OSCR

Temporal Interference Stimulation Enhances Neural Regeneration.

Code ↔ Paper

20 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 20 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Statistical Analysis › In Vitro Analyses ↔ Scripts_TIneurogenesis/Analysis_IF_2Dinvitro/mixed_model.m, lines 78–176 · score 0.94 · likelihood ratio, mixed model, random intercept, technical repeats, full model, reduced model
  2. [2] § Methods › Statistical Analysis › In Vitro Analyses ↔ Scripts_TIneurogenesis/Analysis_IF_3Dinvitro/mixed_model_SAP.m, lines 89–183 · score 0.94 · likelihood ratio, mixed model, random intercept, technical repeats, full model, reduced model
  3. [3] § Methods › Image Analysis › Quantification of Cellular Maturation Stage ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/result_functions/get_DCX_figures_new2.m, lines 213–272 · score 0.84 · postmitotic stage, intermediate stage, proliferative stage, vGCL, dGCL, cell density
  4. [4] § Methods › Image Analysis › Biomarker Density and Cell Count ↔ funcs_pipeline/analyse_data.m, lines 341–452 · score 0.79 · DAB channels, spatial smoothing, slice mask, kernel, Ki67, imfilter
  5. [5] § Methods › Image Analysis › Quantification of BrdU Cells ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/result_functions/get_BrdU_figures_new2.m, lines 89–189 · score 0.72 · NeuN, vGCL, dGCL, BrdU, cell density, hilus
  6. [6] § Results › Theta‐Band TI Stimulation Augments Adult Hippocampal Neurogenesis in an In Vivo Mouse Model of AD ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/result_functions/get_BrdU_figures_new2.m, lines 192–251 · score 0.69 · NeuN, vGCL, dGCL, BrdU, cell density, hilus
  7. [7] § Results › Theta‐Band TI Stimulation Augments Adult Hippocampal Neurogenesis in an In Vivo Mouse Model of AD ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/result_functions/get_DCX_figures_new2.m, lines 213–272 · score 0.67 · postmitotic stages, vGCL, dGCL, cell density, intermediate, GCLs
  8. [8] § Methods › Image Analysis › Quantification of Cellular Maturation Stage ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/result_functions/get_BrdU_figures_new2.m, lines 192–251 · score 0.66 · vGCL, dGCL, BrdU, cell classification, cell density, variable
  9. [9] § Methods › Image Analysis › Biomarker Density and Cell Count ↔ funcs_roi/create_roi_h_mask.m, the whole file · a weak match · score 0.66 · slice mask, regionprops, binary, kernel, DAB, window
