OSCR

Linking changes in sulcal morphometry to cognitive development from childhood to adolescence.

Code ↔ Paper

13 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 13 matches
  1. [1] § Methods › Statistical analysis of brain–behavior associations › Associations between longitudinal structural and cognitive changes › Gene enrichment analysis ↔ abagen/cli/run.py, lines 59–123 · score 0.73 · Allen Human Brain, tissue samples, microarray expression, boundaries, voxel, threshold
  2. [2] § Methods › Modeling the development of sulcal morphometry ↔ code/Fig4/merge_as_labels_1126.R, lines 107–169 · score 0.67 · TIV adjustment, meanD, maxD, TIV adjusted, SL, SA
  3. [3] § Methods › Statistical analysis of brain–behavior associations › Associations between longitudinal structural and cognitive changes › Gene enrichment analysis ↔ abagen/reporting.py, lines 105–169 · score 0.66 · Allen Human Brain, microarray expression, female, MNI, threshold, AHBA
  4. [4] § Methods › Modeling the development of sulcal morphometry ↔ code/Fig2/GAMM4_corrected.R, lines 124–164 · score 0.65 · linear model, smooth terms, full model, coefficient, R2, age
  5. [5] § Results › Age-related development of sulcal morphometry from childhood to adolescence ↔ code/Fig2/GAMM4_corrected.R, lines 29–80 · score 0.56 · meanD, maxD, nested, GAMMs, CT, SL
  6. [6] § Methods › Modeling the development of sulcal morphometry ↔ code/Fig2/GAMM4_corrected.R, lines 29–80 · score 0.56 · meanD, maxD, Outliers, MM, SL, SA
  7. [7] § Results › Sulcus development in children aged 6–14 years is associated with gene expression profiles ↔ abagen/reporting.py, lines 105–169 · score 0.54 · Allen Human Brain, abagen toolbox, regional, Atlas, gene
  8. [8] § Results › Age-related development of sulcal morphometry from childhood to adolescence ↔ code/Fig4/merge_as_labels_1126.R, lines 42–105 · score 0.53 · meanD, maxD, nested, MM, CT, SL
  9. [9] § Methods › Statistical analysis of brain–behavior associations › Associations between longitudinal structural and cognitive changes › Covariance analysis of developmental changes ↔ code/Fig4/C&C_CORR_1126.R, lines 41–85 · score 0.52 · baseline age, partial correlation, interval, gender, cognitive
  10. [10] § Methods › Statistical analysis of brain–behavior associations › Associations between longitudinal structural and cognitive changes › Covariance analysis of developmental changes ↔ code/Fig4/delta_feature_1130.R, lines 140–199 · score 0.52 · baseline age, longitudinal change, interval, cognitive
  11. [11] § Results › Association between sulcal morphometry changes and cognitive performance improvements › Associations between sulcal morphometry changes and working memory gains ↔ code/Fig4/lasso_cc_0106.py, lines 167–228 · score 0.52 · cross validated, LASSO feature, Ridge, predictive, model
  12. [12] § Methods › Statistics and reproducibility › Developmental modeling ↔ code/Fig4/merge_as_labels_1126.R, lines 42–105 · score 0.51 · meanD, maxD, MM, SL, SA, TIV
  13. [13] § Results › Association between sulcal morphometry changes and cognitive performance improvements › Associations between sulcal morphometry changes and working memory gains ↔ code/Fig4/lasso_cc_0106.py, lines 167–228 · score 0.51 · LASSO coefficients, cross validation, Scatter, Predictive, Figure 4, modeling

