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Linking spatially distributed neuronal activation overlap to the limits of perceptual discrimination in rodent primary somatosensory cortex.

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Paper

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The authors' code

MATLAB · 171 lines · 6.2 KB · no license

  1. clear all; clc; close all;
  3. % Create "Extracted Matrices" folder if it doesn't exist
  4. if ~exist('Extracted Matrices', 'dir')
  5. mkdir('Extracted Matrices');
  6. end
  8. % Select .fig files
  9. [fileNames, path1] = uigetfile('*.fig', 'Select Figure Files', 'MultiSelect', 'on');
  10. if isequal(fileNames, 0)
  11. disp('No files selected. Exiting...');
  12. return;
  13. end
  14. if ischar(fileNames)
  15. fileNames = {fileNames};
  16. end
  18. for f = 1:length(fileNames)
  19. file = fileNames{f};
  20. fprintf('\nProcessing %s...\n', file);
  22. fig = openfig(fullfile(path1, file), 'invisible');
  23. ax = findobj(fig, 'Type', 'axes');
  24. scatterData = findobj(ax, 'Type', 'Line');
  26. % Initialize storage
  27. RedPoints = []; MagentaPoints = []; YellowPoints = [];
  28. GreenPoints = []; BluePoints = []; GrayPoints = [];
  30. for i = 1:length(scatterData)
  31. xData = get(scatterData(i), 'XData');
  32. yData = get(scatterData(i), 'YData');
  33. zData = get(scatterData(i), 'ZData');
  34. cData = get(scatterData(i), 'Color');
  36. if isequal(cData, [1, 0, 0])
  37. RedPoints = [RedPoints; xData', yData', zData'];
  38. elseif isequal(cData, [1, 0, 1])
  39. MagentaPoints = [MagentaPoints; xData', yData', zData'];
  40. elseif isequal(cData, [251 177 23]/255)
  41. YellowPoints = [YellowPoints; xData', yData', zData'];
  42. elseif isequal(cData, [0 100 0]/255)
  43. GreenPoints = [GreenPoints; xData', yData', zData'];
  44. elseif isequal(cData, [0, 0, 1])
  45. BluePoints = [BluePoints; xData', yData', zData'];
  46. elseif isequal(cData, [169 169 169]/255)
  47. GrayPoints = [GrayPoints; xData', yData', zData'];
  48. end
  49. end
  51. fileBase = file(1:end-4);
  52. saveName = fullfile('Extracted Matrices', [fileBase, '.mat']);
  53. save(saveName, 'RedPoints', 'MagentaPoints', 'YellowPoints', ...
  54. 'GreenPoints', 'BluePoints', 'GrayPoints');
  56. fprintf('\nExtracted Data Summary for: %s\n', file);
  57. fprintf(' Red (Activated Neurons): %d points\n', size(RedPoints, 1));
  59. close(fig);
  61. %% --- User input for center and capture percentages ---
  62. center = [200 200 200]; % Fixed user-defined center
  63. capturePercentages = [100];
  65. if isempty(RedPoints)
  66. disp('No red points found. Skipping ellipsoid calculation...');
  67. return;
  68. end
  70. % Preallocate results structure
  71. EllipsoidFits = struct([]);
  73. % Loop through each capture percentage
  74. for c = 1:numel(capturePercentages)
  75. capperc = capturePercentages(c);
  76. fprintf('\n--- Running capture percentage: %.1f%% ---\n', capperc);
  78. % Find initial spreads using percentile ranges
  79. half_width = capperc / 2;
  80. bottomrange = 50 - half_width;
  81. toprange = 50 + half_width;
  82. x_range = prctile(RedPoints(:,1), [bottomrange, toprange]);
  83. y_range = prctile(RedPoints(:,2), [bottomrange, toprange]);
