OSCR

Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan.

Code ↔ Paper

14 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 14 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Modelling growth curves of the S-A gradient axis › Distribution selection ↔ Code/for-Normative-Modeling/GAMLSS_model_fitting.ipynb, lines 36–73 · score 0.76 · distribution families, Bayesian Information Criterion, optimal distribution, best fitting, phenotypes, gamlss
  2. [2] § Methods › Data acquisition, quality control, and preprocessing ↔ app/pipelines.py, lines 12–92 · score 0.76 · temporal bandpass filtering, motion correction, regression, scans, pipelines, smoothing
  3. [3] § Methods › Data acquisition, quality control, and preprocessing ↔ app/run.py, lines 1–38 · score 0.67 · functional MRI, processing pipelines, cortical surface, reconstruction, neuroimaging, segmentation
  4. [4] § Methods › Relating structural and geometric hierarchies to the S-A gradient axis › Geometric distance ↔ scripts/surface/process-surfaces-hemisphere.sh, lines 68–119 · score 0.63 · mid thickness surface, wb_command, native, distance
  5. [5] § Results › Sensitivity analyses ↔ G_Validation/Validation_global.m, lines 8–92 · score 0.63 · validation strategies, square error, BSR, HM, LOSO, BLR
  6. [6] § Methods › Mapping the S-A gradient maturation to the adult cognitive spectrum ↔ E_CognitiveSpectrumAnalysis/Stat_terms.m, the whole file · a weak match · score 0.63 · post hoc, cognitive spectrum, Kruskal, Bonferroni, Spearman, width
  7. [7] § Methods › Relating structural and geometric hierarchies to the S-A gradient axis › Intracortical T1w/T2w contrast ratio ↔ scripts/surface/process-surfaces-hemisphere.sh, lines 68–119 · score 0.59 · mid thickness surface, pial surfaces, weighted
  8. [8] § Methods › Relating network segregation/integration to the S-A gradient axis ↔ C_FunctionalSegregation-Integration/Read_Individual_GraphMetrics.m, lines 28–80 · score 0.57 · global Cp, functional segregation, graph, Lp, metrics, node
  9. [9] § Methods › Sensitivity analysis › Balanced resampling analysis ↔ G_Validation/Validation_global.m, lines 94–158 · score 0.56 · validation strategies, peak age, score, global, curves, axis
  10. [10] § Results › Normative growth of the S-A gradient axis across lifespan ↔ B_GrowthPattern/Lifespan_Growth_Axis.m, lines 73–120 · score 0.56 · lifespan growth axis, principal component, growth patterns, PCA, variance, vertices
  11. [11] § Results › Lifespan S-A gradient alignment with adult cognitive spectrum ↔ E_CognitiveSpectrumAnalysis/Stat_terms.m, the whole file · a weak match · score 0.53 · post hoc, cognitive spectrum, Kruskal, width, Phase, axis
  12. [12] § Results › Normative growth of the S-A gradient axis across lifespan ↔ Code/for-Normative-Modeling/GAMLSS_model_fitting.ipynb, lines 36–73 · score 0.53 · scanner head motion, smoothing term, GAMLSS, sex, growth curves, global
  13. [13] § Results › Lifespan association between the S-A axis and structural hierarchies ↔ D_StructuralHierarchies/Lifespan_Growth_Axis.m, lines 1–48 · score 0.52 · lifespan growth axis, principal components, PCA, thickness, variance, hierarchies
  14. [14] § Methods › Relating network segregation/integration to the S-A gradient axis ↔ C_FunctionalSegregation-Integration/Lifespan_Growth_Axis.m, lines 1–48 · score 0.50 · lifespan growth axis, functional segregation, PCA, Cp, vertex, ages