Paper

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The authors' code

R · 169 lines · 6.6 KB · no license · 3 matches

  2. library(dplyr)
  3. library(readr)
  4. library(stringr)
  5. library(purrr)
  6. library(tidyr)
  7. library(tidyverse)
  8. library(readxl)
  9. library(MASS) # 用于稳健回归
  11. # ------------------------- 数据读取和预处理 -------------------------
  12. # 设置工作目录
  13. setwd("E:/lsy_group/7.9reorganize/7.9reorganize/2.data_process/sulcal_data_merge_delete/")
  15. # 1. 读取所有形态特征CSV文件
  16. file_pattern <- "CBDP\\d{4}[A-Z]_FS_default_session_auto_sulcal_morphometry_split_processed\\.csv"
  17. csv_files <- list.files(pattern = file_pattern)
  19. # 读取并合并所有文件
  20. all_data <- lapply(csv_files, function(file) {
  21. subject_id <- str_extract(file, "CBDP0(\\d{3}[A-Z])", group = 1)
  22. read_csv(file) %>%
  23. mutate(subject = paste0(subject_id, "_FS"),
  24. # 修正ID格式：在CBDP后添加双0，确保格式为CBDP00xxX
  25. id = paste0("CBDP0", subject_id))
  26. }) %>% bind_rows()
  28. # 检查数据
  29. if (nrow(all_data) == 0) stop("没有读取到任何数据，请检查文件名格式和路径")
  31. # 2. 读取人口学数据（包含TIV）
  32. demo_data <- readxl::read_excel("E:/lsy_group/7.9reorganize/7.9reorganize/1.data_demographic/merged_final_result.xlsx") %>%
  33. dplyr::select(id, Age, gender, subj_unique, TIV) %>%
  34. mutate(gender = as.factor(gender))
  36. # 3. 合并形态学数据和人口学数据（保留所有原始数据）
  37. merged_data <- all_data %>%
  38. left_join(demo_data, by = "id") #%>%
  39. #dplyr::filter(!is.na(TIV)) # 只过滤掉没有TIV数据的样本
  42. # ------------------------- TIV回归残差计算 -------------------------
  43. # 计算TIV调整后的值（修正残差长度问题）
  44. data_with_tiv_adjusted <- merged_data %>%
  45. group_by(Label) %>%
  46. nest() %>%
  47. mutate(
  48. data_processed = map(data, ~ {
  49. # 安全的残差计算函数
  50. safe_residuals <- function(formula, data) {
  51. # 创建一个与原数据行数相同的NA向量
  52. result <- rep(NA, nrow(data))
  54. # 找到完整的观测值（无缺失值的行）
  55. complete_cases <- complete.cases(data[, all.vars(formula)])
  57. if(sum(complete_cases) > 2) { # 至少需要3个完整观测值
  58. tryCatch({
  59. # 使用稳健回归
  60. model <- MASS::rlm(formula, data = data[complete_cases, ], method = "MM")
  61. result[complete_cases] <- residuals(model)
  62. }, error = function(e) {
  63. tryCatch({
  64. # 如果稳健回归失败，使用普通线性回归
  65. model <- lm(formula, data = data[complete_cases, ])
  66. result[complete_cases] <- residuals(model)
  67. }, error = function(e2) {
  68. # 如果都失败了，保持NA
  69. })
  70. })
  71. }
  72. return(result)
  73. }
  75. .x %>%
  76. mutate(
  77. # 原始形态学特征保持不变
  78. SA = SA,
  79. maxD = maxD,
  80. meanD = meanD,
  81. SW = SW,
  82. SA_tala = SA_tala,
  83. maxD_tala = maxD_tala,
  84. meanD_tala = meanD_tala,
  85. CT = CT,
  86. SL = SL,
  87. SL_tala = SL_tala,
  88. # 添加TIV值
  89. TIV = TIV,
  90. # 添加TIV调整后的值（残差）
  91. SA_TIV_adjusted = safe_residuals(SA ~ TIV, .x),
  92. maxD_TIV_adjusted = safe_residuals(maxD ~ TIV, .x),
  93. meanD_TIV_adjusted = safe_residuals(meanD ~ TIV, .x),
  94. SW_TIV_adjusted = safe_residuals(SW ~ TIV, .x),
