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Experimental quality control induces changes in Allen mouse brain connectomes.

Code ↔ Paper

15 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 15 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Defining injection density and projection density ↔ before_manual_qc/download_knox_conn_data.sh, the whole file · a weak match · score 0.79 · injection fraction, WT experiments, injection density, Allen API, projection density, downloaded
  2. [2] § Methods › Defining injection density and projection density ↔ before_manual_qc/multiply_threshold_inj_proj.sh, the whole file · a weak match · score 0.77 · injection fraction, WT experiments, injection density, Allen API, projection density, tracer
  3. [3] § Methods › Nested leave-one-out cross-validation analysis ↔ run-all-after-manual-QC.sh, lines 46–123 · score 0.70 · nested homogeneous, nested voxel, compile, connectivity models, sensitivity, py
  4. [4] § Methods › Automated quality control ↔ before_manual_qc/create_automated_qc_csv.R, lines 48–94 · score 0.67 · cerebral aqueduct, injection density, thin, misalignment, ventricular, manual QC
  5. [5] § Methods › Manual quality control ↔ before_manual_qc/make_qc_images_bin_threshold.sh, lines 41–122 · score 0.64 · QC images, slice images, projection densities, sh, cropping, manual QC
  6. [6] § Methods › Defining injection density and projection density ↔ before_manual_qc/multiply_threshold_inj_proj.sh, the whole file · a weak match · score 0.61 · injection fraction, injection density, projection density, segmented, connectomes
  7. [7] § Methods › Automated quality control ↔ after_manual_qc/overall_qc_exclusion.R, lines 1–43 · score 0.61 · robust outlier filtering, ventricular voxels, skewness, threshold, injection
  8. [8] § Methods › Rebuilding the regionalized voxel and homogeneous model connectomes ↔ after_manual_qc/build_model_new_excluded.py, lines 47–151 · score 0.59 · build model, normalized connection strengths, hyperparameter, weights, fitted, rebuilt
  9. [9] § Methods › Rebuilding the regionalized voxel and homogeneous model connectomes ↔ run-all-after-manual-QC.sh, lines 1–44 · score 0.58 · mouse_connectivity_models, Allen API, Knox connectome, rebuild, rebuilt, QC
  10. [10] § Methods › Nested leave-one-out cross-validation analysis ↔ after_manual_qc/run_nested_homogeneous_new_excluded.py, lines 35–48 · score 0.57 · nested homogeneous, cross validation, error, models, QC
  11. [11] § Methods › Rich club and community detection analysis ↔ graph_theory/rich_club_connectome_bin.m, lines 1–5 · score 0.57 · Brain Connectivity Toolbox, Rich Club, MATLAB
  12. [12] § Methods › Rebuilding the regionalized voxel and homogeneous model connectomes ↔ run-all-after-manual-QC.sh, lines 46–123 · score 0.51 · hyperparameter selection, py, homogeneous, Rebuilding, rebuilt, model
  13. [13] § Results › Manual QC reveals spatially and qualitatively diverse failures ↔ after_manual_qc/overall_qc_exclusion.R, lines 181–257 · score 0.51 · cortical leaking, Manual QC, nonspecific, misaligned, removal, injections
  14. [14] § Methods › Nested leave-one-out cross-validation analysis ↔ after_manual_qc/run_hyperparameter_selection_new_excluded.py, lines 35–56 · score 0.51 · voxel model, optimal, hyperparameter, fitted, kernel, Nested
  15. [15] § Methods › Rich club and community detection analysis ↔ visualizations/figure_5.R, lines 297–353 · score 0.51 · Louvain community assignments, rich club, connections

