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Glutamatergic signaling underlies brain structural organization for mathematical and reading abilities in children.

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Software and packages ↔ scripts/utility/get_Rpackage_versions.R, lines 1–21 · score 0.84 · R.matlab, BayesFactor, ggplot2, ggrepel, ggseg, psych
  2. [2] § Methods › Null models ↔ scripts/neurotransmitter/utility.py, lines 31–84 · score 0.84 · generates surrogate brain, spatial autocorrelation preserving, distance matrix, randomization, BrainSpace, Moran
  3. [3] § Methods › Maps of neurotransmitter receptors and transporters ↔ scripts/neurotransmitter/parcellate.py, lines 1–15 · score 0.69 · volumetric PET images, Brainnetome atlas, neurotransmitter receptors, parcellated, maps
  4. [4] § Methods › Software and packages ↔ scripts/utility/get_pythonpkg_versions.py, lines 5–39 · score 0.67 · scikit learn, pandas, scipy, Python, numpy, BrainSpace
  5. [5] § Methods › Neurotransmitter mapping of brain organization for math and reading › CMI-HBN discovery cohort ↔ scripts/BF/getBF_neurotransmitter_joint.R, lines 33–93 · score 0.65 · lmBF, Bayes factors, coefficients, BF10, receptors, neurotransmitter
  6. [6] § Methods › Out-of-sample generalization of brain-behavior relationships ↔ scripts/cca/prediction/prediction_cmi_model_to_stanford.R, lines 230–292 · score 0.64 · Upper tail permutation, predicted brain, Pearson correlation, scores, Stanford, CMI
  7. [7] § Methods › Neurotransmitter mapping of brain organization for math and reading › CMI-HBN discovery cohort ↔ scripts/BF/getBF_neurotransmitter.R, lines 28–78 · score 0.63 · lmBF, Bayes factors, BF10, receptors, neurotransmitter
  8. [8] § Methods › Estimation of gray matter volume ↔ scripts/cca/table_top20.R, the whole file · a weak match · score 0.60 · Brainnetome atlas, subcortical, brain regions, ROIs, space
  9. [9] § Results › Network-level NMDA mapping for mathematical ability ↔ scripts/network_analysis/scatter_plot_directionality_network_shirer.R, lines 1–29 · score 0.56 · NMDA receptor density, functional network, Pearson correlation, FDR corrected, Scatter, Stanford
  10. [10] § Methods › Neurotransmitter mapping of brain organization for math and reading › CMI-HBN discovery cohort ↔ scripts/BF/getBF_neurotransmitter_joint.R, lines 1–31 · score 0.56 · mGluR5, weight map, neurotransmitter receptor, GABAA, model, NMDA
  11. [11] § Results › Brain structural organization associated with mathematical ability ↔ scripts/behavior/behavior_analysis.R, lines 244–317 · score 0.54 · math problem solving, math reasoning, numerical operations, CMI HBN, numop, Pearson
  12. [12] § Methods › Participants › CMI-HBN discovery cohort ↔ scripts/behavior/behavior_analysis.R, lines 201–242 · score 0.54 · math problem solving, numerical operations, CMI HBN, females, word, age
  13. [13] § Results › Neurotransmitter mapping for mathematical ability ↔ scripts/BF/getBFr_neurotransmitter.R, lines 1–32 · score 0.52 · replication Bayes factor, math related, neurotransmitter receptors, BF10, NMDA, maps

