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Phase similarity between similar objects indicates representational merging across retrieval training but not sleep.

Code ↔ Paper

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The 16 matches
  1. [1] § Materials and Methods › Data analysis › Representational change analyses › Source localisation ↔ scripts/phase_paper/dnap_class_32_source_reconstruction.py, lines 220–301 · score 0.88 · unit noise gain, rank deficiency, LCMV filters, beamforming, covariance, inverse
  2. [2] § Materials and Methods › Data analysis › EEG preprocessing ↔ scripts/sigma_paper/04_dnap_sigma_sleep-yasa.py, lines 46–82 · score 0.85 · linked mastoid, pre processed, miscellaneous, bandwidth, resampled, E1
  3. [3] § Materials and Methods › Data analysis › Representational change analyses › Linear modelling ↔ scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd, lines 599–722 · score 0.83 · 0–400 ms, 400–700 ms, Treatment contrast, delta, beta, model
  4. [4] § Results › Only the retrieval training intervention induces further representational merging of similar objects in the alpha band › Intervention shift linear mixed-effects modelling ↔ scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd, lines 487–597 · score 0.82 · 0–400 ms, 400–700 ms, Treatment contrast, delta, beta, fitted
  5. [5] § Materials and Methods › Data analysis › EEG preprocessing ↔ scripts/phase_paper/dnap_class_01_preproc.py, lines 441–519 · score 0.81 · AutoReject, bandwidth, resampled, ECG, EMG1, EMG2
  6. [6] § Results › Encoding-driven representational merging of similar objects in theta-band phase › Encoding shift linear mixed-effects modelling ↔ scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd, lines 599–722 · score 0.77 · 0–400 ms, 400–700 ms, bounds, delta, beta, interaction
  7. [7] § Results › Item-level behavioural accuracy to similar-lure objects is predicted by representational merging in the retrieval training intervention ↔ scripts/phase_paper/dnap_class_21_IR-DR_models_items.Rmd, lines 91–133 · score 0.71 · correctly classified, lure accuracy, 400–700 ms, CI, bins, predicted
  8. [8] § Materials and Methods › Data analysis › Behaviour modelling ↔ scripts/phase_paper/dnap_class_15_L-IR-DR_models.Rmd, lines 111–183 · score 0.65 · treatment contrast, lme4, delayed recognition accuracy, outliers, modelling, confidence
  9. [9] § Materials and Methods › Data analysis › EEG phase similarity ↔ scripts/phase_paper/dnap_class_02_read-in_complex.py, lines 173–232 · score 0.62 · 2–30 Hz, 100–700 ms, Morlet, epoch, 100 ms, EEG
  10. [10] § Materials and Methods › Data analysis › EEG phase similarity ↔ scripts/phase_paper/dnap_class_23_read-in_complex_avg.py, lines 170–234 · score 0.62 · 2–30 Hz, 100–700 ms, Morlet, epoch, 100 ms, EEG
  11. [11] § Materials and Methods › Data analysis › Representational change analyses › Source localisation ↔ scripts/phase_paper/dnap_class_32_source_reconstruction.py, lines 220–301 · score 0.62 · inverse solution, LCMV filter, sensor, occipital, phase
  12. [12] § Materials and Methods › Data analysis › Representational change analyses › Source localisation ↔ scripts/phase_paper/dnap_class_33_phase_itpc_source.py, lines 339–381 · score 0.59 · Harvard Oxford atlas, MNI, space, brain, filter, phase
  13. [13] § Materials and Methods › Data analysis › Representational change analyses › Cluster-based permutation tests ↔ scripts/phase_paper/dnap_class_04_phase_same-sim.py, lines 709–772 · score 0.52 · mne.stats.spatio_temporal_cluster_test, tailed, sanity, permutation, phase
  14. [14] § Materials and Methods › Data analysis › Representational change analyses › Cluster-based permutation tests ↔ scripts/phase_paper/dnap_class_24_phase_same-rand_avg.py, lines 523–561 · score 0.52 · mne.stats.spatio_temporal_cluster_test, tailed, sanity, permutation, phase
  15. [15] § Results › Encoding-driven representational merging of similar objects in theta-band phase › Encoding shift source localisation ↔ scripts/phase_paper/dnap_class_36_phase_L-to-IR_graphs_source.py, lines 263–306 · score 0.52 · Harvard Oxford, glass brain, atlas, cluster
  16. [16] § Results › Encoding-driven representational merging of similar objects in theta-band phase › Encoding shift source localisation ↔ scripts/phase_paper/dnap_class_33_phase_itpc_source.py, lines 339–381 · score 0.52 · Harvard Oxford, glass brain, atlas

