OSCR

Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex.

Code ↔ Paper

24 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 24 matches · 20 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Gradient calculation ↔ utils/mapping/makeGradientMap.m, the whole file · a weak match · score 0.83 · gradient magnitude, filled missing, Gaussian kernel, map images, interior, pixel
  2. [2] § Methods › GMM component distances and clustering ↔ main/fig9/analysis/clusterGMMsBD.m, the whole file · a weak match · score 0.76 · hierarchical clustering, Bhattacharyya distance, distance matrix, GMM components, subpopulations, Figure 9
  3. [3] § Methods › GMM component distances and clustering ↔ main/fig9/analysis/clusterGMMsBD.m, the whole file · a weak match · score 0.73 · multivariate Gaussians, Bhattacharyya distance, GMM components, DB, matrix, clustering
  4. [4] § Results › Activity profiles of single neurons varied systematically across sensorimotor cortex › Response duration ↔ main/fig9/plots/formatPDFSubpops.m, the whole file · a weak match · score 0.72 · Target tuning linearity, participation ratio, response duration, Tuning persistence, Tuning sharpness, variation
  5. [5] § Results › Activity profiles of single neurons varied systematically across sensorimotor cortex › Response duration ↔ utils/plots/formatPDFComps.m, the whole file · a weak match · score 0.72 · Target tuning linearity, participation ratio, response duration, Tuning persistence, Tuning sharpness, variation
  6. [6] § Results › Subpopulations spatially overlapped and spanned anatomical areas ↔ main/fig9/analysis/plotSubpopTroughs.m, the whole file · a weak match · score 0.69 · logistic regression, Hindlimb somatomotor, Forelimb somatomotor, trained, axis, clustering
  7. [7] § Results › Subpopulations spatially overlapped and spanned anatomical areas ↔ main/fig9/plots/formatPDFSubpops.m, the whole file · a weak match · score 0.64 · tuning persistence, tuning sharpness, response duration, target tuning, variation, peak
  8. [8] § Results › Subpopulations spatially overlapped and spanned anatomical areas ↔ utils/plots/formatPDFComps.m, the whole file · a weak match · score 0.64 · tuning persistence, tuning sharpness, response duration, target tuning, variation, peak
  9. [9] § Methods › Random boundary analysis ↔ main/fig6/analysis/cvBorderClassifier.m, lines 1–41 · score 0.64 · cross validation, Decoding, hyperparameter, fold, SVM, optimization
  10. [10] § Methods › Nonlinear dimensionality reduction ↔ main/fig7/analysis/tSNEallROIs.m, the whole file · a weak match · score 0.63 · feature matrix, SNE space, perplexity, t-SNE, correlation, dimensional
  11. [11] § Methods › Nonlinear dimensionality reduction ↔ main/fig9/analysis/tSNEallROIsPCA.m, the whole file · a weak match · score 0.63 · feature matrix, SNE space, perplexity, t-SNE, correlation, dimensional
