OSCR

Convergent and divergent brain-cognition development in early 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 · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Imaging data preprocessing ↔ stable_projects/preprocessing/CBIG_fMRI_Preproc2016/utilities/CBIG_preproc_censor_wrapper.m, lines 1–119 · score 0.84 · Lomb Scargle periodogram, 0.08 Hz, fMRI, Uncensored, preprocessing, signal
  2. [2] § Methods › Imaging data preprocessing ↔ stable_projects/preprocessing/CBIG_fMRI_Preproc2016/utilities/CBIG_preproc_censor.m, lines 1–112 · score 0.81 · Lomb Scargle periodogram, 0.08 Hz, fMRI, censored, preprocessing, signal
  3. [3] § Methods › Cognition assessment ↔ stable_projects/predict_phenotypes/ChenTam2022_TRBPC/figure_utilities/matrix_plots/CBIG_TRBPC_supp_relevance_ind.m, the whole file · a weak match · score 0.71 · episodic memory, Processing Speed, inhibition, recall, intelligence, mental
  4. [4] § Results › Convergent and divergent predictive network features between cross-sectional and longitudinal estimates of brain–cognition relationship ↔ stable_projects/predict_phenotypes/Xie2025_LBC/replication/CBIG_LBC_run_step5_consist_inconsist.sh, lines 1–74 · score 0.69 · inconsistent PNFs, CogY0, Network blocks, predictive network feature, FCY0, LMT
  5. [5] § Results › Convergent and divergent predictive network features between cross-sectional and longitudinal estimates of brain–cognition relationship ↔ stable_projects/predict_phenotypes/Xie2025_LBC/replication/CBIG_LBC_run_step5_consist_inconsist.sh, lines 1–74 · score 0.69 · inconsistent PNFs, CogY0, Network blocks, predictive network feature, FCY0, LMT
  6. [6] § Results › Individual differences in longitudinal cognitive changes ↔ stable_projects/predict_phenotypes/Xie2025_LBC/util/plot/CBIG_LBC_cog_stability.m, the whole file · a weak match · score 0.65 · PicVocab, cognitive stability, Longitudinal cognitive, Flanker, Spearman, Picture
  7. [7] § Results › The relationship between FC and cognition strengthens with development ↔ stable_projects/predict_phenotypes/Xie2025_LBC/step3_KRR_predict/script/CBIG_LBC_compute_singleKRR_modelTransferY2.m, lines 86–177 · score 0.62 · CogY2, model transfer, CogY0, FCY2, prediction accuracies, FCY0
  8. [8] § Results › Individual differences in longitudinal cognitive changes ↔ stable_projects/predict_phenotypes/Xie2025_LBC/util/plot/CBIG_LBC_violin_medianIQR.m, the whole file · a weak match · score 0.60 · PicVocab, Longitudinal changes, Violin, bars, median, Flanker
  9. [9] § Results › Convergent and divergent predictive network features between cross-sectional and longitudinal estimates of brain–cognition relationship ↔ stable_projects/predict_phenotypes/Xie2025_LBC/step5_consist_inconsist_PNF/script/CBIG_LBC_ABCD_PNF_compare.m, the whole file · a weak match · score 0.58 · inconsistent PNFs, Network blocks, predictive network feature, transformed, score, matrix
  10. [10] § Results › Convergent and divergent predictive network features between cross-sectional and longitudinal estimates of brain–cognition relationship ↔ stable_projects/predict_phenotypes/Xie2025_LBC/step5_consist_inconsist_PNF/script/CBIG_LBC_ABCD_PNF_compare.m, the whole file · a weak match · score 0.58 · inconsistent PNFs, Network blocks, predictive network feature, transformed, score, matrix
  11. [11] § Results › Models trained on baseline FC to predict baseline cognition improve in accuracy when applied to Year 2 FC and Year 2 cognition ↔ stable_projects/predict_phenotypes/Xie2025_LBC/step3_KRR_predict/script/CBIG_LBC_compute_singleKRR_modelTransferY2.m, lines 86–177 · score 0.54 · CogY2, CogY0, prediction accuracies, FCY2, FCY0, motion
  12. [12] § Results › Baseline FC and longitudinal FC change weakly predict cognitive change ↔ stable_projects/predict_phenotypes/Xie2025_LBC/util/plot/CBIG_LBC_violin_medianIQR.m, the whole file · a weak match · score 0.53 · PicVocab, violin, median, Flanker, box, Picture
  13. [13] § Results › Models trained on baseline FC to predict baseline cognition improve in accuracy when applied to Year 2 FC and Year 2 cognition ↔ stable_projects/preprocessing/CBIG_fMRI_Preproc2016/utilities/CBIG_preproc_plot_QC_RSFC_corr_vs_distance_readdata.m, the whole file · a weak match · score 0.51 · framewise displacement, standard deviation, std, quality, motion, FD