Paper

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The authors' code

R Markdown · 722 lines · 27 KB · no license · 4 matches

  1. ---
  2. title: "dnap_class_exploration"
  3. output: html_document
  4. date: "2024-07-30"
  5. ---
  7. ```{r setup, include=FALSE}
  8. knitr::opts_chunk$set(echo = TRUE)
  10. library(dplyr)
  11. pacman::p_load(tidyverse, tidymodels, lme4, effects, car, emmeans, ggeffects, performance, stringr, ggpubr, rstatix, gridExtra, sjPlot, lmerTest)
  13. ```
  15. ```{r read in data }
  17. dnap_class1 <- read.csv("D:/DNap/Scripts/classified/data/dnap_class_all.csv")
  19. dnap_class_lir1 <- read.csv("D:/DNap/Scripts/classified/data/dnap_class_all_l-ir.csv")
  21. # fix
  23. dnap_class <- dnap_class1 %>%
  24. mutate(id_cond_phase = paste0(ID, condition, phase)) %>%
  25. mutate(KSS = case_when(
  26. id_cond_phase == "26wakeir" ~ 3,
  27. TRUE ~ KSS
  28. ))
  30. dnap_class_lir <- dnap_class_lir1 %>%
  31. mutate(id_cond = paste0(ID, condition)) %>%
  32. mutate(KSS = case_when(
  33. id_cond == "26wake" ~ 3,
  34. TRUE ~ KSS
  35. )) %>%
  36. rename(l = z_phase_sim_l,
  37. ir = z_phase_sim_ir) %>%
  38. pivot_longer(cols = c("l", "ir"), names_to = "phase", values_to = "z_phase_sim")
  40. ```
  42. ```{r outlier KD function}
  44. # outlier detection function
  45. outlierKD <- function(dt, var) {
  46. var_name <- eval(substitute(var),eval(dt))
  47. tot <- sum(!is.na(var_name))
  48. na1 <- sum(is.na(var_name))
  49. m1 <- mean(var_name, na.rm = T)
  50. par(mfrow=c(2, 2), oma=c(0,0,3,0))
  51. boxplot(var_name, main="With outliers")
  52. hist(var_name, main="With outliers", xlab=NA, ylab=NA)
  53. outlier <- boxplot.stats(var_name)$out
  54. mo <- mean(outlier)
  55. var_name <- ifelse(var_name %in% outlier, NA, var_name)
  56. boxplot(var_name, main="Without outliers")
  57. hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
  58. title("Outlier Check", outer=TRUE)
  59. na2 <- sum(is.na(var_name))
  60. message("Outliers identified: ", na2 - na1, " from ", tot, " observations")
  61. message("Proportion (%) of outliers: ", (na2 - na1) / tot*100)
  62. message("Mean of the outliers: ", mo)
  63. m2 <- mean(var_name, na.rm = T)
  64. message("Mean without removing outliers: ", m1)
  65. message("Mean if we remove outliers: ", m2)
  66. response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ")
  67. if(response == "y" | response == "yes"){
  68. dt[as.character(substitute(var))] <- invisible(var_name)
  69. assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
  70. message("Outliers successfully removed", "\n")
  71. return(invisible(dt))
  72. } else{
  73. message("Nothing changed", "\n")
  74. return(invisible(var_name))
  75. }
  76. }
  78. ```
  80. ```{r outliers IR DR}
  81. # IR to DR
  82. dnap_no_out <- dnap_class
  84. outlierKD(dnap_no_out, z_phase_sim)
  86. outlierKD(dnap_no_out, cat_acc_score)
  88. #outlierKD(dnap_no_out, dprime)
  90. dnap_no_outliers <- dnap_no_out %>%
  91. na.omit(z_phase_sim, cat_acc_score)
  93. ```
  95. ```{r outliers L IR }
  97. # L to IR
  98. dnap_no_out_lir <- dnap_class_lir
  100. outlierKD(dnap_no_out_lir, z_phase_sim)
  102. outlierKD(dnap_no_out_lir, cat_acc_score)
  104. dnap_no_outliers_lir <- dnap_no_out_lir %>%
  105. na.omit(z_phase_sim, cat_acc_score)
  107. ```
  111. ```{r dprime model }
  113. # select relevant scores
  114. dnap_dprime1 <- dnap_class %>%
  115. select(ID, condition, phase, dprime) %>%
  116. distinct() %>%
  117. pivot_wider(names_from = "phase", values_from = "dprime", names_prefix = "dprime_" ) %>%
  118. mutate(condition = case_when(
  119. condition == "nap" ~ "Sleep",
  120. condition == "restudy" ~ "Restudy",
  121. condition == "retrieval" ~ "Retrieval",
  122. condition == "wake" ~ "Wake"
  123. ))
  125. dnap_dprime <- dnap_dprime1
  127. # remove outliers
  128. outlierKD(dnap_dprime, dprime_ir)
  129. # dr has no outliers
  131. # reorder condition order
  132. cond_order = c("Retrieval", "Restudy", "Sleep", "Wake")
  133. dnap_dprime$condition <- factor(dnap_dprime$condition, levels = cond_order)
  135. # modelling
  136. dnap_dprime.lmm <- lmer(dprime_dr ~ condition + dprime_ir + (1|ID), data=dnap_dprime)
  137. Anova(dnap_dprime.lmm)
  139. #plotting
  140. dnap_dprime.plot <- Effect(c("condition"), dnap_dprime.lmm, confidence.level=.83) %>%
  141. as_tibble()
  142. (dnap_dprime.plot1 <- ggplot(dnap_dprime.plot,
  143. aes(x=condition, y=fit, ymin=lower, ymax=upper,
  144. colour=condition, fill=condition)) +
  145. geom_point(size=4, shape=15) +
  146. geom_errorbar(width=0.5, linewidth=1.2) +
  147. xlab("Condition") + ylab("Delayed Recognition Accuracy") +
  148. theme(
  149. legend.position = "None") +
  150. theme_bw()+
  151. theme(axis.title.x = element_text(size=16),
  152. axis.title.y = element_text(size=16),
  153. axis.text.x = element_text(size=12),
  154. axis.text.y = element_text(size=12),
