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Spatial biases in visual feature representation of mouse dorsal lateral geniculate nucleus boutons.

Code ↔ Paper

4 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 4 matches
  1. [1] § STAR★Methods › Method details › Data processing of retinal imaging data ↔ pkg/retistruct/demo/retistruct.method.R, lines 44–106 · score 0.60 · flattened retina, Polar coordinates, Retistruct, landmark, reconstructed, spherical
  2. [2] § STAR★Methods › Method details › Data processing of retinal imaging data ↔ pkg/retistruct/R/ReconstructedOutline.R, lines 918–1039 · score 0.55 · flattened retina, spherical retina, reconstructed, matrix, Retistruct
  3. [3] § STAR★Methods › Quantification and statistical analysis › Bouton ROI selection ↔ draw_fig2_example2pFields.m, lines 10–53 · score 0.54 · drifting gratings, static gratings, chosen, scores, stimulus, ROIs
  4. [4] § STAR★Methods › Quantification and statistical analysis › Feature gradient models ↔ draw_fig3_2pSubsets.m, lines 342–405 · score 0.51 · feature deviation, temporal frequency, polyfit, log2, correlation, model

Paper

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

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The authors' code

R · 106 lines · 2.9 KB · no license · 1 match

  1. library(rgl)
  2. oldpar <- par(no.readonly=TRUE) # Save graphics parameters before plotting
  3. ## Set up a 3x3 grid for plotting
  4. par(mfrow=c(3, 3))
  5. par(mar=c(0.5, 0.5, 0.5, 0.5))
  7. ## Load the raw data
  8. dataset <- file.path(system.file(package = "retistruct"), "extdata", "GM509/R-CONTRA")
  9. o <- retistruct.read.dataset(dataset)
  11. ## Load the human annotation of tears
  12. o <- retistruct.read.markup(o)
  14. ## Make this a left eye to help with orientation of points
  15. o$side="Left"
  17. ## Plot of raw data. Axes are reversed to improve comparison with
  18. ## polar plot later
  19. flatplot(o, markup=FALSE)
  20. mtext("A", adj=0, font=2, line=-0.9)
  22. ## Plot the annotation
  23. flatplot(o, datapoints=FALSE, landmarks=FALSE)
  24. mtext("B", adj=0, font=2, line=-0.9)
  26. ## Set up fixed point
  27. o$lambda0 <- 0
  29. ## In v0.6 and below, the code below could be used to triangulate and
  30. ## stitch the outline. In v0.7,
  31. ## RetinalReconstructedOutline$loadOutline() carries out these steps.
  33. ## Initial triangulation (with 500 points)
  34. # n <- 500
  35. # t <- TriangulatedOutline$new(o, n=n)
  37. ## Stitching
  38. # s <- StitchedOutline(t)
  40. ## Triangulate again, to take into account points added by stitching
  41. # m <- TriangulatedOutline(s, n=n,
  42. # suppress.external.steiner=TRUE)
  44. ## Merge the points that have been stitched
  45. # m <- mergePointsEdges(m)
  47. ## Make a rough projection to a sphere
  48. # m <- projectToSphere(m)
  50. r <- NULL
  51. r <- RetinalReconstructedOutline$new()
  52. r$loadOutline(o, debug=FALSE)
  54. ## Plot triangulation and stitching
  55. flatplot(r, datapoints=FALSE, landmarks=FALSE, markup=FALSE)
  56. mtext("C", adj=0, font=2, line=-0.9)
  58. ## Plot the initial gridlines in 2D
  59. par(mfg=c(3, 3))
  60. flatplot(r, grid=TRUE,
  61. datapoints=FALSE, landmarks=FALSE, mesh=FALSE, markup=FALSE,
  62. stitch=FALSE, strain=TRUE)
  63. mtext("Dii", adj=0, font=2, line=-0.9)
  65. ## Plot the intial projection in 3D
  66. par(mfg=c(2, 3))
  67. plot.new()
  68. mtext("Di", adj=0, font=2, line=-0.9)
  70. sphericalplot(r, strain=TRUE, datapoints=FALSE)
  71. view3d(zoom=0.7)
  72. ## Save to SVG
  73. # rgl.postscript("initial-projection.svg", "svg")
  75. r$reconstruct(plot.3d=FALSE)
  77. ## Plot the final projection in 3D and on the grid
  78. par(mfg=c(2, 2))
  79. plot.new()
  80. mtext("Ei", adj=0, font=2, line=-0.9)
  82. sphericalplot(r, strain=TRUE, datapoints=FALSE)
  83. ## Save to SVG
  84. # rgl.postscript("final-projection.svg", "svg")
  86. par(mfg=c(3, 2))
  87. flatplot(r, grid=TRUE,
  88. datapoints=FALSE, landmarks=FALSE, mesh=FALSE, markup=FALSE,
  89. stitch=FALSE, strain=TRUE)
  90. mtext("Eii", adj=0, font=2, line=-0.9)
  92. ## Plot data in polar coordinates and flattend retina
  93. par(mfg=c(2, 1))
  94. projection(r, datapoints=TRUE, landmarks=TRUE, datapoint.contours=FALSE)
  95. mtext("Fi", adj=0, font=2, line=-0.9)
  97. par(mfg=c(3, 1))
  98. flatplot(r, grid=TRUE,
  99. datapoints=TRUE, landmarks=TRUE, mesh=FALSE, markup=FALSE,
  100. stitch=FALSE)
  101. mtext("Fii", adj=0, font=2, line=-0.9)
  103. ## Save to PDF
  104. # dev.print(pdf, file="retistruct-method.pdf", width=6.83, height=6.83)
  106. par(oldpar) # Restore graphics parameters

