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Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.

Code ↔ Paper

17 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 17 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Amplitude-dependent spatial configuration of co-fluctuation scores along the SA axis ↔ scripts/testing_phase_random.m, lines 82–144 · score 0.79 · 50–55 %, 75–80 %, SA axis, shifts, maps, bins
  2. [2] § Results › Amplitude-dependent spatial configuration of co-fluctuation scores along the SA axis ↔ scripts/testing_individual_analysis.m, lines 128–172 · score 0.79 · 50–55 %, 75–80 %, SA axis, maps, score, bins
  3. [3] § Methods › Delineating the changes of co-fluctuation scores across global amplitudes ↔ scripts/fitGAMs_CS_Schaefer200x17.R, lines 1–70 · score 0.75 · tSNR, smooth term, co fluctuation amplitude, regional co fluctuation, co fluctuation score, maximal
  4. [4] § Methods › Delineating the changes of co-fluctuation scores across global amplitudes ↔ scripts/GAMs_fixed.R, lines 22–151 · score 0.74 · GAM smooth term, reduced model, smooth function, nested, curvature, ANOVA
  5. [5] § Methods › MRI acquisition ↔ H3/DREAM/D_core/fsio/ssbloch.m, the whole file · a weak match · score 0.73 · echo planar imaging, flip angle, coil, sequence, resolution, TE
  6. [6] § Methods › Delineating the changes of co-fluctuation scores across global amplitudes ↔ scripts/GAMs_mixed.R, lines 17–128 · score 0.72 · GAM smooth term, reduced model, smooth function, nested, ANOVA, derivatives
  7. [7] § Methods › Delineating the changes of co-fluctuation scores across global amplitudes ↔ scripts/GAMs_fixed.R, lines 22–151 · score 0.68 · tSNR, smooth term, AIC, REML, maximal, sex
  8. [8] § Results › Developmental refinement of opposing co-fluctuation patterns along the SA axis ↔ scripts/testing_hcpd_cs.m, lines 139–193 · score 0.66 · 90–100 %, SA axis, 60 %, 40 %, age, scores
  9. [9] § Methods › MRI data preprocessing ↔ H3/DREAM/D_core/fsio/ssbloch.m, the whole file · a weak match · score 0.65 · high resolution T1, magnetic field, CSF
  10. [10] § Results › Sensitivity and replication analyses ↔ scripts/fit_GAMs_fixed_glasser360_3T.R, lines 1–59 · score 0.61 · head motion, global signal, tSNR, co fluctuation scores, covariate, GS
  11. [11] § Results › Sensitivity and replication analyses ↔ scripts/fit_GAMs_fixed_schaefer400x17_3T.R, lines 1–59 · score 0.61 · head motion, global signal, tSNR, co fluctuation scores, covariate, GS
  12. [12] § Methods › MRI data preprocessing ↔ H1/ccs_04_funcAROMA.sh, the whole file · a weak match · score 0.57 · ICA AROMA, fMRI, nuisance, motion, CCS, smoothing
  13. [13] § Methods › MRI data preprocessing ↔ samplesScripts/ccs_funcproc_template.sh, lines 47–129 · score 0.56 · band pass filtered, detrended, preprocessed, surface, regressed, space
  14. [14] § Methods › MRI data preprocessing ↔ H1/ccs_05_funcpreproc_cortex.sh, lines 54–128 · score 0.54 · band pass filtered, detrended, cortex, surface, linearly, MRI
  15. [15] § Results ↔ scripts/ets_rss_physio_phase_mapping.m, the whole file · a weak match · score 0.53 · heart rate, global RSS, unwrapped, root, frame, signals
  16. [16] § Results ↔ bins_hcp_co_fluc_analysis.m, lines 36–102 · score 0.52 · Static FC, global RSS, timeseries, HCP, co fluctuation, Connectome
  17. [17] § Methods › MRI data preprocessing ↔ scripts/ets_rss_physio_phase_mapping.m, the whole file · a weak match · score 0.51 · heart rate, physiological, HRV, interval, windows, MRI