  10. [10] § Methods › In Vivo Assays › Data Searching and Analysis ↔ Scripts_TIneurogenesis/Analysis_RNAseq_2Dinvitro/VMseq_plots.m, lines 21–120 · score 0.60 · GO enrichment, biological process, Enrichr, library, protein, double
  11. [11] § Methods › In Vivo Assays › Data Searching and Analysis ↔ Scripts_TIneurogenesis/Analysis_proteomics_invivo/Proteomics_plots.m, lines 21–118 · score 0.59 · GO enrichment, biological process, Enrichr, library, protein, double
  12. [12] § Methods › In Vivo Methods › Electrode Implantation ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/Libraries/MIMT-1.54.0.0/MIMT/FEX_dependencies/hyphenate.m, lines 1–115 · score 0.58 · hair, vol, AP, super, micro, cone
  13. [13] § Methods › 3D In Vitro model › Synthesis of the Self‐Assembling Hydrogel ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/Libraries/MIMT-1.54.0.0/MIMT/FEX_dependencies/hyphenate.m, lines 1–115 · score 0.58 · Methyl, gas, PA, gel, drying, mass
  14. [14] § Results › Theta‐Band TI Stimulation Augments Differentiation in In Vitro Cultures of Embryonic NPCs ↔ Scripts_TIneurogenesis/Analysis_IF_2Dinvitro/mixed_model.m, lines 1–76 · score 0.56 · linear mixed, High gamma, Low gamma, treatment, carrier, sham
  15. [15] § Methods › In Vitro Assays › mRNA Extraction and Sequencing ↔ Scripts_TIneurogenesis/Analysis_RNAseq_2Dinvitro/VMseq_plots.m, lines 21–120 · score 0.55 · GO enrichment, biological process, Enrichr, gene
  16. [16] § Methods › In Vitro Assays › mRNA Extraction and Sequencing ↔ Scripts_TIneurogenesis/Analysis_proteomics_invivo/Proteomics_plots.m, lines 21–118 · score 0.55 · GO enrichment, biological process, Enrichr, gene
  17. [17] § Results › Theta‐Band TI Stimulation Augments Adult Hippocampal Neurogenesis in an In Vivo Mouse Model of AD ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/result_functions/get_DCX_figures_new2.m, lines 92–211 · score 0.53 · vGCL, dGCL, cell density, dorsal, ventral, DCX
  18. [18] § Methods › Statistical Analysis › In Vivo Analyses ↔ Scripts_TIneurogenesis/Analysis_IF_2Dinvitro/utils/stats_VMstim.m, lines 153–269 · score 0.53 · Wilcoxon rank sum, Shapiro, ANOVA, threshold
  19. [19] § Methods › Statistical Analysis › In Vivo Analyses ↔ Scripts_TIneurogenesis/Analysis_IF_2Dinvitro/utils/stats_VMstim.m, lines 153–269 · score 0.53 · Wilcoxon rank sum, Holm, Bonferroni
  20. [20] § Results › Theta‐Band TI Stimulation Augments Adult Hippocampal Neurogenesis in an In Vivo Mouse Model of AD ↔ Scripts_TIneurogenesis/Analysis_CellClassification_invivo/result_functions/get_experimenter_tables.m, lines 138–232 · score 0.52 · dorsal GCL, ventral GCL, cell density, classification, mm2, DG