  155. strip.text.x = element_text(size = 16)) +
  156. theme(
  157. panel.grid.major = element_blank(),
  158. panel.grid.minor = element_blank(),
  159. panel.background = element_blank()) +
  160. scale_fill_manual(values = c("#440154FF", "#33638DFF", "#55C667FF", "#DCE319FF")) +
  161. scale_colour_manual(values = c("#440154FF", "#33638DFF", "#55C667FF", "#DCE319FF")) +
  162. theme(
  163. legend.position = "None"))
  165. ggsave("D:/DNap/Scripts/classified/plots/R_plots/dprime_plot.png", plot = dnap_dprime.plot1, width = 19, height = 13, units = "cm")
  166. save_plot("D:/DNap/Scripts/classified/plots/R_plots/svg/dprime_plot.svg", fig=dnap_dprime.plot1, width = 16, height = 10)
  169. # treatment contrasts
  170. #set contrasts for the models
  171. dnap_dprime_contr <- dnap_dprime
  172. dnap_dprime_contr$condition <- as.factor(dnap_dprime_contr$condition)
  173. # check what the contrasts are numbered
  174. contrasts(dnap_dprime_contr$condition)
  175. # retreival = 1
  176. # restudy = 2
  177. # sleep = 3
  178. # wake = 4
  179. contrasts(dnap_dprime_contr$condition) = contr.treatment(4, base=3) # set ret as the control
  180. dnap_dprime_contr.lmm <- lmer(dprime_dr ~ condition + dprime_ir + (1|ID), data=dnap_dprime_contr)
  181. summary(dnap_dprime_contr.lmm)
  183. ```
  186. ```{r time bin exploration setup }
  188. setwd("D:/DNap/EEG")
  190. bounds <- read.table("iaf_long.txt", header = TRUE) %>%
  191. rename(ID = subj,
  192. condition = cond) %>%
  193. mutate(condition = case_when(
  194. condition == "res" ~ "restudy",
  195. condition == "ret" ~ "retrieval",
  196. condition == "nap" ~ "nap",
  197. condition == "wak" ~ "wake"
  198. )) %>%
  199. pivot_wider(names_from = measure, values_from = value) %>%
  200. select(-paf, -cog, -u_alpha_upper, -u_alpha_lower, -l_alpha_upper, -l_alpha_lower) %>%
  201. mutate(id_cond = paste0(ID, condition)) %>%
  202. filter(!id_cond %in% c("5wake", "30nap"))
  204. dnap_bounds <- left_join(dnap_no_outliers, bounds) %>%
  205. mutate(across(ends_with(c("_lower", "_upper")), as.numeric)) %>%
  206. mutate_at(vars(ends_with("_lower")), floor) %>%
  207. mutate_at(vars(ends_with("_upper")), round) %>%
  208. mutate(band = case_when(
  209. frequency >= theta_lower & frequency <= theta_upper ~ "theta",
  210. frequency >= alpha_lower & frequency <= alpha_upper ~ "alpha",
  211. frequency >= beta_lower & frequency <= beta_upper ~ "beta",
  212. frequency < theta_lower ~ "delta"
  213. )) %>%
  214. na.omit(band) %>%
  215. select(-alpha_lower:-alphabeta_upper) %>%
  216. mutate(time_win = case_when(
  217. time_start == 0 ~ "prestim",
  218. time_start < 401 & time_start > 0 ~ "early",
  219. time_start > 401 ~ "late"
  220. ))
  223. dnap_base <- dnap_bounds %>%
  224. group_by(ID, phase, condition, band, time_win) %>%
  225. mutate(phase_mean = mean(z_phase_sim)) %>%
  226. select(ID, phase, condition, band, time_win, phase_mean, lure_category, cat_acc_score, dprime, KSS) %>%
  227. distinct() %>%
  228. ungroup() %>%
  229. pivot_wider(names_from = phase, values_from = c("phase_mean", "cat_acc_score", "dprime", "KSS"), names_sep = "_") %>%
  230. mutate(phase_diff = phase_mean_dr - phase_mean_ir) %>%
  231. distinct() %>%
  232. select(-phase_mean_ir, -phase_mean_dr, -KSS_ir) %>%
  233. na.omit()
  235. ```
  237. ```{r lure cat exploration setup }
  239. dnap_models <- dnap_base %>%
  240. pivot_wider(names_from = "time_win", values_from = "phase_diff") %>%
  241. pivot_wider(names_from = "band", values_from = c("prestim", "early", "late"), names_sep = "_")
  243. ```
  246. ```{r lure category behaviour exploration model}
  248. dnap_beh_lurecat <- dnap_models %>%
  249. select(ID, condition, lure_category, cat_acc_score_ir, cat_acc_score_dr) %>%
  250. mutate(cat_acc_score_time = cat_acc_score_dr - cat_acc_score_ir) %>%
  251. mutate(condition = case_when(
  252. condition == "nap" ~ "Sleep",
  253. condition == "restudy" ~ "Restudy",
  254. condition == "retrieval" ~ "Retrieval",
  255. condition == "wake" ~ "Wake",
  256. )) %>%
  257. mutate(lure_category = case_when(
  258. lure_category == "old" ~ "Original",
  259. lure_category == "new - similar" ~ "Similar",
  260. lure_category == "new - different" ~ "Different",
  261. ))
  263. # set order for conditions and lure categories
  264. cond_order = c("Retrieval", "Restudy", "Sleep", "Wake")
  265. dnap_beh_lurecat$condition <- factor(dnap_beh_lurecat$condition, levels = cond_order)
  267. ### difference score
  268. dnap_lurecat_diff.lmm <- lmer(cat_acc_score_time ~ condition * lure_category + (1|ID), data=dnap_beh_lurecat)
  269. Anova(dnap_lurecat_diff.lmm)
  271. # plot cond * lure_cat
  272. dnap_lurecat_diff.plot <- Effect(c("condition", "lure_category"), dnap_lurecat_diff.lmm, confidence.level=.83) %>%
  273. as_tibble()
  275. (dnap_lurecat_diff.plot1 <- ggplot(dnap_lurecat_diff.plot,
