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A causal role for the posterior corpus callosum in bimanual coordination.

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.

The 6 matches
  1. [1] § Quantification and Statistical Analysis › Error Rate Analysis. ↔ PNAS Upload/Data/Behavioral/Plot_arm_errors_jrr_tyr_delay.m, lines 317–404 · score 0.68 · logistic regression, arm error, error rates, Binomial, Pre, Peri
  2. [2] § Quantification and Statistical Analysis › Error Rate Analysis. ↔ PNAS Upload/Data/Behavioral/Plot_arm_errors_jrr_tyr_delay.m, lines 317–404 · score 0.66 · Logistic regression, error rate, binomial, model, fit, interaction
  3. [3] § Materials and Methods › MRI. › Electrophysiological recordings. ↔ PNAS Upload/C-code/_macros/lfp/m-files/private/load_lfps.m, lines 1–44 · score 0.63 · Alpha Omega, LFP signals, Plexon, electrodes, filtered, band
  4. [4] § Materials and Methods › Behavioral Tasks. ↔ PNAS Upload/R-code/power/doPower.r, lines 60–111 · score 0.60 · 300–500 ms, 300 ms, saccade, bimanual
  5. [5] § Quantification and Statistical Analysis › LFP Power. ↔ PNAS Upload/C-code/_macros/lfp/m-files/private/mt_dtft_gram.m, lines 41–132 · score 0.55 · adaptive algorithm, wk, fs, tapers, LFP
  6. [6] § Quantification and Statistical Analysis › ANOVA. ↔ PNAS Upload/Data/Behavioral/jrr_tyr_double_difference_v2_blue_bar.m, lines 673–819 · score 0.52 · bimanual synchrony, Peri Pre, behavioral, Monkey, apart, blockade

