OSCR

Hyperpolarized <sup>13</sup>C-bicarbonate production correlates with excitatory neuron distribution in the human brain.

Code ↔ Paper

3 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 3 matches
  1. [1] § Results › Gene set enrichment analysis of BP ratio maps reveals neuronal specificity ↔ SpinTestScripts/Schaefer100/GSEAplots_ratiosSchaefer100.py, lines 22–60 · score 0.62 · Ex3b, Ex3a, Ex5a, Ex5b, spatial autocorrelation, Ex2
  2. [2] § Results › Gene set enrichment analysis of BP ratio maps reveals neuronal specificity ↔ SpinTestScripts/Schaefer200/GSEAplots_ratiosSchaefer200.py, lines 22–60 · score 0.62 · Ex3b, Ex3a, Ex5a, Ex5b, spatial autocorrelation, Ex2
  3. [3] § Materials and Methods › Statistical analysis › Correlation analysis ↔ parametricNullDistributionScripts/Schaefer100/SpatialAutocorrelationGSEA.py, lines 23–36 · score 0.58 · brainSMASH, hemisphere, brain map, distance, spatial autocorrelation, parcellations

Paper

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The authors' code

Python · 192 lines · 8.5 KB · no license · 1 match

  1. from brainsmash.mapgen.base import Base
  2. from brainsmash.workbench.geo import cortex
  3. from brainsmash.workbench.geo import parcellate
  4. from brainsmash.mapgen.eval import base_fit
  5. from brainsmash.mapgen.stats import spearmanr, pairwise_r, pearsonr
  6. import numpy as np
  7. from scipy import stats
  8. from brainsmash.mapgen.stats import nonparp
  9. import pandas as pd
  10. import gseapy as gp
  11. from gseapy import barplot, dotplot
  12. import matplotlib.pyplot as plt
  13. import SpatialAutocorrelationGSEA
  14. import glob #For bash like path filenames
  19. # BICARB TO PYRUVATE RATIO -------------------------------
  22. files = sorted(glob.glob("nullDistSchaefer100ratios/NullDistSchaefer100_BP_nperm_1310*.csv"))
  23. arrays = [pd.read_csv(f) for f in files]
  24. ES_nullDistribution_temp = pd.concat(arrays,ignore_index=True).to_numpy()[:100000]
  25. print(ES_nullDistribution_temp.shape)
  27. celltypes = ('Ast','End','Ex1','Ex2','Ex3a','Ex3b','Ex3c','Ex3d','Ex3e','Ex4','Ex5a','Ex5b','Ex6a','Ex6b','Ex8','In1a','In1b',
  28. 'In1c','In2','In3','In4a','In4b','In6a','In6b','In7','In8','Mic','OPC','Oli','Per')
  29. ES_nullDistribution = pd.DataFrame(ES_nullDistribution_temp,columns = list(celltypes))
  31. lake_gmt = "geneset_LAKE.gmt"
  32. geneExpressionCSV = "Schaefer100_lh_expression.csv"
  33. brain_phenotype_file = "formatted_segstatsFilesSchaefer100/formatted_BP-Schaefer100.txt"
  35. BP_empiricalSpearman = SpatialAutocorrelationGSEA.empiricalSpearman(brain_phenotype_file,geneExpressionCSV)
  37. rnk = BP_empiricalSpearman.sort_values(by = 'EmpiricalSpearman',ascending = False)
  39. empiricalPrerank = gp.prerank(rnk = rnk,
  40. gene_sets = "geneset_LAKE.gmt",
  41. ascending = False)
  44. correctedPvalues_temp = []
  45. correctedPvalues = []
  46. for geneSetID in range(len(celltypes)):
  47. n = len(ES_nullDistribution)
  48. correctedPvalues_temp = np.sum(np.abs(ES_nullDistribution.iloc[:,geneSetID]) > abs(
  49. empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[geneSetID,'ES']))/n
  50. correctedPvalues.append(correctedPvalues_temp)
  52. geneSetTerm = empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[:,'Term']
  53. enrichmentScore = empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[:,'ES']
  54. df_correctedPvalues = pd.DataFrame(correctedPvalues)
  55. # FDR correction
  56. FDRCorrectedPvals = pd.Series(stats.false_discovery_control(df_correctedPvalues[0],method = 'BH'))
  57. df_correctedLabelledPvalues = pd.concat([geneSetTerm,enrichmentScore,df_correctedPvalues,FDRCorrectedPvals],ignore_index=True,axis=1)
  58. df_correctedLabelledPvalues = df_correctedLabelledPvalues.set_axis(['geneSetTerm','EmpiricalES','SA_CorrectedPval','FDRandSA_Corrected'],
  59. axis = 1)
  60. print(df_correctedLabelledPvalues)
  64. # BICARB TO PYRUVATE RATIO -------------------------------
  66. significantGenesets = [i for i, j in zip(df_correctedLabelledPvalues['geneSetTerm'],
  67. df_correctedLabelledPvalues['SA_CorrectedPval']) if j <0.05]
  68. print(significantGenesets)
  69. EP = empiricalPrerank.plot(terms = significantGenesets,
  70. figsize = (3,4))
  71. EP.savefig('OneMill_Schaefer100-BP_SA_0.05.pdf',format ='pdf',bbox_inches="tight")
  75. # LACTATE TO PYRUVATE RATIO ES plot -------------------------------
  77. files = sorted(glob.glob("nullDistSchaefer100ratios/NullDistSchaefer100_LP_nperm_1310*.csv"))
  78. arrays = [pd.read_csv(f) for f in files]
  79. ES_nullDistribution_temp = pd.concat(arrays,ignore_index=True).to_numpy()[:100000]
  80. print(ES_nullDistribution_temp.shape)
  82. celltypes = ('Ast','End','Ex1','Ex2','Ex3a','Ex3b','Ex3c','Ex3d','Ex3e','Ex4','Ex5a','Ex5b','Ex6a','Ex6b','Ex8','In1a','In1b',
  83. 'In1c','In2','In3','In4a','In4b','In6a','In6b','In7','In8','Mic','OPC','Oli','Per')