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Shell · 229 lines · 8.3 KB · MIT · 2 matches

  1. #!/bin/bash
  2. # Wrapper script for Step 5: PNF consistency/inconsistency analysis.
  3. #
  4. # Computes predictive network features (PNF) for two models:
  5. # Model 1: FC_Y0 -> CogY0
  6. # Model 2: FC_Delta -> CogDelta
  7. # Then compares sign consistency across models.
  8. # Network matrix plots can be generated locally without cluster submission.
  9. #
  10. # Steps:
  11. # step1 : PNF block for FC_Y0 -> CogY0 (model 1, measure index 2)
  12. # step2 : PNF block for FC_Delta -> CogDelta (model 2, measure index 2)
  13. # step3 : PNF sign consistency/inconsistency comparison (depends on step1+2)
  14. # --all : Submit full pipeline with PBS dependencies
  15. #
  16. # Usage:
  17. # bash CBIG_LBC_run_step5_consist_inconsist.sh --all
  18. # bash CBIG_LBC_run_step5_consist_inconsist.sh --step1
  19. # ...
  20. #
  21. # Written by Yapei Xie and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
  23. # ---------------------------------------------------------------------------
  24. # Load environment config
  25. # ---------------------------------------------------------------------------
  26. SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
  27. source "${SCRIPT_DIR}/config/CBIG_LBC_tested_config.sh"
  29. if [ -z "${LBC_rep_dir}" ]; then
  30. echo "ERROR: LBC_rep_dir not set. Did you source config?"; exit 1
  31. fi
  32. if [ -z "${CBIG_CODE_DIR}" ]; then
  33. echo "ERROR: CBIG_CODE_DIR not set. Did you source CBIG_LBC_tested_config.sh?"; exit 1
  34. fi
  36. export LBC_rep_dir
  37. export CBIG_CODE_DIR
  39. REPO_ROOT="${SCRIPT_DIR}/.."
  40. STEP_DIR="${REPO_ROOT}/step5_consist_inconsist_PNF/script"
  41. LOG_DIR="${LBC_rep_dir}/logs"
  42. PNF_DIR="${LBC_rep_dir}/Results/PNF"
  43. KRR_DIR="${LBC_rep_dir}/Results/KRR_results"
  45. mkdir -p "${LOG_DIR}"
  46. mkdir -p "${PNF_DIR}/FC_Y0"
  47. mkdir -p "${PNF_DIR}/FC_DeltaCogDelta"
  49. # ---------------------------------------------------------------------------
  50. # Input / output paths
  51. # ---------------------------------------------------------------------------
  52. # Model 1: FC_Y0 -> CogY0
  53. cov_mat_model1="${KRR_DIR}/FC_Y0/interpretation/FC_final_Y0/cov_mat.mat"
  54. pnf_standard_model1="${PNF_DIR}/FC_Y0/PNF_FCY0_CogY0_standard_M2.mat"
  55. pnf_networkblock_model1="${PNF_DIR}/FC_Y0/PNF_FCY0_CogY0_NetworkBlock_M2.mat"
  57. # Model 2: FC_Delta -> CogDelta
  58. cov_mat_model2="${KRR_DIR}/FC_DeltaCogDelta/interpretation/Delta_FC/cov_mat.mat"
  59. pnf_standard_model2="${PNF_DIR}/FC_DeltaCogDelta/PNF_FC_Delta_CogDelta_standard_M2.mat"
  60. pnf_networkblock_model2="${PNF_DIR}/FC_DeltaCogDelta/PNF_FC_Delta_CogDelta_NetworkBlock_M2.mat"
  62. # Compare output
  63. compare_output="${PNF_DIR}/Compare_FCY0CogY0_FCdeltaCogDelta_PNFoverlap.mat"
  65. # Measure index (LMT)
  66. MEASURE_INDEX=2
  68. # ---------------------------------------------------------------------------
  69. # Cluster resource settings
  70. # ---------------------------------------------------------------------------