Paper

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The authors' code

MATLAB · 406 lines · 20 KB · CC-BY-4.0 · 2 matches

  1. % Plot_arm_errors_jrr_tyr_delay.m
  2. clc; clear;
  3. %which_monk = 'tyr'; monk_initial = ' T';
  4. %which_monk = 'jrr'; monk_initial = ' J';
  5. which_monk = 'jrr_tyr'; monk_initial = 's J & T';
  7. %acc_file = sprintf('%s_delay_errors_sac_uni_BT_BA.mat',which_monk);
  8. suc_fail_sum_file = sprintf('%s_delay_suc_fails_sac_uni_BT_BA.mat',which_monk);
  9. load(suc_fail_sum_file);
  10. all_suc_control_nums = all_control_successes;
  11. all_suc_inact_nums = all_inact_successes;
  13. acc_file = sprintf('%s_delay_errors_sac_uni_BT_BA.mat',which_monk);
  15. arm_fail_file = sprintf('%s_delay_arm_errors.mat',which_monk);
  17. load(acc_file);
  18. load(arm_fail_file);
  20. num_controls = size(all_control_successes,1);
  21. num_inacts = size(all_inact_successes,1);
  23. if strcmp(which_monk,'zen')
  24. jump_ind = 3;
  25. num_inacts = 9*jump_ind;
  26. else
  27. jump_ind = 2;
  28. end
  31. for inact_ind = 1:num_inacts
  32. for stack_ind = 1:4 % sac, uni, BT, BA
  33. cur_dat = all_inact_fails{inact_ind,stack_ind};
  34. % error table from
  35. cur_go_cue_dat = inact_arm_fails{inact_ind,stack_ind};
  36. if isempty(cur_dat)
  37. inact_arm_errs(inact_ind,stack_ind) = 0;
  38. inact_eye_errs(inact_ind,stack_ind) = 0;
  39. else
  40. err_types = cur_dat(:,3);
  41. statuses = cur_dat(:,4);
  42. go_cues = cur_go_cue_dat(:,2);
  43. cur_arm_errs = find(err_types==5|err_types==6);
  44. cur_eye_errs = find(err_types==1);
  45. cur_post_gocue_errs = find(go_cues~=-99999);
  46. cur_pre_gocue_errs = find(go_cues==-99999);
  47. cur_no_start_errs = find(statuses==-3);
  49. inact_pre_go_eye_errs(inact_ind,stack_ind) = ...
  50. numel(intersect(cur_eye_errs,cur_pre_gocue_errs));
  51. inact_post_go_eye_errs(inact_ind,stack_ind) = ...
  52. numel(intersect(cur_eye_errs,cur_post_gocue_errs));
  53. inact_pre_go_arm_errs(inact_ind,stack_ind) = ...
  54. numel(intersect(cur_arm_errs,cur_pre_gocue_errs));
  55. inact_post_go_arm_errs(inact_ind,stack_ind) = ...
  56. numel(intersect(cur_arm_errs,cur_post_gocue_errs));
  57. inact_no_start_eye_errs(inact_ind,stack_ind) = ...
  58. numel(intersect(cur_no_start_errs,cur_arm_errs));
  59. inact_no_start_arm_errs(inact_ind,stack_ind) = ...
  60. numel(intersect(cur_no_start_errs,cur_eye_errs));
  62. inact_no_start_errs(inact_ind,stack_ind) = numel(cur_no_start_errs);
  63. inact_arm_errs(inact_ind,stack_ind) = numel(cur_arm_errs);
  64. inact_eye_errs(inact_ind,stack_ind) = numel(cur_eye_errs);
  65. end
  66. end
  68. end
  72. for control_ind = 1:num_controls
  73. for stack_ind = 1:4 % sac, uni, BT, BA
  74. cur_dat = all_control_fails{control_ind,stack_ind};
  75. % error table from
  76. cur_go_cue_dat = control_arm_fails{control_ind,stack_ind};
  77. if isempty(cur_dat)
  78. control_arm_errs(control_ind,stack_ind) = 0;
  79. control_eye_errs(control_ind,stack_ind) = 0;
  80. else
  81. err_types = cur_dat(:,3);
  82. statuses = cur_dat(:,4);
  83. go_cues = cur_go_cue_dat(:,2);
  84. cur_arm_errs = find(err_types==5|err_types==6);
  85. cur_eye_errs = find(err_types==1);
  86. cur_post_gocue_errs = find(go_cues~=-99999);
  87. cur_pre_gocue_errs = find(go_cues==-99999);
  88. cur_no_start_errs = find(statuses==-3);
  90. control_pre_go_eye_errs(control_ind,stack_ind) = ...
  91. numel(intersect(cur_eye_errs,cur_pre_gocue_errs));
  92. control_post_go_eye_errs(control_ind,stack_ind) = ...