Paper

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The authors' code

Shell · 123 lines · 5.5 KB · no license · 3 matches

  1. #!/bin/bash
  2. source load-all-modules.sh
  3. set -euo pipefail
  5. ##################################################################################################
  6. cd after_manual_qc
  7. #### NOTE: All scripts here run with injection thresholds of 0.5 and projection thresholds of 0.1###
  9. #####THIS STEP REQUIRES RATINGS. Can skip and using ratings from Nathan et al., 2026, which are in harmonized_ratings
  10. #../derivatives/knox_inj/bin0.5/; ../derivatives/knox_proj/bin0.5/;
  11. #./derivatives/knox_proj/bin0.5/allen_api_not_in_knox; ../derivatives/knox_proj/bin0.5/allen_api_not_in_knox
  13. #Rscript compare_manual_qc_both_raters.R
  15. ###############START HERE########################################
  16. #################################################################
  18. #####run with different binarization thresholds (for automated QC)##
  19. Rscript overall_qc_exclusion.R 0.4 0.05
  20. Rscript overall_qc_exclusion.R 0.5 0.1
  21. Rscript overall_qc_exclusion.R 0.6 0.2
  23. ##################################################################################################
  25. ###very slight modifications needed to mouse_connectivity_models to get things running (legacy python)
  26. ###single change to line 5 of scorers.py
  27. cp scorers_updated.py ../mouse_connectivity_models/paper/figures/model_comparison/helpers/scorers.py
  28. ./patch_api_timeout.sh ###increase time limits for file loads to allow download of all conn. files
  30. oh_rgn_list="../../preprocessed/allen_template_inputs/oh_connectome_rgn_numbers_ccfv3.txt"
  31. knox_region_list="../../preprocessed/allen_template_inputs/knox_connectome_rgn_numbers_ccfv3.txt"
  33. ###rebuild connectomes with the original list of experiments to exclude from Knox et al., 2018
  34. source ../.venv/bin/activate
  35. ./rebuild_oh_connectome.sh "experiments_exclude.json" ${oh_rgn_list} "original"
  36. ./rebuild_oh_connectome.sh "experiments_exclude.json" ${knox_region_list} "original_291"
  37. ./rebuild_knox_connectome.sh "experiments_exclude.json" "original"
  38. ./rebuild_knox_connectome.sh "experiments_exclude.json" "original_oh_211_regions"
  40. ###rebuild connectomes with increased list of experiments to exclude post-QC
  41. ./rebuild_oh_connectome.sh "experiments_exclude_updated.json" ${oh_rgn_list} "rebuilt"
  42. ./rebuild_oh_connectome.sh "experiments_exclude_updated.json" ${knox_region_list} "rebuilt_291"
  43. ./rebuild_knox_connectome.sh "experiments_exclude_updated.json" "rebuilt" ##automatically writes out 291 regions
  44. ./rebuild_knox_connectome.sh "experiments_exclude_updated.json" "rebuilt_oh_211_regions" ##automatically writes out 211 rgns from Oh et al.
  46. ##################SENSITIVITY ANALYSES############################
  47. ###rebuild connectomes with automated-only experiment inclusions
  48. ./rebuild_oh_connectome.sh "experiments_exclude_updated_automated_only.json" ${oh_rgn_list} "rebuilt_auto"
  49. ./rebuild_knox_connectome.sh "experiments_exclude_updated_automated_only.json" "rebuilt_auto"
  52. ##rebuild connectomes with different number of automated lower outliers for inj/proj voxel counts###
  53. ./rebuild_oh_connectome.sh "experiments_exclude_updated_auto_inj0.5_proj0.1_lower_outliers_6.json" ${oh_rgn_list} "rebuilt_lo_6"
  54. ./rebuild_knox_connectome.sh "experiments_exclude_updated_auto_inj0.5_proj0.1_lower_outliers_6.json" "rebuilt_lo_6"
  56. ./rebuild_oh_connectome.sh "experiments_exclude_updated_auto_inj0.5_proj0.1_lower_outliers_8.json" ${oh_rgn_list} "rebuilt_lo_8"
  57. ./rebuild_knox_connectome.sh "experiments_exclude_updated_auto_inj0.5_proj0.1_lower_outliers_8.json" "rebuilt_lo_8"
  59. ############Leave-One-Out Cross-Validation Analyses from Knox et al.##############
  60. ###this step will take a while
  62. python run_hyperparameter_selection_new_excluded.py
  63. python run_nested_voxel_new_excluded.py
  64. python run_nested_homogeneous_new_excluded.py
  66. ###move old table (if it hasn't already been moved)
  67. if [ ! -f "../mouse_connectivity_models/paper/figures/model_comparison/output/cv_results_voxel-standard_homogeneous-standard-original.csv" ]; then
  68. mv ../mouse_connectivity_models/paper/figures/model_comparison/output/cv_results_voxel-standard_homogeneous-standard.csv \
  69. ../mouse_connectivity_models/paper/figures/model_comparison/output/cv_results_voxel-standard_homogeneous-standard-original.csv
  70. fi
  72. python ../mouse_connectivity_models/paper/figures/model_comparison/compile_table.py
  75. #################################################
  76. #############graph theory analyses###############
  77. cd ..
  78. mkdir -p ../derivatives/regionalized_connectomes/
  79. mkdir -p ../derivatives/rich_club/
  80. mkdir -p ../derivatives/community_louvain/
  82. cd graph_theory
  83. Rscript process_flip_regionalized_connectomes.R
  85. matlab -batch "dbstop if error; rich_club_connectome_bin"
  87. matlab -batch "dbstop if error; community_connectome_bin"
  89. ###########visualizations##########################
  90. cd ..
  91. mkdir -p figures
  92. mkdir -p figures/oh/
  93. mkdir -p "../derivatives/excluded_tracer_aggregate_volumes/"
  96. cd visualizations/
  97. ###misc - manually fill in experiments in query.csv with missing injection regions (available on website)
  98. Rscript fill_in_missing_knox_tracer_regions.R
  100. Rscript figure_1_workflow.R
  101. Rscript figure_2.R
  102. Rscript figure_3.R
  104. ##visualize each combination of thresholds and connectome models
  105. Rscript supp_fig_major_div_connectomes.R "knox" 0.2
  106. Rscript supp_fig_major_div_connectomes.R "knox" 0.05
  107. Rscript supp_fig_major_div_connectomes.R "oh" 0.2
  108. Rscript supp_fig_major_div_connectomes.R "oh" 0.05
  111. Rscript figure_4.R "knox" 0.2
  112. Rscript figure_4.R "oh" 0.2
  114. Rscript figure_4.R "knox" 0.05
  115. Rscript figure_4.R "oh" 0.05
  117. Rscript figure_5.R
  119. ####compare LOOCV tables (Knox et al. vs. post-QC)####
  120. Rscript compare_error_tables.R
  122. ###additional sensitivity analyses added
  123. Rscript sensitivity_analysis.R

