OSCR

Large-scale single-neuron recording in the human cortex using an ultra-flexible electrode array.

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] § Results › Single-unit recording of neuronal populations across cortical depths ↔ fig_2_amp_depth_type.m, lines 257–370 · score 0.84 · Kruskal Wallis, penetration depths, TS units exhibited, slightly larger amplitudes, PS units, RS units
  2. [2] § Results › Single-unit recording of neuronal populations across cortical depths ↔ fig_2_statistics_amp_snr_fr_acg_tpd.m, lines 175–273 · score 0.72 · noise ratio, peak duration, trough ratio, ACG, firing rate, SNR
  3. [3] § Methods › Assessment of recording quality ↔ fig_2_statistics_amp_snr_fr_acg_tpd.m, lines 175–273 · score 0.68 · peak duration, trough ratio, ACG, firing rate, SNR, rise
  4. [4] § Results › Stable intraoperative recording of single-unit activity ↔ fig_3_spike_position_dirft_distribution.m, lines 49–116 · score 0.53 · spike position drift, drift distributions, median, needle
  5. [5] § Results › Stimulus and response tuning at single-cell level in human dlPFC ↔ fig4_tuning_listen.m, lines 476–546 · score 0.52 · 0.5–1 s, listened, 0.5 s, tuning
  6. [6] § Results › Stable intraoperative recording of single-unit activity ↔ Fig3_spike_drift_trace_20250220_HS_NoNeedle_NoCuration.m, lines 1–87 · score 0.52 · spike drift, spike position, needle, distance, depth, waveform
  7. [7] § Results › Single-unit recording of neuronal populations across cortical depths ↔ Supp_PSD_VS_depth.m, lines 1–29 · score 0.51 · Power spectral density, PSD, LFP, depths

