OSCR

Cannabinoid modulation of central amygdala population dynamics during threat investigation.

Code ↔ Paper

11 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 11 matches · 3 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Calcium signal acquisition and analysis ↔ Population metrics.m, the whole file · a weak match · score 0.80 · cumulative variance, Mahalanobis distance, concatenated, trajectory, iterative, space · Report a mismatch
  2. [2] § Methods › Calcium signal acquisition and analysis ↔ Spontaneous activity event analysis.m, the whole file · a weak match · score 0.79 · event amplitude, Calcium traces, event frequency, OASIS, deconvolved, curve · Report a mismatch
  3. [3] § Results › CP55940 enhances location-behavior co-representation in CeA SOM neurons ↔ activation_probability_selectivity.m, lines 217 to 234 · score 0.71 · immobility selective cells, mobile selective, activation probability, near odor, quantify · Report a mismatch
  4. [4] § Methods › Calcium signal acquisition and analysis ↔ SVM Decoder Analysis.m, lines 1 to 23 · score 0.69 · support vector machine, SVM, decoder, permuted, decoded, trained · Report a mismatch
  5. [5] § Methods › Calcium signal acquisition and analysis ↔ data analysis-decoding analysis.m, lines 1 to 24 · score 0.68 · SVM classifier, support vector, kernel, decoded, linear, trained · Report a mismatch
  6. [6] § Results › CP55940 enhances location-behavior co-representation in CeA SOM neurons ↔ activation_probability_selectivity.m, lines 178 to 208 · score 0.66 · mobility selective, zone selectivity, activation probabilities, near odor, deviation, median · Report a mismatch
  7. [7] § Results › CP55940 modulation of neuronal population vector trajectories associated with threat-related behavior ↔ Population metrics.m, the whole file · a weak match · score 0.64 · trajectory lengths, Mahalanobis distance, space, shorter, dimensionality, population · Report a mismatch
  8. [8] § Results › CP55940 enhances location-behavior co-representation in CeA SOM neurons ↔ activation_probability_selectivity.m, lines 178 to 208 · score 0.59 · activation probabilities, far odor, near odor, deviation, mobility, zone · Report a mismatch
  9. [9] § Results › CP55940 modulation of SOM neural dynamics associated with threat investigation ↔ Behaviour_aligned_timestamps_and_velocity.m, lines 96 to 137 · score 0.58 · flee events, freezing bouts, freeze onset, odor, behavioral · Report a mismatch
  10. [10] § Methods › Calcium signal acquisition and analysis ↔ Clustering code.m, lines 45 to 90 · score 0.52 · reduce dimensionality, component, variance, PCA, Flee, traces · Report a mismatch
  11. [11] § Methods › Calcium signal acquisition and analysis ↔ Clustering code.m, lines 92 to 136 · score 0.52 · hierarchical clustering, optimal, gap, correlation, PCA, distance · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