Paper

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The authors' code

Jupyter notebook · 226 lines · 8.2 KB · no license · 2 matches

  1. # %% [markdown]
  2. # #### Lifespan normative modeling for functional connectome
  3. # This demo shows a tutorial to establish connectome-based normative models using the generalized additive model for location, scale, and shape (GAMLSS). Taking global system segregation as an example, we used the samples from all healthy populations (N = 33,250) to describe the normative growth patterns of this metric.
  4. #
  5. # To run this tutorial, you need an R environment. Here, we install the `rpy2` package to enable seamless interaction between Python and R:
  7. # %%
  8. # pip3 install rpy2==3.5.12
  10. # %%
  11. # Load the rpy2 extension to enable seamless integration and interaction between Python and R
  12. %load_ext rpy2.ipython
  14. # %%
  15. %%R
  16. ## Use the rpy2 magic command to run R code within a Python environment
  17. ## Install the 'gamlss' package in the R environment
  18. install.packages('gamlss')
  20. # %%
  21. %%R
  22. # load the 'gamlss' package in the R environment
  23. library(gamlss)
  25. # %% [markdown]
  26. # ##### Load Data
  28. # %%
  29. %%R
  31. M_HC <- read.csv("data.csv")
  32. Phenotype_name <- "global system segregation"
  34. head(M_HC)
  36. # %% [markdown]
  37. # ##### Step 1: Select the best data distributions
  38. # While the World Health Organization provides guidelines for modeling anthropometric growth charts (such as head circumference, height, and weight) using the Box‒Cox t-distribution as a starting point, we recognized that the growth curves of brain neuroimaging metrics do not necessarily follow the same underlying distributions. Therefore, we evaluated all continuous distribution families (n=51) for model fitting. To identify the optimal distribution, we fitted GAMLSS with different distributions to four representative global functional metrics (global mean of functional connectome, global variance of functional connectome, global atlas similarity, and global system segregation) and assessed model convergence. We used the Bayesian information criterion (BIC) to compare model fits among the converged models, with a lower BIC indicating a better fit. The `Johnson’s Su (JSU) distribution` consistently provided the best fit across all the evaluated models.
  39. #
  40. # ##### Step 2: GAMLSS model details
  41. # We performed the GAMLSS procedure with the global system segregation as the dependent variable, age as a smoothing term (using the B-spline basis function), sex and in-scanner head motion (mean frame displacement) as other fixed effects, and scanner sites as random effects. We fitted three GAMLSS models with different degrees of freedom (df = 3-5) for the B-spline basis functions in the location (𝜇) parameters, and set default degrees of freedom (df = 3) for the B-spline basis functions in the scale (𝜎) parameters.
  43. # %% [markdown]
  44. # Due to the lengthy runtime, we have provided the precomputed model (Model_global system segregation.RData). You can skip to Step 6, load the model, and proceed with the subsequent analysis.
  46. # %%
  47. %%R
  49. Phenotype_name <- "global system segregation"
  51. con<-gamlss.control(n.cyc=200)
  53. mod1<-gamlss(phenotype~bs(Age,df=3) + Sex + meanFD + random(as.factor(SiteID)),
  54. sigma.fo=~bs(Age) + Sex,
  55. nu.fo=~1,
  56. nu.tau=~1,
  57. family=JSU,
  58. data=M_HC,
  59. control=con)
  60. mod2<-gamlss(phenotype~bs(Age,df=4) + Sex + meanFD + random(as.factor(SiteID)),
  61. sigma.fo=~bs(Age) + Sex,
  62. nu.fo=~1,
  63. nu.tau=~1,
  64. family=JSU,
  65. data=M_HC,
  66. control=con)
  67. mod3<-gamlss(phenotype~bs(Age,df=5) + Sex + meanFD + random(as.factor(SiteID)),
  68. sigma.fo=~bs(Age) + Sex,
  69. nu.fo=~1,
  70. nu.tau=~1,
  71. family=JSU,
  72. data=M_HC,
  73. control=con)
  76. # %% [markdown]
  77. # ##### Step 3: Select the best model
  79. # %%
  80. %%R
  82. conv<-matrix(data=0,nrow=1,ncol=3)
  83. for (i in 1:3) {
  84. command <- paste0( "conv[1, i] <- mod", as.character(i), "$converged" )
  85. eval(parse(text = command))
  86. }
  87. bic_val<-matrix(data=0,nrow=1,ncol=3)
  88. for (i in 1:3) {
  89. command <- paste0( "bic_val[1, i] <- mod", as.character(i), "$sbc" )
  90. eval(parse(text = command))
  91. }
  93. fit_best_order <- as.character(which.min(bic_val))
  94. fit_best_order
  96. # %%
  97. %%R
  98. conv
  100. # %%
  101. %%R
  102. bic_val
  104. # %% [markdown]
  105. # ##### Step 4: Plot normative centiles