Paper

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The authors' code

MATLAB · 364 lines · 14 KB · CC-BY-4.0 · 3 matches

  1. function [total_cells_table,cells_table,stats] = get_DCX_figures_new2(save_path,area)
  3. % Find mat files for final results dataset
  5. cell_categories = {'A', 'B','C', 'D','E', 'F'};
  7. files = dir(fullfile(save_path,'*.mat'));
  8. for i = 1:length(files)
  9. indx(i,1) = contains(files(i).name,'._');
  10. end
  11. files(indx,:) = [];
  13. ind = contains({files.name}','FinalDataset'); % Use only the combined dataset for all slices
  14. files = files(ind);
  16. % Load dataset
  17. final_dataset = load(fullfile(files.folder,files.name));
  19. total_cells_table = final_dataset.allData_final.total_cells_table;
  20. cells_table = final_dataset.allData_final.cells_table;
  22. % Area index
  23. ind_L = contains(cells_table.ROI,'Left_DG');
  24. ind_R = contains(cells_table.ROI,'Right_DG');
  26. %% Generate graphs and stats
  27. save_path_stats = fullfile(save_path,'Stats',area);
  28. group_names = {'Sham', '8Hz'};
  29. groups_legend = {'Sham','Theta'};
  31. %% Total DCX cell numbers
  32. % Total number of DCX cells, density, DG area and DG thickness
  34. variables_test = ["Cell_number", "GCL_area",...
  35. "GCL_thickness", "Cells_GCL","Cells_GCL_dorsal","Cells_GCL_ventral", ...
  36. "GCL_Cell_density", "GCL_dorsal_Cell_density", "GCL_ventral_Cell_density"];
  37. variables_label = ["Cell number", "GCL area (mm^2)", ...
  38. "GCL thickness (μm)", "Cell number (GCL)", "Cell number (dGCL)", "Cell number (vGCL)", ...
  39. "GCL cell density (cells/mm^2)", "dGCL cell density (cells/mm^2)",...
  40. "vGCL cell density (cells/mm^2)"];
  42. for i = 1:length(variables_test)
  44. dependent_varName = char(variables_test(i));
  46. ind_R = contains(total_cells_table.ROI,'Right_DG');
  47. ind_L = contains(total_cells_table.ROI,'Left_DG');
  48. ind_area = contains(total_cells_table.ROI,area);
  50. clear ID_L ID_R grouping_variable_L grouping_variable_R grouping_variable dependent_variable
  52. if strcmp(area,'DG') % If both right and left DG
  54. ID_L = total_cells_table.ID(ind_L);
  55. ID_R = total_cells_table.ID(ind_R);
  57. grouping_variable_L = total_cells_table.Treatment(ind_L);
  58. grouping_variable_R = total_cells_table.Treatment(ind_R);
  60. if strcmp(dependent_varName,'Cell_number') || contains(dependent_varName,'Cells')
  62. dependent_variable = table(sum([total_cells_table.(dependent_varName)(ind_R),...
  63. total_cells_table.(dependent_varName)(ind_L)],2,"omitnan"));
  64. else
  65. dependent_variable = table(mean([total_cells_table.(dependent_varName)(ind_R),...
  66. total_cells_table.(dependent_varName)(ind_L)],2,"omitnan"));
  67. end
  69. [grouping_variable_equal] = compare_cell_arrays(grouping_variable_L,grouping_variable_R);
  71. [ID_equal] = compare_cell_arrays(ID_L,ID_R);
  73. if grouping_variable_equal && ID_equal
  74. grouping_variable = table(total_cells_table.Treatment(ind_R));
  75. end
  77. else
  78. grouping_variable = table(total_cells_table.Treatment(ind_area));
  79. dependent_variable = table(total_cells_table.(dependent_varName)(ind_area));
  80. end
  82. y_label = char(variables_label(i));
  83. % fig_title = strcat(strrep(dependent_varName,"_"," ")," - ",strrep(area,"_"," "));
  84. fig_title = " ";
  85. fig_name = strcat('Total_',dependent_varName,'_',area);
  87. [stats.(area).total_num.All.(dependent_varName)] = simple_Wilcoxon_test_analysis(save_path_stats,grouping_variable,group_names,dependent_variable,y_label,fig_title,fig_name);
