169. Results[8,1]<-"Cohort 2 Partial Correlation, with GR"
  170. Results[8,2]<-round(icc_model[1,1],3)
  171. Results[8,3]<-round(icc_model[2,1],3)
  172. Results[8,4]<-round(icc_model[3,1],3)
  174. ######################################
  175. # Make a plot
  176. MyPlot <- Results[c(2, 6, 7, 8), ]
  177. MyPlot[1,1]<-"Full"
  178. MyPlot[2,1]<-"Partial"
  179. MyPlot[2,3]<-0.424
  180. MyPlot[3,1]<-"Partial Cohort 1"
  181. MyPlot[3,3]<-0.256
  182. MyPlot[4,1]<-"Partial Cohort 2"
  183. MyPlot[4,3]<-0.359
  184. figure2B<-ggplot(MyPlot, aes(x = Model, y = ICC)) +
  185. geom_point(size = 4, color = "limegreen") + # Plot mean points
  186. geom_errorbar(aes(ymin = CI_low, ymax = CI_high), width = 0.2) + # Error bars
  187. theme_classic() +
  188. labs(x = "Type of Correlation + Cohort", y = "ICC") +
  189. theme(axis.title = element_text(size = 22, face = "bold"),
  190. text = element_text(size = 18))+
  191. theme(axis.text.x = element_text(angle = 35, hjust = 1))+
  192. coord_fixed(ratio = 3)
  194. ################################################################################
  195. # Write Results
  196. setwd(figuredir)
  197. ggsave("Figure2B.svg", plot = figure2B, device = "svg")
  198. ggsave("Figure2B.png", plot = figure2B, device = "png")
  199. ggsave("Figure2A.svg", plot = figure2A, device = "svg")
  200. ggsave("Figure2A.png", plot = figure2A, device = "png")
  201. write_xlsx(Results, "ICC_Overview.xlsx")
  203. setwd(datadir)
  204. write.csv(TAU, "CombinedData.csv", row.names = FALSE, quote = FALSE)

b03.Reliability.R at commit 1334b99, under CC0-1.0 · at the source

Overview

Authors: Parmis Khosravi1, Julia O Linke2,3, Anjali D Poe4, Chase Antonacci5, Reut Naim6,7, Elise Cardinale8, Katharina Kircanski9, Anderson Winkler10, Nathan Fox11, Daniel S Pine1, Simone P Haller1
ORCID iDs: Chase Antonacci
  1. Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
  2. Department of Psychology, Johannes-Gutenberg University, Mainz, Germany
  3. Leibniz Institute for Resilience Research, Mainz, Germany
  4. Department of Psychology, Stony Brook University, Stony Brook, NY, United States
  5. Department of Psychology, Stanford University, Stanford, CA, United States
  6. School of Psychological Sciences, Tel-Aviv University, Tel Aviv-Yafo, Israel
  7. Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv-Yafo, Israel
  8. Department of Psychology, Catholic University of America, Washington, DC, United States
  9. Department of Psychology, University of Southern California, Los Angeles, CA, United States
  10. Division of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
  11. Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, United States
Institutions: National Institutes of Health (United States); National Institute of Mental Health (United States); Johannes Gutenberg University Mainz (Germany); Leibniz Institute for Resilience Research (Germany); Stony Brook University (United States); Stanford University (United States); Tel Aviv University (Israel); Catholic University of America (United States); University of Southern California (United States); The University of Texas Rio Grande Valley (United States); University of Maryland, College Park (United States)
Journal: Frontiers in human neuroscience, volume 20, article 1839961
Dates: received 26 March 2026; accepted 16 June 2026; published online 6 July 2026
Type: Brief report · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnhum.2026.1839961 · PMID 42518775 · PMCID PMC13381436 · OpenAlex W7167455255
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Statistics, Spectral & time-frequency, Connectivity, fMRI & imaging
Keywords: children and adolescents, cognitive control, fMRI, neural efficiency, resting state, threat processing
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NICHD NIH HHS (R37 HD017899, R01 HD017899); NIMH NIH HHS (U01 MH093349); Intramural NIH HHS (ZIA MH002782)
Citations: not cited yet (Europe PMC); 22 references in the paper

Abstract

Behaviors arise from coordinated neural activity across diverse spatial and temporal scales. Prior work has linked better task performance and cognitive functioning to patterns of global network connectivity requiring minimal reconfiguration when switching between task demands. This metric indexing similarity in functional connectivity across task and rest has been termed “neural efficiency.” Here we assess stability of neural efficiency over approximately 3 years in adolescence, specificity across two task-rest combinations and associations with anxiety. At approximately age 16 and/or 19, 95 participants completed a resting state scan alongside a cognitive control and/or threat task. Neural efficiency was quantified as partial correlations between intrinsic and task-related functional connectivity patterns across the whole brain. We tested temporal stability across the three-year interval, as well as associations with task performance and anxiety across the two task-rest combinations at the two time points. Neural efficiency values remained relatively stable from mid to late adolescence (ICC[3,1] = 0.51–0.58). The cognitive control task showed higher values than the threat task. Across tasks, neural efficiency was associated with better performance (i.e., reduced interference), although not consistently (r = −0.19, p = 0.26 – r = −0.37, p = 0.021). These effects did not survive correction for multiple testing. No associations were found between neural efficiency and self/parent-reported anxiety. In sum, the metric shows moderate developmental stability and associations with task performance. Task features impact neural efficiency. Given small sample sizes, findings need to be interpreted cautiously.

