OSCR

Protocol for characterization of spatiotemporal network dynamics in cortical and hippocampal assembloids.

Code ↔ Paper

7 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 7 matches
  1. [1] § Troubleshooting › Potential solution ↔ analyze_calcium_imaging.m, lines 9 to 19 · score 0.76 · temporal smoothing window, max_lag, analyze calcium imaging, frame_period, correlated · Report a mismatch
  2. [2] § Step-by-step method details › Network analysis ↔ analyze_calcium_imaging.m, lines 197 to 230 · score 0.72 · spectral coherence, analyze calcium imaging, global synchrony, firing rates, burstiness, connectivity · Report a mismatch
  3. [3] § Step-by-step method details › Network analysis ↔ analyze_calcium_imaging.m, lines 117 to 162 · score 0.69 · functional connectivity matrix, cross correlation, max_lag, ROIs · Report a mismatch
  4. [4] § Step-by-step method details › Network analysis ↔ example_visualization.m, lines 202 to 231 · score 0.69 · spectral coherence, global synchrony, firing rates, metrics, Network, burstiness · Report a mismatch
  5. [5] § Step-by-step method details › Network analysis ↔ analyze_calcium_imaging.m, lines 117 to 162 · score 0.65 · R_pvalue, spike train shuffling, n_shuffles, connection · Report a mismatch
  6. [6] § Step-by-step method details › Network analysis ↔ analyze_calcium_imaging.m, lines 9 to 19 · score 0.53 · max_lag, frame_period, temporal · Report a mismatch
  7. [7] § Step-by-step method details › Network analysis ↔ analyze_calcium_imaging.m, lines 71 to 105 · score 0.53 · inter spike interval, burstiness, ROI · Report a mismatch

