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On-demand seizures facilitate rapid screening of therapeutics for epilepsy.

Code ↔ Paper

6 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.

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  1. [1] § Materials and methods › Statistical analysis and computational processing › Feature calculation ↔ plhg.m, the whole file · a weak match · score 0.93 · high gamma amplitude, Phase locked high, LFP phase, 4–30 Hz, 80–150 Hz, Hilbert
  2. [2] § Materials and methods › Statistical analysis and computational processing › Linear mixed effect models and support vector machines ↔ main_RUN_THIS_FILE.m, lines 231–264 · score 0.71 · support vector machine, ground truth, Accuracy, SVM, error, spontaneous seizure
  3. [3] § Materials and methods › Statistical analysis and computational processing › Feature calculation ↔ calculate_features.m, lines 1–48 · score 0.69 · Phase locked high, high gamma, absolute, root, square, amplitude
  4. [4] § Materials and methods › Statistical analysis and computational processing › Linear mixed effect models and support vector machines ↔ spont_svm_characterization_fig3D.m, lines 1–42 · score 0.68 · support vector machine, ground truth, SVM, spontaneous seizure, baseline, events
  5. [5] § Materials and methods › Statistical analysis and computational processing › Preprocessing ↔ filter_downsample.m, the whole file · a weak match · score 0.62 · notch filter, high pass filter, Butterworth, EEG
  6. [6] § Materials and methods › Statistical analysis and computational processing › Induced activity length determination, thresholds, and calculation of thirds ↔ main_RUN_THIS_FILE.m, lines 103–206 · score 0.52 · Wilcoxon rank sum, trained, thresholds, model, durations, naive