  84. ES_nullDistribution = pd.DataFrame(ES_nullDistribution_temp,columns = list(celltypes))
  86. lake_gmt = "geneset_LAKE.gmt"
  87. geneExpressionCSV = "Schaefer100_lh_expression.csv"
  88. brain_phenotype_file = "formatted_segstatsFilesSchaefer100/formatted_LP-Schaefer100.txt"
  90. LP_empiricalSpearman = SpatialAutocorrelationGSEA.empiricalSpearman(brain_phenotype_file,geneExpressionCSV)
  92. rnk = LP_empiricalSpearman.sort_values(by = 'EmpiricalSpearman',ascending = False)
  94. empiricalPrerank = gp.prerank(rnk = rnk,
  95. gene_sets = "geneset_LAKE.gmt",
  96. ascending = False)
  99. correctedPvalues_temp = []
  100. correctedPvalues = []
  101. for geneSetID in range(len(celltypes)):
  102. n = len(ES_nullDistribution)
  103. correctedPvalues_temp = np.sum(np.abs(ES_nullDistribution.iloc[:,geneSetID]) > abs(
  104. empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[geneSetID,'ES']))/n
  105. correctedPvalues.append(correctedPvalues_temp)
  107. geneSetTerm = empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[:,'Term']
  108. enrichmentScore = empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[:,'ES']
  109. df_correctedPvalues = pd.DataFrame(correctedPvalues)
  110. # FDR correction
  111. FDRCorrectedPvals = pd.Series(stats.false_discovery_control(df_correctedPvalues[0],method = 'BH'))
  112. df_correctedLabelledPvalues = pd.concat([geneSetTerm,enrichmentScore,df_correctedPvalues,FDRCorrectedPvals],ignore_index=True,axis=1)
  113. df_correctedLabelledPvalues = df_correctedLabelledPvalues.set_axis(['geneSetTerm','EmpiricalES','SA_CorrectedPval','FDRandSA_Corrected'],
  114. axis = 1)
  115. print(df_correctedLabelledPvalues)
  119. # LACTATE TO PYRUVATE RATIO ES plot -------------------------------
  121. significantGenesets = [i for i, j in zip(df_correctedLabelledPvalues['geneSetTerm'],
  122. df_correctedLabelledPvalues['FDRandSA_Corrected']) if j <0.25]
  124. terms = empiricalPrerank.res2d.Term
  126. EP = empiricalPrerank.plot(terms = significantGenesets,
  127. figsize = (3,4))
  128. EP.savefig('OneMill_Schaefer100-LP_fdr0.25.pdf',format ='pdf',bbox_inches="tight")
  134. # LACTATE TO BICARBONATE RATIO ES plot -------------------------------
  136. files = sorted(glob.glob("nullDistSchaefer100ratios/NullDistSchaefer100_LB_nperm_1310*.csv"))
  137. arrays = [pd.read_csv(f) for f in files]
  138. ES_nullDistribution_temp = pd.concat(arrays,ignore_index=True).to_numpy()[:100000]
  139. print(ES_nullDistribution_temp.shape)
  142. celltypes = ('Ast','End','Ex1','Ex2','Ex3a','Ex3b','Ex3c','Ex3d','Ex3e','Ex4','Ex5a','Ex5b','Ex6a','Ex6b','Ex8','In1a','In1b',
  143. 'In1c','In2','In3','In4a','In4b','In6a','In6b','In7','In8','Mic','OPC','Oli','Per')
  144. ES_nullDistribution = pd.DataFrame(ES_nullDistribution_temp,columns = list(celltypes))
  146. lake_gmt = "geneset_LAKE.gmt"
  147. geneExpressionCSV = "Schaefer100_lh_expression.csv"
  148. brain_phenotype_file = "formatted_segstatsFilesSchaefer100/formatted_LB-Schaefer100.txt"
  150. LB_empiricalSpearman = SpatialAutocorrelationGSEA.empiricalSpearman(brain_phenotype_file,geneExpressionCSV)
  152. rnk = LB_empiricalSpearman.sort_values(by = 'EmpiricalSpearman',ascending = False)
  154. empiricalPrerank = gp.prerank(rnk = rnk,
  155. gene_sets = "geneset_LAKE.gmt",
  156. ascending = False)
  159. correctedPvalues_temp = []
  160. correctedPvalues = []
  161. for geneSetID in range(len(celltypes)):
  162. n = len(ES_nullDistribution)
  163. correctedPvalues_temp = np.sum(np.abs(ES_nullDistribution.iloc[:,geneSetID]) > abs(
  164. empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[geneSetID,'ES']))/n
  165. correctedPvalues.append(correctedPvalues_temp)
  167. geneSetTerm = empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[:,'Term']
  168. enrichmentScore = empiricalPrerank.res2d.sort_values(by = 'Term').reset_index().loc[:,'ES']
  169. df_correctedPvalues = pd.DataFrame(correctedPvalues)
  170. # FDR correction
  171. FDRCorrectedPvals = pd.Series(stats.false_discovery_control(df_correctedPvalues[0],method = 'BH'))
  172. df_correctedLabelledPvalues = pd.concat([geneSetTerm,enrichmentScore,df_correctedPvalues,FDRCorrectedPvals],ignore_index=True,axis=1)
  173. df_correctedLabelledPvalues = df_correctedLabelledPvalues.set_axis(['geneSetTerm','EmpiricalES','SA_CorrectedPval','FDRandSA_Corrected'],
  174. axis = 1)
  175. print(df_correctedLabelledPvalues)
  179. # LACTATE TO BICARBONATE RATIO ES plot -------------------------------
  181. significantGenesets = [i for i, j in zip(df_correctedLabelledPvalues['geneSetTerm'],
  182. df_correctedLabelledPvalues['FDRandSA_Corrected']) if j <0.25]
  184. terms = empiricalPrerank.res2d.Term
  186. EP = empiricalPrerank.plot(terms = significantGenesets,
  187. figsize = (3,4))
  188. EP.savefig('OneMill_Schaefer100-LB_fdr0.25.pdf',format ='pdf',bbox_inches="tight")