MATLAB · 243 lines · 8.8 KB · no license · 3 matches

  1. %% NEURAL ACTIVITY SELECTIVITY ANALYSIS
  2. % Analyzes calcium imaging data (dF/F) to compute cell activation probabilities
  3. % and behavioral selectivity indices across multiple behavioral states.
  4. %
  5. % REQUIREMENTS:
  6. % - dF/F data in Excel file (rows=timepoints, cols=cells)
  7. % - Cell status column (row 2): 'accepted' or 'rejected'
  8. % - position_binned: logical matrix (timepoints x behaviors) from Anymaze
  9. % - Anymaze.Txt: structure array with behavior labels
  10. %
  11. % OUTPUT:
  12. % - n_o_index: zone selectivity (near odor - far from odor)
  13. % - mobility_index: mobility selectivity (mobile - immobile)
  14. % - cell_probabilities: activation rates by behavioral state
  15. % - behavior_sorted: cell categorization (-1, 0, 1) based on selectivity
  16. %
  19. %% USER SETTINGS
  20. shuffle_behavior = false; % Set to true to randomize behavior markers (for null distribution)
  21. z_threshold = 1.96; % Z-score threshold for activation (95% confidence)
  22. remove_initial_frames = 10;% Number of initial frames to exclude before analysis
  24. %% STEP 1: LOAD DATA
  25. fprintf('Loading dF/F data...\n');
  27. current_dir = uigetdir('', 'Select directory containing dF/F file');
  28. if isequal(current_dir, 0)
  29. error('User cancelled directory selection.');
  30. end
  31. cd(current_dir);
  33. [file_name, file_path] = uigetfile('*.xlsx', 'Select Excel file with dF/F data');
  34. if isequal(file_name, 0)
  35. error('User cancelled file selection.');
  36. end
  38. file_full_path = fullfile(file_path, file_name);
  39. [numeric_data, text_data] = xlsread(file_full_path);
  41. % Parse spreadsheet
  42. cell_ids = text_data(1, 2:end);
  43. cell_status_raw = text_data(2, 2:end);
  44. df_over_f_raw = numeric_data(1:end, 2:end);
  45. times = numeric_data(1:end, 1);
  47. fprintf('Loaded data: %d timepoints, %d cells\n', size(df_over_f_raw, 1), size(df_over_f_raw, 2));
  49. %% STEP 2: FILTER CELLS BY ACCEPTANCE STATUS
  50. % Use strcmp for robust string comparison
  51. accepted_logical = false(1, numel(cell_status_raw));
  52. for m = 1:numel(cell_status_raw)
  53. if strcmp(strtrim(cell_status_raw{m}), 'accepted')
  54. accepted_logical(m) = true;
  55. end
  56. end
  58. num_accepted = sum(accepted_logical);
  59. fprintf('Accepted cells: %d / %d\n', num_accepted, numel(cell_status_raw));
  61. df_over_f = df_over_f_raw(:, accepted_logical);
  62. cell_ids_accepted = cell_ids(accepted_logical);
  64. %% STEP 3: PREPROCESSING - REMOVE NaNs AND NORMALIZE TIME
  65. % Convert time units (multiply by 10 after rounding)
  66. times = round(times, 1) * 10;
  68. % Remove timepoints with NaN in first column (assumes first column indicates valid timepoints)
  69. valid_idx = ~isnan(df_over_f(:, 1));
  70. df_over_f = df_over_f(valid_idx, :);
  71. times = times(valid_idx);
  73. fprintf('Valid timepoints after NaN removal: %d\n', size(df_over_f, 1));
  75. %% STEP 4: NORMALIZATION
  76. % Peak normalization
  77. df_max = max(df_over_f, [], 1); % Max per cell
  78. df_norm = df_over_f ./ df_max; % Normalize by peak
  80. % Z-score normalization (whole session)
  81. df_mean = mean(df_over_f, 1);
  82. df_std = std(df_over_f, 0, 1); % Sample std (divide by N-1)
  83. z_score = (df_over_f - df_mean) ./ df_std;
  85. % Remove initial frames (after Z-scoring to avoid biasing statistics)
  86. z_score(1:remove_initial_frames, :) = [];
  87. times(1:remove_initial_frames) = [];
  89. fprintf('After removing initial %d frames: %d timepoints remain\n', remove_initial_frames, size(z_score, 1));
  91. %% STEP 5: LOAD AND PROCESS BEHAVIORAL DATA
  92. % NOTE: position_binned and Anymaze must be loaded in workspace before running this script
  93. if ~exist('position_binned', 'var') || ~exist('Anymaze', 'var')
  94. error('position_binned and/or Anymaze not found in workspace. Please load behavioral data first.');
  95. end
  97. fprintf('Loading behavioral data from Anymaze...\n');
  99. % Extract behavior states
  100. mobile_idx = find(string(Anymaze.Txt) == 'Mobile');
  101. near_odor_idx = find(string(Anymaze.Txt) == 'InSubstrateZonePeriphery');
  102. far_odor_idx = find(string(Anymaze.Txt) == 'InEmptyZonePeriphery');
  104. mobile_binned = logical(position_binned(:, mobile_idx));
  105. near_odor_binned = logical(position_binned(:, near_odor_idx));
  106. far_binned = logical(position_binned(:, far_odor_idx));
  108. % Optional: shuffle behavior markers for null hypothesis testing
  109. if shuffle_behavior
  110. fprintf('Shuffling behavior markers...\n');
  111. mobile_binned = mobile_binned(randperm(length(mobile_binned)));
  112. near_odor_binned = near_odor_binned(randperm(length(near_odor_binned)));
  113. far_binned = far_binned(randperm(length(far_binned)));
  114. end
  116. %% STEP 6: ALIGN NEURAL AND BEHAVIORAL DATA
  117. % Ensure neural and behavioral data have same length
  118. min_length = min(size(z_score, 1), size(position_binned, 1));