Paper

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The authors' code

MATLAB · 238 lines · 9.2 KB · CC-BY-4.0 · 6 matches

  1. %% Calcium Imaging Analysis Script
  2. % This script analyzes processed calcium imaging data from CaImAn/ezcalcium
  3. % It calculates burstiness, firing rates, spike probabilities, functional connectivity, and synchronization metrics
  5. %% Clear workspace
  6. clear;
  7. clc;
  9. %% User-defined parameters (modify these as needed)
  10. Nsamples = 1000; % Number of samples for spike train generation
  11. frame_period = 0.3875; % Frame period in seconds per frame
  12. max_lag = 13; % Maximum lag for cross-correlation in frames (~5 seconds at default frame rate)
  14. % Temporal smoothing parameters
  15. smooth_window = 1; % Moving average window size for P_spike (1 = no smoothing, >1 = apply smoothing)
  17. % Statistical significance parameters
  18. n_shuffles = 1000; % Number of shuffles for significance testing
  19. significance_alpha = 0.10; % Significance level (e.g., 0.10 for 90% confidence)
  21. %% Select files to process
  22. fprintf('\nSelect processed files to analyze...\n');
  23. [filenames, folderpath] = uigetfile('*.mat', ...
  24. 'Select .mat files containing refined ROIs to analyze', ...
  25. 'MultiSelect', 'on');
  26. if isequal(filenames, 0)
  27. error('No files selected. Exiting.');
  28. end
  30. % Handle single vs multiple file selection
  31. if ischar(filenames)
  32. filenames = {filenames}; % Convert to cell array for consistency
  33. end
  35. filelist = fullfile(folderpath, filenames);
  36. fprintf('Selected %d files for processing.\n\n', numel(filelist));
  38. %% Process each file
  39. for fi = 1:numel(filelist)
  40. fil = filelist{fi};
  41. fprintf('Processing file %d/%d: %s\n', fi, numel(filelist), fil);
  43. %% Load data
  44. % Load main variables
  45. lf = load(fil, 'ROI_center_refined', 'F_raw_refined', ...
  46. 'F_inferred_refined', 'Z_mod_refined');
  48. params = struct();
  49. try
  50. lf_params = load(fil, 'options');
  51. params = lf_params.options;
  52. fprintf(' Loaded existing parameters from file.\n');
  53. catch
  54. fprintf(' No parameters found in file. Using defaults.\n');
  55. end
  57. params.Nsamples = Nsamples;
  59. [n_rois, n_frames] = size(lf.Z_mod_refined);
  60. total_time = n_frames * frame_period;
  62. fprintf(' Number of ROIs: %d\n', n_rois);
  63. fprintf(' Number of frames: %d\n', n_frames);
  64. fprintf(' Total time: %.2f seconds\n', total_time);
  66. %% Initialize output variables
  67. burstiness = zeros(n_rois, 1);
  68. firing_rate_hz = zeros(n_rois, 1);
  69. P_spike = zeros(n_rois, n_frames);
  71. %% Process each ROI
  72. fprintf(' Analyzing ROI spike statistics...\n');
  73. for i = 1:n_rois
  74. sinfo = cont_ca_sampler(lf.Z_mod_refined(i, :), params);
  75. ntrials = size(sinfo.ss, 1);
  77. all_isi = [];
  78. total_spikes = 0;
  79. spike_counts = zeros(1, n_frames);
  81. for trial = 1:ntrials
  82. spike_times = sinfo.ss{trial};
  83. spike_counts = spike_counts + histcounts(spike_times, 0:n_frames);
  85. if numel(spike_times) > 1
  86. isi = diff(spike_times) * frame_period;
  87. all_isi = [all_isi; isi(:)];
  88. end
  90. total_spikes = total_spikes + numel(spike_times);
  91. end
  93. % Burstiness: CV of inter-spike intervals
  94. if ~isempty(all_isi) && std(all_isi) > 0
  95. burstiness(i) = std(all_isi) / mean(all_isi);
  96. else
  97. burstiness(i) = 0;
  98. end
  100. firing_rate_hz(i) = total_spikes / (ntrials * total_time);
  101. P_spike(i, :) = spike_counts / ntrials;
  102. end
  104. fprintf(' Burstiness range: [%.3f, %.3f]\n', min(burstiness), max(burstiness));
  105. fprintf(' Firing rate range: [%.3f, %.3f] Hz\n', min(firing_rate_hz), max(firing_rate_hz));
  107. %% Apply temporal smoothing to P_spike
  108. if smooth_window > 1
  109. fprintf(' Applying temporal smoothing (window size = %d frames)...\n', smooth_window);
  110. P_spike_smoothed = zeros(size(P_spike));
  111. for i = 1:n_rois
  112. P_spike_smoothed(i, :) = movmean(P_spike(i, :), smooth_window);
  113. end
  114. P_spike = P_spike_smoothed;
  115. end
  117. %% Calculate functional connectivity using cross-correlation
  118. fprintf(' Computing functional connectivity matrix...\n');
  120. R = zeros(n_rois, n_rois);
  121. R_pvalue = ones(n_rois, n_rois);
  122. R_significant = false(n_rois, n_rois);
  124. fprintf(' Computing significance thresholds via spike train shuffling (%d shuffles)...\n', n_shuffles);
  126. for i = 1:n_rois
  127. for j = i:n_rois
  128. if i == j
  129. R(i, j) = 1.0;
  130. R_pvalue(i, j) = 0;
  131. R_significant(i, j) = true;
  132. else
  133. [xcorr_vals, ~] = xcorr(P_spike(i, :), P_spike(j, :), max_lag, 'coeff');
  135. % Store signed max correlation (preserves excitatory/inhibitory)
  136. [~, max_idx] = max(abs(xcorr_vals));
  137. actual_corr = xcorr_vals(max_idx);
  138. R(i, j) = actual_corr;
  139. R(j, i) = actual_corr;
  141. % Null distribution via circular shift shuffling
  142. null_corr = zeros(n_shuffles, 1);
  143. for shuffle = 1:n_shuffles
  144. random_shift = randi([1, n_frames - 1]);
  145. P_shuffled = circshift(P_spike(j, :), random_shift);
  147. xcorr_jitter = xcorr(P_spike(i, :), P_shuffled, max_lag, 'coeff');
  148. [~, max_idx_jitter] = max(abs(xcorr_jitter));
  149. null_corr(shuffle) = xcorr_jitter(max_idx_jitter);
  150. end
  152. % Two-tailed test
  153. p_val = sum(abs(null_corr) >= abs(actual_corr)) / n_shuffles;
  154. R_pvalue(i, j) = p_val;
  155. R_pvalue(j, i) = p_val;
  157. is_significant = p_val < significance_alpha;
  158. R_significant(i, j) = is_significant;
  159. R_significant(j, i) = is_significant;
  160. end
  161. end
  162. end
  164. %% Calculate synchronization metrics
  165. upper_triangle_indices = triu(true(n_rois), 1);
  166. unique_correlations = R(upper_triangle_indices);
  167. n_significant = sum(R_significant(upper_triangle_indices));
  168. percent_significant = 100 * n_significant / numel(unique_correlations);
  170. % Global population synchrony (z-score normalized, all neurons)
  171. fprintf(' Computing global population synchrony...\n');
  172. P_spike_norm = zscore(P_spike, 0, 2);
  173. global_synchrony_timeseries = 1 ./ (std(P_spike_norm, 0, 1) + eps);
  174. global_synchrony_index = mean(global_synchrony_timeseries);
  176. % Spectral coherence (frequency-domain, all pairs)
  177. fprintf(' Computing spectral coherence...\n');
  178. coherence_matrix = zeros(n_rois, n_rois);
  179. for i = 1:n_rois
  180. coherence_matrix(i, i) = 1.0;
  181. for j = i+1:n_rois
  182. [Cxy, ~] = mscohere(P_spike(i, :), P_spike(j, :), [], [], [], 1/frame_period);
  183. avg_coherence = mean(Cxy);
  184. coherence_matrix(i, j) = avg_coherence;
  185. coherence_matrix(j, i) = avg_coherence;
  186. end
  187. end
  188. spectral_coherence_index = mean(coherence_matrix(upper_triangle_indices));
  190. fprintf(' Global synchrony index: %.4f\n', global_synchrony_index);
  191. fprintf(' Spectral coherence index: %.4f\n', spectral_coherence_index);
  192. fprintf(' Significant connections: %d/%d (%.1f%%)\n', ...
  193. n_significant, numel(unique_correlations), percent_significant);
  194. fprintf(' Functional connectivity range: [%.3f, %.3f]\n', ...
  195. min(unique_correlations), max(unique_correlations));
  197. %% Save results
  198. fprintf(' Saving results...\n');
  200. original_data = load(fil);
  202. original_data.burstiness = burstiness;
  203. original_data.firing_rate_hz = firing_rate_hz;
  204. original_data.P_spike = P_spike;
  205. original_data.R = R;
  206. original_data.R_pvalue = R_pvalue;
  207. original_data.R_significant = R_significant;
  208. original_data.coherence_matrix = coherence_matrix;
  209. original_data.global_synchrony_timeseries = global_synchrony_timeseries;
  210. original_data.global_synchrony_index = global_synchrony_index;
  211. original_data.spectral_coherence_index = spectral_coherence_index;
  212. original_data.params = params;
  213. original_data.analysis_metadata = struct(...
  214. 'frame_period', frame_period, ...
  215. 'max_lag', max_lag, ...
  216. 'Nsamples', Nsamples, ...
  217. 'smooth_window', smooth_window, ...
  218. 'n_shuffles', n_shuffles, ...
  219. 'significance_alpha', significance_alpha, ...
  220. 'n_rois', n_rois, ...
  221. 'n_frames', n_frames, ...
  222. 'total_time', total_time, ...
  223. 'n_significant_connections', n_significant, ...
  224. 'percent_significant_connections', percent_significant, ...
  225. 'analysis_date', datetime('now'));
  227. save(fil, '-struct', 'original_data');
  229. fprintf(' File updated successfully.\n\n');
  230. end
  232. %% Summary
  233. fprintf('========================================\n');
  234. fprintf('Analysis complete!\n');
  235. fprintf('Processed %d files.\n', numel(filelist));
  236. fprintf('========================================\n');