GSEAplots_ratiosSchaefer100.py at commit 036ea2a, no license · at the source

Overview

Authors: Biranavan Uthayakumar1,2, Dylan A Dingwell1,2, Nicole IC Cappelletto1,2, Nadia D Bragagnolo2, Albert P Chen3, Nathan Ma4, Ruby Endre2, Jesse Gillis5, Hany Soliman6, Charles H Cunningham1,2
ORCID iDs: Nicole IC Cappelletto, Jesse Gillis, Charles H Cunningham
  1. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
  2. Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
  3. GE Healthcare, Toronto, ON, Canada
  4. Pharmacy, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
  5. Department of Physiology, University of Toronto, Toronto, ON, Canada
  6. Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
Institutions: University of Toronto (Canada); Sunnybrook Research Institute; Sunnybrook Health Science Centre (Canada)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1342
Dates: received 15 January 2026; accepted 15 July 2026; published online 1 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1342 · PMID 42689174 · PMCID PMC13537045 · OpenAlex W7172292900
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics
Keywords: human brain, hyperpolarized 13C MRI, cortical metabolism, cell-type distribution, spatial transcriptomics
MeSH: Bicarbonates*, Brain*, Neurons*, Adult, Carbon Isotopes, Female, Humans, Magnetic Resonance Imaging, Male, Pyruvic Acid (* major topic)
Topic: Advanced NMR Techniques and Applications (Spectroscopy, Chemistry), according to OpenAlex
Funding: Canadian Cancer Society (707455); Canadian Institutes of Health Research (PJT152928)
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