run-all-after-manual-QC.sh at commit df87f4e, no license · at the source

Overview

Authors: Vikram Nathan1,2, Stephanie Tullo1, Lizette Herrera-Portillo1,2, Gabriel A Devenyi1,3, Yohan Yee4,5, M Mallar Chakravarty1,3,6
ORCID iDs: Vikram Nathan
  1. Cerebral Imaging Center, Douglas Mental Health University Institute, Montréal, QC, Canada
  2. Integrated Program in Neuroscience, McGill University, Montréal, QC, Canada
  3. Department of Psychiatry, McGill University, Montréal, Canada
  4. Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
  5. Experimental Imaging Centre, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
  6. Department of Biological & Biomedical Engineering, McGill University, Montréal, Canada
Institutions: Douglas Mental Health University Institute (Canada); McGill University (Canada); University of Calgary (Canada)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1310
Dates: received 23 February 2026; accepted 15 June 2026; published online 28 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1310 · PMID 42529501 · PMCID PMC13417618 · OpenAlex W7167062594
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), methods / tools (subfield)
Methods: Connectivity, Machine learning, Statistics, fMRI & imaging, Physiology & signal measures
Keywords: connectomics, quality control, tracer-derived connectivity, Allen mouse brain connectivity atlas (AMBCA)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Douglas Foundation; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé
Citations: not cited yet (Europe PMC); 72 references in the paper