  95. SA_tala_TIV_adjusted = safe_residuals(SA_tala ~ TIV, .x),
  96. maxD_tala_TIV_adjusted = safe_residuals(maxD_tala ~ TIV, .x),
  97. meanD_tala_TIV_adjusted = safe_residuals(meanD_tala ~ TIV, .x),
  98. CT_TIV_adjusted = safe_residuals(CT ~ TIV, .x),
  99. SL_TIV_adjusted = safe_residuals(SL ~ TIV, .x),
  100. SL_tala_TIV_adjusted = safe_residuals(SL_tala ~ TIV, .x)
  101. )
  102. })
  103. ) %>%
  104. dplyr::select(-data) %>%
  105. unnest(data_processed)
  107. # ------------------------- 按Label创建文件 -------------------------
  108. # 创建输出目录
  109. output_dir <- "E:/lsy_group/7.9reorganize/7.9reorganize/4.LASSO_revise/mergeaslabel/"
  110. if (!dir.exists(output_dir)) dir.create(output_dir, recursive = TRUE)
  112. # 获取所有唯一的Label
  113. unique_labels <- unique(data_with_tiv_adjusted$Label)
  115. # 为每个Label创建合并后的文件
  116. walk(unique_labels, ~{
  117. label_data <- data_with_tiv_adjusted %>%
  118. dplyr::filter(Label == .x) %>%
  119. # 重新排列列顺序，让相关信息更有组织
  120. dplyr::select(
  121. # 基本信息
  122. subject, id, Label, Age, gender, subj_unique, TIV,
  123. # 原始形态学特征
  124. SA, maxD, meanD, SW, SA_tala, maxD_tala, meanD_tala, CT, SL, SL_tala,
  125. # TIV调整后的形态学特征
  126. SA_TIV_adjusted, maxD_TIV_adjusted, meanD_TIV_adjusted, SW_TIV_adjusted,
  127. SA_tala_TIV_adjusted, maxD_tala_TIV_adjusted, meanD_tala_TIV_adjusted,
  128. CT_TIV_adjusted, SL_TIV_adjusted, SL_tala_TIV_adjusted,
  129. # 其他所有剩余列
  130. everything()
  131. )
  133. safe_label <- str_replace_all(.x, "[^[:alnum:]_]", "_")
  134. filename <- paste0(output_dir, safe_label, "_merged_with_TIV.csv")
  136. write_csv(label_data, filename)
  137. message("Created merged file with TIV adjustments: ", filename)
  138. message(" - Samples: ", nrow(label_data))
  139. message(" - Features: Original + TIV + TIV-adjusted morphological measures")
  140. })
  142. # ------------------------- 生成汇总报告 -------------------------
  143. message("\n=== 处理完成汇总 ===")
  144. message("共创建了 ", length(unique_labels), " 个按Label合并后的文件")
  145. message("每个文件包含:")
  146. message(" - 原始形态学特征: SA, maxD, meanD, SW, SA_tala, maxD_tala, meanD_tala, CT, SL, SL_tala")
  147. message(" - TIV值: TIV")
  148. message(" - TIV调整后特征: [特征名]_TIV_adjusted")
  149. message(" - 人口学信息: Age, gender, subj_unique")
  150. message("文件保存位置: ", output_dir)
  151. message("注意: 保留了所有原始数据，未进行离群值筛选")
  153. # 可选：创建一个汇总统计文件
  154. summary_stats <- data_with_tiv_adjusted %>%
  155. group_by(Label) %>%
  156. summarise(
  157. n_subjects = n(),
  158. mean_TIV = mean(TIV, na.rm = TRUE),
  159. sd_TIV = sd(TIV, na.rm = TRUE),
  160. mean_age = mean(Age, na.rm = TRUE),
  161. sd_age = sd(Age, na.rm = TRUE),
  162. n_missing_values = sum(is.na(SA) | is.na(maxD) | is.na(meanD) | is.na(SW) |
  163. is.na(SA_tala) | is.na(maxD_tala) | is.na(meanD_tala) |
  164. is.na(CT) | is.na(SL) | is.na(SL_tala)),
  165. .groups = 'drop'
  166. )
  168. write_csv(summary_stats, paste0(output_dir, "summary_by_label.csv"))
  169. message("已创建汇总统计文件: ", paste0(output_dir, "summary_by_label.csv"))