  106. # Using the `getQuantile` function, we obtained normative centiles for each site and sex. By averaging these centiles across all sites and across male and female, we derived the overall lifespan growth curves for the population.
  108. # %%
  109. %%R
  111. quantiles<-c(0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99)
  112. site_unique<-as.character( unique(M_HC$SiteID) )
  113. n_sites <- length(site_unique)
  114. n_quantiles <- length(quantiles)
  115. n_points <- 8021
  116. Centiles_male <- array(NA, dim = c(n_sites, n_quantiles, n_points))
  117. Centiles_female <- array(NA, dim = c(n_sites, n_quantiles, n_points))
  118. x_female <- seq(-0.2, 80, length.out = 8021)
  119. x_male <- seq(-0.2, 80, length.out = 8021)
  121. for (idx_site in 1:length(site_unique)){
  122. print(idx_site)
  123. for (i in 1:length(quantiles)){
  124. command <- paste0("Qua <- getQuantile(mod", fit_best_order ,", quantile=quantiles[i],term='Age',fixed.at=list(Sex=0, SiteID=site_unique[idx_site]), n.points = 8020)")
  125. eval(parse(text = command))
  126. Centiles_female[idx_site, i, ] <- Qua(x_female)
  128. command <- paste0("Qua <- getQuantile(mod", fit_best_order ,", quantile=quantiles[i],term='Age',fixed.at=list(Sex=1, SiteID=site_unique[idx_site]), n.points = 8020)")
  129. eval(parse(text = command))
  130. Centiles_male[idx_site, i, ] <- Qua(x_male)
  131. }
  132. }
  134. # %%
  135. %%R
  136. ################################################################################ plot centiles
  137. Centiles_female <- apply(Centiles_female, c(2, 3), mean)
  138. Centiles_male <- apply(Centiles_male, c(2, 3), mean)
  139. Centiles <- (Centiles_female + Centiles_male) / 2
  141. command <- paste0( "best_model <- mod", fit_best_order)
  142. eval(parse(text = command))
  143. # step : get fitted data for only Age Iterm
  144. Term_contri <- predict(best_model, what = c("mu", "sigma", "nu", "tau"), newdata = M_HC, type="term")
  145. Age_Term <- M_HC$phenotype - Term_contri[,4] - Term_contri[,3] - Term_contri[,2]
  146. plot(M_HC$Age, Age_Term,
  147. col = rgb(0.7, 0.7, 0.7, 0.5),
  148. pch = 16,
  149. xlab = "Age",
  150. ylab = Phenotype_name,
  151. bty = "l",
  152. main = paste0("Lifespan growth curve of ", Phenotype_name))
  153. X <- seq(-0.2, 80, length.out = 8021)
  154. for (i in 1:7) {
  155. if (i == 4) {
  156. lines(X, Centiles[i, ], col = "black", lwd = 2, lty = 1)
  157. } else {
  158. lines(X, Centiles[i, ], col = "black", lwd = 1, lty = 2)
  159. }
  160. }
  162. # %% [markdown]
  163. # ##### Step 5: Save the model
  165. # %%
  166. %%R
  167. ################################################################################ Step 5: Save model and Data
  168. save(best_model, file = paste0("Model_", Phenotype_name ,".RData")) # save model
  170. # %% [markdown]
  171. # ##### Step 6: Load the precomputed model and plot centiles
  173. # %%
  174. %load_ext rpy2.ipython
  176. # %%
  177. %%R
  178. library(gamlss)
  179. M_HC <- read.csv("data.csv")
  180. Phenotype_name <- "global system segregation"
  181. load(paste0("Model_", Phenotype_name ,".RData"))
  183. # %%
  184. %%R
  185. best_model
  187. # %%
  188. %%R
  189. ################################################################################ Step 3: Get centiles
  190. quantiles<-c(0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99)
  191. n_quantiles <- length(quantiles)
  192. n_points <- 8021
  193. Centiles_male <- array(NA, dim = c(n_quantiles, n_points))
  194. Centiles_female <- array(NA, dim = c(n_quantiles, n_points))
  195. x_female <- seq(-0.2, 80, length.out = 8021)
  196. x_male <- seq(-0.2, 80, length.out = 8021)
  198. for (i in 1:length(quantiles)){
  199. Qua <- getQuantile(best_model, quantile=quantiles[i],term='Age',fixed.at=list(Sex=0, SiteID="Site1"), n.points = 8020)
  200. Centiles_female[i,] <- Qua(x_female)
  202. Qua <- getQuantile(best_model, quantile=quantiles[i],term='Age',fixed.at=list(Sex=1, SiteID="Site1"), n.points = 8020)
  203. Centiles_male[i,] <- Qua(x_male)
  204. }
  206. ################################################################################ Step 4: Plot centiles for Site1
  207. Centiles <- (Centiles_female + Centiles_male) / 2
  208. plot(X, Centiles[1, ],
  209. type = "n",
  210. xlab = "Age",
  211. ylab = Phenotype_name,
  212. bty = "l",
  213. ylim = c(0.4, 0.8),
  214. main = paste0("Lifespan growth curve of ", Phenotype_name))
  216. X <- seq(-0.2, 80, length.out = 8021)
  217. for (i in 1:7) {
  218. if (i == 4) {
  219. lines(X, Centiles[i, ], col = "black", lwd = 2, lty = 1)
  220. } else {
  221. lines(X, Centiles[i, ], col = "black", lwd = 1, lty = 2)
  222. }
  223. }
  225. # %%