  93. numel(intersect(cur_eye_errs,cur_post_gocue_errs));
  94. control_pre_go_arm_errs(control_ind,stack_ind) = ...
  95. numel(intersect(cur_arm_errs,cur_pre_gocue_errs));
  96. control_post_go_arm_errs(control_ind,stack_ind) = ...
  97. numel(intersect(cur_arm_errs,cur_post_gocue_errs));
  98. control_no_start_eye_errs(control_ind,stack_ind) = ...
  99. numel(intersect(cur_no_start_errs,cur_arm_errs));
  100. control_no_start_arm_errs(control_ind,stack_ind) = ...
  101. numel(intersect(cur_no_start_errs,cur_eye_errs));
  103. control_no_start_errs(control_ind,stack_ind) = numel(cur_no_start_errs);
  104. control_arm_errs(control_ind,stack_ind) = numel(cur_arm_errs);
  105. control_eye_errs(control_ind,stack_ind) = numel(cur_eye_errs);
  106. end
  107. end
  109. end
  111. % code up error rates here.
  112. control_arm_err_rate = control_arm_errs./(control_arm_errs+all_suc_control_nums)*100;
  113. control_PRE_arm_err_rate = control_arm_err_rate(1:2:end,:);
  114. control_PERI_arm_err_rate = control_arm_err_rate(2:2:end,:);
  115. control_PRE_arm_errs = control_arm_errs(1:2:end,:);
  116. control_PERI_arm_errs = control_arm_errs(2:2:end,:);
  118. control_pre_go_arm_err_rate = control_pre_go_arm_errs./(control_pre_go_arm_errs+all_suc_control_nums)*100;
  119. control_PRE_pre_go_arm_err_rate = control_pre_go_arm_err_rate(1:2:end,:);
  120. control_PERI_pre_go_arm_err_rate = control_pre_go_arm_err_rate(2:2:end,:);
  121. control_PRE_pre_go_arm_errs = control_pre_go_arm_errs(1:2:end,:);
  122. control_PERI_pre_go_arm_errs = control_pre_go_arm_errs(2:2:end,:);
  124. control_post_go_arm_err_rate = control_post_go_arm_errs./(control_post_go_arm_errs+all_suc_control_nums)*100;
  125. control_PRE_post_go_arm_err_rate = control_post_go_arm_err_rate(1:2:end,:);
  126. control_PERI_post_go_arm_err_rate = control_post_go_arm_err_rate(2:2:end,:);
  127. control_PRE_post_go_arm_errs = control_post_go_arm_errs(1:2:end,:);
  128. control_PERI_post_arm_errs = control_post_go_arm_errs(2:2:end,:);
  130. control_eye_err_rate = control_eye_errs./(control_eye_errs+all_suc_control_nums)*100;
  131. control_PRE_eye_err_rate = control_eye_err_rate(1:2:end,:);
  132. control_PERI_eye_err_rate = control_eye_err_rate(2:2:end,:);
  133. control_PRE_eye_errs = control_eye_errs(1:2:end,:);
  134. control_PERI_eye_errs = control_eye_errs(2:2:end,:);
  136. control_pre_go_eye_err_rate = control_pre_go_eye_errs./(control_pre_go_eye_errs+all_suc_control_nums)*100;
  137. control_PRE_pre_go_eye_err_rate = control_pre_go_eye_err_rate(1:2:end,:);
  138. control_PERI_pre_go_eye_err_rate = control_pre_go_eye_err_rate(2:2:end,:);
  139. control_PRE_pre_go_eye_errs = control_pre_go_eye_errs(1:2:end,:);
  140. control_PERI_pre_go_eye_errs = control_pre_go_eye_errs(2:2:end,:);
  142. control_post_go_eye_err_rate = control_post_go_eye_errs./(control_post_go_eye_errs+all_suc_control_nums)*100;
  143. control_PRE_post_go_eye_err_rate = control_post_go_eye_err_rate(1:2:end,:);
  144. control_PERI_post_go_eye_err_rate = control_post_go_eye_err_rate(2:2:end,:);
  145. control_PRE_post_go_eye_errs = control_post_go_eye_errs(1:2:end,:);
  146. control_PERI_post_go_eye_errs = control_post_go_eye_errs(2:2:end,:);
  148. control_no_start_err_rate = control_no_start_errs./(control_no_start_errs+all_suc_control_nums)*100;
  149. control_PRE_no_start_err_rate = control_no_start_err_rate(1:2:end,:);
  150. control_PERI_no_start_err_rate = control_no_start_err_rate(2:2:end,:);
  151. control_PRE_no_start_errs = control_no_start_errs(1:2:end,:);
  152. control_PERI_no_start_errs = control_no_start_errs(2:2:end,:);