retistruct.method.R at commit 4b028b1, no license · at the source

Overview

Authors: Kuwook Cha1, Aline Giselle Rangel Olguin1, Reza Sharif-Naeini1, Erik P Cook1, Arjun Krishnaswamy1
ORCID iDs: Arjun Krishnaswamy
  1. Department of Physiology, McGill University, Montreal, QC, Canada
Institutions: McGill University (Canada)
Journal: iScience, volume 29, issue 8, article 117088
Dates: received 9 March 2025; accepted 21 July 2026; published online 8 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.117088 · PMID 42603969 · PMCID PMC13476559 · OpenAlex W7201985135
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), systems (subfield)
Methods: Connectivity, Statistics, Spectral & time-frequency, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: LGN, visual field specialization, early visual system, neural circuitry, two-photon imaging, axon-localized calcium indicators, chemogenetics, geniculocortical interactions, thalamocortical feedback
Topic: Hemispheric Asymmetry in Neuroscience (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Alfred P. Sloan Foundation; Canada Research Chairs Program; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Quebec Sante; The Scottish Rite Charitable Foundation; Canadian Institutes of Health Research
Citations: not cited yet (Europe PMC); 118 references in the paper
Research resources: rabbit anti-DsRed RRID:AB_10013483, goat anti-osteopontin (1:1000) RRID:AB_2194992, Alexa Fluor 488 RRID:AB_2340375, chicken anti-GFP (1:1000) RRID:AB_300798, Cy3 RRID:AB_92570, Cy3 RRID:AB_92588, Mouse: VGluT2-Cre RRID:IMSR_JAX, Mouse: Rosa26-CAG-GCaMP6f RRID:IMSR_JAX:028865, Mouse: Ntsr1-Cre RRID:IMSR_JAX:03026, Ntsr1-Cre RRID:IMSR_JAX:030264, MATLAB RRID:SCR_001622, R RRID:SCR_001905, ImageJ RRID:SCR_002285, Psychtoolbox RRID:SCR_002881, Python RRID:SCR_008394, ScanImage RRID:SCR_014307, Isolectin RRID:SCR_014365