  12. [12] § Methods › Gradient calculation ↔ utils/mapping/makeMetricMap.m, the whole file · a weak match · score 0.59 · Gaussian kernel, map images, pixel, bins, distance, locations
  13. [13] § Methods › Computing map images from single-cell properties ↔ utils/mapping/makeMetricMap.m, the whole file · a weak match · score 0.59 · Gaussian smooth, irregular, empty, weights, pixels, colormap
  14. [14] § Methods › Random boundary analysis ↔ main/fig6/analysis/sampleRandomBorders.m, lines 1–113 · score 0.59 · rejection sampling, endpoints, orientation, segment, bounding, 500 um
  15. [15] § Methods › Gaussian mixture modeling and cluster selection ↔ utils/gmm/fitAreaGMMs.m, the whole file · a weak match · score 0.58 · Gaussian Mixture Models, feature vectors, fit, GMMs
  16. [16] § Methods › Computing map images from single-cell properties ↔ utils/mapping/makeGradientMap.m, the whole file · a weak match · score 0.57 · Gaussian smooth, irregular, empty, zero, pixels, colormap
  17. [17] § Methods › Gaussian mixture modeling and cluster selection ↔ utils/gmm/fitGMM.m, the whole file · a weak match · score 0.57 · feature vectors, fitgmdist, ICL, covariance, optimal, modeled
  18. [18] § Results › Subpopulations spatially overlapped and spanned anatomical areas ↔ main/fig10/fig10_main.m, lines 30–40 · score 0.56 · Hindlimb somatomotor, Forelimb somatomotor, Forelimb motor, Anterior, subpopulations, maps
  19. [19] § Methods › Gaussian mixture modeling and cluster selection ↔ utils/gmm/fitGMM.m, the whole file · a weak match · score 0.54 · fitted GMMs, BIC, ICL, posterior, components, modeling
  20. [20] § Methods › PCA-based alternative to feature-based analyses ↔ main/fig9/fig9_analysis.m, lines 47–49 · score 0.51 · PC coefficients, VARIMAX, rotated, PCA, Figure 9
  21. [21] § Results › Dense sampling of sensorimotor cortex reveals heterogeneous tuning › Behavior ↔ main/fig2/plots/plotTrajectories.m, the whole file · a weak match · score 0.51 · finger centroid trajectories, looping, kinematics, position, Figure 2
  22. [22] § Results › Neighboring sensorimotor regions shared PETH features in complex spatial patterns ↔ utils/gmm/fitAreaGMMs.m, the whole file · a weak match · score 0.51 · Gaussian Mixture Model, feature vector, fit, GMM, neurons
  23. [23] § Results › Neighboring sensorimotor regions shared PETH features in complex spatial patterns ↔ utils/gmm/fitAllROIsGMM.m, the whole file · a weak match · score 0.51 · Gaussian Mixture Model, feature vector, fit, GMM, neurons
  24. [24] § Results › Dense sampling of sensorimotor cortex reveals heterogeneous tuning › Behavior ↔ main/fig2/plots/plotTrajectories.m, the whole file · a weak match · score 0.50 · Finger centroid trajectories, nose, mouth, kinematic, lift, traces