  154. control_no_start_eye_err_rate = control_no_start_eye_errs./(control_no_start_eye_errs+all_suc_control_nums)*100;
  155. control_PRE_no_start_eye_err_rate = control_no_start_eye_err_rate(1:2:end,:);
  156. control_PERI_no_start_eye_err_rate = control_no_start_eye_err_rate(2:2:end,:);
  157. control_PRE_no_start_eye_errs = control_no_start_eye_errs(1:2:end,:);
  158. control_PERI_no_start_eye_errs = control_no_start_eye_errs(2:2:end,:);
  160. control_no_start_arm_err_rate = control_no_start_arm_errs./(control_no_start_arm_errs+all_suc_control_nums)*100;
  161. control_PRE_no_start_arm_err_rate = control_no_start_arm_err_rate(1:2:end,:);
  162. control_PERI_no_start_arm_err_rate = control_no_start_arm_err_rate(2:2:end,:);
  163. control_PRE_no_start_arm_errs = control_no_start_arm_errs(1:2:end,:);
  164. control_PERI_no_start_arm_errs = control_no_start_arm_errs(2:2:end,:);
  167. % inact err rates
  168. inact_arm_err_rate = inact_arm_errs./(inact_arm_errs+all_suc_inact_nums)*100;
  169. inact_PRE_arm_err_rate = inact_arm_err_rate(1:2:end,:);
  170. inact_PERI_arm_err_rate = inact_arm_err_rate(2:2:end,:);
  171. inact_PRE_arm_errs = inact_arm_errs(1:2:end,:);
  172. inact_PERI_arm_errs = inact_arm_errs(2:2:end,:);
  174. inact_pre_go_arm_err_rate = inact_pre_go_arm_errs./(inact_pre_go_arm_errs+all_suc_inact_nums)*100;
  175. inact_PRE_pre_go_arm_err_rate = inact_pre_go_arm_err_rate(1:2:end,:);
  176. inact_PERI_pre_go_arm_err_rate = inact_pre_go_arm_err_rate(2:2:end,:);
  177. inact_PRE_pre_go_arm_errs = inact_pre_go_arm_errs(1:2:end,:);
  178. inact_PERI_pre_go_arm_errs = inact_pre_go_arm_errs(2:2:end,:);
  180. inact_post_go_arm_err_rate = inact_post_go_arm_errs./(inact_post_go_arm_errs+all_suc_inact_nums)*100;
  181. inact_PRE_post_go_arm_err_rate = inact_post_go_arm_err_rate(1:2:end,:);
  182. inact_PERI_post_go_arm_err_rate = inact_post_go_arm_err_rate(2:2:end,:);
  183. inact_PRE_post_go_arm_errs = inact_post_go_arm_errs(1:2:end,:);
  184. inact_PERI_post_arm_errs = inact_post_go_arm_errs(2:2:end,:);
  186. inact_eye_err_rate = inact_eye_errs./(inact_eye_errs+all_suc_inact_nums)*100;
  187. inact_PRE_eye_err_rate = inact_eye_err_rate(1:2:end,:);
  188. inact_PERI_eye_err_rate = inact_eye_err_rate(2:2:end,:);
  189. inact_PRE_eye_errs = inact_eye_errs(1:2:end,:);
  190. inact_PERI_eye_errs = inact_eye_errs(2:2:end,:);
  192. inact_pre_go_eye_err_rate = inact_pre_go_eye_errs./(inact_pre_go_eye_errs+all_suc_inact_nums)*100;
  193. inact_PRE_pre_go_eye_err_rate = inact_pre_go_eye_err_rate(1:2:end,:);
  194. inact_PERI_pre_go_eye_err_rate = inact_pre_go_eye_err_rate(2:2:end,:);
  195. inact_PRE_pre_go_eye_errs = inact_pre_go_eye_errs(1:2:end,:);
  196. inact_PERI_pre_go_eye_errs = inact_pre_go_eye_errs(2:2:end,:);
  198. inact_post_go_eye_err_rate = inact_post_go_eye_errs./(inact_post_go_eye_errs+all_suc_inact_nums)*100;
  199. inact_PRE_post_go_eye_err_rate = inact_post_go_eye_err_rate(1:2:end,:);
  200. inact_PERI_post_go_eye_err_rate = inact_post_go_eye_err_rate(2:2:end,:);
  201. inact_PRE_post_go_eye_errs = inact_post_go_eye_errs(1:2:end,:);
  202. inact_PERI_post_go_eye_errs = inact_post_go_eye_errs(2:2:end,:);
  204. inact_no_start_err_rate = inact_no_start_errs./(inact_no_start_errs+all_suc_inact_nums)*100;
  205. inact_PRE_no_start_err_rate = inact_no_start_err_rate(1:2:end,:);
  206. inact_PERI_no_start_err_rate = inact_no_start_err_rate(2:2:end,:);
  207. inact_PRE_no_start_errs = inact_no_start_errs(1:2:end,:);
  208. inact_PERI_no_start_errs = inact_no_start_errs(2:2:end,:);