Paper

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The authors' code

MATLAB · 279 lines · 10 KB · Apache-2.0 · 2 matches

  1. %% Welcome & Introduction ----------------------------------------------------
  2. % On-Demand Seizures Facilitate Rapid Screening of Therapeutics for Epilepsy
  3. % Authors: Yuzhang Chen, Brian Litt, Flavia Vitale, Hajime Takano
  4. % DOI: https://doi.org/10.7554/eLife.101859
  6. % This code is to be used with the pre-processed source data below:
  7. % SOURCE DATA LOCATION
  9. % ----------------------------------------------------------------------------
  11. % Clears All Variables From Workplace & Command Line Output
  13. clear all; close all; clc;
  15. %% KEY: Set directory to Source File Directory -------------------------------
  17. directory = 'E:\eLife Export\';
  19. %% General Parameters & SubFolder List Compilation ---------------------------
  21. % Generate complete subfolder list, identify start and end of EEG data
  22. complete_list = dir(directory); dirFlags = [complete_list.isdir]; subFolders = complete_list(dirFlags);
  23. real_folder_st = find(ismember({subFolders.name},'EEG_000_START'));
  24. real_folder_end = find(ismember({subFolders.name},'EEG_999_END'));
  26. % Select Folders & Put into List Called SubFolders
  27. subFolders = subFolders(real_folder_st + 1:real_folder_end - 1);
  28. clear complete_list dirFlags real_folder_st real_folder_end;
  30. %% Parameters ----------------------------------------------------------------
  32. % Booleans
  33. % First Run? 0 - No; 1 - Yes
  34. first_run = 0;
  35. % Do Features Need to Be Calculated? 0 - No; 1 - Yes
  36. feat_calc = 1;
  37. % Should Figures Be Plotted? 0 - No; 1 - Yes How Long? plot_duration (s)
  38. to_plot = 0; plot_duration = 95;
  40. % Key Global Variables
  41. % Sampling Rate
  42. fs = 2000;
  43. % Feature window size (s). Window displacement (s).
  44. winLen = 0.5; winDisp = 0.25; overlap_per = winDisp/winLen*100;
  45. % Feature List - 1:12 include all features. Refer to calculate_features for
  46. % more information
  47. feature_list = [1:12];
  48. % Band Power Ranges (Hz)
  49. bp_filters = [1, 30; 30, 300; 300, fs/2];
  50. % Seizure Countdown/Cooldown Period For Automated Seizure Detection (s)
  51. countdown_sec = 5;
  53. %% Filters Extracted Data ----------------------------------------------------
  55. if first_run
  56. for folder_num = 1:length(subFolders)
  57. path_extract = strcat(directory,subFolders(folder_num).name,'\');
  58. filter_downsample(path_extract,fs,plot_duration);
  59. end
  60. end
  62. %% Feature Calculation -------------------------------------------------------
  64. if feat_calc && first_run
  65. for folder_num = 1:length(subFolders)
  66. path_extract = strcat(directory,subFolders(folder_num).name,'\');
  67. calculate_features(path_extract,1,feature_list,winLen, winDisp, bp_filters);
  68. end
  69. end
  71. %% Figure 3 A B & 4 A B - Plots of Individual Seizures -----------------------
  73. % Figure 3 A & B - Epileptic Induction Plots
  75. folder_num = find({subFolders.name} == "EEG_100_KA_THY_SST_CHR");
  76. path_extract = strcat(directory,subFolders(folder_num).name,'\');
  78. % Figure 3 A
  80. seizure = 21; time_idx = [6, 16; 18, 28; 42, 52]; filtered = 1; plot_duration = 65;
  81. plot_select_pairs_fig3A3B4A4B(path_extract, seizure, time_idx, plot_duration, filtered);
  83. % Figure 3 B
  85. seizure = 35; time_idx = [12, 22; 24, 34; 46, 56]; filtered = 1; plot_duration = 65;
  86. plot_select_pairs_fig3A3B4A4B(path_extract, seizure, time_idx, plot_duration, filtered);
  88. % Figure 4 A & B Naive Induction Plots
  90. folder_num = find({subFolders.name} == "EEG_110_NA_THY");
  91. path_extract = strcat(directory,subFolders(folder_num).name,'\');
  93. % Figure 4 A
  95. seizure = 1; time_idx = [8, 18; 22, 32; 34, 44]; filtered = 1; plot_duration = 55;
  96. plot_select_pairs_fig3A3B4A4B(path_extract, seizure, time_idx, plot_duration, filtered);
  98. % Figure 4 B
  100. seizure = 22; time_idx = [8, 18; 22, 32; 34, 44]; filtered = 1; plot_duration = 55;
  101. plot_select_pairs_fig3A3B4A4B(path_extract, seizure, time_idx, plot_duration, filtered);
  103. %% Seizure Duration Calculations and Thresholding - Figure A2 B C D E --------
  105. % Loads Seizure Model
  107. % Seizure_model_spont's model was trained on Animal 100 % Seizure 49
  108. % Training Function - fitcknn(X,Y) where X is merged temp_output_array and
  109. % Y is kmeans(X,3)
  111. load('seizure_model_spont.mat')
  113. max_trial = 200;
  115. % Loads 'To Fix' File For Manual Seizure Duration Fix (~15% of Trials)
  117. to_fix_chart = readmatrix(strcat(directory,"To Fix.csv"));
  119. % -------------------------------------------------------------------------
  121. % Merged sz_parameters and output_array
  123. animal_info = readtable(strcat(directory,'Animal Master.csv'));
  125. merged_output_array = [];
  126. merged_sz_parameters = [];
  128. % Performs seizure calculation
  130. for folder_num = 1:length(subFolders)
  132. path_extract = strcat(directory,subFolders(folder_num).name,'\');
  133. [seizure_duration,min_thresh,output_array,sz_parameters,to_fix_chart] = predict_seizure_duration(path_extract,sz_model,countdown_sec,to_fix_chart,to_plot,subFolders, max_trial,0);
  134. merged_output_array = [merged_output_array, output_array];
  135. merged_sz_parameters = [merged_sz_parameters; sz_parameters];
  136. seizure_duration_list(folder_num) = {seizure_duration};
  137. min_thresh_list(folder_num) = min_thresh;
  139. end
  141. % Threshold Comparisons
  143. pw = [min_thresh_list.power];
  144. pw (pw == -1 ) = NaN;
  145. dur = [min_thresh_list.duration];