analyze_calcium_imaging.m, under CC-BY-4.0 · at the source

Overview

Authors: Asia R Guzman1, Colin M McCrimmon1, Daniel Toker1, Bennett G Novitch2,3,4, Momoko Watanabe5,6, Ranmal A Samarasinghe1,3,4
ORCID iDs: Asia R Guzman, Ranmal A Samarasinghe
  1. Department of Neurology, University of California, Los Angeles, David Geffen School of Medicine, Los Angeles, CA 90095, USA
  2. Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
  3. Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
  4. Intellectual Development and Disabilities Research Center, University of California, Los Angeles, Los Angeles, CA 90095, USA
  5. Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA
  6. Sue & Bill Gross Stem Cell Research Center, School of Medicine, University of California, Irvine, Irvine, CA, USA
Institutions: University of California, Los Angeles (United States); University of California, Irvine (United States)
Journal: STAR protocols, volume 7, issue 2, article 104463
Dates: published online 27 March 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.xpro.2026.104463 · PMID 41904955 · PMCID PMC13054417 · OpenAlex W7141397090
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), cellular / molecular (subfield)
Methods: Connectivity, Machine learning, Evoked potentials, Graphs, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuroscience, Stem Cells, Organoids
MeSH: Cell Culture Techniques*, Cerebral Cortex*, Hippocampus*, Nerve Net*, Organoids*, Cell Differentiation, Humans, Induced Pluripotent Stem Cells, Interneurons (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (R01MH130061, 5K08NS119747); Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles (49660); Intellectual and Developmental Disabilities Research Center (P50HD103557); Citizens United for Research in Epilepsy (20204012, 20244243); Simons Foundation (717153)
Citations: not cited yet (Europe PMC); 14 references in the paper
Research resources: MATLAB R2024b RRID:SCR_001622, Fiji v2.14.0 RRID:SCR_002285, ImageJ v1.54f (included with Fiji) RRID:SCR_003070, Leica Application Suite X (LAS X) v3.7 RRID:SCR_013673, Calcium Imaging Data Analysis (CaImAn) RRID:SCR_021533