  89. close all
  90. end
  92. %% Categories - DCX cell number
  94. variables_test = ["Cell_number", "Distance", "Normalised_distance", "Cells_GCL",...
  95. "Cells_GCL_dorsal","Cells_GCL_ventral", ...
  96. "GCL_Cell_density", "GCL_dorsal_Cell_density", "GCL_ventral_Cell_density"];
  97. variables_label = ["Cell number", "Distance (μm)", "Distance", "Cell number (GCL)",...
  98. "Cell number (dGCL)", "Cell number (vGCL)", ...
  99. "GCL cell density (cells/mm^2)", "dGCL cell density (cells/mm^2)",...
  100. "vGCL cell density (cells/mm^2)"];
  102. % Area index
  103. ind_area = contains(cells_table.ROI,area);
  105. for j = 1:length(variables_test)
  107. % dependent_varName = [];
  108. dependent_varName = char(variables_test(j));
  110. sham_mean = [];
  111. sham_std = [];
  112. theta_mean = [];
  113. theta_std = [];
  115. for i = 1:length(cell_categories)
  117. field_name = strcat('Cat',cell_categories(i));
  118. field_name = field_name{1,:};
  120. ind_cat = strcmp(cells_table.Category,cell_categories(i));
  122. clear dependent_variable ID_L ID_R grouping_variable_L grouping_variable_R grouping_variable
  124. if strcmp(area,'DG') % If both right and left DG
  126. % Category DCX cells (DG)
  127. ID_L = cells_table.ID(ind_cat&ind_L);
  128. ID_R = cells_table.ID(ind_cat&ind_R);
  130. grouping_variable_L = cells_table.Treatment(ind_cat&ind_L);
  131. grouping_variable_R = cells_table.Treatment(ind_cat&ind_R);
  133. dependent_variable(:,1) = cells_table.(dependent_varName)(ind_cat&ind_R);
  134. dependent_variable(:,2) = cells_table.(dependent_varName)(ind_cat&ind_L);
  136. if strcmp(dependent_varName,'Cell_number') || contains(dependent_varName,'Cells')
  137. dependent_variable = sum(dependent_variable,2,'omitnan');
  138. else
  139. dependent_variable = mean(dependent_variable,2,'omitnan');
  140. end
  142. [grouping_variable_equal] = compare_cell_arrays(grouping_variable_L,grouping_variable_R);
  144. [ID_equal] = compare_cell_arrays(ID_L,ID_R);
  146. if grouping_variable_equal && ID_equal
  147. grouping_variable = table(cells_table.Treatment(ind_cat&ind_R));
  148. end
  149. else
  150. grouping_variable = table(cells_table.Treatment(ind_cat&ind_area));
  151. dependent_variable = table(cells_table.(dependent_varName)(ind_cat&ind_area));
  152. end
  154. y_label = char(variables_label(j));
  155. fig_title = strcat('Category',{' '},cell_categories(i));
  156. fig_name = strcat(dependent_varName,'_cat',cell_categories(i));
  157. fig_name = fig_name{1};
  159. [stats.categories.(area).(dependent_varName).(field_name)] = simple_Wilcoxon_test_analysis(save_path_stats,grouping_variable,group_names,dependent_variable,y_label,fig_title,fig_name);
  161. ind_Sham = contains(grouping_variable.Var1,'Sham');
  162. ind_Theta = contains(grouping_variable.Var1,'8Hz');
  164. sham_mean(i,1) = mean(dependent_variable.Var1(ind_Sham),'omitnan');
  165. sham_std(i,1) = std(dependent_variable.Var1(ind_Sham),'omitnan')...
  166. /sqrt(length(dependent_variable.Var1(ind_Sham&~isnan(dependent_variable.Var1))));
  168. theta_mean(i,1) = mean(dependent_variable.Var1(ind_Theta),'omitnan');
  169. theta_std(i,1) = std(dependent_variable.Var1(ind_Theta),'omitnan')...
  170. /sqrt(length(dependent_variable.Var1(ind_Theta&~isnan(dependent_variable.Var1))));
  172. end
  174. graph = figure;
  175. y_final = [sham_mean,theta_mean];
  176. error_final = [sham_std,theta_std];