Paper

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

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The authors' code

MATLAB · 273 lines · 9.8 KB · CC-BY-4.0 · 2 matches

  1. %% 请deepseek整合了一下代码 可以一起出6张统计图并print 25 50 75 100分位值
  3. % STEP1 load data
  4. clc;
  5. clear;
  6. close all;
  8. filepath ='D:\930_paper_plotting\Unit_info\Unit_info\Unit_info_SU_patients_extend';
  9. filenames = dir(fullfile(filepath, "*.mat"));
  10. fs = 25640;
  12. % 筛选以"Unit_info"开头的文件
  13. filtered_files = filenames(startsWith({filenames.name}, 'Unit_info'));
  15. % STEP2 savepath setting
  16. savepath = 'D:\930_paper_plotting\fig\rebuttal\fig_2_256_staitstics_box_1';
  17. if ~exist(savepath, 'dir')
  18. mkdir(savepath);
  19. end
  20. %%
  21. % 定义所有需要分析的指标及其参数
  22. metrics = {
  23. % struct('name', 'Amplitude', 'unit', 'μV', 'var', 'amplitude', ...
  24. % 'bin_size', 5, 'window_size', 250, 'hist_ylim', [0 100]);
  25. %
  26. % struct('name', 'SNR', 'unit', '', 'var', 'SNR', ...
  27. % 'bin_size', 0.2, 'window_size', 20, 'hist_ylim', [0 100]);
  28. %
  29. % struct('name', 'FR', 'unit', 'Hz', 'var', 'FR', ...
  30. % 'bin_size', 1, 'window_size', 50, 'hist_ylim', [0 200]);
  31. %
  32. % struct('name', 'ACG', 'unit', 'ms', 'var', 'acg_tau_rise', ...
  33. % 'bin_size', 1, 'window_size', 50, 'hist_ylim', [0 250]);
  34. %
  35. % struct('name', 'TTPduration', 'unit', 'ms', 'var', 'Duration_ms', ...
  36. % 'bin_size', 0.05, 'window_size', 2, 'hist_ylim', [0 80]);
  37. struct('name', 'PTratio', 'unit', '', 'var', 'Ratio_Fol_Peak', ...
  38. 'bin_size', 0.02, 'window_size',1.25, 'hist_ylim', [0 50])
  40. % 以下不用
  41. % struct('name', 'TTPduration', 'unit', 'ms', 'var', 'trough_to_peak_latency', ...
  42. % 'bin_size', 0.02, 'window_size', 1, 'hist_ylim', [0 100]);
  43. % struct('name', 'PTratio', 'unit', '', 'var', 'peak_trough_ratio', ...
  44. % 'bin_size', 0.2, 'window_size', 5, 'get_function', @(t) max(t.peak_trough_ratio_1st, t.peak_trough_ratio_2nd))
  48. };
  50. % 主循环 - 处理每个指标
  51. for m_idx = 1:length(metrics)
  52. metric = metrics{m_idx};
  54. % STEP3 绘制累计直方图 (保持原始代码结构)
  56. all_hiscount = [];
  57. all_values = [];
  59. figure;
  60. hold on;
  62. % 遍历每个患者文件
  63. for f = 1:length(filtered_files)
  64. % 载入患者数据
  65. load(fullfile(filepath, filtered_files(f).name));
  67. % 处理特殊指标
  68. if isfield(metric, 'get_function')
  69. % 峰谷比特殊处理
  70. X_unit_cell = metric.get_function(table_all_chns);
  71. elseif isfield(metric, 'conversion')
  72. % TTP单位转换处理
  73. X_unit_cell = metric.conversion(table_all_chns.(metric.var));
  74. else
  75. % 其他指标直接获取
  76. X_unit_cell = table_all_chns.(metric.var);
  77. end
  79. % 初始化患者直方图数据
  80. patient_histcounts = zeros(1, floor(metric.window_size / metric.bin_size));
  82. % 遍历每个簇
  83. for ii = 1:length(X_unit_cell)
  84. % 直接使用数值数组索引
  85. x_spk = double(X_unit_cell(ii, :));
  86. all_values = [all_values, x_spk]; % 累积所有患者
  88. % 计算直方图
  89. [N, edges] = histcounts(x_spk, 0:metric.bin_size:metric.window_size);
  90. patient_histcounts = patient_histcounts + N; % 累加直方图数据
  91. end
  93. % 累加所有患者的直方图数据
  94. if isempty(all_hiscount)
  95. all_hiscount = patient_histcounts;
  96. else
  97. all_hiscount = all_hiscount + patient_histcounts;
  98. end
  99. end
  101. % 绘制累计直方图
  102. x = metric.bin_size:metric.bin_size:metric.window_size;
  103. bar(x, all_hiscount, 'FaceColor', [0 0 0], 'FaceAlpha', 0.4, 'EdgeColor', 'none');
  105. % 计算中位数和四分位数
  106. value_media = median(all_values);
  107. value_25 = prctile(all_values, 25);
  108. value_75 = prctile(all_values, 75);
  109. value_max = max(all_values);
  111. fprintf('%-15s - Quartiles: 25%%=%.3f, 50%%(median)=%.3f, 75%%=%.3f, 100%%(max)=%.3f, N=%d\n',...
  112. metric.name, value_25, value_media, value_75, value_max, length(all_values));
  114. % 绘制中位数竖线
  115. xline(value_75, '--', 'Color', [99, 76, 153]/255, 'LineWidth', 1, 'DisplayName', '75%');
  116. xline(value_25, '--', 'Color', [99, 76, 153]/255, 'LineWidth', 1, 'DisplayName', '25%');
  117. xline(value_media, '--', 'Color', [188, 60, 50]/255, 'LineWidth', 1, 'DisplayName', 'Median');
  119. % 绘图设置
  120. ylabel('Counts', 'FontSize', 7);
  121. xlabel([metric.name ' (' metric.unit ')'], 'FontSize', 6);
  123. if isfield(metric, 'hist_ylim')
  124. ylim(metric.hist_ylim);
  125. else
  126. ylim([0, max(all_hiscount) * 1.1]);
  127. end
  129. xlim([0, metric.window_size]);
  131. % 坐标轴设置 (保持原始设置)
  132. box on;
  133. ax = gca;
  134. ax.LineWidth = 0.5;
  135. ax.XColor = 'k';
  136. ax.YColor = 'k';
  137. ax.XAxis.Visible = 'on';
  138. ax.YAxis.Visible = 'on';
  139. ax.Box = 'off';
  140. ax.FontSize = 6;
  141. ax.YTickMode = 'auto';