Abstract

The Allen Mouse Brain Connectivity Atlas (AMBCA) is widely used to represent structural connectivity in the mouse brain. The AMBCA consists of tracer injection experiments where neuronal projections axonally connected to the initial injection site are labeled. The resulting whole-brain structural connectomes, derived from a subset of these experiments in C57BL/6 mice, have been used in several studies of connectomic architectures. However, through close inspection of n = 437 distinct experiments used in a publicly-available connectome (Knox et al., 2018), we observed experiments with off-target injections, diffuse projections, unrealistically small injections and projections, and anatomical misalignments, affecting the accuracy and applicability of these connectivity experiments. We applied a combined automated and manual quality control (QC) and identified n = 56 (~13% of the original n = 437) experiments representing a wide variety of injection and projection failures across the brain. Automated QC was used to detect extreme injection and projection sizes and misalignments, while manual QC was used to detect subtle off-target tracer spreading. Using the remaining n = 381 experiments, we rebuilt two different connectomes using previously-published methods; specifically: the regionalized voxel model from Knox et al. (2018), and the homogeneous model from Oh et al. (2014). Our rebuilt connectomes show strong losses in connectivity between regions with limited evidence of structural connectivity by other methods (e.g., hippocampus-medulla, cerebellum-isocortex) and gains in connectivity between regions with strong connectivity evidence (hypothalamus-cerebellum, hypothalamus-isocortex). Finally, we analyzed the rich club and community organization to demonstrate the potential downstream impacts on the representation of the overall structural connectome architectures of our QC’d connectomes and observed subtle whole-brain organizational changes. We present our rebuilt connectomes, and particularly highlight the regionalized voxel model, as more accurate representations of structural connectivity derived from the AMBCA.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 15 matches between paragraphs and lines of code.

vik16nathan/allen_connectome_qc

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: df87f4e22d1c1b8407fc2d5bfc6c781e93c8a243, 10 August 2026
Languages: Shell (14), R (13), Python (10), MATLAB (2)
Size: 161 files, 39 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, environment (pyproject.toml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (13 files), NumPy (9 files), AllenSDK (6 files), ggplot2 (5 files), scikit-learn (5 files), pandas (4 files), patchwork (4 files), ANTs (3 files), pheatmap (3 files), Brain Connectivity Toolbox (2 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
40 files
At the source: github.com/vik16nathan/allen_connectome_qc

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 39 scripts, each with its path and the digest of its content;
  • 15 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 code is stored on https://github.com/vik16nathan/allen_connectome_qc. All input data is publicly available and downloaded from the Allen API.

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

  • Funding: added Douglas Foundation; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 4 keywords, 72 references.

Cite

This paper

Nathan, V., Tullo, S., Herrera-Portillo, L., Devenyi, G. A., Yee, Y., & Chakravarty, M. M. (2026). Experimental quality control induces changes in Allen mouse brain connectomes. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1310. https://doi.org/10.1162/imag.a.1310

BibTeX

@article{nathan2026experimental,
author = {Nathan, Vikram and Tullo, Stephanie and Herrera-Portillo, Lizette and Devenyi, Gabriel A and Yee, Yohan and Chakravarty, M Mallar},
title = {{Experimental quality control induces changes in Allen mouse brain connectomes}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1310},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1310},
url = {https://doi.org/10.1162/imag.a.1310},
pmid = {42529501},
pmcid = {PMC13417618}
}

RIS

TY - JOUR
AU - Nathan, Vikram
AU - Tullo, Stephanie
AU - Herrera-Portillo, Lizette
AU - Devenyi, Gabriel A
AU - Yee, Yohan
AU - Chakravarty, M Mallar
TI - Experimental quality control induces changes in Allen mouse brain connectomes
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/28
VL - 4
SP - IMAG.a.1310
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1310
UR - https://doi.org/10.1162/imag.a.1310
LA - en
ER -

CSL-JSON

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

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