  84. z_range = prctile(RedPoints(:,3), [bottomrange, toprange]);
  86. xspread = (x_range(2) - x_range(1)) / 2;
  87. yspread = (y_range(2) - y_range(1)) / 2;
  88. zspread = (z_range(2) - z_range(1)) / 2;
  90. % Initial check for capture
  91. inside = checkEllipsoid(xspread, yspread, zspread, center, RedPoints);
  92. captured_percent = 100 * nnz(inside) / size(RedPoints, 1);
  94. % Growth loop
  95. axgrowths = [1 10]; % Two passes with different growth steps
  96. maxIter = 10000;
  98. for g = 1:numel(axgrowths)
  99. if captured_percent >= capperc
  100. disp("Already captured enough in first trial. Skipping rescue capture.")
  101. break; % Already captured enough — skip the next growth phase
  102. end
  103. axgrowth = axgrowths(g);
  104. iter = 0;
  106. while captured_percent < capperc && iter < maxIter
  107. iter = iter + 1;
  109. % Try growing each axis
  110. spreads = [xspread, yspread, zspread];
  111. labels = {'xspread', 'yspread', 'zspread'};
  112. axesIdx = [1, 2, 3];
  114. for i = axesIdx
  115. test_spreads = spreads;
  116. test_spreads(i) = test_spreads(i) + axgrowth;
  117. temp_inside = checkEllipsoid(test_spreads(1), test_spreads(2), test_spreads(3), center, RedPoints);
  118. new_percent = 100 * nnz(temp_inside) / size(RedPoints, 1);
  119. if new_percent > captured_percent
  120. spreads(i) = test_spreads(i);
  121. inside = temp_inside;
  122. captured_percent = new_percent;
  123. end
  124. end
  126. xspread = spreads(1);
  127. yspread = spreads(2);
  128. zspread = spreads(3);
  130. if captured_percent >= capperc
  131. break;
  132. end
  133. end
  134. end
  136. % Compute volume of the ellipsoid within voxel grid
  137. dx = 1; dy = 1; dz = 5;
  138. [X, Y, Z] = ndgrid(0:dx:400, 0:dy:400, 0:dz:2000);
  139. ellipsoidMask = ((X - center(1)).^2 / xspread^2 + ...
  140. (Y - center(2)).^2 / yspread^2 + ...
  141. (Z - center(3)).^2 / zspread^2) <= 1;
  143. voxelVolume = dx * dy * dz; % in um³
  144. clippedVolume = nnz(ellipsoidMask) * voxelVolume;
  146. % Store and display results
  147. EllipsoidFits(c).capturePercent = capperc;
  148. EllipsoidFits(c).center = center;
  149. EllipsoidFits(c).axes_um = [xspread yspread zspread];
  150. EllipsoidFits(c).totalRed = size(RedPoints, 1);
  151. EllipsoidFits(c).numCaptured = nnz(inside);
  152. EllipsoidFits(c).capturedPercent = captured_percent;
  153. EllipsoidFits(c).clippedVolume_um3 = clippedVolume;
  155. fprintf(' Center: [%.2f, %.2f, %.2f] um\n', center);
  156. fprintf(' Axes (x, y, z): %.2f, %.2f, %.2f um\n', xspread, yspread, zspread);
  157. fprintf(' Red Points: %d\n', size(RedPoints, 1));
  158. fprintf(' Captured: %d (%.2f%%)\n', nnz(inside), captured_percent);
  159. fprintf(' Volume: %.2f um³\n', clippedVolume);
  160. end
  161. end
  162. % Optional: Save the results
  163. save(saveName, 'EllipsoidFits', '-append');
  165. %% --- Function to check if the points are inside the ellipsoid ---
  166. function inside = checkEllipsoid(xspread, yspread, zspread, center, RedPoints)
  167. x_shifted = (RedPoints(:,1) - center(1)).^2 / xspread^2;
  168. y_shifted = (RedPoints(:,2) - center(2)).^2 / yspread^2;
  169. z_shifted = (RedPoints(:,3) - center(3)).^2 / zspread^2;
  170. inside = (x_shifted + y_shifted + z_shifted) <= 1;
  171. end