Whole-brain metabolic topography measured with hyperpolarized 13C-pyruvate MRI in 40 healthy individuals was correlated with microarray transcriptomic data from the Allen Human Brain Atlas. Spatial autocorrelation was addressed using both model-based and non-model-based methods, and two parcellation atlases with different numbers of regions were employed to reduce the likelihood of false positives. Brain regions with higher expression of transcripts characteristic of excitatory neurons showed an elevated bicarbonate-to-pyruvate ratio, indicating a higher flux through pyruvate dehydrogenase. Specifically, the Ex2 and Ex4 excitatory neuron subtypes showed the strongest enrichment among all cell-type gene sets. Collectively, these findings connect regional variations in metabolic neuroimaging profiles to underlying patterns of cellular gene expression, offering a framework for understanding the molecular basis of tissue-level metabolic organization.

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 3 matches between paragraphs and lines of code.

Zenodo 20575695

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 5 files
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.20575695

brinuthayakumar/hpmr-genetranscript_imagingneuroscience

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 036ea2a8315df4c309316c1170d214d2fd8065ef, 6 June 2026
Languages: Python (17), R (1)
Size: 87 files, 18 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (15 files), pandas (15 files), SciPy (15 files), Matplotlib (12 files), BrainSMASH (11 files), abagen (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
19 files
At the source: github.com/brinUthayakumar/HPMR-GeneTranscript_ImagingNeuroscience/tree/0.0.1

The paper's code and data availability statement is in the Data section.

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;
  • 18 scripts, each with its path and the digest of its content;
  • 3 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 and Code Availability

All correlation and GSEA data, as well as the code to reproduce the figures and results, are available at https://doi.org/10.5281/zenodo.20575695.

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 3, 28 September 2026

  • Funding: added Canadian Cancer Society: 707455; Canadian Institutes of Health Research: PJT152928

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 5 keywords, 10 MeSH terms, 49 references.

Cite

This paper

Uthayakumar, B., Dingwell, D. A., Cappelletto, N. I., Bragagnolo, N. D., Chen, A. P., Ma, N., Endre, R., Gillis, J., Soliman, H., & Cunningham, C. H. (2026). Hyperpolarized &lt;sup&gt;13&lt;/sup&gt;C-bicarbonate production correlates with excitatory neuron distribution in the human brain. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1342. https://doi.org/10.1162/imag.a.1342

BibTeX

@article{uthayakumar2026hyperpolarized,
author = {Uthayakumar, Biranavan and Dingwell, Dylan A and Cappelletto, Nicole IC and Bragagnolo, Nadia D and Chen, Albert P and Ma, Nathan and Endre, Ruby and Gillis, Jesse and Soliman, Hany and Cunningham, Charles H},
title = {{Hyperpolarized \&lt;sup\&gt;13\&lt;/sup\&gt;C-bicarbonate production correlates with excitatory neuron distribution in the human brain}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = sep,
volume = {4},
pages = {IMAG.a.1342},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1342},
url = {https://doi.org/10.1162/imag.a.1342},
pmid = {42689174},
pmcid = {PMC13537045}
}

RIS

TY - JOUR
AU - Uthayakumar, Biranavan
AU - Dingwell, Dylan A
AU - Cappelletto, Nicole IC
AU - Bragagnolo, Nadia D
AU - Chen, Albert P
AU - Ma, Nathan
AU - Endre, Ruby
AU - Gillis, Jesse
AU - Soliman, Hany
AU - Cunningham, Charles H
TI - Hyperpolarized &lt;sup&gt;13&lt;/sup&gt;C-bicarbonate production correlates with excitatory neuron distribution in the human brain
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/09/01
VL - 4
SP - IMAG.a.1342
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1342
UR - https://doi.org/10.1162/imag.a.1342
LA - en
ER -

CSL-JSON

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"id": "10.1162/imag.a.1342",
"type": "article-journal",
"title": "Hyperpolarized &lt;sup&gt;13&lt;/sup&gt;C-bicarbonate production correlates with excitatory neuron distribution in the human brain",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Uthayakumar",
"given": "Biranavan"
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{
"family": "Cappelletto",
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},
{
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{
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{
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}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1342",
"DOI": "10.1162/imag.a.1342",
"PMID": "42689174",
"PMCID": "PMC13537045",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1342",
"language": "en",
"issued": {
"date-parts": [
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1
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}
}

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