  178. b = bar(y_final,'FaceColor',[.7 .7 .7],'EdgeColor',[0 0 0],'LineWidth',1);
  180. b(1).FaceColor = [.7 .7 .7];
  181. b(2).FaceColor = [.2 .6 .5];
  183. box off
  184. removeToolbarExplorationButtons(b)
  186. ngroups = size(y_final, 1);
  187. nbars = size(y_final, 2);
  189. % Calculate width for each bar group
  190. groupwidth = min(0.8, nbars/(nbars + 1.5));
  191. hold on
  192. for i = 1:nbars
  193. x = (1:ngroups) - groupwidth/2 + (2*i-1) * groupwidth / (2*nbars);
  194. hold on
  195. b2 = errorbar(x, y_final(:,i), error_final(:,i),'LineStyle','none',...
  196. 'Color', 'k','linewidth', 1);
  197. end
  199. xticklabels(cell_categories)
  200. ylabel(y_label)
  201. %title(strcat(strrep(dependent_varName,"_"," ")," -"," ",strrep(area,"_"," ")))
  202. fig_name = strcat('Cat_',dependent_varName,'_',area);
  203. legend(groups_legend,'Location','northeast')
  204. box off
  206. save_images_path = fullfile(save_path_stats,'Figures');
  208. saveas(graph,fullfile(save_images_path,'\',strcat(fig_name,'.tif')))
  210. close all
  211. end
  213. %% Stages - DCX
  215. cell_stages = {'Proliferative stage', 'Intermediate stage','Postmitotic stage'};
  216. cell_stages2 = {'Proliferative', 'Intermediate','Postmitotic'};
  218. variables_test = ["Cell_number", "Cells_GCL",...
  219. "Cells_GCL_dorsal","Cells_GCL_ventral", ...
  220. "GCL_Cell_density", "GCL_dorsal_Cell_density", "GCL_ventral_Cell_density"];
  221. variables_label = ["Cell number", "Cell number (GCL)",...
  222. "Cell number (dGCL)", "Cell number (vGCL)", ...
  223. "GCL cell density (cells/mm^2)", "dGCL cell density (cells/mm^2)",...
  224. "vGCL cell density (cells/mm^2)"];
  226. for j = 1:length(variables_test)
  228. dependent_varName = char(variables_test(j));
  230. sham_mean = [];
  231. sham_std = [];
  232. theta_mean = [];
  233. theta_std = [];
  235. idx = 1;
  237. for i = 1:2:length(cell_categories)
  239. field_name = cell_stages2(idx);
  240. field_name = field_name{1,:};
  242. ind_cat1 = strcmp(cells_table.Category,cell_categories(i));
  243. ind_cat2 = strcmp(cells_table.Category,cell_categories(i+1));
  245. clear grouping_variable_C1 grouping_variable_C2 ID_C1 ID_C2 dependent_variable_R dependent_variable_L dependent_variable grouping_variable
  247. if strcmp(area,'DG') % If both right and left DG
  249. grouping_variable_C1(:,1) = cells_table.Treatment(ind_cat1&ind_R);
  250. grouping_variable_C1(:,2) = cells_table.Treatment(ind_cat1&ind_L);
  251. grouping_variable_C2(:,1) = cells_table.Treatment(ind_cat2&ind_R);
  252. grouping_variable_C2(:,2) = cells_table.Treatment(ind_cat2&ind_L);
  254. ID_C1(:,1) = cells_table.ID(ind_cat1&ind_R);
  255. ID_C1(:,2) = cells_table.ID(ind_cat1&ind_L);
  256. ID_C2(:,1) = cells_table.ID(ind_cat2&ind_R);
  257. ID_C2(:,2) = cells_table.ID(ind_cat2&ind_L);
  259. dependent_variable_R(:,1) = cells_table.(dependent_varName)(ind_cat1&ind_R);
  260. dependent_variable_R(:,2) = cells_table.(dependent_varName)(ind_cat2&ind_R);
  261. dependent_variable_L(:,1) = cells_table.(dependent_varName)(ind_cat1&ind_L);
  262. dependent_variable_L(:,2) = cells_table.(dependent_varName)(ind_cat2&ind_L);
  264. dependent_variable = [sum(dependent_variable_R,2,"omitnan"),sum(dependent_variable_L,2,"omitnan")];
  266. if strcmp(dependent_varName,'Cell_number') || strcmp(dependent_varName,'Cells')
  267. dependent_variable = table(sum(dependent_variable,2,'omitnan'));
  268. else
  269. dependent_variable = table(mean(dependent_variable,2,'omitnan'));