Abstract

This protocol generates neural assembloids from human induced pluripotent stem cells (hiPSCs) to model human brain circuitry. It details the differentiation of excitatory-predominant hippocampal (Hc) and cortical (Cx) organoids and their fusion with inhibitory interneuron-predominant ganglionic eminence (GE) organoids. We then detail procedures for maintaining assembloids to promote interneuron migration and network integration, followed by functional assessment using two-photon calcium imaging to measure neuronal activity.

For complete details on the use and execution of this protocol, please refer to McCrimmon et al.1

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 7 matches between paragraphs and lines of code.

Zenodo 18763819

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
2 files
At the source: zenodo.org/records/18763819

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 2 scripts, each with its path and the digest of its content;
  • 7 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

The accession number for the original MATLAB code (analyze_calcium_imaging.m, network analysis steps 49-50) and example data reported in this paper is Zenodo: https://zenodo.org/records/18763819. All other software used is publicly available and cited in the key resources table. The original published data generated using the methods described here are available from the lead contact upon reasonable request.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 3 keywords, 9 MeSH terms, 5 funders, 13 references, 5 RRIDs.

Cite

This paper

Guzman, A. R., McCrimmon, C. M., Toker, D., Novitch, B. G., Watanabe, M., & Samarasinghe, R. A. (2026). Protocol for characterization of spatiotemporal network dynamics in cortical and hippocampal assembloids. STAR protocols, 7(2), 104463. https://doi.org/10.1016/j.xpro.2026.104463

BibTeX

@article{guzman2026protocol,
author = {Guzman, Asia R and McCrimmon, Colin M and Toker, Daniel and Novitch, Bennett G and Watanabe, Momoko and Samarasinghe, Ranmal A},
title = {{Protocol for characterization of spatiotemporal network dynamics in cortical and hippocampal assembloids}},
journal = {STAR protocols},
year = {2026},
month = mar,
volume = {7},
number = {2},
pages = {104463},
publisher = {Elsevier},
issn = {2666-1667},
doi = {10.1016/j.xpro.2026.104463},
url = {https://doi.org/10.1016/j.xpro.2026.104463},
pmid = {41904955},
pmcid = {PMC13054417}
}

RIS

TY - JOUR
AU - Guzman, Asia R
AU - McCrimmon, Colin M
AU - Toker, Daniel
AU - Novitch, Bennett G
AU - Watanabe, Momoko
AU - Samarasinghe, Ranmal A
TI - Protocol for characterization of spatiotemporal network dynamics in cortical and hippocampal assembloids
T2 - STAR protocols
J2 - STAR Protoc
PY - 2026
DA - 2026/03/27
VL - 7
IS - 2
SP - 104463
SN - 2666-1667
PB - Elsevier
DO - 10.1016/j.xpro.2026.104463
UR - https://doi.org/10.1016/j.xpro.2026.104463
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.xpro.2026.104463",
"type": "article-journal",
"title": "Protocol for characterization of spatiotemporal network dynamics in cortical and hippocampal assembloids",
"container-title": "STAR protocols",
"author": [
{
"family": "Guzman",
"given": "Asia R"
},
{
"family": "McCrimmon",
"given": "Colin M"
},
{
"family": "Toker",
"given": "Daniel"
},
{
"family": "Novitch",
"given": "Bennett G"
},
{
"family": "Watanabe",
"given": "Momoko"
},
{
"family": "Samarasinghe",
"given": "Ranmal A"
}
],
"container-title-short": "STAR Protoc",
"volume": "7",
"issue": "2",
"page": "104463",
"DOI": "10.1016/j.xpro.2026.104463",
"PMID": "41904955",
"PMCID": "PMC13054417",
"ISSN": "2666-1667",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.xpro.2026.104463",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
27
]
]
}
}

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