GAMLSS_model_fitting.ipynb at commit 36c819e, no license · at the source

Overview

Authors: Qiongling Li1,2,3, Xinyuan Liang1,2,3, Debin Zeng4, Tengda Zhao1,2,3, Xuhong Liao5, Gaolang Gong1,2,3,6, Qian Wang1,2,3, Chenxuan Pang1,2,3, Qian Yu1,2,3, Xiaoxi Dong1, Yirong He1, Yanchao Bi1,7,8, Pindong Chen9, Rui Chen1, Yuan Chen10,11,12,13, Taolin Chen14,15,16, Jingliang Cheng10,11,12,13, Yuqi Cheng17, Zaixu Cui6, Zhengjia Dai1,2,3
and 56 other authorsYao Deng1, Yuyin Ding1, Qi Dong1, Dingna Duan1,2,3, Jia-Hong Gao8,18,19, Qiyong Gong14,15,16, Ying Han20, Zaizhu Han1,3, Chu-Chung Huang21, Ruiwang Huang1,3,22, Ran Huo23, Lingjiang Li24,25, Ching-Po Lin26,27, Qixiang Lin1,2,3, Bangshan Liu24,25, Chao Liu1,3, Ningyu Liu1, Ying Liu23, Yong Liu28, Jing Lu1, Leilei Ma1, Weiwei Men18,19, Shaozheng Qin1,2,3,6, Wen Qin29, Jiang Qiu30,31,32, Shijun Qiu33,34, Tianmei Si35, Shuping Tan36, Yanqing Tang37, Sha Tao1, Dawei Wang38, Fei Wang39, Jiali Wang1, Pan Wang40, Xiaoqin Wang30,31, Yanpei Wang1, Dongtao Wei30,31, Yankun Wu35, Peng Xie41,42, Xiufeng Xu43, Yuehua Xu1,2,3, Zhilei Xu1,2,3, Liyuan Yang1,2,3, Chunshui Yu29, Huishu Yuan23, Zilong Zeng1,2,3, Haibo Zhang1, Xi Zhang44, Gai Zhao1, Yanting Zheng33, Suyu Zhong28, MCADI, DIDA-MDD Working Group, Mingrui Xia1,2,3, Shuyu Li1, Yong He1,2,3,6
ORCID iDs: Qiongling Li, Xinyuan Liang, Debin Zeng, Tengda Zhao, Xuhong Liao, Gaolang Gong, Yanchao Bi, Yuan Chen, Jingliang Cheng, Zaixu Cui, Jia-Hong Gao, Qiyong Gong, Zaizhu Han, Chu-Chung Huang, Ruiwang Huang, Ching-Po Lin, Qixiang Lin, Chao Liu, Yong Liu, Shaozheng Qin, Wen Qin, Jiang Qiu, Shijun Qiu, Tianmei Si, Yanqing Tang, Sha Tao, Fei Wang, Dongtao Wei, Peng Xie, Zhilei Xu, Chunshui Yu, Suyu Zhong, Mingrui Xia, Shuyu Li, Yong He
44 affiliations
  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University,Beijing, China
  2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University,Beijing, China
  3. IDG/McGovern Institute for Brain Research, Beijing Normal University,Beijing, China
  4. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University,Beijing, China
  5. School of Systems Science, Beijing Normal University,Beijing, China
  6. Chinese Institute for Brain Research,Beijing, China
  7. School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University,Beijing, China
  8. IDG/McGovern Institute for Brain Research, Peking University,Beijing, China
  9. Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences,Beijing, China
  10. Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University,Zhengzhou, China
  11. Henan Engineering Technology Research Center for detection and application of brain function,Zhengzhou, China
  12. Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Zhengzhou, China
  13. Henan Engineering Research Center of medical imaging intelligent diagnosis and treatment, Zhengzhou, China
  14. Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology and Medical Imaging, West China Hospital of Sichuan University,Chengdu, Sichuan China
  15. Psychoradiology Key Laboratory of Sichuan Province, West China Hospital of Sichuan University,Chengdu, Sichuan China
  16. Xiamen Key Lab of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian China
  17. Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine,Hangzhou, Zhejiang China
  18. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University,Beijing, China
  19. Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University,Beijing, China
  20. Department of Neurology, Xuanwu Hospital of Capital Medical University,Beijing, China
  21. Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University,Shanghai, China
  22. School of Psychology, South China Normal University,Guangzhou, China
  23. Department of Radiology, Peking University Third Hospital,Beijing, China
  24. Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University,Changsha, China
  25. Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health,Changsha, China