  142. ax.XTick = 0 : metric.window_size / 5 : metric.window_size;
  145. % 图形窗口尺寸设置 (保持原始设置)
  146. set(gcf, 'Units', 'centimeters', 'Position', [0 0 5 5]);
  147. set(gcf, 'PaperUnits', 'centimeters');
  148. set(gcf, 'PaperSize', [5 5]);
  149. set(gcf, 'PaperPositionMode', 'auto');
  152. % 保存直方图
  153. save_name_pdf = fullfile(savepath, ['All_Patients_' metric.name '_Histogram.pdf']);
  154. save_name_png = fullfile(savepath, ['All_Patients_' metric.name '_Histogram.png']);
  155. exportgraphics(gcf, save_name_pdf, 'ContentType', 'vector', 'Resolution', 600);
  156. saveas(gcf, save_name_png);
  157. close(gcf);
  160. end
  162. %% supplementray，一起plot
  164. clc; clear
  165. filepath ='D:\930_paper_plotting\Unit_info\Unit_info\Unit_info_SU_patients_extend';
  166. filenames = dir(fullfile(filepath, "*.mat"));
  167. fs = 25640;
  169. filtered_files = filenames(startsWith({filenames.name}, 'Unit_info'));
  171. savepath = 'D:\930_paper_plotting\fig\rebuttal\fig_supp_3_256_stablity_results';
  172. if ~exist(savepath, 'dir')
  173. mkdir(savepath);
  174. end
  175. %%
  177. % 需要分析的字段
  178. metrics = {
  179. % 'amplitude', 'Amplitude (μV)', [0, 250], 'boxchart_amplitude_all_patient_with_color';
  180. % 'SNR', 'Signal-to-noise ratio', [0, 11], 'boxchart_SNR_all_patient_with_color';
  181. % 'FR', 'Firing rate (Hz)', [-5, 50], 'boxchart_FR_all_patient_with_color';
  182. % 'acg_tau_rise', 'ACG score (ms)', [-5, 60], 'boxchart_ACG_all_patient_with_color';
  183. % 'trough_to_peak_latency', 'Trough-to-peak duration (ms)', [0, 1], 'boxchart_TPduration_all_patient_with_color';
  184. % 'PTratio', 'peak-to-trough ratio', [0, 5], 'boxchart_PTratio_all_patient_with_color';
  185. 'Duration_ms', 'Trough-to-peak duration (ms)', [0, 2.5], 'boxchart_TPduration_all_patient_with_color';
  186. 'Ratio_Fol_Peak', 'peak-to-trough ratio', [0, 1.5], 'boxchart_PTratio_all_patient_with_color';
  187. };
  191. % 颜色设置
  192. xColors = [95 73 145; 60 130 130; 207 192 100] / 255;
  193. num_files = length(filtered_files);
  194. myColormap = interp1(1:size(xColors, 1), xColors, linspace(1, size(xColors, 1), num_files), 'linear');
  196. for m = 1:size(metrics, 1)
  197. varname = metrics{m, 1};
  198. y_label = metrics{m, 2};
  199. y_lim = metrics{m, 3};
  200. save_name = metrics{m, 4};
  202. % ===== 收集数据 =====
  203. patient_data = {};
  204. for f = 1:num_files
  205. load(fullfile(filepath, filtered_files(f).name));
  206. switch varname
  207. case 'amplitude'
  208. X_unit_cell = table_all_chns.amplitude;
  209. case 'SNR'
  210. X_unit_cell = table_all_chns.SNR;
  211. case 'FR'
  212. X_unit_cell = table_all_chns.FR;
  213. case 'acg_tau_rise'
  214. X_unit_cell = table_all_chns.acg_tau_rise;
  215. case 'Duration_ms'
  216. X_unit_cell = table_all_chns.Duration_ms;
  217. case 'Ratio_Fol_Peak'
  218. X_unit_cell = table_all_chns.Ratio_Fol_Peak;
  219. end
  221. % print
  222. patient_data{f} = double(X_unit_cell);
  223. values = double(X_unit_cell);
  224. fprintf('Patient %d (%s) - %s:\n', f, filtered_files(f).name, varname);
  225. fprintf(' Mean: %.3f | Std: %.3f | Min: %.3f | Max: %.3f | n = %d\n', ...
  226. mean(values), std(values), min(values), max(values), numel(values));
  227. end
  229. % ===== 绘图 =====
  230. figure; hold on;
  231. all_data = vertcat(patient_data{:});
  232. group_labels = arrayfun(@(x) repmat(x, size(patient_data{x}, 1), 1), 1:num_files, 'UniformOutput', false);
  233. group_labels = vertcat(group_labels{:});
  235. for k = 1:num_files
  236. bc = boxchart(categorical(group_labels(group_labels == k)), all_data(group_labels == k), 'Notch', 'on');
  237. bc.BoxFaceColor = myColormap(k, :);
  238. bc.BoxFaceAlpha = 0.5;
  239. bc.LineWidth = 1;
  240. bc.MarkerStyle = '.';
  241. bc.MarkerColor = myColormap(k, :);
  242. end
  244. xticklabels({'Pt.01', 'Pt.02', 'Pt.03', 'Pt.04', 'Pt.05', 'Pt.06', 'Pt.07', 'Pt.08', 'Pt.09', 'Pt.10','Pt.11'});
  245. ylabel(y_label, 'FontSize', 7);
  246. ylim(y_lim);
  248. % 坐标轴样式
  249. box on;
  250. ax = gca;
  251. ax.LineWidth = 0.5;
  252. ax.XColor = 'k';
  253. ax.YColor = 'k';
  254. ax.FontSize = 7;
  255. ax.YAxisLocation = 'left';
  256. ax.YAxis(1).TickDirection = 'in';
  257. ax.YAxis(1).TickLength = [0.01, 0.01];
  258. ax.YGrid = 'off';
  259. ax.XGrid = 'off';
  260. ax.Box = 'off';
  262. % 尺寸设置
  263. set(gcf, 'Units', 'centimeters', 'Position', [0 0 6 7]);
  264. set(gcf, 'PaperUnits', 'centimeters', 'PaperSize', [6 7]);
  265. set(gcf, 'PaperPositionMode', 'auto');
  267. % 保存
  268. save_name_png = fullfile(savepath, [save_name '.png']);
  269. save_name_pdf = fullfile(savepath, [save_name '.pdf']);
  270. saveas(gcf, save_name_pdf);
  271. saveas(gcf, save_name_png);
  272. close(gcf);
  273. end