  119. z_score = z_score(1:min_length, :);
  120. times = times(1:min_length);
  122. mobile_binned = mobile_binned(1:min_length);
  123. near_odor_binned = near_odor_binned(1:min_length);
  124. far_binned = far_binned(1:min_length);
  126. fprintf('Aligned neural and behavioral data to %d timepoints\n', min_length);
  128. %% STEP 7: SEGMENT NEURAL DATA BY BEHAVIOR
  129. mobile_z = z_score(mobile_binned, :);
  130. immobile_z = z_score(~mobile_binned, :);
  131. near_odor_z = z_score(near_odor_binned, :);
  132. far_odor_z = z_score(far_binned, :);
  134. %% STEP 8: COMPUTE ACTIVATION PROBABILITIES
  135. % Activation = Z-score >= threshold
  136. z_binary = z_score >= z_threshold;
  137. z_p = sum(z_binary, 1) ./ size(z_score, 1);
  139. % By behavioral state
  140. ne_binary = far_odor_z >= z_threshold;
  141. ne_p = sum(ne_binary, 1) ./ size(far_odor_z, 1);
  143. no_binary = near_odor_z >= z_threshold;
  144. no_p = sum(no_binary, 1) ./ size(near_odor_z, 1);
  146. mo_binary = mobile_z >= z_threshold;
  147. mo_p = sum(mo_binary, 1) ./ size(mobile_z, 1);
  149. im_binary = immobile_z >= z_threshold;
  150. im_p = sum(im_binary, 1) ./ size(immobile_z, 1);
  152. fprintf('Activation probabilities computed (threshold Z >= %.2f)\n', z_threshold);
  154. %% STEP 9: COMPUTE SELECTIVITY INDICES (Single animal)
  155. n_o_index = no_p - ne_p; % Near odor vs far odor
  156. mobility_index = mo_p - im_p; % Mobile vs immobile
  158. % Compile cell probabilities for export
  159. cell_probabilities = [z_p; ne_p; no_p; mo_p; im_p]';
  160. behavior_labels = {'Overall', 'FarOdor', 'NearOdor', 'Mobile', 'Immobile'};
  162. %% STEP 10: POPULATION ANALYSIS (pooled across animals)
  163. fprintf('\n--- POPULATION ANALYSIS (pooled data) ---\n');
  164. fprintf('Enter pooled selectivity indices from all animals.\n');
  166. behavior_code_total = zeros(2, 1); % Preallocate
  167. try
  168. behavior_code_total(1, :) = input('Input zone selectivity indices [1 x N array]: ');
  169. behavior_code_total(2, :) = input('Input mobility selectivity indices [1 x N array]: ');
  170. catch
  171. error('Failed to parse input. Please provide numeric arrays.');
  172. end
  174. behavior_code_total = behavior_code_total'; % Transpose to [N x 2]
  175. num_pooled_cells = size(behavior_code_total, 1);
  176. fprintf('Loaded %d cells from pooled dataset\n', num_pooled_cells);
  178. %% STEP 11: THRESHOLD SELECTIVITY BY MEDIAN ABSOLUTE DEVIATION
  179. % Compute MAD for each selectivity measure
  180. mad_threshold = mad(behavior_code_total, 1); % MAD across cells for each measure
  182. fprintf('Zone selectivity MAD threshold: %.4f\n', mad_threshold(1));
  183. fprintf('Mobility selectivity MAD threshold: %.4f\n', mad_threshold(2));
  185. % Categorize cells: -1 (low), 0 (intermediate), +1 (high)
  186. behavior_sorted = zeros(2, num_pooled_cells);
  188. % Zone selectivity
  189. for i = 1:num_pooled_cells
  190. if behavior_code_total(i, 1) <= -mad_threshold(1)
  191. behavior_sorted(1, i) = -1; % Far odor preferring
  192. elseif behavior_code_total(i, 1) >= mad_threshold(1)
  193. behavior_sorted(1, i) = 1; % Near odor preferring
  194. else
  195. behavior_sorted(1, i) = 0; % Non-selective
  196. end
  197. end
  199. % Mobility selectivity
  200. for i = 1:num_pooled_cells
  201. if behavior_code_total(i, 2) <= -mad_threshold(2)
  202. behavior_sorted(2, i) = -1; % Immobile preferring
  203. elseif behavior_code_total(i, 2) >= mad_threshold(2)
  204. behavior_sorted(2, i) = 1; % Mobile preferring
  205. else
  206. behavior_sorted(2, i) = 0; % Non-selective
  207. end
  208. end
  210. %% STEP 12: DEFINE RESPONSIVE CELL POPULATIONS
  211. % Extract binary responses based on selectivity categories
  212. near_resp = behavior_sorted(1, :) == 1; % Near odor selective
  213. far_resp = behavior_sorted(1, :) == -1; % Far odor selective
  214. mobile_resp = behavior_sorted(2, :) == 1; % Mobile selective
  215. immobile_resp = behavior_sorted(2, :) == -1; % Immobile selective
  217. %% STEP 13: COMPUTE SET OVERLAPS
  218. % Quantify cells with multiple selectivities
  219. A = far_resp; % A = near_resp; for near-odor selectivity
  220. B = immobile_resp; % Immobile selective cells
  221. C = mobile_resp; % Mobile selective cells
  223. % Cardinality
  224. n_A = sum(A);
  225. n_B = sum(B);
  226. n_C = sum(C);
  228. % Pairwise intersections
  229. n_AB = sum(A & B);
  230. n_BC = sum(B & C);
  231. n_AC = sum(A & C);
  233. % Triple intersection
  234. n_ABC = sum(A & B & C);
  236. %% OUTPUT SUMMARY
  237. fprintf('\n=== ANALYSIS COMPLETE ===\n');
  238. fprintf('Key outputs:\n');
  239. fprintf(' n_o_index: zone selectivity (near odor - far odor)\n');
  240. fprintf(' mobility_index: mobility selectivity (mobile - immobile)\n');
  241. fprintf(' cell_probabilities: [N x 5] activation rates\n');
  242. fprintf(' behavior_sorted: [2 x N] cell categorization\n');
  243. fprintf(' behavior_code_total: [N x 2] pooled selectivity indices\n');