Paper

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The authors' code

R · 104 lines · 3.3 KB · CC-BY-NC-4.0 · 2 matches

  1. # -------------------------------------------------------------------------
  2. # Author: Yuan Zhang
  3. # Date: 2026-04-13
  4. #
  5. # Compute Bayes Factors (BF10) for association between Mode 2 brain GMV
  6. # weight maps and neurotransmitter receptor maps for CMI joint CCA model
  7. #
  8. # Brain map source:
  9. # *_coef.csv
  10. # Weight column used:
  11. # xloading_m2
  12. # Sign is flipped by multiplying by -1 before analysis
  13. # -------------------------------------------------------------------------
  15. setwd("/Users/zhangyuan/Google Drive/2023_math_reading_neurotransmitter/GitHub")
  16. rm(list = ls())
  18. library(BayesFactor)
  20. # -------------------------------------------------------------------------
  21. # Load neurotransmitter receptor data
  22. # -------------------------------------------------------------------------
  23. fname_receptors <- "data/neurotransmitter/receptor_data_bn246.csv"
  24. receptors <- read.csv(fname_receptors, header = FALSE)
  26. # Receptor names (correspond to columns in receptors CSV)
  27. receptor_names <- c(
  28. "5HT1a", "5HT1b", "5HT2a", "5HT4", "5HT6", "5HTT", "A4B2", "CB1",
  29. "D1", "D2", "DAT", "GABAa", "H3", "M1", "mGluR5", "MOR", "NET",
  30. "NMDA", "VAChT"
  31. )
  33. # -------------------------------------------------------------------------
  34. # Function to compute Bayes Factors for a single Mode 2 brain map
  35. # -------------------------------------------------------------------------
  36. compute_bayes_factors_mode2 <- function(coef_file, bmap_name, output_file,
  37. weight_col = "xloading_m2",
  38. flip_sign = TRUE) {
  39. # Load coefficient file
  40. coef_df <- read.csv(coef_file)
  42. if (!(weight_col %in% colnames(coef_df))) {
  43. stop(paste(weight_col, "not found in", coef_file))
  44. }
  46. # Extract Mode 2 brain map
  47. bmap <- coef_df[[weight_col]]
  49. # Flip sign if requested
  50. if (flip_sign) {
  51. bmap <- -1 * bmap
  52. }
  54. # Use only first 218 rows to match receptor maps
  55. bmap <- bmap[1:218]
  57. # Build dataframe
  58. df <- data.frame(
  59. bmap = bmap,
  60. receptors[1:218, ]
  61. )
  63. # Assign receptor names
  64. colnames(df)[2:ncol(df)] <- receptor_names
  66. # Ensure valid column names for formulas
  67. colnames(df) <- make.names(colnames(df))
  69. # Compute BF10 for each receptor
  70. bf10 <- c()
  72. for (i in 2:ncol(df)) {
  73. formula <- as.formula(paste("bmap ~", colnames(df)[i]))
  74. bf <- lmBF(formula, data = df)
  75. bf_value <- exp(bf@bayesFactor$bf)
  76. bf10 <- c(bf10, bf_value)
  77. }
  79. # Create results dataframe
  80. res <- data.frame(
  81. bmap = bmap_name,
  82. receptor = receptor_names,
  83. BF10 = bf10
  84. )
  86. # Reorder rows to match previous output style
  87. idx <- c(9:11, 15, 18, 12, 1:7, 14, 19, 17, 8, 13, 16)
  88. res <- res[idx, c("bmap", "receptor", "BF10")]
  90. # Save results
  91. write.csv(res, file = output_file, row.names = FALSE)
  92. cat("Bayes Factor results saved to:", output_file, "\n")
  93. }
  95. # -------------------------------------------------------------------------
  96. # Run for current CMI domain-specific datasets
  97. # -------------------------------------------------------------------------
  99. # CMI shared
  100. compute_bayes_factors_mode2(
  101. coef_file = "results/cca/cmi/wholebrain_cca_cmi_math_reading_combined/CCA_PCA_roi_gmv_brainnetome_mathreadingstd_ageinmodel_coef.csv",
  102. bmap_name = "CMI_shared_mode2",
  103. output_file = "results/neurotransmitter/cmi/shared/bf10_CMI_shared_mode2_receptors.csv"
  104. )

getBF_neurotransmitter_joint.R at commit 20c0f67, under CC-BY-NC-4.0 · at the source