fig_2_statistics_amp_snr_fr_acg_tpd.m, under CC-BY-4.0 · at the source

Overview

Authors: Shun Wu1, Zhiqiang Yan2, Cen Kong1,3, Xiaofan Jiang2, Qiufeng Dong2, Youkun Qian4, Guangyuan Chen1,3, Beibei Chen2, Chi Ren1, Junfeng Lu4, Xia Li2, Zhengtuo Zhao1,3, Xue Li1,3
ORCID iDs: Guangyuan Chen, Chi Ren, Junfeng Lu, Zhengtuo Zhao, Xue Li
  1. Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences,Shanghai, China
  2. Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University,Xi’an, China
  3. University of Chinese Academy of Sciences,Beijing, China
  4. Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University,Shanghai, China
Institutions: Chinese Academy of Sciences (China); Air Force Medical University (China); University of Chinese Academy of Sciences (China); Fudan University (China)
Journal: Nature communications, volume 17, issue 1, article 5156
Dates: received 17 October 2025; accepted 12 March 2026; published online 13 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71443-7 · PMID 41974677 · PMCID PMC13249945 · OpenAlex W7154020675
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), human (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Extracellular recording, Biomedical engineering
MeSH: Cerebral Cortex*, Electrodes, Implanted*, Neurons*, Action Potentials, Adult, Female, Humans, Male, Middle Aged (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: X.L. is supported by National Science and Technology Innovation 2030 Major Program grant 2021ZD0202202 and Shanghai Municipal Science and Technology Major Project grant 2021SHZDZX; Z.Z. is supported by Shanghai Municipal Science and Technology Major Project grant 2018SHZDZX05, Shanghai Municipal Science and Technology Major Project grant 2021SHZDZX and National Science and Technology Innovation 2030 Major Program grant 2022ZD0210300
Citations: cited by 1 paper (Europe PMC); 59 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

Zenodo 18774630

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (13)
Size: 13 files, 13 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 (5 files), Signal Processing Toolbox (2 files), Neurodata Without Borders (PyNWB, MatNWB) (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
13 files
At the source:

Code availability statement

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  • it points to the authors' code: Zenodo 18774630

Read it in the paper: doi.org/10.1038/s41467-026-71443-7.