  210. inact_no_start_eye_err_rate = inact_no_start_eye_errs./(inact_no_start_eye_errs+all_suc_inact_nums)*100;
  211. inact_PRE_no_start_eye_err_rate = inact_no_start_eye_err_rate(1:2:end,:);
  212. inact_PERI_no_start_eye_err_rate = inact_no_start_eye_err_rate(2:2:end,:);
  213. inact_PRE_no_start_eye_errs = inact_no_start_eye_errs(1:2:end,:);
  214. inact_PERI_no_start_eye_errs = inact_no_start_eye_errs(2:2:end,:);
  216. inact_no_start_arm_err_rate = inact_no_start_arm_errs./(inact_no_start_arm_errs+all_suc_inact_nums)*100;
  217. inact_PRE_no_start_arm_err_rate = inact_no_start_arm_err_rate(1:2:end,:);
  218. inact_PERI_no_start_arm_err_rate = inact_no_start_arm_err_rate(2:2:end,:);
  219. inact_PRE_no_start_arm_errs = inact_no_start_arm_errs(1:2:end,:);
  220. inact_PERI_no_start_arm_errs = inact_no_start_arm_errs(2:2:end,:);
  222. arm_err_out=fun_plot_errs(control_PRE_arm_err_rate,inact_PRE_arm_err_rate,...
  223. control_PERI_arm_err_rate,inact_PERI_arm_err_rate,monk_initial,'arm err')
  224. eye_err_out=fun_plot_errs(control_PRE_eye_err_rate,inact_PRE_eye_err_rate,...
  225. control_PERI_eye_err_rate,inact_PERI_eye_err_rate,monk_initial,'eye err')
  226. no_start_err_out=fun_plot_errs(control_PRE_no_start_err_rate,inact_PRE_no_start_err_rate,...
  227. control_PERI_no_start_err_rate,inact_PERI_no_start_err_rate,monk_initial,'no start err')
  228. pre_go_eye_err_out=fun_plot_errs(control_PRE_pre_go_eye_err_rate,inact_PRE_pre_go_eye_err_rate,...
  229. control_PERI_pre_go_eye_err_rate,inact_PERI_pre_go_eye_err_rate,monk_initial,'eye err (Before Go)')
  230. post_go_eye_err_out=fun_plot_errs(control_PRE_post_go_eye_err_rate,inact_PRE_post_go_eye_err_rate,...
  231. control_PERI_post_go_eye_err_rate,inact_PERI_post_go_eye_err_rate,monk_initial,'eye err (After Go)')
  232. pre_go_arm_err_out=fun_plot_errs(control_PRE_pre_go_arm_err_rate,inact_PRE_pre_go_arm_err_rate,...
  233. control_PERI_pre_go_arm_err_rate,inact_PERI_pre_go_arm_err_rate,monk_initial,'arm err (Before Go)')
  234. post_go_arm_err_out=fun_plot_errs(control_PRE_post_go_arm_err_rate,inact_PRE_post_go_arm_err_rate,...
  235. control_PERI_post_go_arm_err_rate,inact_PERI_post_go_arm_err_rate,monk_initial,'arm err (After Go)')
  236. no_start_eye_err_out=fun_plot_errs(control_PRE_no_start_eye_err_rate,inact_PRE_no_start_eye_err_rate,...
  237. control_PERI_no_start_eye_err_rate,inact_PERI_no_start_eye_err_rate,monk_initial,'no start eye err')
  238. no_start_arm_err_out=fun_plot_errs(control_PRE_no_start_arm_err_rate,inact_PRE_no_start_arm_err_rate,...
  239. control_PERI_no_start_arm_err_rate,inact_PERI_no_start_arm_err_rate,monk_initial,'no start arm err')
  241. %% binomial test
  243. inact_errs_of_interest = inact_post_go_arm_errs;
  244. control_errs_of_interest = control_post_go_arm_errs;
  246. inact_suc_sum = sum(all_suc_inact_nums); % sum of all successes
  247. control_suc_sum = sum(all_suc_control_nums); % sum of all successes
  248. control_arm_err_sum = sum(control_errs_of_interest); % sum of all arm errs
  250. inact_pre_suc_sum = sum(all_suc_inact_nums(1:jump_ind:end,:));
  251. inact_peri_suc_sum = sum(all_suc_inact_nums(2:jump_ind:end,:));
  253. inact_pre_arm_err_sum = sum(inact_errs_of_interest(1:jump_ind:end,:));
  254. inact_peri_arm_err_sum = sum(inact_errs_of_interest(2:jump_ind:end,:));
  256. control_pre_suc_sum = sum(all_suc_control_nums(1:jump_ind:end,:));
  257. control_peri_suc_sum = sum(all_suc_control_nums(2:jump_ind:end,:));
  259. control_pre_arm_err_sum = sum(control_errs_of_interest(1:jump_ind:end,:));