Overview

Authors: Yuan Zhang1, Hyesang Chang1, Dawlat El-Said1, Vinod Menon1,2,3,4
ORCID iDs: Yuan Zhang, Hyesang Chang, Dawlat El-Said, Vinod Menon
  1. Department of Psychiatry & Behavioral Sciences, Stanford University,Stanford, CA USA
  2. Department of Neurology & Neurological Sciences, Stanford University,Stanford, CA USA
  3. Stanford Wu Tsai Neurosciences Institute, Stanford University,Stanford, CA USA
  4. Graduate School of Education, Stanford University,Stanford, CA USA
Institutions: Stanford University (United States)
Journal: Nature communications, volume 17, issue 1, article 8327
Dates: received 20 October 2025; accepted 18 June 2026; published online 4 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75102-9 · PMID 42401539 · PMCID PMC13470007 · OpenAlex W7167349436
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: PET / SPECT (modality), human (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, fMRI & imaging
Keywords: Cognitive neuroscience, Psychology
MeSH: Brain*, Glutamic Acid*, Mathematics*, Reading*, Brain Mapping, Child, Female, Humans, Male, Positron-Emission Tomography, Receptors, N-Methyl-D-Aspartate, Signal Transduction (* major topic)
Topic: Cognitive and developmental aspects of mathematical skills (Statistics and Probability, Mathematics), according to OpenAlex
Funding: NIH (HD094623, HD059205, MH084164, and MH121069); Stanford Maternal & Child Health Research Institute Postdoctoral Support Award
Citations: cited by 1 paper (Europe PMC); 119 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 13 matches between paragraphs and lines of code.

Zenodo 20574388

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: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

scsnl/yz_smri_math_reading_nt_2025

License: CC-BY-NC-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 20c0f67a9e2e1ea998ad15144a885cdb291f3c8c, 6 June 2026
Languages: R (35), Python (6), MATLAB (4)
Size: 838 files, 45 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (12 files), NumPy (5 files), BayesFactor (4 files), SciPy (4 files), pandas (3 files), SPM (3 files), reshape2 (2 files), tidyverse (2 files), BrainSMASH (1 file), BrainSpace (1 file), easystats (1 file), ggseg (1 file), Statistics and Machine Learning Toolbox (1 file), neuromaps (1 file), psych (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
47 files
At the source: github.com/scsnl/YZ_sMRI_math_reading_NT_2025/tree/v1.0

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:

  • it points to the authors' code: Zenodo 20574388

Read it in the paper: doi.org/10.1038/s41467-026-75102-9.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 45 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);
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Data

Datasets cited

Code and data availability statement

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

  • it points to the authors' code: Zenodo 20574388
  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-75102-9.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 12 MeSH terms, 2 funders, 112 references.

Cite

This paper

Zhang, Y., Chang, H., El-Said, D., & Menon, V. (2026). Glutamatergic signaling underlies brain structural organization for mathematical and reading abilities in children. Nature communications, 17(1), 8327. https://doi.org/10.1038/s41467-026-75102-9

BibTeX

@article{zhang2026glutamatergic,
author = {Zhang, Yuan and Chang, Hyesang and El-Said, Dawlat and Menon, Vinod},
title = {{Glutamatergic signaling underlies brain structural organization for mathematical and reading abilities in children}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8327},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75102-9},
url = {https://doi.org/10.1038/s41467-026-75102-9},
pmid = {42401539},
pmcid = {PMC13470007}
}

RIS

TY - JOUR
AU - Zhang, Yuan
AU - Chang, Hyesang
AU - El-Said, Dawlat
AU - Menon, Vinod
TI - Glutamatergic signaling underlies brain structural organization for mathematical and reading abilities in children
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/04
VL - 17
IS - 1
SP - 8327
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75102-9
UR - https://doi.org/10.1038/s41467-026-75102-9
LA - en
ER -

CSL-JSON

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