  26. Institute of Neuroscience, National Yang Ming Chiao Tung University,Taipei, China
  27. Department of Education and Research, Taipei City Hospital,Taipei, China
  28. Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications,Beijing, China
  29. Department of Radiology & Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology & State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital,Tianjin, China
  30. Key Laboratory of Cognition and Personality (SWU), Ministry of Education,Chongqing, China
  31. Department of Psychology, Southwest University,Chongqing, China
  32. Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University,Chongqing, China
  33. Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou, China
  34. State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou, China
  35. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University,Beijing, China
  36. Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School,Beijing, China
  37. Department of Psychiatry, Shengjing Hospital of China Medical University,Shenyang, China
  38. Department of Radiology, Qilu Hospital of Shandong University,Ji’nan, China
  39. Department of Psychiatry, The First Affiliated Hospital of China Medical University,Shenyang, China
  40. Department of Neurology, Tianjin Huanhu Hospital, Tianjin University,Tianjin, China
  41. Chongqing Key Laboratory of Neurobiology,Chongqing, China
  42. Department of Neurology, The First Affiliated Hospital of Chongqing Medical University,Chongqing, China
  43. Department of Psychiatry, First Affiliated Hospital of Kunming Medical University,Kunming, Yunnan China
  44. Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital,Beijing, China
Institutions: Beijing Normal University (China); Beihang University (China); Chinese Institute for Brain Research (China); Peking University (China); Chinese Academy of Sciences (China); First Affiliated Hospital of Zhengzhou University (China); West China Hospital of Sichuan University (China); Zhejiang University (China); Capital Medical University (China); East China Normal University (China); South China Normal University (China); Peking University Third Hospital (China); Central South University (China); Second Xiangya Hospital of Central South University (China); National Yang Ming Chiao Tung University (Taiwan); Taipei City Hospital (Taiwan); Beijing University of Posts and Telecommunications (China); Tianjin Medical University General Hospital (China); Ministry of Education of the People's Republic of China (China); Southwest University (China); First Affiliated Hospital of Guangzhou University of Chinese Medicine (China); Peking University Sixth Hospital (China); Sheng Jing Hospital (China); Qilu Hospital of Shandong University (China); First Hospital of China Medical University (China); Tianjin University (China); Chongqing Medical University (China); The Affiliated Yongchuan Hospital of Chongqing Medical University (China); First Affiliated Hospital of Kunming Medical University (China); Chinese PLA General Hospital (China)
Journal: Nature communications, volume 17, issue 1, article 4951
Dates: received 4 May 2025; accepted 16 March 2026; published online 7 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71270-w · PMID 41946727 · PMCID PMC13234122 · OpenAlex W7151605549
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), developmental (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Graphs, fMRI & imaging, Physiology & signal measures
Keywords: Functional magnetic resonance imaging, Cognitive neuroscience
MeSH: Cerebral Cortex*, Longevity*, Neurodevelopment*, Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Connectome, Female, Humans, Infant, Infant, Newborn, Magnetic Resonance Imaging, Male, Middle Aged, 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) (82021004, 31830034)
Citations: cited by 6 papers (Europe PMC); 70 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 14 matches between paragraphs and lines of code.