  260. control_peri_arm_err_sum = sum(control_errs_of_interest(2:jump_ind:end,:));
  262. stack_str = {'Saccade','uni','together','apart','bimanual'};
  264. for stack_ind = 1:5
  266. if stack_ind ==5
  267. inact_pre_mat = [zeros(inact_pre_suc_sum(3)+inact_pre_suc_sum(4),1); ...
  268. ones(inact_pre_arm_err_sum(3)+inact_pre_arm_err_sum(4),1); ];
  269. inact_peri_mat = [zeros(inact_peri_suc_sum(3)+inact_peri_suc_sum(4),1); ...
  270. ones(inact_peri_arm_err_sum(3)+inact_peri_arm_err_sum(4),1); ];
  272. control_pre_mat = [zeros(control_pre_suc_sum(3)+control_pre_suc_sum(4),1); ...
  273. ones(control_pre_arm_err_sum(3)+control_pre_arm_err_sum(4),1); ];
  274. control_peri_mat = [zeros(control_peri_suc_sum(3)+control_peri_suc_sum(4),1); ...
  275. ones(control_peri_arm_err_sum(3)+control_peri_arm_err_sum(4),1); ];
  276. else
  277. inact_pre_mat = [zeros(inact_pre_suc_sum(stack_ind),1); ...
  278. ones(inact_pre_arm_err_sum(stack_ind),1); ];
  279. inact_peri_mat = [zeros(inact_peri_suc_sum(stack_ind),1); ...
  280. ones(inact_peri_arm_err_sum(stack_ind),1); ];
  281. control_pre_mat = [zeros(control_pre_suc_sum(stack_ind),1); ...
  282. ones(control_pre_arm_err_sum(stack_ind),1); ];
  283. control_peri_mat = [zeros(control_peri_suc_sum(stack_ind),1); ...
  284. ones(control_peri_arm_err_sum(stack_ind),1); ];
  285. end
  287. [inact_z(stack_ind),~,~,inact_pv(stack_ind)]= ...
  288. fun_compare_bino_prob(inact_pre_mat',inact_peri_mat');
  289. [control_z(stack_ind),~,~,control_pv(stack_ind)]= ...
  290. fun_compare_bino_prob(control_pre_mat',control_peri_mat');
  291. [IC_z(stack_ind),~,~,IC_pv(stack_ind)]= ...
  292. fun_compare_bino_prob(control_peri_mat',inact_peri_mat'); % inact peri vs. control peri
  293. fprintf('binomial test (inact_peri vs. inact_pre) : %s arm overall error rate \n',which_monk);
  294. fprintf('%s: %.2f (%.2f-%.2f) p:%.4f\n',...
  295. stack_str{stack_ind},(mean(inact_peri_mat)-mean(inact_pre_mat))*100,mean(inact_peri_mat)*100,...
  296. mean(inact_pre_mat)*100,inact_pv(stack_ind));
  298. fprintf('binomial test (control_peri vs. control_pre) : %s arm overall error rate \n',which_monk);
  299. fprintf('%s: %.2f (%.2f-%.2f) p:%.4f\n',...
  300. stack_str{stack_ind},(mean(control_peri_mat)-mean(control_pre_mat))*100,mean(control_peri_mat)*100,...
  301. mean(control_pre_mat)*100,control_pv(stack_ind));
  303. fprintf('binomial test (inact_peri vs. control_peri) : %s arm overall error rate \n',which_monk);
  304. fprintf('%s: %.2f (%.2f-%.2f) p:%.4f\n',...
  305. stack_str{stack_ind},(mean(inact_peri_mat)-mean(control_peri_mat))*100,mean(inact_peri_mat)*100,...
  306. mean(control_peri_mat)*100,IC_pv(stack_ind));
  308. fprintf('double difference (inact_peri-inact_pre)-(control_peri-control_pre) : %s arm overall error rate \n',which_monk);
  309. fprintf('%s: %.2f (%.2f-%.2f) \n',...
  310. stack_str{stack_ind},(mean(inact_peri_mat)-mean(inact_pre_mat))*100 ...
  311. -(mean(control_peri_mat)-mean(control_pre_mat))*100,...
  312. (mean(inact_peri_mat)-mean(inact_pre_mat))*100,...
  313. (mean(control_peri_mat)-mean(control_pre_mat))*100);
  315. end
  317. % binomial test for together vs. apart in control_peri
  318. control_peri_togethr_mat = [zeros(control_peri_suc_sum(3),1); ...
  319. ones(control_peri_arm_err_sum(3),1); ];
  320. control_peri_apart_mat = [zeros(control_peri_suc_sum(4),1); ...
  321. ones(control_peri_arm_err_sum(4),1); ];
  322. [control_peri_bino_z,~,~,control_peri_bino_p]= ...
  323. fun_compare_bino_prob(control_peri_togethr_mat',control_peri_apart_mat');