activation_probability_selectivity.m at commit 80d175e, no license · at the source

Overview

Authors: Farhana Yasmin1, Saptarnab Naskar1, Danyal Zaidi1, Isaac Kandil1, Michelle Kwon1, Luis E. Rosas-Vidal1, Sachin Patel1
ORCID iDs: Saptarnab Naskar, Luis E. Rosas-Vidal, Sachin Patel
  1. Stephen M. Stahl Center for Psychiatric Neuroscience, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine,Chicago, IL USA
Institutions: Northwestern University (United States)
Journal: Nature communications, volume 17, issue 1, article 10127
Dates: received 12 February 2025; accepted 9 September 2026; published online 2 October 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-77957-4 · PMID 42827137 · PMCID PMC13633270 · OpenAlex W7216594906
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Graphs, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Emotion, Synaptic transmission, Neuroscience
MeSH: Cannabinoids*, Central Amygdaloid Nucleus*, Fear*, Neurons*, Animals, Avoidance Learning, gamma-Aminobutyric Acid, Male, Mice, Mice, Inbred C57BL, Receptor, Cannabinoid, CB1, Somatostatin (* major topic)
Topic: Cannabis and Cannabinoid Research (Pharmacology (medical), Medicine), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (MH119817)
Citations: not cited yet (Europe PMC); 86 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