  276. aes(x=lure_category, y=fit,
  277. colour=lure_category, fill=lure_category, shape=lure_category)) +
  278. geom_point(size=4.5) +
  279. geom_errorbar(size=1, width=0.5, aes(ymin=lower, ymax=upper)) +
  280. #geom_hline(yintercept=0, linetype=2) +
  281. xlab("Lure Category") + ylab("Recognition Accuracy Change (%)") +
  282. labs(fill = "Lure Category",
  283. colour = "Lure Category",
  284. shape = "Lure Category") +
  285. facet_wrap(~condition, ncol=4) +
  286. theme_bw()+
  287. theme(plot.title = element_text(hjust = 0.5, size=22),
  288. axis.title.y = element_text(size=22),
  289. axis.text.y = element_text(size=22),
  290. strip.text.x = element_text(size = 22),
  291. legend.text=element_text(size=22),
  292. legend.title=element_text(size=22),
  293. axis.title.x=element_blank(),
  294. axis.text.x=element_blank(),
  295. axis.ticks.x=element_blank()) +
  296. #theme_apa() +
  297. #theme(panel.spacing = unit(1, "lines")) +
  298. theme(
  299. panel.grid.major = element_blank(),
  300. panel.grid.minor = element_blank(),
  301. #panel.background = element_blank()
  302. ) +
  303. theme(
  304. legend.position = "bottom") +
  305. scale_colour_manual(values = c("#440154FF", "#33638DFF", "#55C667FF"))) #, "#DCE319FF"
  307. ggsave("D:/DNap/Scripts/classified/plots/R_plots/cond_lure-acc_diff_plot.png", plot=dnap_lurecat_diff.plot1, width = 12, height = 7, units = "in")
  308. save_plot("D:/DNap/Scripts/classified/plots/R_plots/svg/cond_lure-acc_diff_plot.svg", fig=dnap_lurecat_diff.plot1, width = 24, height = 14)
  310. #treatment contrasts
  311. # object_type*condition
  312. dnap_beh_lurecat_con <- dnap_beh_lurecat
  313. dnap_beh_lurecat_con$obj_cond <- interaction(dnap_beh_lurecat_con$lure_category, dnap_beh_lurecat_con$condition)
  314. levels(dnap_beh_lurecat_con$obj_cond) #check levels
  315. # set base, one condition at a time
  316. dnap_beh_lurecat_con$obj_cond <- relevel(dnap_beh_lurecat_con$obj_cond, ref = "Similar.Sleep")
  317. # model
  318. dnap_lurecat_diff_con.lmm <- lmer(cat_acc_score_time ~ obj_cond + (1|ID), data=dnap_beh_lurecat_con)
  319. summary(dnap_lurecat_diff_con.lmm)
  321. ```
  324. ```{r ratio setup and model - all}
  326. ## overall average setup
  327. dnap_ratio_all <- left_join(dnap_no_outliers, bounds) %>%
  328. filter(frequency >= 4,
  329. time_start >= 200) %>%
  330. group_by(ID, phase, condition) %>%
  331. mutate(phase_mean = mean(z_phase_sim)) %>%
  332. select(ID, phase, condition, phase_mean, lure_category, cat_acc_score, KSS) %>%
  333. distinct() %>%
  334. ungroup() %>%
  335. pivot_wider(names_from = phase, values_from = c("phase_mean", "cat_acc_score", "KSS"), names_sep = "_") %>%
  336. mutate(phase_diff = phase_mean_dr - phase_mean_ir) %>%
  337. distinct() %>%
  338. select(-phase_mean_ir, -phase_mean_dr, -KSS_ir) %>%
  339. na.omit() %>%
  340. rename(acc_ir = cat_acc_score_ir,
  341. acc_dr = cat_acc_score_dr) %>%
  342. mutate(lure_category = case_when(
  343. lure_category == "old" ~ "old",
  344. lure_category == "new - similar" ~ "sim",
  345. lure_category == "new - different" ~ "diff"
  346. )) %>%
  347. pivot_wider(names_from = "lure_category", values_from = c("acc_ir", "acc_dr"), names_sep = "_") %>%
  348. mutate(sd_ratio_ir = acc_ir_sim - acc_ir_diff,
  349. sd_ratio_dr = acc_dr_sim - acc_dr_diff,
  350. sd_ratio_diff = sd_ratio_dr - sd_ratio_ir,
  351. condition = case_when(
  352. condition == "retrieval" ~ "Retrieval",
  353. condition == "restudy" ~ "Restudy",
  354. condition == "wake" ~ "Wake",
  355. condition == "nap" ~ "Sleep"
  356. ))
  359. # #check multicolinearity with vif() # good
  360. # #vif(dnap_ratio_all.lmm)
  363. # model with difference score
  364. cond_order_diff = c("Wake", "Retrieval", "Sleep", "Restudy")
  365. dnap_ratio_all$condition <- factor(dnap_ratio_all$condition, levels = cond_order_diff)
  367. # just behaviour ***
  368. dnap_ratio_all_diff.lmm <- lmer(sd_ratio_diff ~ condition + (1|ID), data = dnap_ratio_all)
  369. Anova(dnap_ratio_all_diff.lmm)
  371. dnap_ratio_all_diff.plot <- Effect(c("condition"), dnap_ratio_all_diff.lmm, confidence.level=.83) %>%
  372. as_tibble()
  374. (dnap_ratio_all_diff.plot1 <- ggplot(dnap_ratio_all_diff.plot,
  375. aes(x=condition, y=fit, colour = condition)) +
  376. geom_point() +
  377. geom_errorbar(aes(ymin=lower, ymax=upper), width=0.3) +
  378. geom_hline(yintercept=0, linetype=2) +
  379. xlab("Condition") + ylab("Similar:Different Lure Accuracy") +
  380. theme_bw()+
  381. theme(
  382. legend.position = "None",
  383. text = element_text(size = 20)) +
  384. scale_colour_manual(values = c("#440154FF", "#33638DFF", "#55C667FF", "#DCE319FF")))
  386. ggsave("D:/DNap/Scripts/classified/plots/R_plots/cond_lure-ratio_diff_plot.png", plot=dnap_ratio_all_diff.plot1, width = 8, height = 6, units = "in")
  387. save_plot("D:/DNap/Scripts/classified/plots/R_plots/svg/cond_lure-ratio_diff_plot.svg", fig=dnap_ratio_all_diff.plot1, width = 16, height = 12)