sunlianglong/brainchart-fc-lifespan

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 36c819e2190adcf411ef3ad4b04232f8e3f5d7a5, 3 April 2025
Languages: MATLAB (10), Jupyter (1)
Size: 398 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FreeSurfer (4 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
12 files
At the source: github.com/sunlianglong/BrainChart-FC-Lifespan/tree/main

QionglingLi/LifespanGradient

License: BSD-3-Clause
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: e63aa0a9c1c7eb01c871235d9a1ddb5beeed0f81, 6 March 2026
Languages: MATLAB (13), R (2)
Size: 57 files, 15 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (6 files), BrainSpace (2 files), ggplot2 (1 file), GIfTI library for MATLAB (1 file), Parallel Computing Toolbox (1 file), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
17 files
At the source: github.com/QionglingLi/LifespanGradient

gitlab.qunex.yale.edu/

License: none: the authors keep all their rights
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Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
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At the source: gitlab.qunex.yale.edu/

DCAN-Labs/abcd-hcp-pipeline

License: BSD-3-Clause
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 0fe7f449057b85eab9727d22a3302ce91499b86a, 16 January 2026
Languages: Python (4), Shell (2)
Size: 24 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (Dockerfile, requirements.txt, app/requirements.txt), continuous integration, documentation
Not found: CITATION.cff, tests
Tools: FreeSurfer (2 files), FSL (1 file), HCP Pipelines (1 file), PyBIDS (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
8 files
At the source: github.com/DCAN-Labs/abcd-hcp-pipeline

