OSCR

Anti-Seizure Medications Alter Functional and Effective Connectivity as Measured With Intracranial Electroencephalography.

Code ↔ Paper

13 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 13 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › CCEP Parameters: N1, N2, and RMS ↔ CCEP_offon_RMS.m, lines 182–259 · score 0.89 · 85–250 ms, 10–300 ms, 10–30 ms, 85 ms, RMS, N1
  2. [2] § Methods › Calculating Functional Connectivity and Graph Theoretical Measures ↔ Figure5.m, lines 1–57 · score 0.85 · Global efficiency, Small worldness, Clustering coefficient, local efficiency, modularity, metrics
  3. [3] § Methods › Statistical Analyses on Effective and Functional Connectivity ↔ CCEP_offon_RMS_multi_v2.m, lines 143–206 · score 0.78 · stim rec pairs, linear regression, fitting, family, metrics, model
  4. [4] § Methods › Calculating Functional Connectivity and Graph Theoretical Measures ↔ Pipeline_RestingState.m, lines 12–86 · score 0.75 · bandpass filter, 0.1–1 Hz, cycles, transform, phase, power
  5. [5] § Results › Effects of ASMs on Graph Theoretical Measures ↔ Figure5.m, lines 1–57 · score 0.75 · global efficiency, small worldness, clustering coefficient, local efficiency, modularity, strength
  6. [6] § Methods › CCEP Parameters: N1, N2, and RMS ↔ ccep_N1N2.m, the whole file · a weak match · score 0.75 · 10–30 ms, N2 responses, findpeak, 85 ms, 250 ms, N1
  7. [7] § Results › Effects of ASMs on CCEPs ↔ CCEP_offon_collect_drectns_perm.m, lines 248–286 · score 0.69 · FDR correction, N1 latency, N2 latency, N1 amplitude, N2 amplitude, CCEPs
  8. [8] § Results › Effects of ASMs on CCEPs ↔ CCEP_offon_collect.m, lines 52–192 · score 0.63 · N1 latency, N2 latency, N1 amplitude, N2 amplitude, recording channel, peaks
  9. [9] § Results › Effects of ASMs on CCEPs ↔ CCEP_offon_RMS_multi_v2.m, lines 143–206 · score 0.58 · linear regression, ASM state, patient ID, variables, RMS, 50 ms
  10. [10] § Results › Effects of ASMs on Functional Connectivity ↔ Pipeline_RestingState.m, lines 12–86 · score 0.58 · 0.1–1 Hz, slow fluctuations, iEEG, 0.1 Hz, electrodes
  11. [11] § Results › Effects of ASMs on CCEPs ↔ CCEP_offon_RMS_multi_v2.m, lines 208–263 · score 0.55 · 10–300 ms, Stimulated channels, recording channels, box, RMS, 10 ms
  12. [12] § Methods › Statistical Analyses on Effective and Functional Connectivity ↔ CCEP_offon_collect_drectns_perm.m, lines 1–18 · score 0.52 · Benjamini Hochberg, FDR, permutation, epileptogenic, EPZ, N1
  13. [13] § Methods › sEEG Data Acquisition and Preprocessing ↔ CCEP_processing.m, the whole file · a weak match · score 0.51 · FieldTrip, preprocessed, poststimulation, baseline, zero, CCEP