  71. walltime_pnf="4:00:00"
  72. mem_pnf="32G"
  73. walltime_compare="1:00:00"
  74. mem_compare="16G"
  76. # ---------------------------------------------------------------------------
  77. # Helper: build a MATLAB command string with addpath and random sleep
  78. # ---------------------------------------------------------------------------
  79. matlab_cmd() {
  80. local mf="$1"
  81. local delay=$((RANDOM % 60))
  82. echo "sleep ${delay} && matlab -nodisplay -nosplash -r \"\
  83. addpath(genpath('${STEP_DIR}')); \
  84. addpath(genpath('${REPO_ROOT}/util')); \
  85. ${mf} exit;\""
  86. }
  88. # ---------------------------------------------------------------------------
  89. # Helper: submit a PBS job with optional afterok dependency
  90. # Args: <name> <walltime> <mem> <hold_ids> <cmd>
  91. # Sets LAST_JOB_ID to the submitted numeric job ID
  92. # ---------------------------------------------------------------------------
  93. submit_job() {
  94. local name="$1"
  95. local walltime="$2"
  96. local mem="$3"
  97. local hold="$4"
  98. local cmd="$5"
  100. local depend_flag=""
  101. if [ -n "${hold}" ]; then
  102. depend_flag="-W depend=afterok:${hold}"
  103. fi
  105. local job_id
  106. job_id=$(echo "$cmd" | qsub \
  107. -N "$name" \
  108. -l "walltime=${walltime},mem=${mem},nodes=1:ppn=1" \
  109. -V \
  110. -m ae \
  111. -e "${LOG_DIR}/${name}_err.txt" \
  112. -o "${LOG_DIR}/${name}_out.txt" \
  113. ${depend_flag})
  115. if [ $? -ne 0 ] || [ -z "$job_id" ]; then
  116. echo "ERROR: Job submission failed for ${name}."; exit 1
  117. fi
  118. job_id="${job_id%%.*}"
  119. echo "Submitted job: ${name} [ID: ${job_id}]"
  120. LAST_JOB_ID="${job_id}"
  121. }
  123. # ---------------------------------------------------------------------------
  124. # Step submission functions
  125. # ---------------------------------------------------------------------------
  127. submit_step1() {
  128. local hold="${1:-}"
  129. local mf="CBIG_LBC_ABCD_PNF_block("
  130. mf="${mf}'${cov_mat_model1}', "
  131. mf="${mf}${MEASURE_INDEX}, "
  132. mf="${mf}'${pnf_standard_model1}', "
  133. mf="${mf}'${pnf_networkblock_model1}');"
  134. submit_job "PNF_block_model1" "${walltime_pnf}" "${mem_pnf}" "${hold}" "$(matlab_cmd "$mf")"
  135. }
  137. submit_step2() {
  138. local hold="${1:-}"
  139. local mf="CBIG_LBC_ABCD_PNF_block("
  140. mf="${mf}'${cov_mat_model2}', "
  141. mf="${mf}${MEASURE_INDEX}, "
  142. mf="${mf}'${pnf_standard_model2}', "
  143. mf="${mf}'${pnf_networkblock_model2}');"
  144. submit_job "PNF_block_model2" "${walltime_pnf}" "${mem_pnf}" "${hold}" "$(matlab_cmd "$mf")"
  145. }
  147. submit_step3() {
  148. local hold="${1:-}"
  149. local mf="CBIG_LBC_ABCD_PNF_compare("
  150. mf="${mf}'${pnf_networkblock_model1}', "
  151. mf="${mf}'${pnf_networkblock_model2}', "
  152. mf="${mf}'${pnf_standard_model1}', "
  153. mf="${mf}'${pnf_standard_model2}', "
  154. mf="${mf}'${compare_output}');"
  155. submit_job "PNF_compare" "${walltime_compare}" "${mem_compare}" "${hold}" "$(matlab_cmd "$mf")"