BioMedIA/dhcp-structural-pipeline

License: Apache-2.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: aa214f285fb6dd919125584dd2476fc447dffb59, 15 November 2020
Languages: C++ (721), C/C++ (470), C (21), Shell (17), CUDA (14), Python (4), JavaScript (1)
Size: 1,662 files, 1,248 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (Dockerfile)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: FSL (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
1,250 files
At the source: github.com/BioMedIA/dhcp-structural-pipeline

iBEAT-V2/iBEAT-V2.0-Docker

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f701061578ecc4411d428ca57fecbaa271d727c4, 13 January 2026
Size: 1 file, 0 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
1 file
At the source: github.com/iBEAT-V2/iBEAT-V2.0-Docker

ecr05/MSM_HOCR

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: c9d899676c3fc15b55ff2863765951533e35ad8d, 21 July 2022
Languages: C++ (30), C/C++ (15), Shell (1)
Size: 72 files, 46 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Connectome Workbench (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
47 files
At the source: github.com/ecr05/MSM_HOCR

StuartJO/plotSurfaceROIBoundary

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 38521732528110a1a07f9c08bfcc4ad998b8b92f, 8 April 2025
Languages: MATLAB (10)
Size: 34 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files), FreeSurfer (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
12 files
At the source: github.com/StuartJO/plotSurfaceROIBoundary

NeuroanatomyAndConnectivity/gradient_analysis

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5b2ac635e08110d5720938b887011f809c3ee1f8, 23 July 2017
Languages: Jupyter (9), Python (1), Shell (1)
Size: 59 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 9 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (10 files), Matplotlib (7 files), pandas (6 files), NiBabel (5 files), SciPy (5 files), seaborn (5 files), h5py (3 files), Nilearn (3 files), Pillow (3 files), FSL (2 files), scikit-learn (2 files), NetworkX (1 file), Connectome Workbench (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/NeuroanatomyAndConnectivity/gradient_analysis

Zenodo 18764982

License: BSD-3-Clause
State: the link answers, verified on 29 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: Statistics and Machine Learning Toolbox (6 files), BrainSpace (2 files), ggplot2 (1 file), GIfTI library for MATLAB (1 file), Parallel Computing Toolbox (1 file), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
17 files
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-71270-w.

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:

  • 10 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,362 scripts, each with its path and the digest of its content;
  • 14 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-71270-w.

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, 76 authors, 2 keywords, 18 MeSH terms, 1 funder, 69 references.

Cite

This paper

Li, Q., Liang, X., Zeng, D., Zhao, T., Liao, X., Gong, G., Wang, Q., Pang, C., Yu, Q., Dong, X., He, Y., Bi, Y., Chen, P., Chen, R., Chen, Y., Chen, T., Cheng, J., Cheng, Y., Cui, Z., . . . He, Y. (2026). Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan. Nature communications, 17(1), 4951. https://doi.org/10.1038/s41467-026-71270-w