Paper

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The authors' code

MATLAB · 389 lines · 16 KB · GPL-3.0 · 3 matches

  1. function CCEP_offon_RMS_multi_v2(Sbj_Metadatas)
  2. % CCEP_offon_RMS_multi_v2: Linear regression to compare RMS between ASM-ON vs ASM-OFF
  3. % analyzing connectivity between stimulated and recorded regions
  4. %
  5. % Model per stim-rec pair: RMS ~ ASM_State + PatientID (main effects only)
  6. % Tests ASM effect on connectivity for each stim-rec combination
  7. % Family-wise Bonferroni correction across all 48 comparisons (16 cells × 3 metrics)
  8. %
  9. % Output: 4×4 heatmaps showing p-values (columns=stim, rows=rec)
  10. % v2: updated to analyze the data with linear regression
  12. % Initialize data collection structures
  13. for_crisscross = {'SOZ','EPZ','IZ','Healthy'};
  15. % Cell arrays to store data for each stim-rec pair
  16. % Dimensions: [4 stim types, 4 rec types]
  17. rms_data_stim_rec = cell(4, 4);
  18. rms_n1_data_stim_rec = cell(4, 4);
  19. rms_n2_data_stim_rec = cell(4, 4);
  20. patient_id_stim_rec = cell(4, 4);
  22. % Initialize cells
  23. for i1 = 1:4
  24. for i2 = 1:4
  25. rms_data_stim_rec{i1, i2} = [];
  26. rms_n1_data_stim_rec{i1, i2} = [];
  27. rms_n2_data_stim_rec{i1, i2} = [];
  28. patient_id_stim_rec{i1, i2} = {};
  29. end
  30. end
  32. %% Start the loop for subject metadata
  33. for s = 1:length(Sbj_Metadatas)
  35. Sbj_Metadata = Sbj_Metadatas{s};
  36. sbj_ID = Sbj_Metadata.sbj_ID;
  37. AllBlockInfo = readtable(fullfile(Sbj_Metadata.project_root,[Sbj_Metadata.project_name '_BlockInfo.xlsx']));
  39. offblocks = find(strcmp(AllBlockInfo.sbj_ID,sbj_ID) & strcmp(AllBlockInfo.task_type,'offmed'));
  40. onblocks = find(strcmp(AllBlockInfo.sbj_ID,sbj_ID) & strcmp(AllBlockInfo.task_type,'onmed'));
  42. offmed_block = AllBlockInfo.BlockList{offblocks(1)};
  43. onmed_block = AllBlockInfo.BlockList{onblocks(1)};
  45. %% Find analyzed channel pairs
  46. fileList = dir(fullfile(Sbj_Metadata.results,'off_on_peaks','peak_info_*.mat'));
  47. analyzed_chans = cellfun(@(x) erase(x, {'peak_info_','.mat'}), {fileList.name}', 'UniformOutput', false);
  49. %% Get good bipolar channels
  50. ref = 'bp';
  51. off_ccep = load(fullfile(Sbj_Metadata.iEEG_data,offmed_block, 'CCEPdir', [offmed_block '_ccep_' ref '_' analyzed_chans{1} '.mat']));
  52. bp_chans = off_ccep.CCEPs.label;
  53. clear off_ccep
  55. % Find non-artefactual channels in bipolar data
  56. off_info = load(fullfile(Sbj_Metadata.iEEG_data, offmed_block, [offmed_block '_info.mat']));
  57. on_info = load(fullfile(Sbj_Metadata.iEEG_data, onmed_block, [onmed_block '_info.mat']));
  59. bp_good_chans_off = get_info_goodchans_bp(off_info.info, bp_chans);
  60. bp_good_chans_on = get_info_goodchans_bp(on_info.info, bp_chans);
  62. bp_good_chans = bp_good_chans_off(ismember(bp_good_chans_off, bp_good_chans_on));
  64. %% Get channel characters for stimulation pairs
  65. importance = {'SOZ','EPZ','IZ','Healthy'};
  67. % Find character of each pair of analyzed (stimulated) electrodes
  68. events_chans = strcat(off_info.info.events.StimCh1,repmat({'-'},[length(off_info.info.events.StimCh1),1]),off_info.info.events.StimCh2);