  389. # treatment contrasts
  390. #set contrasts for the models
  391. dnap_ratio_all$condition <- as.factor(dnap_ratio_all$condition)
  392. # check what the contrasts are numbered
  393. contrasts(dnap_ratio_all$condition)
  394. # wake = 1
  395. # retrieval = 2
  396. # sleep = 3
  397. # resudy = 4
  398. contrasts(dnap_ratio_all$condition) = contr.treatment(4, base=1) # set wake as the control
  399. dnap_ratio_all_diff.lmm <- lmer(sd_ratio_diff ~ condition + (1|ID), data = dnap_ratio_all)
  400. summary(dnap_ratio_all_diff.lmm)
  402. ```
  404. ```{r behaviour descriptives }
  406. # ratio
  407. dnap_ratio_all_avgs <- dnap_ratio_all %>%
  408. group_by(condition) %>%
  409. na.omit(sd_ratio_ir, sd_ratio_dr, sd_ratio_diff) %>%
  410. mutate(mean_ratio_ir = mean(sd_ratio_ir),
  411. sd_ratio_ir = sd(sd_ratio_ir),
  412. mean_ratio_dr = mean(sd_ratio_dr),
  413. sd_ratio_dr = sd(sd_ratio_dr),
  414. mean_ratio_diff = mean(sd_ratio_diff),
  415. sd_ratio_diff = sd(sd_ratio_diff)) %>%
  416. select(condition, mean_ratio_ir, sd_ratio_ir, mean_ratio_dr, sd_ratio_dr, mean_ratio_diff, sd_ratio_diff) %>%
  417. distinct() %>%
  418. mutate(across(where(is.numeric), round, 2))
  421. # similar and different lure accuracy separated
  422. dnap_accuracy_avg <- dnap_models %>%
  423. select(condition, lure_category, cat_acc_score_ir, cat_acc_score_dr) %>%
  424. filter(lure_category != "old") %>%
  425. mutate(cat_acc_score_diff = cat_acc_score_dr - cat_acc_score_ir) %>%
  426. group_by(condition, lure_category) %>%
  427. mutate(mean_acc_ir = mean(cat_acc_score_ir),
  428. sd_acc_ir = sd(cat_acc_score_ir),
  429. mean_acc_dr = mean(cat_acc_score_dr),
  430. sd_acc_dr = sd(cat_acc_score_dr),
  431. mean_acc_diff = mean(cat_acc_score_diff),
  432. sd_acc_diff = sd(cat_acc_score_diff),
  433. lure_category = case_when(
  434. lure_category == "new - similar" ~ "sim",
  435. lure_category == "new - different" ~ "diff"
  436. )) %>%
  437. ungroup() %>%
  438. select(condition, lure_category, mean_acc_ir, sd_acc_ir, mean_acc_dr, sd_acc_dr, mean_acc_diff, sd_acc_diff) %>%
  439. distinct() %>%
  440. pivot_wider(names_from = "lure_category", values_from = c("mean_acc_ir", "sd_acc_ir", "mean_acc_dr", "sd_acc_dr", "mean_acc_diff", "sd_acc_diff"), names_sep = "_")%>%
  441. mutate(across(where(is.numeric), round, 2))
  443. dnap_accuracy_avg_sim <- dnap_accuracy_avg %>%
  444. mutate(acc_ir = paste0(mean_acc_ir_sim, " (", sd_acc_ir_sim, ")"),
  445. acc_dr = paste0(mean_acc_dr_sim, " (", sd_acc_dr_sim, ")"),
  446. acc_diff = paste0(mean_acc_diff_sim, " (", sd_acc_diff_sim, ")")) %>%
  447. select(condition, starts_with("acc_"))
  449. dnap_accuracy_avg_diff <- dnap_accuracy_avg %>%
  450. mutate(acc_ir = paste0(mean_acc_ir_diff, " (", sd_acc_ir_diff, ")"),
  451. acc_dr = paste0(mean_acc_dr_diff, " (", sd_acc_dr_diff, ")"),
  452. acc_diff = paste0(mean_acc_diff_diff, " (", sd_acc_diff_diff, ")")) %>%
  453. select(condition, starts_with("acc_"))
  455. ```
  459. model with only retrieval condition, late alpha subject level
  461. ```{r retreival only models for late alpha }
  463. dnap_ratio_broad_ret <- dnap_models %>%
  464. select(-dprime_ir, -dprime_dr) %>%
  465. rename(acc_ir = cat_acc_score_ir,
  466. acc_dr = cat_acc_score_dr) %>%
  467. mutate(lure_category = case_when(
  468. lure_category == "old" ~ "old",
  469. lure_category == "new - similar" ~ "sim",
  470. lure_category == "new - different" ~ "diff"
  471. )) %>%
  472. pivot_wider(names_from = "lure_category", values_from = c("acc_ir", "acc_dr"), names_sep = "_") %>%
  473. mutate(sd_ratio_ir = acc_ir_sim - acc_ir_diff,
  474. sd_ratio_dr = acc_dr_sim - acc_dr_diff,
  475. sd_ratio_diff = sd_ratio_dr - sd_ratio_ir) %>%
  476. filter(condition == "retrieval")
  479. # difference score
  480. #late_alpha does not predict accuracy for similar lures
  481. dnap_ret_sim_late_ratio_diff.lmm <- lm(sd_ratio_diff ~ late_alpha, data = dnap_ratio_broad_ret)
  482. Anova(dnap_ret_sim_late_ratio_diff.lmm)
  484. ```
  487. ```{r model phase per cond, band, and time window bin }
  489. dnap_base_ch <- dnap_bounds %>%
  490. group_by(ID, phase, condition, band, time_win, ch_name) %>%
  491. mutate(phase_mean = mean(z_phase_sim)) %>%
  492. select(ID, phase, condition, band, time_win, ch_name, phase_mean, KSS) %>%
  493. distinct() %>%
  494. ungroup() %>%
  495. pivot_wider(names_from = phase, values_from = c("phase_mean", "KSS"), names_sep = "_") %>%
  496. mutate(phase_diff = phase_mean_dr - phase_mean_ir) %>%
  497. distinct() %>%
  498. select(-phase_mean_ir, -phase_mean_dr, -KSS_ir) %>%
  499. na.omit() %>%
  500. filter(time_win != "prestim") %>%
  501. mutate(time_win = case_when(
  502. time_win == "early" ~ "Early (0-0.4s)",
  503. time_win == "late" ~ "Late (0.4-0.7s)"),
  504. band = case_when(
  505. band == "delta" ~ "Delta",