  146. dur (dur == -1) = NaN;
  147. ep = table2array(animal_info(:,5));
  149. % Wilcoxon Rank Sum Test
  151. pow_ep_vs_nv = ranksum(pw(ep == 1), pw(ep == 0))
  152. dur_ep_vs_nv = ranksum(dur(ep == 1), dur(ep == 0))
  154. % Epileptic Calculations
  156. ep_pow_mean = mean(pw(ep == 1))
  157. ep_pow_sd = std(pw(ep == 1))
  159. ep_dur_mean = mean(dur(ep == 1))
  160. ep_dur_sd = std(dur(ep == 1))
  162. min_thresh_success = [min_thresh_list.avg_success];
  163. ep_mean_success = mean(min_thresh_success(ep == 1))
  164. ep_sd_success = std(min_thresh_success(ep == 1))
  166. % Wilcoxon Rank Sum Test
  168. succ_ep_vs_nv = ranksum(min_thresh_success(ep == 1), min_thresh_success(ep == 0))
  170. % Naive Calculations
  172. nv_pow_mean = nanmean(pw(ep == 0))
  173. nv_pow_sd = nanstd(pw(ep == 0))
  175. nv_dur_mean = nanmean(dur(ep == 0))
  176. nv_dur_sd = nanstd(dur(ep == 0))
  178. nv_mean_success = mean(min_thresh_success(ep == 0))
  179. nv_sd_success = std(min_thresh_success(ep == 0))
  181. % Figure A2 B C D E
  183. % The number of animals in 'Animal Master.csv' has to equal the number of
  184. % animals that were processed.
  186. threshold_and_success_rate_plot_func_figA2(directory,min_thresh_list,seizure_duration_list,1)
  188. % Overall Accuracy Within 5 Secs. Must Perform With All Animals!
  190. num_within_5sec = sum(to_fix_chart((to_fix_chart(:,1) > 99),6));
  191. animal_in_to_fix = size(to_fix_chart((to_fix_chart(:,1) > 99),6),1);
  192. accuracy_within_5sec = 1 - (animal_in_to_fix - num_within_5sec)/size(merged_sz_parameters,1)
  194. % Epileptic Only Accuracy
  196. total = sum(merged_sz_parameters (:,1) == 111) + sum(merged_sz_parameters (:,1) == 112) + sum(merged_sz_parameters (:,1) <= 107 & merged_sz_parameters (:,1) >= 100);
  197. fixed = sum(to_fix_chart(:,1) == 111 & to_fix_chart(:,6) == 0) + sum(to_fix_chart(:,1) == 112 & to_fix_chart(:,6) == 0) + sum(to_fix_chart(:,1) >= 100 & to_fix_chart(:,1) <= 107 & to_fix_chart(:,6) == 0);
  198. accuracy_within_5sec_ep = 1 - fixed/total
  200. % Naive Only Accuracy
  202. total = sum(merged_sz_parameters (:,1) >= 113 & merged_sz_parameters (:,1) <= 116) + sum(merged_sz_parameters (:,1) <= 110 & merged_sz_parameters (:,1) >= 108);
  203. fixed = sum(to_fix_chart(:,1) >= 113 & to_fix_chart(:,1) <= 116 & to_fix_chart(:,6) == 0) + sum(to_fix_chart(:,1) >= 108 & to_fix_chart(:,1) <= 110 & to_fix_chart(:,6) == 0);
  204. accuracy_within_5sec_nv = 1 - fixed/total
  206. clear min_thresh seizure_duration output_array sz_parameters animal_info
  208. %% Output Data To R ----------------------------------------------------------
  210. animal_info = readtable(strcat(directory,'Animal Master.csv'));
  212. % Special Case For Drug Trials. Only Export Above Threshold Ones W Pairing
  213. drug = 0;
  214. % Removes Second Stim Indices.
  215. second_stim = 1;
  217. for folder_num = 1:length(subFolders)
  219. path_extract = strcat(directory,subFolders(folder_num).name,'\');
  221. if folder_num == 1
  222. [final_divided,sz_parameters,feature_list] = extract_data_R(animal_info,path_extract,seizure_duration_list,[],folder_num,drug,second_stim,1);
  223. else
  224. [final_divided,sz_parameters,feature_list] = extract_data_R(animal_info,path_extract,seizure_duration_list,feature_list,folder_num,drug,second_stim,1);
  225. end
  227. end
  229. clear animal_info
  231. %% Spontaneous Seizure Support Vector Machine Plotting - Figure 3 D ----------
  233. % Appends Baseline Signals
  235. path_extract = strcat(directory,"EEG_END_BASELINE_FOR_SVM_ALL_ANIMALS",'\');
  236. [~,~,output_array_base,sz_param_base] = predict_seizure_duration(path_extract,sz_model,0,to_fix_chart,0,subFolders,2000,0);
  238. svm_merged_output_array = [merged_output_array, output_array_base];
  239. svm_merged_sz_parameters = [merged_sz_parameters; sz_param_base];
  241. svm_values = spont_svm_characterization_fig3D(svm_merged_output_array,svm_merged_sz_parameters);
  243. % Extracts Predictions and Ground Truth
  245. output_values = svm_values(:,1);
  246. true_output_values = svm_values(:,2);
  248. % Find Indices For Truth
  249. idx_evk = find(true_output_values == 3);
  250. idx_failed = find(true_output_values == 2);
  252. % True Positive
  253. evk_accuracy = sum(output_values(idx_evk,:) == true_output_values(idx_evk,:)) / length(idx_evk) * 100
  255. % True Negative
  256. failed_accuracy = sum(output_values(idx_failed,:) == true_output_values(idx_failed,:)) / length(idx_failed) * 100
  258. % Type I and Type II Errors
  259. false_positive = sum(output_values(idx_failed,:) == 3) / length(idx_failed) * 100
  260. false_negative = sum(output_values(idx_evk,:) == 2) / length(idx_evk) * 100
  262. % Note: Above Values were for both epileptic and naive. Extracting only
  263. % values of accuracy from epileptic animals (from the outputted figures)
  264. % gives the values reported in Figure 3 E
  266. %% Evoked Seizures Processing - Figures 3 C and 4 D --------------------------
  268. % Figure 3 C
  270. [final_feature_output, subdiv_index, merged_sz_duration] = spont_evok_plot_func_fig3C(merged_output_array,merged_sz_parameters,seizure_duration_list,directory,subFolders);
  272. set(gcf, 'Position', [469 445 636 521])
  274. % Figure 4 D
  275. [final_feature_output, subdiv_index, merged_sz_duration] = naiv_ep_plot_func_fig4D(merged_output_array,merged_sz_parameters,seizure_duration_list,directory,subFolders,1,4);
  276. set(gcf, 'Position', [207 516 1025 362])
  278. [final_feature_output, subdiv_index, merged_sz_duration] = naiv_ep_plot_func_fig4D(merged_output_array,merged_sz_parameters,seizure_duration_list,directory,subFolders,5,200);
  279. set(gcf, 'Position', [207 516 1025 362])