  69. analyzed_chans_character = cell([length(analyzed_chans),1]);
  70. for c = 1:length(analyzed_chans_character)
  71. xxx=find(ismember(events_chans,analyzed_chans{c}));
  72. analyzed_chans_character(c) = off_info.info.events.elec_type(xxx(1));
  73. end
  75. analyzed_chans_split = strsplit_SA(analyzed_chans);
  77. %% Loop through each analyzed stim channel pair
  78. corrSheet = readtable(Sbj_Metadata.labelfile);
  79. for c = 1:length(analyzed_chans)
  81. % Load peak info for this stim pair
  82. load(fullfile(Sbj_Metadata.results,'off_on_peaks',['peak_info_' analyzed_chans{c} '.mat']),'peak_info')
  84. % Remove bad channels
  85. peak_info = peak_info(ismember(peak_info.label, bp_good_chans), :);
  87. % Remove stimulation channels themselves
  88. peak_info_spl = strsplit_SA(peak_info.label);
  89. peak_info = peak_info(~any(ismember(peak_info_spl, analyzed_chans_split(c,:)), 2), :);
  90. peak_info_spl = strsplit_SA(peak_info.label);
  91. % remove non-gray channels
  92. peakinfo_chan_WMvsGM = assign_classes(peak_info_spl(:,1), peak_info_spl(:,2), corrSheet.Label, lower(corrSheet.WMvsGM),{'gray','white','csf','skull'});
  93. peak_info = peak_info(~strcmp(peakinfo_chan_WMvsGM,'gray'),:);
  94. peak_info_spl = strsplit_SA(peak_info.label);
  96. % Get recording channel classes
  97. peakinfo_chan_classes = assign_classes(peak_info_spl(:,1), peak_info_spl(:,2), ...
  98. off_info.info.channelinfo.Label, off_info.info.channelinfo.chan_info, importance);
  100. % Extract RMS values (off and on states)
  101. rms_off = peak_info.off_rms;
  102. rms_on = peak_info.on_rms;
  103. rms_n1_off = peak_info.off_rmsN1;
  104. rms_n1_on = peak_info.on_rmsN1;
  105. rms_n2_off = peak_info.off_rmsN2;
  106. rms_n2_on = peak_info.on_rmsN2;
  108. % Get stimulation channel class index
  109. stim_chan_class_idx = find(strcmp(for_crisscross, analyzed_chans_character{c}));
  111. % For each recording channel class, accumulate data
  112. for rec_class_idx = 1:4
  113. rec_class = for_crisscross{rec_class_idx};
  115. % Find indices of recording channels in this class
  116. rec_idx = strcmp(peakinfo_chan_classes, rec_class);
  118. if sum(rec_idx) > 0
  119. % Combine OFF and ON data
  120. rms_combined = [rms_off(rec_idx); rms_on(rec_idx)];
  121. rms_n1_combined = [rms_n1_off(rec_idx); rms_n1_on(rec_idx)];
  122. rms_n2_combined = [rms_n2_off(rec_idx); rms_n2_on(rec_idx)];
  124. % ASM state indicator (0=off, 1=on)
  125. n_off = sum(rec_idx);
  126. n_on = sum(rec_idx);
  127. asm_state = [zeros(n_off, 1); ones(n_on, 1)];
  129. % Patient ID
  130. patient_ids = [repmat({sbj_ID}, n_off, 1); repmat({sbj_ID}, n_on, 1)];
  132. % Store in cell array
  133. rms_data_stim_rec{stim_chan_class_idx, rec_class_idx} = [rms_data_stim_rec{stim_chan_class_idx, rec_class_idx}; rms_combined];
  134. rms_n1_data_stim_rec{stim_chan_class_idx, rec_class_idx} = [rms_n1_data_stim_rec{stim_chan_class_idx, rec_class_idx}; rms_n1_combined];
  135. rms_n2_data_stim_rec{stim_chan_class_idx, rec_class_idx} = [rms_n2_data_stim_rec{stim_chan_class_idx, rec_class_idx}; rms_n2_combined];