  506. band == "theta" ~ "Theta",
  507. band == "alpha" ~ "Alpha",
  508. band == "beta" ~ "Beta"
  509. ))
  511. dnap_base_ch_ret <- dnap_base_ch %>%
  512. filter(condition == "retrieval")
  514. band_order <- c("Delta", "Theta", "Alpha", "Beta")
  515. dnap_base_ch_ret$band <- factor(dnap_base_ch_ret$band, levels = band_order)
  517. dnap.band_time_predict <- lmer(phase_diff ~ band * time_win + (1|ID) + (1|ch_name), data = dnap_base_ch_ret)
  519. Anova(dnap.band_time_predict)
  520. #make table
  521. anova_tab <- Anova(dnap.band_time_predict)
  522. anova_df <- as.data.frame(anova_tab) %>%
  523. mutate(
  524. Chisq = ifelse(Chisq >= 0.005,
  525. format(round(Chisq, 2), nsmall = 2, scientific = FALSE),
  526. format(signif(Chisq, 1), scientific = FALSE)),
  528. `Pr(>Chisq)` = case_when(
  529. round(`Pr(>Chisq)`, 3) == 0 ~ "<.001",
  530. TRUE ~ sub("^0\\.", ".", format(round(`Pr(>Chisq)`, 3), nsmall = 3))
  531. )
  532. )
  534. # time*band
  535. dnap.band_time_predict.plot <- Effect(c("band","time_win"), dnap.band_time_predict,confidence.level=.83) %>%
  536. as_tibble()
  538. (dnap.band_time_predict.plot1 <- ggplot(dnap.band_time_predict.plot,
  539. aes(x=band, y=fit, colour=band)) +
  540. geom_point(size = 3) +
  541. geom_errorbar(aes(ymin=lower, ymax=upper), width = 0.2) +
  542. facet_wrap(~time_win) +
  543. xlab("Band") + ylab("Phase Similarity Shift") +
  544. theme_bw() +
  545. theme(
  546. legend.position = "none",
  547. text = element_text(size = 20)) +
  548. scale_colour_manual(values = c("#440154FF", "#33638DFF", "#55C667FF", "#DCE319FF")))
  550. ggsave("D:/DNap/Scripts/classified/plots/R_plots/phase_sim_bands_time_ret_model_plot.png", plot = dnap.band_time_predict.plot1, width = 8, height = 6, units = "in")
  551. save_plot("D:/DNap/Scripts/classified/plots/R_plots/svg/phase_sim_bands_time_ret_model_plot.svg", fig = dnap.band_time_predict.plot1, width = 16, height = 12)
  553. # band
  554. dnap.band_predict.plot <- Effect(c("band"), dnap.band_time_predict,confidence.level=.83) %>%
  555. as_tibble()
  557. (dnap.band_predict.plot1 <- ggplot(dnap.band_predict.plot,
  558. aes(x=band, y=fit, colour=band)) +
  559. geom_point(size = 3) +
  560. geom_errorbar(aes(ymin=lower, ymax=upper), width = 0.2) +
  561. xlab("Band") + ylab("Phase Similarity Shift") +
  562. theme_bw() +
  563. theme(
  564. legend.position = "none",
  565. text = element_text(size = 20)) +
  566. scale_colour_manual(values = c("#440154FF", "#33638DFF", "#55C667FF", "#DCE319FF")))
  568. ggsave("D:/DNap/Scripts/classified/plots/R_plots/phase_sim_bands_ret_model_plot.png", plot = dnap.band_predict.plot1, width = 8, height = 6, units = "in")
  569. save_plot("D:/DNap/Scripts/classified/plots/R_plots/svg/phase_sim_bands_ret_model_plot.svg", fig = dnap.band_predict.plot1, width = 16, height = 12)
  571. ## treatment contrasts
  573. # band
  574. dnap_base_ch_ret$band <- as.factor(dnap_base_ch_ret$band)
  575. contrasts(dnap_base_ch_ret$band) = contr.treatment(4, base=3) # set each band as the control
  576. # check what the contrasts are numbered
  577. contrasts(dnap_base_ch_ret$band)
  578. # delta = 1
  579. # theta = 2
  580. # alpha = 3
  581. # beta = 4
  582. # model
  583. dnap.band_time_predict_con1 <- lmer(phase_diff ~ band + (1|ID) + (1|ch_name), data = dnap_base_ch_ret)
  584. summary(dnap.band_time_predict_con1)
  586. # band*time
  587. dnap_base_ch_ret_con <- dnap_base_ch_ret
  588. dnap_base_ch_ret_con$band_time <- interaction(dnap_base_ch_ret_con$time_win, dnap_base_ch_ret_con$band)
  589. levels(dnap_base_ch_ret_con$band_time) #check levels
  590. # set base, one condition at a time
  591. # dnap_base_ch_ret_con$band_time <- relevel(dnap_base_ch_ret_con$band_time, ref = "Early (0-0.4s).Alpha")
  592. dnap_base_ch_ret_con$band_time <- relevel(dnap_base_ch_ret_con$band_time, ref = "Late (0.4-0.7s).Alpha")
  593. # model
  594. dnap.band_time_predict_con2.lmm <- lmer(phase_diff ~ band_time + (1|ID) + (1|ch_name), data = dnap_base_ch_ret_con)
  595. summary(dnap.band_time_predict_con2.lmm)
  597. ```
  599. ```{r does band and time predict phase similarity L to IR }
  601. dnap_cond_predict_lir <- left_join(dnap_no_outliers_lir, bounds) %>%
  602. mutate(across(ends_with(c("_lower", "_upper")), as.numeric)) %>%
  603. mutate_at(vars(ends_with("_lower")), floor) %>%
  604. mutate_at(vars(ends_with("_upper")), round) %>%
  605. mutate(band = case_when(
  606. frequency >= theta_lower & frequency <= theta_upper ~ "theta",
  607. frequency >= alpha_lower & frequency <= alpha_upper ~ "alpha",
  608. frequency >= beta_lower & frequency <= beta_upper ~ "beta",
  609. frequency < theta_lower ~ "delta"
  610. )) %>%
  611. na.omit(band) %>%
  612. select(-alpha_lower:-alphabeta_upper) %>%
  613. mutate(time_win = case_when(
  614. time_start == 0 ~ "prestim",
  615. time_start < 401 & time_start > 0 ~ "early",