  156. }
  158. # ---------------------------------------------------------------------------
  159. # Usage
  160. # ---------------------------------------------------------------------------
  161. if [ -z "${1}" ]; then
  162. echo "Usage: bash CBIG_LBC_run_step5_consist_inconsist.sh --stepN"
  163. echo " --all : Submit full pipeline with PBS dependencies"
  164. echo " --step1 : PNF block for FC_Y0 -> CogY0 (model 1)"
  165. echo " --step2 : PNF block for FC_Delta -> CogDelta (model 2)"
  166. echo " --step3 : PNF sign consistency comparison (requires step1+2 outputs)"
  167. exit 0
  168. fi
  170. # ---------------------------------------------------------------------------
  171. # --all: submit full pipeline with job dependencies
  172. # ---------------------------------------------------------------------------
  173. if [ "${1}" == "--all" ]; then
  174. echo "=========================================="
  175. echo "Submitting Step 5 PNF pipeline"
  176. echo "Started: $(date)"
  177. echo "=========================================="
  179. # Validate inputs
  180. [ ! -f "${cov_mat_model1}" ] && echo "ERROR: ${cov_mat_model1} not found." && exit 1
  181. [ ! -f "${cov_mat_model2}" ] && echo "ERROR: ${cov_mat_model2} not found." && exit 1
  183. # Steps 1 and 2 are independent — submit in parallel
  184. submit_step1 ""
  185. JOB1="${LAST_JOB_ID}"
  186. submit_step2 ""
  187. JOB2="${LAST_JOB_ID}"
  189. # Step 3 depends on both step 1 and step 2
  190. submit_step3 "${JOB1}:${JOB2}"
  192. echo ""
  193. echo "=========================================="
  194. echo "All jobs submitted. Monitor with: qstat"
  195. echo "Output directory: ${PNF_DIR}"
  196. echo "Logs: ${LOG_DIR}/"
  197. echo "=========================================="
  199. # ---------------------------------------------------------------------------
  200. # Individual steps
  201. # ---------------------------------------------------------------------------
  202. elif [ "${1}" == "--step1" ]; then
  203. echo "Step 1: PNF block for FC_Y0 -> CogY0 (model 1)"
  204. [ ! -f "${cov_mat_model1}" ] && echo "ERROR: ${cov_mat_model1} not found." && exit 1
  205. submit_step1 ""
  206. echo "Logs: ${LOG_DIR}/PNF_block_model1_out.txt"
  208. elif [ "${1}" == "--step2" ]; then
  209. echo "Step 2: PNF block for FC_Delta -> CogDelta (model 2)"
  210. [ ! -f "${cov_mat_model2}" ] && echo "ERROR: ${cov_mat_model2} not found." && exit 1
  211. submit_step2 ""
  212. echo "Logs: ${LOG_DIR}/PNF_block_model2_out.txt"
  214. elif [ "${1}" == "--step3" ]; then
  215. echo "Step 3: PNF sign consistency comparison"
  216. [ ! -f "${pnf_networkblock_model1}" ] && \
  217. echo "ERROR: ${pnf_networkblock_model1} not found. Has step1 finished?" && exit 1
  218. [ ! -f "${pnf_networkblock_model2}" ] && \
  219. echo "ERROR: ${pnf_networkblock_model2} not found. Has step2 finished?" && exit 1
  220. submit_step3 ""
  221. echo "Logs: ${LOG_DIR}/PNF_compare_out.txt"
  223. else
  224. echo "ERROR: Unknown flag: ${1}"
  225. echo "Run without arguments to see usage."
  226. exit 1
  227. fi
  229. exit 0