main_RUN_THIS_FILE.m at commit 318b9ac, under Apache-2.0 · at the source

Overview

Authors: Yuzhang Chen1,2, Brian Litt3,4,5, Flavia Vitale2,3,4,5, Hajime Takano4,6
ORCID iDs: Yuzhang Chen, Flavia Vitale, Hajime Takano
  1. Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania Philadelphia United States
  2. Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center Philadelphia United States
  3. Department of Bioengineering, University of Pennsylvania Philadelphia United States
  4. Department of Neurology, Perelman School of Medicine, University of Pennsylvania Philadelphia United States
  5. Center for Neuroengineering and Therapeutics, University of Pennsylvania Philadelphia United States
  6. Division of Neurology, Department of Pediatrics, The Children’s Hospital of Philadelphia Philadelphia United States
Institutions: University of Pennsylvania (United States); Philadelphia VA Medical Center (United States); Children's Hospital of Philadelphia (United States)
Journal: eLife, volume 13, article RP101859
Dates: published online 30 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.101859 · PMID 42059433 · PMCID PMC13132545 · OpenAlex W4405065339
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), mouse (organism), epilepsy (population)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Connectivity, Complexity, Machine learning, Physiology & signal measures
Keywords: epilepsy, optogenetics, EEG, Mouse
MeSH: Anticonvulsants*, Epilepsy*, Seizures*, Animals, Diazepam, Disease Models, Animal, Drug Evaluation, Preclinical, Kainic Acid, Male, Mice, Optogenetics (* major topic)
Journal subjects: Neuroscience
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (NS-091006-07, P50HD105354, R01NS082046, NS122038-01); Mirowski Family Foundation; Neil and Barbara Smit; Jonathan and Bonnie Rothberg; CHOP AEF
Citations: cited by 1 paper (Europe PMC); 68 references in the paper
Research resources: C57BL/6J-Thy1-ChR2-YFP mice RRID:IMSR_JAX:007612, MATLAB RRID:SCR_001622, R Project for Statistical Computing RRID:SCR_001905