Paper

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The authors' code

R · 228 lines · 11 KB · no license · 2 matches

  1. library(dplyr)
  2. library(R.matlab)
  3. library(ggsegGlasser)
  4. library(ggsegSchaefer)
  5. library(ggseg)
  6. library(ggplot2)
  7. library(ggseg3d)
  8. library(cifti)
  9. library(stringr)
  10. library(factoextra)
  11. library(matrixStats)
  12. library(scales)
  13. library(Hmisc)
  14. library(tidyr)
  15. library(cocor)
  16. library(mgcv)
  17. library(gratia)
  18. library(tidyverse)
  19. library(numDeriv)
  22. #### FIT GAM SMOOTH ####
  23. ## Function to fit a GAM (measure ~ s(smooth_var, k = knots, fx = set_fx) + covariates)).
  24. ## measure：cs
  25. ## region: V1...
  26. ## smooth_var: bin_label
  27. ## covariates: sex + age + tSNR + mean_fd + mean_gs + mean_hr + mean_br
  28. ## knots: 3
  29. ## set_fx = TRUE
  30. ## stats_only = FALSE
  31. gam.fit.smooth <- function(region, smooth_var, covariates, knots, set_fx = FALSE, stats_only = FALSE){
  32. # compatible settings.
  33. parcel <- region
  34. #Fit the gam
  35. modelformula <- as.formula(sprintf("%s ~ s(%s, k = %s, fx = %s) + %s", region, smooth_var, knots, set_fx, covariates))
  36. gam.model <- gam(modelformula, method = "REML", data = gam.data)
  37. gam.results <- summary(gam.model)
  38. summary(gam.model)
  39. p.pv <- gam.results$p.pv
  40. p.t <- gam.results$p.t
  41. gam_check_k <- k.check(gam.model)
  42. aic_values <- AIC(gam.model)
  43. # browser()
  45. #GAM derivatives
  46. #Get derivatives of the smooth function using finite differences
  47. derv <- derivatives(gam.model, term = sprintf('s(%s)',smooth_var), interval = "simultaneous", unconditional = F) #derivative at 200 indices of smooth_var with a simultaneous CI
  48. #Identify derivative significance window(s)
  49. derv <- derv %>% #add "sig" column (TRUE/FALSE) to derv
  50. mutate(sig = !(0 > lower & 0 < upper)) #derivative is sig if the lower CI is not < 0 while the upper CI is > 0 (i.e., when the CI does not include 0)
  51. derv$sig_deriv = derv$derivative*derv$sig #add "sig_deriv derivatives column where non-significant derivatives are set to 0
  53. # second derivatives
  54. derv2l <- derivatives(gam.model, term = sprintf('s(%s)',smooth_var), order = 2)
  55. mean.derivative_2l <- mean(derv2l$derivative)
  56. # first derivatives
  57. derv1l <- derivatives(gam.model, term = sprintf('s(%s)',smooth_var), order = 1)
  59. # curvature.
  60. d1 <- derv1l$derivative
  61. d2 <- derv2l$derivative
  62. curvature <- abs(d2) / (1 + d1^2)^(3/2)
  63. mean.curvature <- mean(curvature)
  64. # print(curvature)
  65. # print(length(curvature))
  66. # print(mean(curvature))
  68. #GAM statistics
  69. #F value for the smooth term and GAM-based significance of the smooth term
  70. gam.smooth.F <- gam.results$s.table[3]
  71. gam.smooth.pvalue <- gam.results$s.table[4]
  73. #Calculate the magnitude and significance of the smooth term effect by comparing full and reduced models
  74. ##Compare a full model GAM (with the smooth term) to a nested, reduced model (with covariates only)
  75. nullmodel <- as.formula(sprintf("%s ~ %s", region, covariates)) #no smooth term
  76. gam.nullmodel <- gam(nullmodel, method = "REML", data = gam.data)
  77. gam.nullmodel.results <- summary(gam.nullmodel)
  79. ##Full versus reduced model anova p-value
  80. anova.smooth.pvalue <- anova.gam(gam.nullmodel,gam.model,test='Chisq')$`Pr(>Chi)`[2]
  82. ##Full versus reduced model direction-dependent partial R squared
  83. ### effect size
  84. sse.model <- sum((gam.model$y - gam.model$fitted.values)^2)
  85. sse.nullmodel <- sum((gam.nullmodel$y - gam.nullmodel$fitted.values)^2)
  86. partialRsq <- (sse.nullmodel - sse.model)/sse.nullmodel
  87. ### effect direction
  88. mean.derivative <- mean(derv$derivative)
  89. if(mean.derivative < 0){ #if the average derivative is less than 0, make the effect size estimate negative
  90. partialRsq <- partialRsq*-1}
  92. #Derivative-based temporal characteristics
  93. #Age of developmental change onset
  94. if(sum(derv$sig) > 0){ #if derivative is significant at at least 1 age
  95. change.onset <- min(derv$data[derv$sig==T])
  96. } #find first age in the smooth where derivative is significant
  97. if(sum(derv$sig) == 0){ #if gam derivative is never significant
  98. change.onset <- NA
  99. } #assign NA
  101. #Age of maximal developmental change
  102. if(sum(derv$sig) > 0){
  103. derv$abs_sig_deriv = round(abs(derv$sig_deriv),5) #absolute value significant derivatives
  104. maxval <- max(derv$abs_sig_deriv) #find the largest derivative
  105. window.peak.change <- derv$data[derv$abs_sig_deriv == maxval] #identify the age(s) at which the derivative is greatest in absolute magnitude
  106. peak.change <- mean(window.peak.change)} #identify the age of peak developmental change
  107. if(sum(derv$sig) == 0){
  108. peak.change <- NA
  109. }
  111. #Age of decrease onset
  112. if(sum(derv$sig) > 0){
  113. decreasing.range <- derv$data[derv$sig_deriv < 0] #identify all ages with a significant negative derivative (i.e., smooth_var indices where y is decreasing)
  114. if(length(decreasing.range) > 0)
  115. decrease.onset <- min(decreasing.range) #find youngest age with a significant negative derivative
  116. if(length(decreasing.range) == 0)
  117. decrease.onset <- NA
  118. }
  119. if(sum(derv$sig) == 0){
  120. decrease.onset <- NA
  121. }
  123. #Age of increase offset
  124. if(sum(derv$sig) > 0){
  125. increasing.range <- derv$data[derv$sig_deriv > 0] #identify all ages with a significant positive derivative (i.e., smooth_var indices where y is increasing)
  126. if(length(increasing.range) > 0)
  127. increase.offset <- max(increasing.range) #find oldest age with a significant positive derivative
  128. if(length(increasing.range) == 0)
  129. increase.offset <- NA
  130. }
  131. if(sum(derv$sig) == 0){