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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;
  • 13 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 availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41467-026-71443-7.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 2 keywords, 9 MeSH terms, 2 funders, 58 references.

Cite

This paper

Wu, S., Yan, Z., Kong, C., Jiang, X., Dong, Q., Qian, Y., Chen, G., Chen, B., Ren, C., Lu, J., Li, X., Zhao, Z., & Li, X. (2026). Large-scale single-neuron recording in the human cortex using an ultra-flexible electrode array. Nature communications, 17(1), 5156. https://doi.org/10.1038/s41467-026-71443-7

BibTeX

@article{wu2026large,
author = {Wu, Shun and Yan, Zhiqiang and Kong, Cen and Jiang, Xiaofan and Dong, Qiufeng and Qian, Youkun and Chen, Guangyuan and Chen, Beibei and Ren, Chi and Lu, Junfeng and Li, Xia and Zhao, Zhengtuo and Li, Xue},
title = {{Large-scale single-neuron recording in the human cortex using an ultra-flexible electrode array}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5156},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71443-7},
url = {https://doi.org/10.1038/s41467-026-71443-7},
pmid = {41974677},
pmcid = {PMC13249945}
}

RIS

TY - JOUR
AU - Wu, Shun
AU - Yan, Zhiqiang
AU - Kong, Cen
AU - Jiang, Xiaofan
AU - Dong, Qiufeng
AU - Qian, Youkun
AU - Chen, Guangyuan
AU - Chen, Beibei
AU - Ren, Chi
AU - Lu, Junfeng
AU - Li, Xia
AU - Zhao, Zhengtuo
AU - Li, Xue
TI - Large-scale single-neuron recording in the human cortex using an ultra-flexible electrode array
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/13
VL - 17
IS - 1
SP - 5156
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71443-7
UR - https://doi.org/10.1038/s41467-026-71443-7
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71443-7",
"type": "article-journal",
"title": "Large-scale single-neuron recording in the human cortex using an ultra-flexible electrode array",
"container-title": "Nature communications",
"author": [
{
"family": "Wu",
"given": "Shun"
},
{
"family": "Yan",
"given": "Zhiqiang"
},
{
"family": "Kong",
"given": "Cen"
},
{
"family": "Jiang",
"given": "Xiaofan"
},
{
"family": "Dong",
"given": "Qiufeng"
},
{
"family": "Qian",
"given": "Youkun"
},
{
"family": "Chen",
"given": "Guangyuan"
},
{
"family": "Chen",
"given": "Beibei"
},
{
"family": "Ren",
"given": "Chi"
},
{
"family": "Lu",
"given": "Junfeng"
},
{
"family": "Li",
"given": "Xia"
},
{
"family": "Zhao",
"given": "Zhengtuo"
},
{
"family": "Li",
"given": "Xue"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5156",
"DOI": "10.1038/s41467-026-71443-7",
"PMID": "41974677",
"PMCID": "PMC13249945",
"ISSN": "2041-1723",
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"URL": "https://doi.org/10.1038/s41467-026-71443-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
13
]
]
}
}

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Distributed neural codes of the 3D position in the marmoset frontal cortex and hippocampus.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, 1 reference, author Zhengtuo Zhao
[9] doi:10.7554/elife.110588 [code]
Opening the black box toward a modular approach to spike sorting.
Journal: eLife
In common: Neurodata Without Borders (PyNWB, MatNWB), extracellular electrophysiology (units, LFP), 3 references
[10] doi:10.1126/sciadv.aef0343 [code]
Learning induces activation-mechanism-dependent neural plasticity in an intracortical microstimulation task.
Journal: Science advances
In common: Signal Processing Toolbox, Statistics and Machine Learning Toolbox, 3 references

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