Paper

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

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The authors' code

MATLAB · 95 lines · 3.9 KB · CC0-1.0 · 2 matches

  1. function [imgRGB, imgMask, xBounds, yBounds, smoothGrad, cmap] = makeGradientMap(metricVals, valPositions, gridRes, xLimits, yLimits, cmapInfo, sdPts, sdGrad)
  2. % Creates a gridded color image from smoothed metric values, interpolates
  3. % missing grid pieces, computes 2D gradient, Gaussian kernel smoothes and
  4. % maps colors to a colormap
  5. %
  6. % Note: cmapInfo is overloaded, allowing multiple kinds of color mappings
  7. % See below:
  8. %
  9. % Inputs:
  10. % metricVals N x 1 value data
  11. % valPositions N x 2 XY locations of the values
  12. % gridRes # grid cells on the short side.
  13. % xLimits 1 x 2 [xmin, xmax], lateral extent for binning
  14. % yLimits 1 x 2 [ymin, ymax], vertical extent
  15. % cmapInfo
  16. % 1 x 3 RGB base color
  17. % or
  18. % (char) name of a colorcet palette
  19. % or
  20. % K x 3 RGB base colormap to be interpolated
  21. % sdPts standard deviation of irregular Gaussian smoothing
  22. % on original XY points and their metric values
  23. % sdGrad standard deviation of image smoothing kernel
  24. %
  25. % Outputs:
  26. % gradImg nY-nX-3 RGB image of gradient magnitudes (smoothed).
  27. % imgMask nY-nX mask (1 = cell had data).
  28. % xBounds [xmin xmax].
  29. % yBounds [ymin ymax].
  30. % smoothGrad nY-nX smoothed gradient values before RGB.
  31. % cmap 3 x 256 colormap that the image is sampled from.
  33. % -------------------------- INPUTS
  34. % 1) Parse inputs
  35. if ~ischar(cmapInfo) && size(cmapInfo,2) ~= 3
  36. error('Invalid cmapInfo, check available options')
  37. end
  39. % If not provided, choose sd proportional to grid span / resolution
  40. if nargin < 8 || isempty(sdGrad)
  41. sdPts = 0.2;
  42. end
  43. if nargin < 8 || isempty(sdGrad)
  44. sdGrad = min(diff(xLimits),diff(yLimits)) / (2*gridRes);
  45. end
  47. % Get data, smooth with irregular Gaussian smoothing first
  48. metricVals = irregularGaussianSmooth(valPositions, metricVals, sdPts);
  50. % -------------------------- BINNING
  51. % 2) Compute grid layout
  52. % Using xLimits, yLimits, gridRes (short side)
  53. [xEdges, yEdges, xBounds, yBounds, cellSize] = computeGrid2D(xLimits, yLimits, gridRes);
  55. % 3) Bin data into grid, a cell array of values per bin
  56. binnedGrid = binVals2D(metricVals, valPositions, xEdges, yEdges);
  58. % -------------------------- SUMMARY OPERATION (MEAN)
  59. % 4) Compute raw means + mask
  60. meanGrid = cellfun(@(x) mean(x), binnedGrid); % per-cell mean (NaN where empty)
  61. imgMask = double(~isnan(meanGrid)); % mask of cells that received data (1=yes)
  63. % -------------------------- INTERPOLATION
  64. % 5) Fill missing data within the interior of the image via interpolation
  65. [meanGrid, imgMask] = interpMap2D(meanGrid, imgMask);
  67. % -------------------------- SPATIAL GRADIENT
  68. % 6) Compute gradient magnitude without bleeding NaNs at edges
  69. % Create a copy (G) where outside-hull cells are replaced by the nearest
  70. % inside-hull value (via distance transform), so gradients compute cleanly.
  71. G = meanGrid;
  72. maskInside = (imgMask == 1);
  73. outsideIdx = find(~maskInside);
  74. [~, nearestIdx] = bwdist(maskInside); % For each position, nearest true pixel index
  75. G(outsideIdx) = meanGrid(nearestIdx(outsideIdx));
  77. % Compute gradients using true physical spacing (cellSize).
  78. [Gy, Gx] = gradient(G, cellSize, cellSize);
  79. gradMag = sqrt(Gx.^2 + Gy.^2);
  81. % Restore NaNs outside the hull so we don't display artifacts.
  82. gradMag(~maskInside) = NaN;
  84. % -------------------------- SMOOTHING
  85. % 7) Gaussiankernel smooth the gradient magnitude, then reapply hull mask
  86. % Replace NaNs with zeros temporarily for filtering; then mask invalids again.
  87. tempGrad = gradMag;
  88. tempGrad(isnan(tempGrad)) = 0; % avoid NaNs spreading in filter
  89. smoothGrad = imgaussfilt(tempGrad, sdGrad);
  90. smoothGrad(~maskInside) = NaN; % restore outside as NaN
  92. % -------------------------- MAP IMAGE TO RGB
  93. % 13) Build imgRGB from normalized, smoothed gradient map
  95. [imgRGB, cmap] = grid2RGB(smoothGrad, imgMask, cmapInfo);