  270. end
  272. [grouping_variable_equal] = compare_cell_arrays(grouping_variable_C1,grouping_variable_C2);
  274. [ID_equal] = compare_cell_arrays(ID_C1,ID_C2);
  276. if grouping_variable_equal && ID_equal
  277. grouping_variable = table(cells_table.Treatment(ind_cat1&ind_R));
  278. end
  280. else
  282. ID_C1 = cells_table.ID(ind_cat1&ind_area);
  283. ID_C2 = cells_table.ID(ind_cat2&ind_area);
  284. grouping_variable_C1 = cells_table.Treatment(ind_cat1&ind_area);
  285. grouping_variable_C2 = cells_table.Treatment(ind_cat2&ind_area);
  287. dependent_variable(:,1) = cells_table.(dependent_varName)(ind_cat1&ind_area);
  288. dependent_variable(:,2) = cells_table.(dependent_varName)(ind_cat2&ind_area);
  290. [grouping_variable_equal] = compare_cell_arrays(grouping_variable_C1,grouping_variable_C2);
  292. [ID_equal] = compare_cell_arrays(ID_C1,ID_C2);
  294. if grouping_variable_equal && ID_equal
  296. if strcmp(dependent_varName,'Cell_number') || strcmp(dependent_varName,'Cells') || contains(dependent_varName,'density')
  297. dependent_variable = table(sum(dependent_variable,2,'omitnan'));
  298. else
  299. dependent_variable = table(mean(dependent_variable,2,'omitnan'));
  300. end
  302. grouping_variable = table(cells_table.Treatment(ind_cat1&ind_area));
  304. end
  305. end
  307. y_label = char(variables_label(j));
  308. fig_title = cell_stages(idx);
  309. fig_name = strcat(dependent_varName,'_',cell_stages2(idx));
  310. fig_name = fig_name{1};
  312. [stats.Stages.(area).(dependent_varName).(field_name)] = simple_Wilcoxon_test_analysis(save_path_stats,grouping_variable,group_names,dependent_variable,y_label,fig_title,fig_name);
  314. ind_Sham = contains(grouping_variable.Var1,'Sham');
  315. ind_Theta = contains(grouping_variable.Var1,'8Hz');
  317. sham_mean(idx,1) = mean(dependent_variable.Var1(ind_Sham),'omitnan');
  318. sham_std(idx,1) = std(dependent_variable.Var1(ind_Sham),'omitnan')...
  319. /sqrt(length(dependent_variable.Var1(ind_Sham&~isnan(dependent_variable.Var1))));
  321. theta_mean(idx,1) = mean(dependent_variable.Var1(ind_Theta),'omitnan');
  322. theta_std(idx,1) = std(dependent_variable.Var1(ind_Theta),'omitnan')...
  323. /sqrt(length(dependent_variable.Var1(ind_Theta&~isnan(dependent_variable.Var1))));
  325. idx = idx + 1;
  326. end
  328. graph = figure;
  329. y_final = [sham_mean,theta_mean];
  330. error_final = [sham_std,theta_std];
  332. b = bar(y_final,'FaceColor',[.7 .7 .7],'EdgeColor',[0 0 0],'LineWidth',1);
  334. b(1).FaceColor = [.7 .7 .7];
  335. b(2).FaceColor = [.2 .6 .5];
  337. box off
  338. removeToolbarExplorationButtons(b)
  340. ngroups = size(y_final, 1);
  341. nbars = size(y_final, 2);
  342. % Calculating the width for each bar group
  343. groupwidth = min(0.8, nbars/(nbars + 1.5));
  344. hold on
  345. for i = 1:nbars
  346. x = (1:ngroups) - groupwidth/2 + (2*i-1) * groupwidth / (2*nbars);
  347. hold on
  348. b2 = errorbar(x, y_final(:,i), error_final(:,i),'LineStyle','none',...
  349. 'Color', 'k','linewidth', 1);
  350. end
  352. xticklabels(cell_stages2)
  353. ylabel(y_label)
  354. title(strcat('Neurogenesis stages'))% -'," ",strrep(area,"_"," ")))
  355. fig_name = strcat(dependent_varName,'_stages_',area);
  356. legend(groups_legend,'Location','northeast')
  357. box off
  359. save_images_path = fullfile(save_path_stats,'Figures');
  361. saveas(graph,fullfile(save_images_path,'\',strcat(fig_name,'.tif')))
  363. close all
  364. end