  325. inact_peri_togethr_mat = [zeros(inact_peri_suc_sum(3),1); ...
  326. ones(inact_peri_arm_err_sum(3),1); ];
  327. inact_peri_apart_mat = [zeros(inact_peri_suc_sum(4),1); ...
  328. ones(inact_peri_arm_err_sum(4),1); ];
  329. [inact_peri_bino_z,~,~,inact_peri_bino_p]= ...
  330. fun_compare_bino_prob(inact_peri_togethr_mat',inact_peri_apart_mat');
  332. % 2025-01-06 JK added double difference (inact peri-inact pre) - (control peri-control pre)
  334. % 2025-01-07 logistic regresssion of success/fails
  336. % Logistic regression allows us to model the probability of success as a function of:
  337. %
  338. % Group (experiment vs. control),
  339. % Phase (pre vs. peri),
  340. % Interaction between group and phase.
  341. % This method directly identifies whether the effects of group, phase, or their interaction are statistically significant.
  342. %
  344. % Example: Long-format data
  345. % Columns: [Group, Phase, Outcome, Count]
  346. % Group: 0 = Control, 1 = Experiment
  347. % Phase: 0 = Pre, 1 = Peri
  348. % Outcome: 0 = Fail, 1 = Success
  349. % Count: Observed frequency
  350. for stack_ind = 2:4
  351. data = [
  352. 1, 0, 1, inact_pre_suc_sum(stack_ind); % Experiment-Pre-Success
  353. 1, 0, 0, inact_pre_arm_err_sum(stack_ind); % Experiment-Pre-Fail
  354. 1, 1, 1, inact_peri_suc_sum(stack_ind); % Experiment-Peri-Success
  355. 1, 1, 0, inact_peri_arm_err_sum(stack_ind); % Experiment-Peri-Fail
  356. 0, 0, 1, control_pre_suc_sum(stack_ind); % Control-Pre-Success
  357. 0, 0, 0, control_pre_arm_err_sum(stack_ind); % Control-Pre-Fail
  358. 0, 1, 1, control_peri_suc_sum(stack_ind); % Control-Peri-Success
  359. 0, 1, 0, control_peri_arm_err_sum(stack_ind); % Control-Peri-Fail
  360. ];
  363. group = data(1:2:end, 1); % Group (Experiment/Control)
  364. phase = data(1:2:end, 2); % Phase (Pre/Peri)
  365. success_count = data(1:2:end, 4); % Success Counts
  366. fail_count = data(2:2:end, 4); % Fail Counts
  367. total_count = success_count + fail_count;
  369. % Create a table for logistic regression
  370. tbl = table(group, phase, fail_count, total_count, ...
  371. 'VariableNames', {'Group', 'Phase', 'FailCount', 'TotalCount'});
  373. % Fit logistic regression model
  374. glm = fitglm(tbl, 'FailCount ~ Group*Phase', ...
  375. 'Distribution', 'binomial', 'BinomialSize', tbl.TotalCount)
  376. end
  378. % show error double difference (inact_peri-inact_pre)-(control_peri-control_pre)
  379. % Monkey J
  380. % uni: 0.90 (0.46--0.44)
  381. % together: -0.34 (-0.34-0.00)
  382. % apart: 0.47 (-0.42--0.89)
  384. % Monkey T
  385. % uni: 0.76=(2.12-1.30)-(1.63-1.57)
  386. % together: 1.37=(5.58-1.96)-(3.62-1.37) (phase p<0.01; Group:Phase
  387. % N.S.)
  388. % apart: -11.47=(7.29-3.73)-(18.71-3.68) (phase p<0.001; Group:Phase
  389. % p<0.001)
  391. % Monkeys J and T
  392. % uni: 0.89
  393. % together: 0.64
  394. % apart: -6.21 (phase p<0.001; Group:Phase p<0.001)
  397. %2025-11-30 JK added
  398. % The reviewer asked to generate error rates
  399. % Showing Blockade Pre and Blocakde Peri now
  401. inact_pre_arm_error_rates = inact_pre_arm_err_sum./(inact_pre_suc_sum+inact_pre_arm_err_sum)*100;
  402. inact_peri_arm_error_rates = inact_peri_arm_err_sum./(inact_peri_suc_sum+inact_peri_arm_err_sum)*100;
  403. control_pre_arm_error_rates = control_pre_arm_err_sum./(control_pre_suc_sum+control_pre_arm_err_sum)*100;
  404. control_peri_arm_error_rates = control_peri_arm_err_sum./(control_peri_suc_sum+control_peri_arm_err_sum)*100;