  136. patient_id_stim_rec{stim_chan_class_idx, rec_class_idx} = [patient_id_stim_rec{stim_chan_class_idx, rec_class_idx}; patient_ids];
  137. end
  138. end
  139. end
  141. end
  143. %% Fit linear regression for each stim-rec pair and extract p-values
  144. fprintf('\n========== LINEAR REGRESSION RESULTS PER STIM-REC PAIR ==========\n');
  145. fprintf('Model: RMS ~ ASM_State + PatientID (main effects only)\n');
  146. fprintf('Family-wise Bonferroni correction: α = 0.05 / 48 = %.6f\n\n', 0.05/48);
  148. % Initialize p-value matrices for each metric
  149. pval_rms_matrix = nan(4, 4);
  150. pval_rms_n1_matrix = nan(4, 4);
  151. pval_rms_n2_matrix = nan(4, 4);
  153. % Store model results
  154. models_rms = cell(4, 4);
  155. models_rms_n1 = cell(4, 4);
  156. models_rms_n2 = cell(4, 4);
  158. % Alpha for family-wise correction
  159. alpha_fam = 0.05 / 48;
  161. for stim_idx = 1:4
  162. for rec_idx = 1:4
  164. % Get data for this stim-rec pair
  165. rms_data = cell2mat(rms_data_stim_rec{stim_idx, rec_idx});rms_data_stim_rec{stim_idx, rec_idx}=rms_data;
  166. rms_n1_data = cell2mat(rms_n1_data_stim_rec{stim_idx, rec_idx});rms_n1_data_stim_rec{stim_idx, rec_idx}=rms_n1_data;
  167. rms_n2_data = cell2mat(rms_n2_data_stim_rec{stim_idx, rec_idx});rms_n2_data_stim_rec{stim_idx, rec_idx}=rms_n2_data;
  168. patient_ids = patient_id_stim_rec{stim_idx, rec_idx};
  170. % Only fit model if we have data
  171. if ~isempty(rms_data)
  172. % Create ASM state vector (0=off, 1=on)
  173. n_data = length(rms_data);
  174. asm_state = [zeros(n_data/2, 1); ones(n_data/2, 1)];
  176. % Create tables for regression
  177. tbl_rms = table(categorical(asm_state), categorical(patient_ids), rms_data, ...
  178. 'VariableNames', {'ASM_State', 'PatientID', 'RMS'});
  179. tbl_rms_n1 = table(categorical(asm_state), categorical(patient_ids), rms_n1_data, ...
  180. 'VariableNames', {'ASM_State', 'PatientID', 'RMS_N1'});
  181. tbl_rms_n2 = table(categorical(asm_state), categorical(patient_ids), rms_n2_data, ...
  182. 'VariableNames', {'ASM_State', 'PatientID', 'RMS_N2'});
  184. % Fit models
  185. try
  186. mdl_rms = fitlm(tbl_rms, 'RMS ~ ASM_State + PatientID');
  187. mdl_rms_n1 = fitlm(tbl_rms_n1, 'RMS_N1 ~ ASM_State + PatientID');
  188. mdl_rms_n2 = fitlm(tbl_rms_n2, 'RMS_N2 ~ ASM_State + PatientID');
  190. % Store models
  191. models_rms{stim_idx, rec_idx} = mdl_rms;
  192. models_rms_n1{stim_idx, rec_idx} = mdl_rms_n1;
  193. models_rms_n2{stim_idx, rec_idx} = mdl_rms_n2;
  195. % Extract p-value for ASM_State effect (second row in coefficients)
  196. pval_rms_matrix(stim_idx, rec_idx) = mdl_rms.Coefficients.pValue(2);
  197. pval_rms_n1_matrix(stim_idx, rec_idx) = mdl_rms_n1.Coefficients.pValue(2);
  198. pval_rms_n2_matrix(stim_idx, rec_idx) = mdl_rms_n2.Coefficients.pValue(2);
  200. catch ME
  201. fprintf('Warning: Model fit failed for stim=%s, rec=%s\n', for_crisscross{stim_idx}, for_crisscross{rec_idx});
  202. fprintf(' Error: %s\n', ME.message);
  203. end
  204. end
  205. end
  206. end
  208. %% Create visualization (4x4 heatmaps with p-values)
  209. figure('position',[0 0 1800 500])
  210. h_rms = zeros(size(pval_rms_matrix));
  211. h_rmsN1 = zeros(size(pval_rms_matrix));