Abstract

Humans and other mammals show spatial biases in their perception of visual features. Although the primary visual cortex is considered a source of these biases, earlier structures may also contribute. Recent work shows feature biases in the retina, but little is known about how they evolve as signals flow through the dorsal lateral geniculate nucleus of the thalamus (dLGN). Using in vivo calcium imaging, we investigated spatial frequency, orientation, direction, and temporal frequency representations in both the retina and dLGN boutons. We found modest location-dependent biases in the representation of each feature across visual space for boutons, while such biases were weaker in the retina. dLGN representations emerged from functionally and anatomically defined bouton subsets. Selective ablation of cortical feedback to dLGN modulated feature biases but did not eliminate them. Together, these results suggest that dLGN integrates retinal and cortical inputs to create spatially biased feature maps for V1.

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 4 matches between paragraphs and lines of code.

Swamylab/Cha_et_al_Mouse_dLGN_FeatureMaps

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 47c77dc466243aaead3bc17cae377a9a8311894a, 3 June 2026
Languages: MATLAB (18)
Size: 20 files, 18 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (3 files), Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/Swamylab/Cha_et_al_Mouse_dLGN_FeatureMaps

davidcsterratt/retistruct

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4b028b1bc5a4dd0206483be55905b6ded746eec1, 8 June 2025
Languages: R (96), MATLAB (10), C (4), Shell (1)
Size: 396 files, 111 scripts
Software Heritage: archived
Found in: the resources table
Holds: README, environment (pkg/retistruct/DESCRIPTION, pkg/retistructdemos/DESCRIPTION), tests, continuous integration, documentation
Not found: license file, CITATION.cff
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
112 files
At the source: github.com/davidcsterratt/retistruct

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;
  • 129 scripts, each with its path and the digest of its content;
  • 4 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

Datasets cited

Data and code availability

Data to reproduce analysis and main figures have been deposited at https://doi.org/10.5061/dryad.tmpg4f5f9 and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Accession numbers are listed in the key resources table. Further raw data for this study is available upon reasonable request to the lead contact AK ().

Code to analyze data and generate main figures has been deposited at https://github.com/Swamylab/Cha_et_al_Mouse_dLGN_FeatureMaps and is publicly available as of the date of publication. Accession numbers are listed in the key resources table. Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, AK ().

Other items: Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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

  • Authors: added Arjun Krishnaswamy (0000-0002-7706-4657); removed Arjun Krishnaswamy

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 9 keywords, 6 funders, 117 references, 17 RRIDs.

Cite

This paper

Cha, K., Rangel Olguin, A. G., Sharif-Naeini, R., Cook, E. P., & Krishnaswamy, A. (2026). Spatial biases in visual feature representation of mouse dorsal lateral geniculate nucleus boutons. iScience, 29(8), 117088. https://doi.org/10.1016/j.isci.2026.117088

BibTeX

@article{cha2026spatial,
author = {Cha, Kuwook and Rangel Olguin, Aline Giselle and Sharif-Naeini, Reza and Cook, Erik P and Krishnaswamy, Arjun},
title = {{Spatial biases in visual feature representation of mouse dorsal lateral geniculate nucleus boutons}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {8},
pages = {117088},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117088},
url = {https://doi.org/10.1016/j.isci.2026.117088},
pmid = {42603969},
pmcid = {PMC13476559}
}

RIS

TY - JOUR
AU - Cha, Kuwook
AU - Rangel Olguin, Aline Giselle
AU - Sharif-Naeini, Reza
AU - Cook, Erik P
AU - Krishnaswamy, Arjun
TI - Spatial biases in visual feature representation of mouse dorsal lateral geniculate nucleus boutons
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/08
VL - 29
IS - 8
SP - 117088
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117088
UR - https://doi.org/10.1016/j.isci.2026.117088
LA - en
ER -

CSL-JSON

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