  132. increase.offset <- NA
  133. }
  135. #Age of maturation
  136. if(sum(derv$sig) > 0){
  137. change.offset <- max(derv$data[derv$sig==T])
  138. } #find last age in the smooth where derivative is significant
  139. if(sum(derv$sig) == 0){
  140. change.offset <- NA
  141. }
  142. full.results <- cbind(parcel, gam.smooth.F, gam.smooth.pvalue, partialRsq, anova.smooth.pvalue,
  143. change.onset, peak.change, decrease.onset, increase.offset, change.offset,
  144. mean.curvature, mean.derivative_2l)
  145. stats.results <- cbind(parcel, gam.smooth.F, gam.smooth.pvalue, partialRsq, anova.smooth.pvalue)
  146. if(stats_only == TRUE)
  147. return(list(results=stats.results, p.pv=p.pv, p.t=p.t, gam_check_k=gam_check_k, aic_values=aic_values))
  148. if(stats_only == FALSE)
  149. # return(full.results)
  150. return(list(results=full.results, p.pv=p.pv, p.t=p.t, gam_check_k=gam_check_k, aic_values=aic_values))
  151. }
  154. #### PREDICT GAM SMOOTH FITTED VALUES ####
  155. ## Function to predict fitted values of a measure based on a fitted GAM smooth
  156. ## (measure ~ s(smooth_var, k = knots, fx = set_fx) + covariates)) and a prediction df.
  157. gam.smooth.predict <- function(region, smooth_var, covariates, knots, set_fx = FALSE, increments){
  158. parcel <- region
  159. region <- str_replace(region, "-", ".")
  160. modelformula <- as.formula(sprintf("%s ~ s(%s, k = %s, fx = %s) + %s", region, smooth_var, knots, set_fx, covariates))
  161. gam.model <- gam(modelformula, method = "REML", data = gam.data)
  162. gam.results <- summary(gam.model)
  163. #Extract gam input data
  164. df <- gam.model$model #extract the data used to build the gam, i.e., a df of y + predictor values
  165. #Create a prediction data frame
  166. np <- increments #number of predictions to make; predict at np increments of smooth_var
  167. thisPred <- data.frame(init = rep(0,np)) #initiate a prediction df
  168. theseVars <- attr(gam.model$terms,"term.labels") #gam model predictors (smooth_var + covariates)
  169. varClasses <- attr(gam.model$terms,"dataClasses") #classes of the model predictors and y measure
  170. thisResp <- as.character(gam.model$terms[[2]]) #the measure to predict
  171. for (v in c(1:length(theseVars))) { #fill the prediction df with data for predictions. These data will be used to predict the output measure (y) at np increments of the smooth_var, holding other model terms constant
  172. thisVar <- theseVars[[v]]
  173. thisClass <- varClasses[thisVar]
  174. if (thisVar == smooth_var) {
  175. thisPred[,smooth_var] = seq(min(df[,smooth_var],na.rm = T),max(df[,smooth_var],na.rm = T), length.out = np) #generate a range of np data points, from minimum of smooth term to maximum of smooth term
  176. } else {
  177. switch (thisClass,
  178. "numeric" = {thisPred[,thisVar] = median(df[,thisVar])}, #make predictions based on median value
  179. "factor" = {thisPred[,thisVar] = levels(df[,thisVar])[[1]]}, #make predictions based on first level of factor
  180. "ordered" = {thisPred[,thisVar] = levels(df[,thisVar])[[1]]} #make predictions based on first level of ordinal variable
  181. )
  182. }
  183. }
  184. pred <- thisPred %>% select(-init)
  185. #Generate predictions based on the gam model and predication data frame
  186. predicted.smooth <- fitted_values(object = gam.model, data = pred)
  187. predicted.smooth <- predicted.smooth %>% select(all_of(smooth_var), fitted, se, lower, upper)
  188. smooth.fit <- list(parcel, predicted.smooth)
  189. return(smooth.fit)
  190. }
  193. #### CALCULATE SMOOTH ESTIMATES ####
  194. ## Function to estimate the zero-averaged gam smooth function
  195. gam.estimate.smooth <- function(region, smooth_var, covariates, knots, set_fx = FALSE, increments){
  196. # compatible settings.
  197. parcel <- region
  198. modelformula <- as.formula(sprintf("%s ~ s(%s, k = %s, fx = %s) + %s", region, smooth_var, knots, set_fx, covariates))
  199. gam.model <- gam(modelformula, method = "REML", data = gam.data)
  200. gam.results <- summary(gam.model)
  201. #Extract gam input data
  202. df <- gam.model$model #extract the data used to build the gam, i.e., a df of y + predictor values
  203. #Create a prediction data frame
  204. np <- increments #number of predictions to make; predict at np increments of smooth_var
  205. thisPred <- data.frame(init = rep(0,np)) #initiate a prediction df
  206. theseVars <- attr(gam.model$terms,"term.labels") #gam model predictors (smooth_var + covariates)
  207. varClasses <- attr(gam.model$terms,"dataClasses") #classes of the model predictors and y measure
  208. thisResp <- as.character(gam.model$terms[[2]]) #the measure to predict
  209. for (v in c(1:length(theseVars))) { #fill the prediction df with data for predictions. These data will be used to predict the output measure (y) at np increments of the smooth_var, holding other model terms constant
  210. thisVar <- theseVars[[v]]
  211. thisClass <- varClasses[thisVar]
  212. if (thisVar == smooth_var) {
  213. thisPred[,smooth_var] = seq(min(df[,smooth_var],na.rm = T),max(df[,smooth_var],na.rm = T), length.out = np) #generate a range of np data points, from minimum of smooth term to maximum of smooth term
  214. } else {
  215. switch (thisClass,
  216. "numeric" = {thisPred[,thisVar] = median(df[,thisVar])}, #make predictions based on median value
  217. "factor" = {thisPred[,thisVar] = levels(df[,thisVar])[[1]]}, #make predictions based on first level of factor
  218. "ordered" = {thisPred[,thisVar] = levels(df[,thisVar])[[1]]} #make predictions based on first level of ordinal variable
  219. )
  220. }
  221. }
  222. pred <- thisPred %>% select(-init)
  223. #Estimate the smooth trajectory
  224. estimated.smooth <- smooth_estimates(object = gam.model, data = pred)
  225. estimated.smooth <- estimated.smooth %>% select(smooth_var, est)
  226. return(estimated.smooth)
  227. }