merge_as_labels_1126.R at commit fb98cd7, no license · at the source

Overview

Authors: Yijin Shan1, Huiting Qiao1, Yirong He2, Lei Chu1, Debin Zeng3, Xiaoxi Dong4, Tengda Zhao4,5,6, Xuhong Liao7, Xiaodan Chen4,5,6, Yunman Xia4,5,6, Tianyuan Lei4,5,6, Lianglong Sun4,5,6, Weiwei Men8,9, Rui Chen4, Leilei Ma4, Xiaoyu Ren4, Yanpei Wang4, Daoyang Wang10, Mingming Hu4, Zhiying Pan4
and 7 other authorsShuping Tan11, Jia-Hong Gao8,9,12, Shaozheng Qin4,5,6,13, Sha Tao4, Qi Dong4, Yong He4,5,6,13, Shuyu Li4
ORCID iDs: Debin Zeng, Xuhong Liao, Yunman Xia, Lianglong Sun, Jia-Hong Gao, Shaozheng Qin, Sha Tao, Yong He, Shuyu Li
13 affiliations
  1. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University,Beijing, China
  2. Institute for Brain Research and Rehabilitation, South China Normal University,Guangzhou, China
  3. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University,Shanghai, China
  4. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University,Beijing, China
  5. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University,Beijing, China
  6. IDG/McGovern Institute for Brain Research, Beijing Normal University,Beijing, China
  7. School of Systems Science, Beijing Normal University,Beijing, China
  8. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University,Beijing, China
  9. Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University,Beijing, China
  10. Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University,Hangzhou, China
  11. Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School,Beijing, China
  12. IDG/McGovern Institute for Brain Research, Peking University,Beijing, China
  13. Chinese Institute for Brain Research,Beijing, China
Institutions: National Academy of Sciences of Belarus (Belarus)
Journal: Communications biology, volume 9, issue 1, article 767
Dates: received 21 September 2025; accepted 18 March 2026; published online 8 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-09956-6 · PMID 41951806 · PMCID PMC13237371 · OpenAlex W7152075899
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Smoothing, state filtering, decompositions
Keywords: Neuroscience, Cognitive neuroscience
MeSH: Cerebral Cortex*, Child Development*, Cognition*, Adolescent, Child, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Neurodevelopment (* major topic)
Topic: Infant Development and Preterm Care (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82021004, 31521063); Beijing Normal University (BNU) (Z15110000391512); China Postdoctoral Science Foundation (2025M772874); Funder(s):the Scientific and Technological Innovation 2030 - Major Projects Grant Reference Number: 2021ZD0200500
Citations: not cited yet (Europe PMC); 99 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

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rmarkello/abagen

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: dc4a007e4e902e51f97251390c8d1bbf7e58c6d3, 29 September 2023
Languages: Python (52), Shell (1)
Size: 130 files, 53 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt, setup.cfg, setup.py, docs/requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (23 files), pandas (22 files), abagen (20 files), NiBabel (11 files), SciPy (7 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
55 files
At the source: github.com/rmarkello/abagen

murraylab/brainsmash

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f6a9c375ba2e591acbc2edc161fbcff12609749d, 18 February 2024
Languages: Python (18)
Size: 83 files, 18 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt, setup.py), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: NumPy (11 files), NiBabel (3 files), SciPy (3 files), BrainSMASH (2 files), scikit-learn (2 files), Matplotlib (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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20 files
At the source: github.com/murraylab/brainsmash

yijinshan1/sulcus2025

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: fb98cd73c7f3e476b1e48be5858bb39d2cf24302, 28 January 2026
Languages: Python (12), R (6)
Size: 63 files, 18 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (11 files), Matplotlib (10 files), NumPy (8 files), SciPy (6 files), seaborn (6 files), tidyverse (6 files), ggplot2 (3 files), NiBabel (3 files), statsmodels (3 files), scikit-learn (2 files), abagen (1 file), BrainSMASH (1 file), ggpubr (1 file), mgcv (1 file), Nilearn (1 file), psych (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
18 files
At the source: github.com/yijinshan1/sulcus2025

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 27 authors, 2 keywords, 11 MeSH terms, 4 funders, 95 references.

Cite

This paper

Shan, Y., Qiao, H., He, Y., Chu, L., Zeng, D., Dong, X., Zhao, T., Liao, X., Chen, X., Xia, Y., Lei, T., Sun, L., Men, W., Chen, R., Ma, L., Ren, X., Wang, Y., Wang, D., Hu, M., . . . Li, S. (2026). Linking changes in sulcal morphometry to cognitive development from childhood to adolescence. Communications biology, 9(1), 767. https://doi.org/10.1038/s42003-026-09956-6