  212. h_rmsN2 = zeros(size(pval_rms_matrix));
  214. % Plot 1: RMS (10-300ms)
  215. subplot(1, 3, 1)
  216. imagesc(pval_rms_matrix', [0.0001 0.05])
  217. colormap(gca, master_ColorMaps('hawaii'))
  218. cbar1 = colorbar;
  219. cbar1.Label.String = 'p-value';
  220. set(gca, 'XTick', 1:4, 'XTickLabel', for_crisscross,'FontSize', 12);
  221. set(gca, 'YTick', 1:4, 'YTickLabel', for_crisscross);
  222. xlabel('Stimulation Channel');
  223. ylabel('Recording Channel');
  224. title('RMS (10-300ms)');
  225. grid off
  227. % Add p-values and significance boxes
  228. for stim_idx = 1:4
  229. for rec_idx = 1:4
  230. pval = pval_rms_matrix(stim_idx, rec_idx);
  231. if ~isnan(pval)
  232. if pval < 0.0001
  233. text(stim_idx, rec_idx, 'p<0.0001', 'Color', 'k', ...
  234. 'HorizontalAlignment', 'center', 'FontSize', 12);
  235. else
  236. text(stim_idx, rec_idx, num2str(pval, '%.4f'), 'Color', 'k', ...
  237. 'HorizontalAlignment', 'center', 'FontSize', 12);
  238. end
  240. % Draw colored box if Bonferroni-corrected significant
  241. if pval < alpha_fam
  242. % Determine direction of effect: mean(ON) - mean(OFF)
  243. n_each = length(rms_data_stim_rec{stim_idx, rec_idx}) / 2;
  244. if n_each > 0
  245. rms_vals = rms_data_stim_rec{stim_idx, rec_idx};
  246. mean_off = mean(rms_vals(1:n_each));
  247. mean_on = mean(rms_vals(n_each+1:end));
  248. if mean_on < mean_off
  249. box_color = 'b'; % Blue: decrease from OFF to ON
  250. h_rms(stim_idx, rec_idx) = -1;
  251. else
  252. box_color = 'r'; % Red: increase from OFF to ON
  253. h_rms(stim_idx, rec_idx) = 1;
  254. end
  255. else
  256. box_color = 'k';
  257. end
  258. rectangle('Position', [stim_idx-0.5, rec_idx-0.5, 1, 1], ...
  259. 'EdgeColor', box_color, 'LineWidth', 2.5);
  260. end
  261. end
  262. end
  263. end
  265. % Plot 2: RMS-N1 (10-30ms)
  266. subplot(1, 3, 2)
  267. imagesc(pval_rms_n1_matrix', [0.0001 0.05])
  268. colormap(gca, master_ColorMaps('hawaii'))
  269. cbar2 = colorbar;
  270. cbar2.Label.String = 'p-value';
  271. set(gca, 'XTick', 1:4, 'XTickLabel', for_crisscross,'FontSize', 12);
  272. set(gca, 'YTick', 1:4, 'YTickLabel', for_crisscross);
  273. xlabel('Stimulation Channel');
  274. ylabel('Recording Channel');
  275. title('RMS-N1 (10-30ms)');
  276. grid off
  278. % Add p-values and significance boxes
  279. for stim_idx = 1:4
  280. for rec_idx = 1:4
  281. pval = pval_rms_n1_matrix(stim_idx, rec_idx);
  282. if ~isnan(pval)
  283. if pval < 0.0001
  284. text(stim_idx, rec_idx, 'p<0.0001', 'Color', 'k', ...
  285. 'HorizontalAlignment', 'center', 'FontSize', 12);
  286. else
  287. text(stim_idx, rec_idx, num2str(pval, '%.4f'), 'Color', 'k', ...
  288. 'HorizontalAlignment', 'center', 'FontSize', 12);
  289. end
  291. % Draw colored box if Bonferroni-corrected significant
  292. if pval < alpha_fam
  293. % Determine direction of effect: mean(ON) - mean(OFF)
  294. n_each = length(rms_n1_data_stim_rec{stim_idx, rec_idx}) / 2;
  295. if n_each > 0
  296. rms_vals = rms_n1_data_stim_rec{stim_idx, rec_idx};
  297. mean_off = mean(rms_vals(1:n_each));
  298. mean_on = mean(rms_vals(n_each+1:end));
  299. if mean_on < mean_off