GAMs_fixed.R at commit ed94e54, no license · at the source

Overview

Authors: De-Zhi Jin1, Changsong Zhou2, Xi-Nian Zuo3,4,5, Joshua Faskowitz6, Ting Xu7,8, Ye He1
ORCID iDs: De-Zhi Jin, Changsong Zhou, Xi-Nian Zuo, Ting Xu, Ye He
  1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing, China
  2. Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University,Kowloon Tong, Hong Kong
  3. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University,Beijing, China
  4. National Basic Science Data Center, Beijing, China
  5. Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University,Beijing, China
  6. Department of Psychological and Brain Sciences, Indiana University,Bloomington, IN USA
  7. Center for the Integrative Developmental Neuroscience, Child Mind Institute,New York, NY USA
  8. Kunming Institute of Zoology, Chinese Academy of Sciences,Kunming, China
Institutions: Beijing University of Posts and Telecommunications (China); Hong Kong Baptist University (Hong Kong SAR China); Beijing Normal University (China); Indiana University Bloomington (United States); Child Mind Institute (United States); Chinese Academy of Sciences (China)
Journal: Nature communications, volume 17, issue 1, article 9138
Dates: received 19 September 2025; accepted 14 July 2026; published online 28 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-76011-7 · PMID 42649187 · PMCID PMC13518953 · OpenAlex W7171492208
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, fMRI & imaging, Physiology & signal measures
Keywords: Cognitive neuroscience, Dynamic networks
MeSH: Nerve Net*, Sensorimotor Cortex*, Adolescent, Adult, Brain, Brain Mapping, Child, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82102134)
Citations: cited by 1 paper (Europe PMC); 115 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