CBIG_LBC_run_step5_consist_inconsist.sh at commit e3c8c66, under MIT · at the source

Overview

Authors: Yapei Xie1,2,3,4, Shaoshi Zhang1,2,3,4,5, Csaba Orban1,2,3,4, Leon Qi Rong Ooi1,2,3,4,5, Ru Kong1,2,3,4, Dorothea L Floris6,7, Xi-Nian Zuo8,9,10, Elvisha Dhamala11,12,13, Avram J Holmes14, Lucina Q Uddin15, Thomas E Nichols16, Adriana Di Martino17, B T Thomas Yeo1,2,3,4,5,18
ORCID iDs: Yapei Xie, Shaoshi Zhang, Csaba Orban, Leon Qi Rong Ooi, Ru Kong, Dorothea L Floris, Xi-Nian Zuo, Elvisha Dhamala, Lucina Q Uddin, Thomas E Nichols, B T Thomas Yeo
18 affiliations
  1. Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  2. Department of Medicine, Healthy Longevity Translational Research Programme, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
  4. N.1 Institute for Health, National University of Singapore, Singapore, Singapore
  5. Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
  6. Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
  7. Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
  8. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
  9. National Basic Science Data Center, Beijing, China
  10. Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
  11. Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY USA
  12. Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY USA
  13. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY USA
  14. Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ USA
  15. Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA USA
  16. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
  17. Autism Center, Child Mind Institute, New York, NY USA
  18. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA USA
Institutions: National University of Singapore (Singapore); Radboud University Nijmegen (Netherlands); University of Zurich (Switzerland); Donders Centre for Cognitive Neuroimaging (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands); Beijing Normal University (China); State Key Laboratory of Cognitive Neuroscience and Learning; Feinstein Institute for Medical Research (United States); Hofstra University (United States); Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (United States); Zucker Hillside Hospital (United States); Rutgers, The State University of New Jersey (United States); University of California, Los Angeles (United States); University of Oxford (United Kingdom); Child Mind Institute (United States); Massachusetts General Hospital (United States); Athinoula A. Martinos Center for Biomedical Imaging (United States)
Journal: Nature communications, volume 17, issue 1, article 6868
Dates: received 25 June 2025; accepted 12 May 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73668-y · PMID 42191703 · PMCID PMC13388698 · OpenAlex W7162416950
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Keywords: Cognitive neuroscience, Computational neuroscience
MeSH: Adolescent Development*, Brain*, Cognition*, Adolescent, Brain Mapping, Cross-Sectional Studies, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Nerve Net (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIDA NIH HHS (U01 DA041025, U01 DA041120, U01 DA041089, U01 DA041134, U01 DA041174, U01 DA050989, U01 DA041048, U01 DA051039, U01 DA041022, U24 DA041147, U01 DA041106, U01 DA050988, U01 DA051016, U01 DA041028, U01 DA051018, U01 DA041093, U01 DA041148, U01 DA050987, U01 DA051038, U24 DA041123, U01 DA041117, U01 DA041156, U01 DA051037); NIMH NIH HHS (R01 MH133334)
Citations: cited by 1 paper (Europe PMC); 96 references in the paper

Abstract

How functional brain networks and cognition co-evolve during adolescent development remains poorly understood. Using baseline and Year 2 data from 2949 individuals in the Adolescent Brain Cognitive Development Study, we trained kernel ridge regression models to predict cognitive ability from resting-state functional connectivity. We find that baseline functional connectivity more strongly predicts future cognitive ability than baseline cognitive ability. Models trained on baseline functional connectivity to predict baseline cognition generalize better to Year 2 functional connectivity and cognition, suggesting that brain–cognition relationships strengthen over time. Intriguingly, baseline functional connectivity outperforms longitudinal functional connectivity change in predicting future cognitive ability. While longitudinal functional connectivity change is less reliable than baseline functional connectivity – intraclass correlation coefficient 0.24 vs. 0.56 – shortening scan duration to reduce reliability of baseline functional connectivity does not eliminate the predictive gap. Furthermore, neither baseline functional connectivity nor functional connectivity change meaningfully predicts longitudinal change in cognitive ability. We also identify converging and diverging predictive network features across cross-sectional and longitudinal brain-cognition models – a multivariate twist on Simpson’s paradox – with clear sex-specific patterns. Overall, in early adolescence, stable individual differences in brain functional network organization play a more critical role than dynamic changes in shaping future cognitive outcomes.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 13 matches between paragraphs and lines of code.