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

Overview

Authors: Jung Uk Kang1, Lawrence H. Snyder1, Eric Mooshagian1,2
ORCID iDs: Jung Uk Kang, Lawrence H. Snyder, Eric Mooshagian
  1. Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110
  2. Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093
Institutions: Washington University in St. Louis (United States)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 18, article e2516541123
Dates: received 2 July 2025; accepted 28 March 2026; published online 29 April 2026; in print 5 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2516541123 · PMID 42054369 · PMCID PMC13143020 · OpenAlex W4411989833
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.07.02.662886
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Machine learning, Connectivity, Physiology & signal measures
Keywords: bimanual coordination, callosal blockade, corpus callosum, interhemispheric communication, parietal cortex
MeSH: Corpus Callosum*, Psychomotor Performance*, Functional Laterality, Humans, Lidocaine, Magnetic Resonance Imaging, Male, Movement, Parietal Lobe (* major topic)
Journal subjects: Biological Sciences, Neuroscience
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI) (EY-012135); NINDS (NS-123435)
Citations: cited by 1 paper (Europe PMC); 116 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.

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Zenodo 19341112

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, Materials, and Software Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (11 files), Chronux (4 files), Signal Processing Toolbox (4 files), FieldTrip (3 files), Parallel Computing Toolbox (1 file), Wavelet Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
317 files
At the source: doi.org/10.5281/zenodo.19341112

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 9 MeSH terms, 2 funders, 111 references.

Cite

This paper

Kang, J. U., Snyder, L. H., & Mooshagian, E. (2026). A causal role for the posterior corpus callosum in bimanual coordination. Proceedings of the National Academy of Sciences of the United States of America, 123(18), e2516541123. https://doi.org/10.1073/pnas.2516541123

BibTeX

@article{kang2026causal,
author = {Kang, Jung Uk and Snyder, Lawrence H. and Mooshagian, Eric},
title = {{A causal role for the posterior corpus callosum in bimanual coordination}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = apr,
volume = {123},
number = {18},
pages = {e2516541123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2516541123},
url = {https://doi.org/10.1073/pnas.2516541123},
pmid = {42054369},
pmcid = {PMC13143020}
}

RIS

TY - JOUR
AU - Kang, Jung Uk
AU - Snyder, Lawrence H.
AU - Mooshagian, Eric
TI - A causal role for the posterior corpus callosum in bimanual coordination
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/04/29
VL - 123
IS - 18
SP - e2516541123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2516541123
UR - https://doi.org/10.1073/pnas.2516541123
LA - en
ER -

CSL-JSON

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"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
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"family": "Kang",
"given": "Jung Uk"
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"container-title-short": "Proc Natl Acad Sci U S A",
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"PMID": "42054369",
"PMCID": "PMC13143020",
"ISSN": "0027-8424",
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