makeGradientMap.m at commit 6a547f1, under CC0-1.0 · at the source

Overview

Authors: Sohrab Salimian1, Harrison Grier1, Matthew Tyler Kaufman2,3,4
ORCID iDs: Sohrab Salimian, Harrison Grier, Matthew Tyler Kaufman
  1. Committee on Computational Neuroscience, The University of Chicago Chicago United States
  2. Department of Organismal Biology and Anatomy, The University of Chicago Chicago United States
  3. Neuroscience Institute, The University of Chicago Chicago United States
  4. NSF-Simons National Institute for Theory and Mathematics in Biology Chicago United States
Institutions: University of Chicago (United States)
Journal: eLife, volume 14, article RP109240
Dates: published online 3 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.109240 · PMID 42396973 · PMCID PMC13331482 · OpenAlex W4417400079
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), systems (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: motor cortex, somatosensory cortex, two-photon imaging, reaching and grasping, Mouse
MeSH: Neurons*, Sensorimotor Cortex*, Animals, Brain Mapping, Male, Mice, Mice, Inbred C57BL (* major topic)
Journal subjects: Neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (NCS 1835390, DMS-2235451); National Institute of Neurological Disorders and Stroke (R01 NS121535, T32 NS121763); Simons Foundation (876393SPI, MP-TMPS-00005320); Alfred P. Sloan Foundation; Whitehall Foundation
Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

Cortical control of movement is a distributed computation spanning multiple densely interconnected regions. Although we have rich anatomical atlases and a coarse understanding of how function maps to areas and subregions, we lack a detailed account of how behaviorally relevant activity is organized across the cortical sheet. Here, we trained head-fixed mice to perform a 15-target reach-to-grasp task while we performed cellular-resolution, two-photon calcium imaging across five regions of sensorimotor cortex (>39,000 layer 2/3 neurons). We characterized each neuron’s trial-averaged peri-event activity with interpretable metrics and mapped these response properties across areas, revealing large-scale spatial structure. Neuronal response profiles often shifted abruptly at anatomical borders: motor areas showed sharper tuning and more linear relationships with target location, whereas somatosensory areas displayed more heterogeneous response patterns. Neural response properties also differed according to somatotopic representation. Nonlinear dimensionality reduction of the neural feature matrix revealed that areas varied in their average response profiles, but that areas did not have well-separated feature distributions; instead, each area contained subpopulations. Neurons in each subpopulation had characteristic response profiles and were distributed across multiple cortical areas. The spatial distributions of the subpopulations overlapped, with neurons from different subpopulations salt-and-pepper intermingled in the overlap zones. Together, these results describe novel activity structure across sensorimotor cortex and identify several distinct but spatially overlapping subpopulations with characteristic activity patterns during reach-to-grasp behavior.

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 24 matches between paragraphs and lines of code.

kaufmanlab/SGK26-public

License: CC0-1.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 6a547f1dc7e2b4e2b4d072fac9d772d0496435a8, 1 July 2026
Languages: MATLAB (130)
Size: 134 files, 130 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (19 files), Image Processing Toolbox (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
132 files
At the source: github.com/kaufmanlab/SGK26-public

Code availability

Code used to create the figures is available on GitHub: https://github.com/kaufmanlab/SGK26-public, copy archived at Grier, 2026.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 130 scripts, each with its path and the digest of its content;
  • 24 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 availability

Data is available at https://doi.org/10.6084/m9.figshare.32642106.

The following dataset was generated:

SalimianS GrierHA KaufmanMT 2026Data from "Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex" eLife 2026figshare10.6084/m9.figshare.32642106PMC1333148242396973

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 5 keywords, 7 MeSH terms, 5 funders, 86 references.

Cite

This paper

Salimian, S., Grier, H., & Kaufman, M. T. (2026). Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex. eLife, 14, RP109240. https://doi.org/10.7554/elife.109240

BibTeX

@article{salimian2026neural,
author = {Salimian, Sohrab and Grier, Harrison and Kaufman, Matthew Tyler},
title = {{Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex}},
journal = {eLife},
year = {2026},
month = jul,
volume = {14},
pages = {RP109240},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.109240},
url = {https://doi.org/10.7554/elife.109240},
pmid = {42396973},
pmcid = {PMC13331482}
}

RIS

TY - JOUR
AU - Salimian, Sohrab
AU - Grier, Harrison
AU - Kaufman, Matthew Tyler
TI - Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/07/03
VL - 14
SP - RP109240
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.109240
UR - https://doi.org/10.7554/elife.109240
LA - en
ER -

CSL-JSON

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