figshare 21956801

License: CC-BY-4.0
State: the link answers, verified on 5 October 2026
Evidence: files inventoried
Languages: MATLAB (12)
Size: 12 files, 12 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files)
Availability: 1 check, the latest on 5 October 2026: the link answers (HTTP 200)
  • 5 October 2026: the link answers (HTTP 200)
12 files
At the source:

luisrosasvidal/Rosas-Vidal2025

License: GPL-3.0
State: the link answers, verified on 5 October 2026
Evidence: files inventoried
Commit: e61af6fe4943f1d4d2e8e49db9a1653f79a2d0c3, 13 March 2025
Languages: MATLAB (4)
Size: 6 files, 4 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 (1 file)
Availability: 1 check, the latest on 5 October 2026: the link answers
  • 5 October 2026: the link answers
6 files
At the source: github.com/luisrosasvidal/Rosas-Vidal2025

fyasmin09/yasmin_et_al_2026

License: none: the authors keep all their rights
State: the link answers, verified on 5 October 2026
Evidence: files inventoried
Commit: 80d175e4f19507609510c2191c208e39b8f5a11a, 16 June 2026
Languages: MATLAB (6)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 5 October 2026: the link answers
  • 5 October 2026: the link answers
7 files
At the source: github.com/fyasmin09/yasmin_et_al_2026.git

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-77957-4.

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  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • 11 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Read it in the paper: doi.org/10.1038/s41467-026-77957-4.

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Version 1, 6 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 12 MeSH terms, 1 funder, 83 references.

Cite

This paper

Yasmin, F., Naskar, S., Zaidi, D., Kandil, I., Kwon, M., Rosas-Vidal, L. E., & Patel, S. (2026). Cannabinoid modulation of central amygdala population dynamics during threat investigation. Nature communications, 17(1), 10127. https://doi.org/10.1038/s41467-026-77957-4

BibTeX

@article{yasmin2026cannabinoid,
author = {Yasmin, Farhana and Naskar, Saptarnab and Zaidi, Danyal and Kandil, Isaac and Kwon, Michelle and Rosas-Vidal, Luis E. and Patel, Sachin},
title = {{Cannabinoid modulation of central amygdala population dynamics during threat investigation}},
journal = {Nature communications},
year = {2026},
month = oct,
volume = {17},
number = {1},
pages = {10127},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-77957-4},
url = {https://doi.org/10.1038/s41467-026-77957-4},
pmid = {42827137},
pmcid = {PMC13633270}
}

RIS

TY - JOUR
AU - Yasmin, Farhana
AU - Naskar, Saptarnab
AU - Zaidi, Danyal
AU - Kandil, Isaac
AU - Kwon, Michelle
AU - Rosas-Vidal, Luis E.
AU - Patel, Sachin
TI - Cannabinoid modulation of central amygdala population dynamics during threat investigation
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/10/02
VL - 17
IS - 1
SP - 10127
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-77957-4
UR - https://doi.org/10.1038/s41467-026-77957-4
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-77957-4",
"type": "article-journal",
"title": "Cannabinoid modulation of central amygdala population dynamics during threat investigation",
"container-title": "Nature communications",
"author": [
{
"family": "Yasmin",
"given": "Farhana"
},
{
"family": "Naskar",
"given": "Saptarnab"
},
{
"family": "Zaidi",
"given": "Danyal"
},
{
"family": "Kandil",
"given": "Isaac"
},
{
"family": "Kwon",
"given": "Michelle"
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{
"family": "Rosas-Vidal",
"given": "Luis E."
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{
"family": "Patel",
"given": "Sachin"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "10127",
"DOI": "10.1038/s41467-026-77957-4",
"PMID": "42827137",
"PMCID": "PMC13633270",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-77957-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
2
]
]
}
}

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