BibTeX

@article{shan2026linking,
author = {Shan, Yijin and Qiao, Huiting and He, Yirong and Chu, Lei and Zeng, Debin and Dong, Xiaoxi and Zhao, Tengda and Liao, Xuhong and Chen, Xiaodan and Xia, Yunman and Lei, Tianyuan and Sun, Lianglong and Men, Weiwei and Chen, Rui and Ma, Leilei and Ren, Xiaoyu and Wang, Yanpei and Wang, Daoyang and Hu, Mingming and Pan, Zhiying and Tan, Shuping and Gao, Jia-Hong and Qin, Shaozheng and Tao, Sha and Dong, Qi and He, Yong and Li, Shuyu},
title = {{Linking changes in sulcal morphometry to cognitive development from childhood to adolescence}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {767},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09956-6},
url = {https://doi.org/10.1038/s42003-026-09956-6},
pmid = {41951806},
pmcid = {PMC13237371}
}

RIS

TY - JOUR
AU - Shan, Yijin
AU - Qiao, Huiting
AU - He, Yirong
AU - Chu, Lei
AU - Zeng, Debin
AU - Dong, Xiaoxi
AU - Zhao, Tengda
AU - Liao, Xuhong
AU - Chen, Xiaodan
AU - Xia, Yunman
AU - Lei, Tianyuan
AU - Sun, Lianglong
AU - Men, Weiwei
AU - Chen, Rui
AU - Ma, Leilei
AU - Ren, Xiaoyu
AU - Wang, Yanpei
AU - Wang, Daoyang
AU - Hu, Mingming
AU - Pan, Zhiying
AU - Tan, Shuping
AU - Gao, Jia-Hong
AU - Qin, Shaozheng
AU - Tao, Sha
AU - Dong, Qi
AU - He, Yong
AU - Li, Shuyu
TI - Linking changes in sulcal morphometry to cognitive development from childhood to adolescence
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/08
VL - 9
IS - 1
SP - 767
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09956-6
UR - https://doi.org/10.1038/s42003-026-09956-6
LA - en
ER -

CSL-JSON

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}

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The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41467-026-71270-w [code]
Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan.
Journal: Nature communications
In common: Nilearn, NiBabel, ggplot2, 6 other tools, developmental, 4 references, 7 authors
[2] doi:10.1038/s42003-026-10276-y [code]
The cellular correlates and adolescent reorganisation of cortical myelination networks in the common marmoset.
Journal: Communications biology
In common: BrainSMASH, mgcv, NiBabel, 8 other tools, structural MRI / diffusion, 6 references
[3] doi:10.1073/pnas.2519586123 [code]
Personalized functional topography-based multisite brain age prediction modeling reveals divergent neurodevelopment in major depression.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: BrainSMASH, ggpubr, statsmodels, 4 other tools, developmental, structural MRI / diffusion, 6 references, author Yong He
[4] doi:10.1038/s41467-026-74153-2 [code]
Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder.
Journal: Nature communications
In common: abagen, BrainSMASH, ggpubr, 8 other tools, 6 references
[5] doi:10.1093/braincomms/fcag097 [code]
Long-term effects of preterm birth on cortical folding trajectories in early childhood.
Journal: Brain communications
In common: developmental, 13 references
[6] doi:10.7554/elife.103097 [code]
Canonical neurodevelopmental trajectories of structural and functional manifolds.
Journal: eLife
In common: mgcv, Nilearn, ggpubr, 8 other tools, developmental, structural MRI / diffusion, 3 references
[7] doi:10.64898/2026.03.06.709690
Genetic insights on the mechanisms of human cortical folding
Journal: bioRxiv (preprint)
In common: 11 references
[8] doi:10.1371/journal.pbio.3003856 [code]
Aging and metabolism contribute separately to brain-body health.
Journal: PLoS biology
In common: Nilearn, NiBabel, statsmodels, 6 other tools, structural MRI / diffusion, 6 references
[9] doi:10.1038/s41467-026-71719-y [code]
Brain functional-structural gradient coupling reflects development, behavior and genetic influences.
Journal: Nature communications
In common: abagen, Nilearn, NiBabel, 7 other tools, developmental, 4 references
[10] doi:10.1038/s41467-026-73072-6 [code]
Mapping the spatiotemporal continuum of structural connectivity development across the human connectome in youth.
Journal: Nature communications
In common: mgcv, psych, NiBabel, 5 other tools, developmental, structural MRI / diffusion, 5 references

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