  300. box_color = 'b'; % Blue: decrease from OFF to ON
  301. h_rmsN1(stim_idx, rec_idx) = -1;
  302. else
  303. box_color = 'r'; % Red: increase from OFF to ON
  304. h_rmsN1(stim_idx, rec_idx) = 1;
  305. end
  306. else
  307. box_color = 'k';
  308. end
  309. rectangle('Position', [stim_idx-0.5, rec_idx-0.5, 1, 1], ...
  310. 'EdgeColor', box_color, 'LineWidth', 2.5);
  311. end
  312. end
  313. end
  314. end
  316. % Plot 3: RMS-N2 (85-250ms)
  317. subplot(1, 3, 3)
  318. imagesc(pval_rms_n2_matrix', [0.0001 0.05])
  319. colormap(gca, master_ColorMaps('hawaii'))
  320. cbar3 = colorbar;
  321. cbar3.Label.String = 'p-value';
  322. set(gca, 'XTick', 1:4, 'XTickLabel', for_crisscross,'FontSize', 12);
  323. set(gca, 'YTick', 1:4, 'YTickLabel', for_crisscross);
  324. xlabel('Stimulation Channel');
  325. ylabel('Recording Channel');
  326. title('RMS-N2 (85-250ms)');
  327. grid off
  329. % Add p-values and significance boxes
  330. for stim_idx = 1:4
  331. for rec_idx = 1:4
  332. pval = pval_rms_n2_matrix(stim_idx, rec_idx);
  333. if ~isnan(pval)
  334. if pval < 0.0001
  335. text(stim_idx, rec_idx, 'p<0.0001', 'Color', 'k', ...
  336. 'HorizontalAlignment', 'center', 'FontSize', 12);
  337. else
  338. text(stim_idx, rec_idx, num2str(pval, '%.4f'), 'Color', 'k', ...
  339. 'HorizontalAlignment', 'center', 'FontSize', 12);
  340. end
  343. % Draw colored box if Bonferroni-corrected significant
  344. if pval < alpha_fam
  345. % Determine direction of effect: mean(ON) - mean(OFF)
  346. n_each = length(rms_n2_data_stim_rec{stim_idx, rec_idx}) / 2;
  347. if n_each > 0
  348. rms_vals = rms_n2_data_stim_rec{stim_idx, rec_idx};
  349. mean_off = mean(rms_vals(1:n_each));
  350. mean_on = mean(rms_vals(n_each+1:end));
  351. if mean_on < mean_off
  352. box_color = 'b'; % Blue: decrease from OFF to ON
  353. h_rmsN2(stim_idx, rec_idx) = -1;
  354. else
  355. box_color = 'r'; % Red: increase from OFF to ON
  356. h_rmsN2(stim_idx, rec_idx) = 1;
  357. end
  358. else
  359. box_color = 'k';
  360. end
  361. rectangle('Position', [stim_idx-0.5, rec_idx-0.5, 1, 1], ...
  362. 'EdgeColor', box_color, 'LineWidth', 2.5);
  363. end
  364. end
  365. end
  366. end
  368. sgtitle({'Linear Regression: ASM Effect on Connectivity (RMS ~ ASM_State + PatientID)'; ...
  369. sprintf('Columns = Stimulated Region | Rows = Recorded Region | Red box = p < %.6f (Bonferroni corrected)', alpha_fam)});
  371. %% Save figure and results
  372. res_dir = fullfile(Sbj_Metadatas{1}.project_root, 'COMBINED_RESULTS', 'offon_RMS_multi_v2');
  373. if ~isfolder(res_dir)
  374. mkdir(res_dir);
  375. end
  377. print(fullfile(res_dir, 'RMS_regression_connectivity_pvalues.png'), '-dpng', '-r300');
  379. %% Save results
  380. save(fullfile(res_dir, 'RMS_regression_connectivity_results.mat'), ...
  381. 'models_rms', 'models_rms_n1', 'models_rms_n2', ...
  382. 'pval_rms_matrix', 'pval_rms_n1_matrix', 'pval_rms_n2_matrix', ...
  383. 'rms_data_stim_rec', 'rms_n1_data_stim_rec', 'rms_n2_data_stim_rec', ...
  384. 'patient_id_stim_rec', 'alpha_fam', 'for_crisscross',...
  385. 'h_rms','h_rmsN1','h_rmsN2');
  387. fprintf('\n\nResults saved to: %s\n', res_dir);
  389. end