Abstract

Animal models of epilepsy are critical in drug development and therapeutic testing. However, dominant methods for evaluating epilepsy treatments face a tradeoff between higher throughput and etiological relevance. Screening models are either based on acutely induced seizures in wild-type, naive animals or spontaneous seizures in chronically epileptic animals. Each has its disadvantages – acute convulsant or kindling-induced seizures do not account for the myriad neuropathological changes in the diseased, epileptic brains, and spontaneous behavioral seizures are sparse in chronically epileptic models, making it time-intensive to sufficiently power experiments. In this study, we developed the Opto-IHK (optogenetically induced seizures in intrahippocampal kainate mice) model, a mechanistic approach to precipitate seizures ‘on demand’ in chronically epileptic mice. We briefly synchronized principal cells in the CA1 region of the diseased hippocampus to reliably induce stereotyped on-demand behavioral seizures. These induced seizures resembled naturally occurring spontaneous seizures in the epileptic animals and could be stopped by commonly prescribed anti-seizure medications such as levetiracetam and diazepam. Furthermore, we showed that seizures induced in chronically epileptic animals differed from those in naive animals, highlighting the importance of evaluating therapeutics in the diseased circuit. Taken together, we envision the Opto-IHK model to accelerate the evaluation of both pharmacological and closed-loop interventions for epilepsy.

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 6 matches between paragraphs and lines of code.

yuzhangc/Evoked_Seizures

License: Apache-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 318b9ac6dd65ba908645cecb957872662497131c, 29 April 2026
Languages: MATLAB (15), R (4)
Size: 25 files, 19 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files), ggplot2 (4 files), Signal Processing Toolbox (4 files), tidyverse (4 files), ggpubr (2 files), lmerTest (2 files), rstatix (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
21 files
At the source: github.com/yuzhangc/Evoked_Seizures

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 19 scripts, each with its path and the digest of its content;
  • 6 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

No dataset and no data link were found in the paper.

Data availability

All data have been deposited at https://doi.org/10.26275/4uue-pck4 and are publicly available. The code for the analyses presented in this paper is also accessible at https://github.com/yuzhangc/Evoked_Seizures (copy archived at Chen, 2025).

The following dataset was generated:

TakanoH VitaleF 2025On-Demand Seizures Facilitate Rapid Screening of Therapeutics for EpilepsyPennsieve Platform10.26275/4uue-pck4PMC1313254542059433

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 4 keywords, 11 MeSH terms, 5 funders, 67 references, 3 RRIDs.

Cite

This paper

Chen, Y., Litt, B., Vitale, F., & Takano, H. (2026). On-demand seizures facilitate rapid screening of therapeutics for epilepsy. eLife, 13, RP101859. https://doi.org/10.7554/elife.101859

BibTeX

@article{chen2026demand,
author = {Chen, Yuzhang and Litt, Brian and Vitale, Flavia and Takano, Hajime},
title = {{On-demand seizures facilitate rapid screening of therapeutics for epilepsy}},
journal = {eLife},
year = {2026},
month = apr,
volume = {13},
pages = {RP101859},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.101859},
url = {https://doi.org/10.7554/elife.101859},
pmid = {42059433},
pmcid = {PMC13132545}
}

RIS

TY - JOUR
AU - Chen, Yuzhang
AU - Litt, Brian
AU - Vitale, Flavia
AU - Takano, Hajime
TI - On-demand seizures facilitate rapid screening of therapeutics for epilepsy
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/04/30
VL - 13
SP - RP101859
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.101859
UR - https://doi.org/10.7554/elife.101859
LA - en
ER -

CSL-JSON

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"given": "Yuzhang"
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"container-title-short": "Elife",
"volume": "13",
"page": "RP101859",
"DOI": "10.7554/elife.101859",
"PMID": "42059433",
"PMCID": "PMC13132545",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.101859",
"language": "en",
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