Zenodo 19219794

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references deposited at Crossref
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.19219794

thomasyeolab/standalone_xie2025_lbc

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e3c8c6648c6ce77580aeeaad56a53017b18fdf83, 25 March 2026
Languages: MATLAB (1555), Shell (217), C (113), Python (92), C++ (65), C/C++ (58), R (8), Jupyter (4)
Size: 4,889 files, 2,112 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (external_packages/python/mapalign-master/requirements.txt, external_packages/python/mapalign-master/setup.py, external_packages/python/yapf-master/setup.cfg, external_packages/python/yapf-master/setup.py), tests, documentation, 2 notebooks
Not found: CITATION.cff, continuous integration
Tools: FreeSurfer (129 files), Statistics and Machine Learning Toolbox (73 files), FieldTrip (66 files), NumPy (30 files), SPM (25 files), SciPy (16 files), GIfTI library for MATLAB (13 files), FSL (12 files), scikit-learn (12 files), Connectome Workbench (11 files), Matplotlib (8 files), PyTorch (7 files), Image Processing Toolbox (6 files), pandas (6 files), seaborn (6 files), MRtrix3 (4 files), AFNI (3 files), lme4 (3 files), circlize (2 files), ComplexHeatmap (2 files), igraph (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), ANTs (1 file), fdr_bh (Benjamini-Hochberg FDR) (1 file), ggplot2 (1 file), Signal Processing Toolbox (1 file), NiBabel (1 file), Nilearn (1 file), tedana (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2,000 files
At the source: github.com/ThomasYeoLab/Standalone_Xie2025_LBC/tree/v0.01.0

Code availability

Image preprocessing was conducted using previously published pipelines available at https://github.com/ThomasYeoLab/Standalone_CBIG_fMRI_Preproc2016, archived at 10.5281/zenodo.1972324592. Study-specific preprocessing scripts are available at https://github.com/ThomasYeoLab/ABCD_scripts, archived at 10.5281/zenodo.1972325593. Analysis code is publicly available at https://github.com/ThomasYeoLab/Standalone_Xie2025_LBC and archived at 10.5281/zenodo.1921979494.

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,998 scripts, each with its path and the digest of its content;
  • 13 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The ABCD data are publicly available through the NIH Brain Development Cohorts (NBDC) Data Hub. Researchers with access to the ABCD data will be able to download the data from https://nbdc-datashare.lassoinformatics.com. Source data supporting the findings of this study are provided with this paper. Source data are provided with this paper.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Journal: Nature communications → Nature Communications
  • Publisher: n/a → Nature Publishing Group
  • Received: n/a → 2025-06-25
  • Accepted: n/a → 2026-05-12
  • Issue date: 2026-05 → 2026
  • Keywords: added Cognitive neuroscience; Computational neuroscience
  • References: 0 → 89

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 12 MeSH terms, 2 funders, 0 references.

Cite

This paper

Xie, Y., Zhang, S., Orban, C., Ooi, L. Q. R., Kong, R., Floris, D. L., Zuo, X.-N., Dhamala, E., Holmes, A. J., Uddin, L. Q., Nichols, T. E., Di Martino, A., & Yeo, B. T. T. (2026). Convergent and divergent brain-cognition development in early adolescence. Nature communications, 17(1), 6868. https://doi.org/10.1038/s41467-026-73668-y

BibTeX

@article{xie2026convergent,
author = {Xie, Yapei and Zhang, Shaoshi and Orban, Csaba and Ooi, Leon Qi Rong and Kong, Ru and Floris, Dorothea L and Zuo, Xi-Nian and Dhamala, Elvisha and Holmes, Avram J and Uddin, Lucina Q and Nichols, Thomas E and Di Martino, Adriana and Yeo, B T Thomas},
title = {{Convergent and divergent brain-cognition development in early adolescence}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6868},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73668-y},
url = {https://doi.org/10.1038/s41467-026-73668-y},
pmid = {42191703},
pmcid = {PMC13388698}
}