CCEP_offon_RMS_multi_v2.m at commit d5a7054, under GPL-3.0 · at the source

Overview

Authors: Serdar Akkol1, Helen E. Brinyark2, Rebekah Chatfield1,3, Aparna Vaddiparti1, Rachel June Smith3, Benjamin C. Cox1,4
  1. Department of Neurology University of Alabama at Birmingham Birmingham Alabama USA
  2. Department of Biomedical Engineering University of Alabama at Birmingham Birmingham Alabama USA
  3. Department of Electrical and Computer Engineering University of Alabama at Birmingham Birmingham Alabama USA
  4. Birmingham VA Medical Center Neurology Service Birmingham Alabama USA
Institutions: University of Alabama at Birmingham (United States); UAB Medicine; Birmingham VA Medical Center (United States)
Journal: Brain and behavior, volume 16, issue 6, article e71507
Dates: received 24 November 2025; accepted 6 May 2026; published online 27 May 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/brb3.71507 · PMID 42204888 · PMCID PMC13240049 · OpenAlex W7162634591
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), epilepsy (population)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Connectivity, Graphs, Physiology & signal measures
Keywords: broadband high frequency activity, graph theoretical measures, medication side effect, non‐involved zone, seizure onset zone
MeSH: Anticonvulsants*, Brain*, Drug Resistant Epilepsy*, Nerve Net*, Seizures*, Adult, Electric Stimulation, Electrocorticography, Electroencephalography, Evoked Potentials, Female, Humans, Male, Middle Aged, Neural Pathways, Young Adult (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: American Epilepsy Society Junior Investigator Award (1042632); CURE Epilepsy Taking Flight Award (1061181)); Citizens United for Research in Epilepsy (CURE); American Epilepsy Society
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Background: Anti‐seizure medications (ASMs) control seizures through distinct neuronal mechanisms. While suppressing seizures, putative spill‐over effects can lead to multiple cognitive side effects, some of which can be explained by the modulation of brain connectivity. This modulation can be studied with functional and effective connectivity. While functional connectivity provides information on statistical dependencies between two brain regions, effective connectivity as measured with cortico‐cortical evoked potentials (CCEPs) through single pulse electrical stimulation (SPES) can elucidate the underlying mechanisms of the effective connectivity with high temporal and spatial resolution. CCEPs are often performed while patients are on ASMs; however, the effect of ASMs on CCEPs is largely unknown given limited opportunity to experimentally study this in the human brain. We hypothesized that ASMs would alter the connectivity within the seizure network more often than in healthy brain areas.

Objective: We aimed to understand the effects of ASMs on functional and effective connectivity.