GetEllipsoidDataOnly.m at commit 2570706, no license · at the source

Overview

Authors: Madison Jiang1, Joseph J. Pancrazio2, Thomas J. Smith1
  1. School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
  2. Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States
Institutions: The University of Texas at Dallas (United States)
Journal: Frontiers in computational neuroscience, volume 20, article 1876230
Dates: received 8 May 2026; accepted 31 July 2026; published online 14 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fncom.2026.1876230 · PMID 42666294 · PMCID PMC13522154 · OpenAlex W7203443586
Open access: gold, a free copy (OpenAlex)
Status: code verified
Keywords: computational model, intracortical microstimulation, rodent, sensory discrimination, somatosensory cortex
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute for Health and Care Research
Citations: not cited yet (Europe PMC); 27 references in the paper

Abstract

Introduction: Intracortical microstimulation (ICMS) of the primary somatosensory cortex can evoke localized tactile percepts, yet the spatial factors that influence perceptual discrimination remain poorly defined. In prior work, we showed that discrimination accuracy between behaviorally evaluated ICMS-evoked percepts declines as stimulation sites converge across cortical depths and adjacent cortical columns. Those results suggest that overlap in neuronal recruitment may constrain perceptual differentiation.

Methods: Here, we combine simulated data from a biophysically realistic computational model of the somatosensory cortex with previously collected behavioral data from rats to quantify how overlap in ICMS-evoked activation volume relates to discrimination performance. Within the model, ICMS patterns investigated behaviorally were simulated, and activation volumes were estimated by fitting a range of 50–100% capture ellipsoids to the spatial distribution of activated somata. Overlap in activation volumes between pairs of ICMS patterns was then quantified using the intersection-over-union (IoU) metric.

Results: Across both single- and four-shank microelectrode array configurations, we found that discrimination accuracy decreased in an exponential decay-like relationship (R2 = 0.88) as model-derived activation volume overlap increased. Independent of depth vs. lateral separation between ICMS pattern pairs, minimal overlap (IoU < 1%) was associated with high discrimination accuracy (>70%; average of 85%), whereas IoU values exceeding 20% corresponded to near-chance performance.

Discussion: These results suggest that ICMS-evoked activation volume overlap between stimulation sites may provide mechanistic insight into the spatial limits of perceptual discrimination in ICMS applications. More broadly, these findings may help guide future investigations aimed at determining appropriate electrode spacing and stimulation strategies for sensory neuroprosthetic design.

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

Repository

Its files are read in the Code ↔ Paper reader above.

msj220001/Ssctx-column-model-4-electrode-ICMS

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2570706badd0c5f75b6d92fccb96ffbfe6114ae0, 6 May 2026
Languages: NEURON (612), Shell (76), Python (53), MATLAB (4)
Size: 999 files, 745 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (737 files), Matplotlib (50 files), NumPy (50 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
746 files
At the source: github.com/msj220001/Ssctx-column-model-4-electrode-ICMS

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;
  • 745 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/msj220001/Ssctx-column-model-4-electrode-ICMS.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 5 keywords, 1 funder, 26 references.

Cite

This paper

Jiang, M., Pancrazio, J. J., & Smith, T. J. (2026). Linking spatially distributed neuronal activation overlap to the limits of perceptual discrimination in rodent primary somatosensory cortex. Frontiers in computational neuroscience, 20, 1876230. https://doi.org/10.3389/fncom.2026.1876230

BibTeX

@article{jiang2026linking,
author = {Jiang, Madison and Pancrazio, Joseph J. and Smith, Thomas J.},
title = {{Linking spatially distributed neuronal activation overlap to the limits of perceptual discrimination in rodent primary somatosensory cortex}},
journal = {Frontiers in computational neuroscience},
year = {2026},
month = aug,
volume = {20},
pages = {1876230},
publisher = {Frontiers Media SA},
issn = {1662-5188},
doi = {10.3389/fncom.2026.1876230},
url = {https://doi.org/10.3389/fncom.2026.1876230},
pmid = {42666294},
pmcid = {PMC13522154}
}

RIS

TY - JOUR
AU - Jiang, Madison
AU - Pancrazio, Joseph J.
AU - Smith, Thomas J.
TI - Linking spatially distributed neuronal activation overlap to the limits of perceptual discrimination in rodent primary somatosensory cortex
T2 - Frontiers in computational neuroscience
J2 - Front Comput Neurosci
PY - 2026
DA - 2026/08/14
VL - 20
SP - 1876230
SN - 1662-5188
PB - Frontiers Media SA
DO - 10.3389/fncom.2026.1876230
UR - https://doi.org/10.3389/fncom.2026.1876230
LA - en
ER -

CSL-JSON

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"id": "10.3389/fncom.2026.1876230",
"type": "article-journal",
"title": "Linking spatially distributed neuronal activation overlap to the limits of perceptual discrimination in rodent primary somatosensory cortex",
"container-title": "Frontiers in computational neuroscience",
"author": [
{
"family": "Jiang",
"given": "Madison"
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{
"family": "Pancrazio",
"given": "Joseph J."
},
{
"family": "Smith",
"given": "Thomas J."
}
],
"container-title-short": "Front Comput Neurosci",
"volume": "20",
"page": "1876230",
"DOI": "10.3389/fncom.2026.1876230",
"PMID": "42666294",
"PMCID": "PMC13522154",
"ISSN": "1662-5188",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fncom.2026.1876230",
"language": "en",
"issued": {
"date-parts": [
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]
}
}

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