BibTeX

@article{li2026spatiotemporal,
author = {Li, Qiongling and Liang, Xinyuan and Zeng, Debin and Zhao, Tengda and Liao, Xuhong and Gong, Gaolang and Wang, Qian and Pang, Chenxuan and Yu, Qian and Dong, Xiaoxi and He, Yirong and Bi, Yanchao and Chen, Pindong and Chen, Rui and Chen, Yuan and Chen, Taolin and Cheng, Jingliang and Cheng, Yuqi and Cui, Zaixu and Dai, Zhengjia and Deng, Yao and Ding, Yuyin and Dong, Qi and Duan, Dingna and Gao, Jia-Hong and Gong, Qiyong and Han, Ying and Han, Zaizhu and Huang, Chu-Chung and Huang, Ruiwang and Huo, Ran and Li, Lingjiang and Lin, Ching-Po and Lin, Qixiang and Liu, Bangshan and Liu, Chao and Liu, Ningyu and Liu, Ying and Liu, Yong and Lu, Jing and Ma, Leilei and Men, Weiwei and Qin, Shaozheng and Qin, Wen and Qiu, Jiang and Qiu, Shijun and Si, Tianmei and Tan, Shuping and Tang, Yanqing and Tao, Sha and Wang, Dawei and Wang, Fei and Wang, Jiali and Wang, Pan and Wang, Xiaoqin and Wang, Yanpei and Wei, Dongtao and Wu, Yankun and Xie, Peng and Xu, Xiufeng and Xu, Yuehua and Xu, Zhilei and Yang, Liyuan and Yu, Chunshui and Yuan, Huishu and Zeng, Zilong and Zhang, Haibo and Zhang, Xi and Zhao, Gai and Zheng, Yanting and Zhong, Suyu and {MCADI} and {DIDA-MDD Working Group} and Xia, Mingrui and Li, Shuyu and He, Yong},
title = {{Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {4951},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71270-w},
url = {https://doi.org/10.1038/s41467-026-71270-w},
pmid = {41946727},
pmcid = {PMC13234122}
}

RIS

TY - JOUR
AU - Li, Qiongling
AU - Liang, Xinyuan
AU - Zeng, Debin
AU - Zhao, Tengda
AU - Liao, Xuhong
AU - Gong, Gaolang
AU - Wang, Qian
AU - Pang, Chenxuan
AU - Yu, Qian
AU - Dong, Xiaoxi
AU - He, Yirong
AU - Bi, Yanchao
AU - Chen, Pindong
AU - Chen, Rui
AU - Chen, Yuan
AU - Chen, Taolin
AU - Cheng, Jingliang
AU - Cheng, Yuqi
AU - Cui, Zaixu
AU - Dai, Zhengjia
AU - Deng, Yao
AU - Ding, Yuyin
AU - Dong, Qi
AU - Duan, Dingna
AU - Gao, Jia-Hong
AU - Gong, Qiyong
AU - Han, Ying
AU - Han, Zaizhu
AU - Huang, Chu-Chung
AU - Huang, Ruiwang
AU - Huo, Ran
AU - Li, Lingjiang
AU - Lin, Ching-Po
AU - Lin, Qixiang
AU - Liu, Bangshan
AU - Liu, Chao
AU - Liu, Ningyu
AU - Liu, Ying
AU - Liu, Yong
AU - Lu, Jing
AU - Ma, Leilei
AU - Men, Weiwei
AU - Qin, Shaozheng
AU - Qin, Wen
AU - Qiu, Jiang
AU - Qiu, Shijun
AU - Si, Tianmei
AU - Tan, Shuping
AU - Tang, Yanqing
AU - Tao, Sha
AU - Wang, Dawei
AU - Wang, Fei
AU - Wang, Jiali
AU - Wang, Pan
AU - Wang, Xiaoqin
AU - Wang, Yanpei
AU - Wei, Dongtao
AU - Wu, Yankun
AU - Xie, Peng
AU - Xu, Xiufeng
AU - Xu, Yuehua
AU - Xu, Zhilei
AU - Yang, Liyuan
AU - Yu, Chunshui
AU - Yuan, Huishu
AU - Zeng, Zilong
AU - Zhang, Haibo
AU - Zhang, Xi
AU - Zhao, Gai
AU - Zheng, Yanting
AU - Zhong, Suyu
AU - MCADI
AU - DIDA-MDD Working Group
AU - Xia, Mingrui
AU - Li, Shuyu
AU - He, Yong
TI - Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/07
VL - 17
IS - 1
SP - 4951
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71270-w
UR - https://doi.org/10.1038/s41467-026-71270-w
LA - en
ER -

CSL-JSON

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