Methods: We recruited seven patients undergoing invasive monitoring with depth electrodes for medically refractory epilepsy, five of whom underwent SPES with 5 mA, 150 µsec per phase square wave pulses at 1 Hz frequency while the patients were on ASMs as part of ongoing research projects (ASM‐ON). Since patients did not have their typical seizures, a second SPES session was performed by the clinical team while off ASMs to reduce their seizure threshold (ASM‐OFF). We recorded a total of 565 bipolar channels and electrically stimulated a total of 17 seizure onset zone (SOZ), 6 early propagation zone (EPZ), 36 irritative zone (IZ), and 64 non‐involved zone (NIZ) electrode pairs across five patients. We compared the amplitude and latency of early and late voltage deflections (N1, N2) and root mean square values of CCEPs between two sessions. In a partly overlapping cohort of five patients, we also recorded 1‐h long rest sessions (ASM‐ON and ASM‐OFF) to show the changes in functional connectivity and graph theoretical measures obtained from the slow fluctuations in broadband high frequency activity.

Results: ASMs preferentially modulated excitability within the epileptic network, with the highest rates of significant CCEP amplitude changes observed for seizure network (SOZ→SOZ: 15.9% N1, 15.5% N2; EPZ→EPZ: 15.6% N1, 11.1% N2; IZ→IZ: 11.6% N1, 12.4% N2) compared to NIZ→NIZ (5.7% N1, 7.4% N2), and with amplitude effects consistently exceeding latency effects across all tissue class combinations (p < 0.05, group significance at q < 0.05). We also found that the RMS based connectivity and functional connectivity were altered more often outside of the seizure network.

Conclusions: Overall, we found that ASMs altered the effective connectivity within the seizure network more than within non‐involved regions, whereas functional connectivity was altered more often within non‐involved regions. This study is the first study revealing with high spatiotemporal resolution that ASMs can alter brain effective and functional connectivity in multiple different ways.

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 13 matches between paragraphs and lines of code.

sakkol/CCEP_ASM_connectivity

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d5a705408d62a06db316e36fac2f15463ce26377, 3 April 2026
Languages: MATLAB (24)
Size: 26 files, 24 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (6 files), FieldTrip (2 files), Signal Processing Toolbox (1 file), shadedErrorBar (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
26 files
At the source: github.com/sakkol/CCEP_ASM_connectivity

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;
  • 24 scripts, each with its path and the digest of its content;
  • 13 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 Statement

The code used for analyses will be available at: https://github.com/sakkol/CCEP_ASM_connectivity. The dataset used for the current study is available from the corresponding author on reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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, issue, pages, dates, 6 authors, 5 keywords, 16 MeSH terms, 4 funders, 70 references.

Cite

This paper

Akkol, S., Brinyark, H. E., Chatfield, R., Vaddiparti, A., Smith, R. J., & Cox, B. C. (2026). Anti-Seizure Medications Alter Functional and Effective Connectivity as Measured With Intracranial Electroencephalography. Brain and behavior, 16(6), e71507. https://doi.org/10.1002/brb3.71507

BibTeX

@article{akkol2026anti,
author = {Akkol, Serdar and Brinyark, Helen E. and Chatfield, Rebekah and Vaddiparti, Aparna and Smith, Rachel June and Cox, Benjamin C.},
title = {{Anti-Seizure Medications Alter Functional and Effective Connectivity as Measured With Intracranial Electroencephalography}},
journal = {Brain and behavior},
year = {2026},
month = jun,
volume = {16},
number = {6},
pages = {e71507},
publisher = {Wiley},
issn = {2162-3279},
doi = {10.1002/brb3.71507},
url = {https://doi.org/10.1002/brb3.71507},
pmid = {42204888},
pmcid = {PMC13240049}
}

RIS

TY - JOUR
AU - Akkol, Serdar
AU - Brinyark, Helen E.
AU - Chatfield, Rebekah
AU - Vaddiparti, Aparna
AU - Smith, Rachel June
AU - Cox, Benjamin C.
TI - Anti-Seizure Medications Alter Functional and Effective Connectivity as Measured With Intracranial Electroencephalography
T2 - Brain and behavior
J2 - Brain Behav
PY - 2026
DA - 2026/06/01
VL - 16
IS - 6
SP - e71507
SN - 2162-3279
PB - Wiley
DO - 10.1002/brb3.71507
UR - https://doi.org/10.1002/brb3.71507
LA - en
ER -

CSL-JSON

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