zuoxinian/CCS

License: GPL-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9a1fc10bb560f5a2aa802c7f6fcc80c5a44366ee, 12 September 2024
Languages: MATLAB (563), Python (90), Shell (74), Jupyter (2), R (1)
Size: 1,947 files, 730 scripts
Software Heritage: not archived
Found in: the text, “MRI data preprocessing”
Holds: README, license file, environment (H3/AmygdalaGo-BOLT/requirements_autodl.txt), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: FreeSurfer (88 files), EEGLAB (75 files), PyTorch (55 files), FSL (42 files), NumPy (40 files), AFNI (21 files), Statistics and Machine Learning Toolbox (19 files), NiBabel (12 files), OpenCV (10 files), SimpleITK (10 files), GIfTI library for MATLAB (9 files), FieldTrip (8 files), ANTs (6 files), Image Processing Toolbox (5 files), Matplotlib (5 files), SciPy (5 files), SPM (4 files), Pillow (3 files), Brain Connectivity Toolbox (2 files), Signal Processing Toolbox (2 files), MONAI (2 files), Connectome Workbench (2 files), scikit-image (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/zuoxinian/CCS

dzjin5678/co-fluctuation-scores

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ed94e54c614fc0f2597236f626e81b7881080363, 5 June 2026
Languages: MATLAB (24), R (24)
Size: 88 files, 48 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (23 files), tidyverse (22 files), ggseg (21 files), Statistics and Machine Learning Toolbox (17 files), FieldTrip (6 files), Signal Processing Toolbox (4 files), BrainSpace (3 files), mgcv (3 files), ComplexHeatmap (1 file), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/dzjin5678/co-fluctuation-scores

Zenodo 20722002

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (23 files), tidyverse (22 files), ggseg (21 files), Statistics and Machine Learning Toolbox (17 files), FieldTrip (6 files), Signal Processing Toolbox (4 files), BrainSpace (3 files), mgcv (3 files), ComplexHeatmap (1 file), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-76011-7.

Tracing map

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  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 826 scripts, each with its path and the digest of its content;
  • 17 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 12 MeSH terms, 1 funder, 114 references.

Cite

This paper

Jin, D.-Z., Zhou, C., Zuo, X.-N., Faskowitz, J., Xu, T., & He, Y. (2026). Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis. Nature communications, 17(1), 9138. https://doi.org/10.1038/s41467-026-76011-7

BibTeX

@article{jin2026human,
author = {Jin, De-Zhi and Zhou, Changsong and Zuo, Xi-Nian and Faskowitz, Joshua and Xu, Ting and He, Ye},
title = {{Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {9138},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76011-7},
url = {https://doi.org/10.1038/s41467-026-76011-7},
pmid = {42649187},
pmcid = {PMC13518953}
}

RIS

TY - JOUR
AU - Jin, De-Zhi
AU - Zhou, Changsong
AU - Zuo, Xi-Nian
AU - Faskowitz, Joshua
AU - Xu, Ting
AU - He, Ye
TI - Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/28
VL - 17
IS - 1
SP - 9138
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76011-7
UR - https://doi.org/10.1038/s41467-026-76011-7
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-76011-7",
"type": "article-journal",
"title": "Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis",
"container-title": "Nature communications",
"author": [
{
"family": "Jin",
"given": "De-Zhi"
},
{
"family": "Zhou",
"given": "Changsong"
},
{
"family": "Zuo",
"given": "Xi-Nian"
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{
"family": "Faskowitz",
"given": "Joshua"
},
{
"family": "Xu",
"given": "Ting"
},
{
"family": "He",
"given": "Ye"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9138",
"DOI": "10.1038/s41467-026-76011-7",
"PMID": "42649187",
"PMCID": "PMC13518953",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-76011-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
28
]
]
}
}

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