RIS

TY - JOUR
AU - Xie, Yapei
AU - Zhang, Shaoshi
AU - Orban, Csaba
AU - Ooi, Leon Qi Rong
AU - Kong, Ru
AU - Floris, Dorothea L
AU - Zuo, Xi-Nian
AU - Dhamala, Elvisha
AU - Holmes, Avram J
AU - Uddin, Lucina Q
AU - Nichols, Thomas E
AU - Di Martino, Adriana
AU - Yeo, B T Thomas
TI - Convergent and divergent brain-cognition development in early adolescence
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/26
VL - 17
IS - 1
SP - 6868
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73668-y
UR - https://doi.org/10.1038/s41467-026-73668-y
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-73668-y",
"type": "article-journal",
"title": "Convergent and divergent brain-cognition development in early adolescence",
"container-title": "Nature communications",
"author": [
{
"family": "Xie",
"given": "Yapei"
},
{
"family": "Zhang",
"given": "Shaoshi"
},
{
"family": "Orban",
"given": "Csaba"
},
{
"family": "Ooi",
"given": "Leon Qi Rong"
},
{
"family": "Kong",
"given": "Ru"
},
{
"family": "Floris",
"given": "Dorothea L"
},
{
"family": "Zuo",
"given": "Xi-Nian"
},
{
"family": "Dhamala",
"given": "Elvisha"
},
{
"family": "Holmes",
"given": "Avram J"
},
{
"family": "Uddin",
"given": "Lucina Q"
},
{
"family": "Nichols",
"given": "Thomas E"
},
{
"family": "Di Martino",
"given": "Adriana"
},
{
"family": "Yeo",
"given": "B T Thomas"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "6868",
"DOI": "10.1038/s41467-026-73668-y",
"PMID": "42191703",
"PMCID": "PMC13388698",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73668-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
26
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

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:10.1162/imag.a.1222 [code]
Network-based near-scalp personalized brain stimulation targets.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: tedana, Connectome Workbench, AFNI, 16 other tools, 7 references, 3 authors
[2] doi:10.1002/epi.70323 [code]
Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy.
Journal: Epilepsia
In common: tedana, Connectome Workbench, AFNI, 16 other tools, fMRI, 4 references, 3 authors
[3] doi:10.1038/s41467-026-76011-7 [code]
Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.
Journal: Nature communications
In common: Connectome Workbench, AFNI, GIfTI library for MATLAB, 16 other tools, 9 references, author Xi-Nian Zuo
[4] doi:10.1002/hbm.70483 [code]
Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.
Journal: Human brain mapping
In common: tedana, Connectome Workbench, AFNI, 17 other tools, fMRI, 6 references
[5] doi:10.1016/j.neuron.2026.04.011 [code]
Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex.
Journal: Neuron
In common: tedana, Connectome Workbench, AFNI, 16 other tools, fMRI, 6 references
[6] doi:10.1038/s41467-026-71151-2 [code]
Common and distinct neural correlates of social interaction processing and theory of mind in narratives.
Journal: Nature communications
In common: MRtrix3, fdr_bh (Benjamini-Hochberg FDR), Connectome Workbench, 18 other tools, 4 references
[7] doi:10.1371/journal.pbio.3003856 [code]
Aging and metabolism contribute separately to brain-body health.
Journal: PLoS biology
In common: tedana, Connectome Workbench, AFNI, 18 other tools, 3 references
[8] doi:10.1038/s41398-026-04025-2 [code]
Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.
Journal: Translational psychiatry
In common: tedana, Connectome Workbench, AFNI, 19 other tools, 3 references
[9] doi:10.1016/j.isci.2026.116903 [code]
Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation.
Journal: iScience
In common: tedana, Connectome Workbench, AFNI, 16 other tools, fMRI, 4 references
[10] doi:10.1038/s41467-026-76452-0 [code]
Music evokes shared neural representations of imagined narratives across sensory modalities.
Journal: Nature communications
In common: tedana, Connectome Workbench, AFNI, 18 other tools, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.