  616. time_start > 401 ~ "late"
  617. )) %>%
  618. filter(time_win != "prestim") %>%
  619. group_by(ID, phase, band, time_win, ch_name) %>%
  620. mutate(z_phase_mean = mean(z_phase_sim)) %>%
  621. ungroup() %>%
  622. select(ID, phase, band, time_win, z_phase_mean, ch_name) %>%
  623. distinct() %>%
  624. pivot_wider(names_from = phase, values_from = z_phase_mean) %>%
  625. mutate(phase_diff = ir-l) %>%
  626. select(-l, -ir) %>%
  627. mutate(time_win = case_when(
  628. time_win == "early" ~ "Early (0-0.4s)",
  629. time_win == "late" ~ "Late (0.4-0.7s)"),
  630. band = case_when(
  631. band == "delta" ~ "Delta",
  632. band == "theta" ~ "Theta",
  633. band == "alpha" ~ "Alpha",
  634. band == "beta" ~ "Beta"
  635. ))
  637. time_order <- c("Early (0-0.4s)", "Late (0.4-0.7s)")
  638. band_order <- c("Delta", "Theta", "Alpha", "Beta")
  640. dnap_cond_predict_lir$time_win <- factor(dnap_cond_predict_lir$time_win, levels = time_order)
  641. dnap_cond_predict_lir$band <- factor(dnap_cond_predict_lir$band, levels = band_order)
  643. dnap.cond_predict_lir <- lmer(phase_diff ~ band * time_win + (1|ID) + (1|ch_name), data = dnap_cond_predict_lir)
  645. Anova(dnap.cond_predict_lir)
  646. #make table
  647. anova_tab <- Anova(dnap.cond_predict_lir)
  648. anova_df <- as.data.frame(anova_tab) %>%
  649. mutate(
  650. Chisq = ifelse(Chisq >= 0.005,
  651. format(round(Chisq, 2), nsmall = 2, scientific = FALSE),
  652. format(signif(Chisq, 1), scientific = FALSE)),
  654. `Pr(>Chisq)` = case_when(
  655. round(`Pr(>Chisq)`, 3) == 0 ~ "<.001",
  656. TRUE ~ sub("^0\\.", ".", format(round(`Pr(>Chisq)`, 3), nsmall = 3))
  657. )
  658. )
  660. dnap.cond_predict_lir_time.plot <- Effect(c("band", "time_win"), dnap.cond_predict_lir, confidence.level=.83) %>%
  661. as_tibble()
  663. (dnap.cond_predict_lir_time.plot1 <- ggplot(dnap.cond_predict_lir_time.plot,
  664. aes(x=band, y=fit, colour=band)) +
  665. geom_point(size = 3) +
  666. geom_errorbar(aes(ymin=lower, ymax=upper), width = 0.2) +
  667. xlab("Band") + ylab("Phase Similarity Shift") +
  668. facet_wrap(~time_win) +
  669. theme_bw() +
  670. theme(
  671. legend.position = "none",
  672. text = element_text(size = 20)) +
  673. scale_colour_manual(values = c("#440154FF", "#33638DFF", "#55C667FF", "#DCE319FF")))
  675. ggsave("D:/DNap/Scripts/classified/plots/R_plots/phase_sim_time_bands_l-to-ir_model_plot.png", plot = dnap.cond_predict_lir_time.plot1, width = 8, height = 6, units = "in")
  676. save_plot("D:/DNap/Scripts/classified/plots/R_plots/svg/phase_sim_time_bands_l-to-ir_model_plot.svg", fig = dnap.cond_predict_lir_time.plot1, width = 16, height = 12)
  678. # band
  679. dnap.cond_predict_lir.plot <- Effect(c("band"), dnap.cond_predict_lir, confidence.level=.83) %>%
  680. as_tibble()
  682. (dnap.cond_predict_lir.plot1 <- ggplot(dnap.cond_predict_lir.plot,
  683. aes(x=band, y=fit, colour=band)) +
  684. geom_point(size = 3) +
  685. geom_errorbar(aes(ymin=lower, ymax=upper), width = 0.2) +
  686. xlab("Band") + ylab("Phase Similarity Shift") +
  687. theme_bw() +
  688. theme(
  689. legend.position = "none",
  690. text = element_text(size = 20)) +
  691. scale_colour_manual(values = c("#440154FF", "#33638DFF", "#55C667FF", "#DCE319FF")))
  693. ggsave("D:/DNap/Scripts/classified/plots/R_plots/phase_sim_bands_l-to-ir_model_plot.png", plot = dnap.cond_predict_lir.plot1, width = 8, height = 6, units = "in")
  694. save_plot("D:/DNap/Scripts/classified/plots/R_plots/svg/phase_sim_bands_l-to-ir_model_plot.svg", fig = dnap.cond_predict_lir.plot1, width = 16, height = 12)
  696. ## treatment contrasts
  698. # band
  699. dnap_cond_predict_lir$band <- as.factor(dnap_cond_predict_lir$band)
  700. contrasts(dnap_cond_predict_lir$band) = contr.treatment(4, base=3) # set each band as the control
  701. # check what the contrasts are numbered
  702. contrasts(dnap_cond_predict_lir$band)
  703. # delta = 1
  704. # theta = 2
  705. # alpha = 3
  706. # beta = 4
  707. # model
  708. dnap.cond_predict_lir_con <- lmer(phase_diff ~ band + (1|ID) + (1|ch_name), data = dnap_cond_predict_lir)
  709. summary(dnap.cond_predict_lir_con)
  711. # band*time
  712. dnap_cond_predict_lir_con <- dnap_cond_predict_lir
  713. dnap_cond_predict_lir_con$band_time <- interaction(dnap_cond_predict_lir_con$time_win, dnap_cond_predict_lir_con$band)
  714. levels(dnap_cond_predict_lir_con$band_time) #check levels
  715. # set base, one condition at a time
  716. # dnap_cond_predict_lir_con$band_time <- relevel(dnap_cond_predict_lir_con$band_time, ref = "Early (0-0.4s).Beta")
  717. dnap_cond_predict_lir_con$band_time <- relevel(dnap_cond_predict_lir_con$band_time, ref = "Late (0.4-0.7s).Alpha")
  718. # model
  719. dnap.band_time_predict_lir_con2.lmm <- lmer(phase_diff ~ band_time + (1|ID) + (1|ch_name), data = dnap_cond_predict_lir_con)
  720. summary(dnap.band_time_predict_lir_con2.lmm)
  722. ```