get_DCX_figures_new2.m, under CC-BY-4.0 · at the source

Overview

Authors: Sofia Peressotti1,2,3,4, Maria Garcia Garrido2, Patrycja Dzialecka2,3, Rachel Man Hoi Law4, Roberto Portillo‐Lara1, Bethany Geary3,5, Elena Faillace1, Shirine Merlo‐Nikpay Aslie1, Marcelina Wojewska2, Maria Otero‐Jimenez2, Martina Genta1, Luqiao Tan2,3, Karen Duff3,6, Javier Alegre‐Abarrategui2, Rylie Green1, Nir Grossmann2,3
ORCID iDs: Sofia Peressotti, Maria Garcia Garrido, Patrycja Dzialecka, Rachel Man Hoi Law, Roberto Portillo‐Lara, Bethany Geary, Elena Faillace, Shirine Merlo‐Nikpay Aslie, Maria Otero‐Jimenez, Martina Genta, Karen Duff, Javier Alegre‐Abarrategui, Rylie Green, Nir Grossmann
  1. Bioengineering Department Imperial College London South Kensington London UK
  2. Department of Brain Sciences Imperial College London Hammersmith Hospital London UK
  3. UK Dementia Research Institute London UK
  4. Psychiatry and Fundamental Neuroscience Department University of Geneva Geneva Switzerland
  5. Medical Research Council Protein Phosphorylation and Ubiquitylation Unit University of Dundee Dundee UK
  6. Institute of Neurology University College London London UK
Institutions: University of Geneva (Switzerland); Hammersmith Hospital (United Kingdom); UK Dementia Research Institute (United Kingdom); Imperial College London (United Kingdom); University of Dundee (United Kingdom); MRC Protein Phosphorylation and Ubiquitylation Unit (United Kingdom); Medical Research Council (United Kingdom); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom)
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), volume 13, issue 37, article e24341
Dates: received 22 December 2025; accepted 2 April 2026; published online 28 April 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/advs.202524341 · PMID 42047177 · PMCID PMC13334665 · OpenAlex W7156974573
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), Alzheimer's / dementia (population), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Complexity, fMRI & imaging, Single-unit activity, calcium imaging, Machine learning
Keywords: temporal interference stimulation, neural regeneration, neural progenitor cells, adult neurogenesis, alzheimer's disease, neuromodulation
MeSH: Alzheimer Disease*, Deep Brain Stimulation*, Hippocampus*, Nerve Regeneration*, Neural Stem Cells*, Neurogenesis*, Animals, Disease Models, Animal, Mice (* major topic)
Topic: Planarian Biology and Electrostimulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: European Research Council (771985); Engineering and Physical Sciences Research Council (UKEP/W004844/1); UK Dementia Research Institute; National Institute for Health and Care Research; Imperial Biomedical Research Centre
Citations: cited by 1 paper (Europe PMC); 195 references in the paper

Abstract

Neural regeneration therapies aim to treat neurodegeneration by promoting the proliferation and maturation of exogenous or endogenous neural progenitor cells (NPCs). However, their efficacy has been limited. Deep brain stimulation (DBS) via implanted electrodes has been shown to promote neurogenesis in vitro and in vivo. Still, its invasiveness precludes deployment in research and widespread clinical use. Temporal interference (TI) has emerged as a strategy for non‐invasive, high‐precision DBS using multiple kHz‐range electric fields to target the deep brain. Here, we validate the potential of TI stimulation for neural regeneration augmentation in the central nervous system (CNS). First, we showed that TI stimulation modulated at the theta‐band frequency enhances the maturation of embryonic neural progenitor cells in vitro. We then demonstrate that theta‐band TI stimulation targeting the hippocampus enhances endogenous hippocampal neurogenesis in an in vivo mouse model of Alzheimer's disease‐like amyloidosis. By uncovering frequency‐specific control of stem cell fate, we propose a clinically relevant regeneration strategy that avoids pharmacological or genetic manipulation. Our results enable focal, non‐invasive augmentation of deep‐brain neural regeneration via electrical stimulation.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 20 matches between paragraphs and lines of code.