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 1 match between paragraphs and lines of code.

NIMH-SDAN/Neural.Efficiency.2025

License: CC0-1.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1334b99907732b1dbef79973433d41fe31a3e7f2, 1 June 2025
Languages: R (7), Shell (4), MATLAB (3), Python (2)
Size: 21 files, 16 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 (7 files), ggplot2 (3 files), MRIQC (3 files), NumPy (3 files), pandas (3 files), car (2 files), fMRIPrep (2 files), imageio (2 files), lme4 (2 files), lmerTest (2 files), Matplotlib (2 files), NiBabel (2 files), SciPy (2 files), easystats (1 file), h5py (1 file), scikit-learn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
18 files
At the source: github.com/NIMH-SDAN/Neural.Efficiency.2025

The paper's code and data availability statement is in the Data section.

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  • 16 scripts, each with its path and the digest of its content;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

In accordance with the NIH Data Management and Sharing Policy, individual imaging data collected at the NIH will be shared in a public repository for participants who consented to public data sharing. These data will be available immediately following publication through OpenNeuro: https://openneuro.org/datasets/ds007812. Some individual differences data were acquired at the University of Maryland. These data, with matching identifiers, are available upon reasonable request from SH or NF, respectively, but are not publicly available due to privacy restrictions. Analytical code is available on GitHub: https://github.com/NIMH-SDAN/Neural.Efficiency.2025.

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, 11 authors, 6 keywords, 3 funders, 22 references.

Cite

This paper

Khosravi, P., Linke, J. O., Poe, A. D., Antonacci, C., Naim, R., Cardinale, E., Kircanski, K., Winkler, A., Fox, N., Pine, D. S., & Haller, S. P. (2026). Developmental stability of task-rest neural efficiency in youth using a threat and cognitive control task. Frontiers in human neuroscience, 20, 1839961. https://doi.org/10.3389/fnhum.2026.1839961

BibTeX

@article{khosravi2026developmental,
author = {Khosravi, Parmis and Linke, Julia O and Poe, Anjali D and Antonacci, Chase and Naim, Reut and Cardinale, Elise and Kircanski, Katharina and Winkler, Anderson and Fox, Nathan and Pine, Daniel S and Haller, Simone P},
title = {{Developmental stability of task-rest neural efficiency in youth using a threat and cognitive control task}},
journal = {Frontiers in human neuroscience},
year = {2026},
month = jul,
volume = {20},
pages = {1839961},
publisher = {Frontiers Media SA},
issn = {1662-5161},
doi = {10.3389/fnhum.2026.1839961},
url = {https://doi.org/10.3389/fnhum.2026.1839961},
pmid = {42518775},
pmcid = {PMC13381436}
}

RIS

TY - JOUR
AU - Khosravi, Parmis
AU - Linke, Julia O
AU - Poe, Anjali D
AU - Antonacci, Chase
AU - Naim, Reut
AU - Cardinale, Elise
AU - Kircanski, Katharina
AU - Winkler, Anderson
AU - Fox, Nathan
AU - Pine, Daniel S
AU - Haller, Simone P
TI - Developmental stability of task-rest neural efficiency in youth using a threat and cognitive control task
T2 - Frontiers in human neuroscience
J2 - Front Hum Neurosci
PY - 2026
DA - 2026/07/06
VL - 20
SP - 1839961
SN - 1662-5161
PB - Frontiers Media SA
DO - 10.3389/fnhum.2026.1839961
UR - https://doi.org/10.3389/fnhum.2026.1839961
LA - en
ER -

CSL-JSON

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"id": "10.3389/fnhum.2026.1839961",
"type": "article-journal",
"title": "Developmental stability of task-rest neural efficiency in youth using a threat and cognitive control task",
"container-title": "Frontiers in human neuroscience",
"author": [
{
"family": "Khosravi",
"given": "Parmis"
},
{
"family": "Linke",
"given": "Julia O"
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{
"family": "Poe",
"given": "Anjali D"
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{
"family": "Antonacci",
"given": "Chase"
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{
"family": "Naim",
"given": "Reut"
},
{
"family": "Cardinale",
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{
"family": "Kircanski",
"given": "Katharina"
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{
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"given": "Anderson"
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{
"family": "Fox",
"given": "Nathan"
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{
"family": "Pine",
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"volume": "20",
"page": "1839961",
"DOI": "10.3389/fnhum.2026.1839961",
"PMID": "42518775",
"PMCID": "PMC13381436",
"ISSN": "1662-5161",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnhum.2026.1839961",
"language": "en",
"issued": {
"date-parts": [
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[7] doi:10.1016/j.xcrm.2026.102766 [code]
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[10] doi:10.1093/braincomms/fcag176 [code]
Tau topography subtypes account for clinical heterogeneity and longitudinal trajectories in early-onset Alzheimer's disease.
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