dnap_class_15_L-IR-DR_models.Rmd, no license · at the source

Overview

Authors: Hayley Bree Caldwell1, Alex Chatburn1, Kurt Lushington1, Simon Hanslmayr2, Sebastian Michelmann3
ORCID iDs: Hayley Bree Caldwell, Alex Chatburn, Kurt Lushington, Simon Hanslmayr, Sebastian Michelmann
  1. School of Psychology, Adelaide University, Adelaide, South Australia, Australia
  2. School of Psychology and Neuroscience, Centre for Neurotechnology, University of Glasgow, Glasgow, United Kingdom
  3. Department of Psychology, New York University, New York City, NY, United States
Institutions: Adelaide University (Australia); The University of Adelaide (Australia); University of Glasgow (United Kingdom); New York University (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1321
Dates: received 23 February 2026; accepted 6 July 2026; published online 31 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1321 · PMID 42549201 · PMCID PMC13430988 · OpenAlex W7167899992
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Spectral & time-frequency
Keywords: memory consolidation, retrieval training, sleep, EEG phase, representational change, gist extraction
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 120 references in the paper

Abstract

Retrieval training (i.e., cued recall) is theorised to induce rapid memory consolidation, similarly to sleep. Across consolidation, related neural representations become increasingly similar; yet, representational change has never been directly compared between sleep and retrieval training to test similarities in their underlying mechanisms. In this study, 30 subjects (27F, 18–34, M = 22.17) completed 4 separate sessions in which they (1) learnt object–word pairs, followed by (2) immediate recognition testing, (3) one of four 120-min interventions (retrieval training, restudy, sleep, or wake), and (4) delayed recognition testing. We compared EEG phase similarity between similar and different objects to assess the time, frequency, and anatomical distribution of representational similarity across encoding (learning to immediate recognition), and each intervention (immediate to delayed recognition). We hypothesised that EEG phase patterns for similar objects would become more similar (i.e., representational merging) across retrieval training and sleep interventions, and predict a greater endorsement of similar-object lures. We found increased representational similarity between similar objects across encoding in the theta-band and occipital sources. Crucially, additional representational merging was only observed across the retrieval training intervention, in the alpha-band and parieto-occipital sources. Despite retrieval training leading to reduced performance in discriminating similar-objects lures, greater representational merging across retrieval training predicted greater discrimination of similar-object lures. Together, these findings suggest that sleep and retrieval training induce different memory transformations across equivalent timescales. Retrieval training may generally provoke rapid gist extraction, with greater neocortical integration supporting episodic discrimination. Conversely, sleep may selectively maintain short-term task-relevant episodic and semantic details.

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 16 matches between paragraphs and lines of code.

OSF mygh9

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (41), R (10)
Size: 166 files, 51 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Holds: README, 10 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: MNE-Python (41 files), Matplotlib (39 files), NumPy (37 files), SciPy (32 files), car (10 files), easystats (10 files), emmeans (10 files), lme4 (10 files), tidyverse (10 files), pandas (8 files), seaborn (5 files), autoreject (4 files), ggpubr (4 files), Nilearn (4 files), NiBabel (3 files), rstatix (3 files), lmerTest (2 files), broom (1 file), Pingouin (1 file), Plotly (1 file), scikit-learn (1 file), YASA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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52 files
At the source: doi.org/10.17605/OSF.IO/MYGH9

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;
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Data

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Data and Code Availability

The data and code used to produce all reported analyses are publicly available on Open Science Framework: https://doi.org/10.17605/OSF.IO/MYGH9. Access to the full dataset collected is available upon request by contacting the corresponding author.

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

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Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 118 references.

Cite

This paper

Caldwell, H. B., Chatburn, A., Lushington, K., Hanslmayr, S., & Michelmann, S. (2026). Phase similarity between similar objects indicates representational merging across retrieval training but not sleep. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1321. https://doi.org/10.1162/imag.a.1321

BibTeX

@article{caldwell2026phase,
author = {Caldwell, Hayley Bree and Chatburn, Alex and Lushington, Kurt and Hanslmayr, Simon and Michelmann, Sebastian},
title = {{Phase similarity between similar objects indicates representational merging across retrieval training but not sleep}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1321},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1321},
url = {https://doi.org/10.1162/imag.a.1321},
pmid = {42549201},
pmcid = {PMC13430988}
}

RIS

TY - JOUR
AU - Caldwell, Hayley Bree
AU - Chatburn, Alex
AU - Lushington, Kurt
AU - Hanslmayr, Simon
AU - Michelmann, Sebastian
TI - Phase similarity between similar objects indicates representational merging across retrieval training but not sleep
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/31
VL - 4
SP - IMAG.a.1321
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1321
UR - https://doi.org/10.1162/imag.a.1321
LA - en
ER -

CSL-JSON

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"title": "Phase similarity between similar objects indicates representational merging across retrieval training but not sleep",
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"author": [
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"family": "Caldwell",
"given": "Hayley Bree"
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"family": "Chatburn",
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"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
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"DOI": "10.1162/imag.a.1321",
"PMID": "42549201",
"PMCID": "PMC13430988",
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"date-parts": [
[
2026,
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The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1093/nc/niag029 [code]
A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.
Journal: Neuroscience of consciousness
In common: autoreject, Pingouin, easystats, 15 other tools, cognitive, 2 references
[2] doi:10.1038/s41597-026-07350-9 [code]
An open multi-center MEG-EEG dataset for studying conscious visual perception.
Journal: Scientific data
In common: autoreject, Pingouin, easystats, 13 other tools, EEG
[3] doi:10.1016/j.isci.2026.116586 [code]
Condition-specific neural signatures of reactivation during post-retrieval rest: An EEG study.
Journal: iScience
In common: MNE-Python, seaborn, scikit-learn, 4 other tools, EEG, cognitive, 8 references
[4] doi:10.1038/s41597-026-07377-y [code]
An open-access multi-site fMRI dataset for investigating conscious visual perception.
Journal: Scientific data
In common: autoreject, Pingouin, easystats, 13 other tools
[5] doi:10.1162/imag.a.1245 [code]
Towards precision EEG connectomics: Evaluating the benefits of dense sampling.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: autoreject, Pingouin, rstatix, 12 other tools, EEG
[6] doi:10.1093/cercor/bhag113 [code]
Long-term reliability and stability of parameterized resting state EEG: evidence from a five-year follow-up.
Journal: Cerebral cortex (New York, N.Y. : 1991)
In common: easystats, broom, emmeans, 11 other tools, EEG, 2 references
[7] doi:10.1038/s41467-026-74824-0 [code]
Learning regularities in noise engages both neural predictive activity and representational changes.
Journal: Nature communications
In common: autoreject, Pingouin, easystats, 10 other tools, cognitive, 2 references
[8] doi:10.1038/s41598-026-41129-7 [code]
Non-linear relationships between auditory mismatch responses and the inharmonicity of complex sounds.
Journal: Scientific reports
In common: autoreject, broom, emmeans, 9 other tools, EEG, cognitive, 3 references
[9] doi:10.34133/csbj.0042 [code]
Using Steady-State Visual Evoked Potentials to Characterize Wide-Ranging Retinopathy Linked to &lt;i&gt;CRB1&lt;/i&gt;: Implications for Clinical Trials.
Journal: Computational and structural biotechnology journal
In common: Pingouin, easystats, car, 12 other tools, EEG
[10] doi:10.1073/pnas.2603114123 [code]
The human hippocampus can pattern separate memories by meaning.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: rstatix, easystats, broom, 10 other tools, cognitive, 1 reference

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