Zenodo 15747448

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 Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (36 files), Statistics and Machine Learning Toolbox (14 files), Matplotlib (2 files), NumPy (2 files), SciPy (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
378 files
At the source: doi.org/10.5281/zenodo.15747448

pdzialecka/TAH-IHC-analysis

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f6c50e1d96ae5cf00b4948b30480f568189c593e, 16 January 2024
Languages: MATLAB (54)
Size: 62 files, 54 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (15 files), Statistics and Machine Learning Toolbox (5 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
55 files
At the source: github.com/pdzialecka/TAH-IHC-analysis

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

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 432 scripts, each with its path and the digest of its content;
  • 20 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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

Data Availability Statement

RNA‐seq data and proteomics data have been deposited at GEO (access code: GSE300971). The data is private, and it can be accessed with the reviewer access token (uvkpqckmxpshnif). Proteomics data have been deposited to ProteomeXchange via the PRIDE database. The data can be accessed with the project accession code (PXD066537) and token (f7Vjr5NtaS58). The microscopy data reported in this paper will be shared by the lead contact upon request. All the original code used in this study has been deposited at Zenodo, which is publicly available as of the date of submission (doi: https://doi.org/10.5281/zenodo.15747448) and Github for the in vivo IHC analysis: https://github.com/pdzialecka/TAH‐IHC‐analysis (https://github.com/pdzialecka/TAH-IHC-analysis). Any additional information required to reanalyze the data reported in this paper is available from the corresponding authors upon 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, 16 authors, 6 keywords, 9 MeSH terms, 5 funders, 190 references.

Cite

This paper

Peressotti, S., Garcia Garrido, M., Dzialecka, P., Hoi Law, R. M., Portillo‐Lara, R., Geary, B., Faillace, E., Merlo‐Nikpay Aslie, S., Wojewska, M., Otero‐Jimenez, M., Genta, M., Tan, L., Duff, K., Alegre‐Abarrategui, J., Green, R., & Grossmann, N. (2026). Temporal Interference Stimulation Enhances Neural Regeneration. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 13(37), e24341. https://doi.org/10.1002/advs.202524341

BibTeX

@article{peressotti2026temporal,
author = {Peressotti, Sofia and Garcia Garrido, Maria and Dzialecka, Patrycja and Hoi Law, Rachel Man and Portillo‐Lara, Roberto and Geary, Bethany and Faillace, Elena and Merlo‐Nikpay Aslie, Shirine and Wojewska, Marcelina and Otero‐Jimenez, Maria and Genta, Martina and Tan, Luqiao and Duff, Karen and Alegre‐Abarrategui, Javier and Green, Rylie and Grossmann, Nir},
title = {{Temporal Interference Stimulation Enhances Neural Regeneration}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = apr,
volume = {13},
number = {37},
pages = {e24341},
publisher = {Wiley},
issn = {2198-3844},
doi = {10.1002/advs.202524341},
url = {https://doi.org/10.1002/advs.202524341},
pmid = {42047177},
pmcid = {PMC13334665}
}

RIS

TY - JOUR
AU - Peressotti, Sofia
AU - Garcia Garrido, Maria
AU - Dzialecka, Patrycja
AU - Hoi Law, Rachel Man
AU - Portillo‐Lara, Roberto
AU - Geary, Bethany
AU - Faillace, Elena
AU - Merlo‐Nikpay Aslie, Shirine
AU - Wojewska, Marcelina
AU - Otero‐Jimenez, Maria
AU - Genta, Martina
AU - Tan, Luqiao
AU - Duff, Karen
AU - Alegre‐Abarrategui, Javier
AU - Green, Rylie
AU - Grossmann, Nir
TI - Temporal Interference Stimulation Enhances Neural Regeneration
T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
J2 - Adv Sci (Weinh)
PY - 2026
DA - 2026/04/28
VL - 13
IS - 37
SP - e24341
SN - 2198-3844
PB - Wiley
DO - 10.1002/advs.202524341
UR - https://doi.org/10.1002/advs.202524341
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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