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The longitudinal development of intrinsic timescales in infancy and their relation to alpha brain rhythm.

Code ↔ Paper

9 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 9 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Analysis › Relationship between intrinsic timescales and properties of alpha activity ↔ 5_Tau_Correlations_AlphaProperties.R, lines 40–128 · score 0.72 · alpha band, alpha properties, oscillatory power, peak frequency, R2, fit
  2. [2] § Materials and methods › Analysis › Longitudinal analysis ↔ 4_Tau_Development_LMMAnalysis.R, lines 71–128 · score 0.70 · random slope, random intercept, lme4, LMM, Squared, covariates
  3. [3] § Materials and methods › Electroencephalography parameters › Intrinsic timescales ↔ tau_estimate.py, lines 167–234 · score 0.67 · exponential fit models, autocorrelation decays, optimization, curves, lag, timescale
  4. [4] § Materials and methods › Analysis › Relationship between intrinsic timescales and properties of alpha activity ↔ AdditionalScripts/SAF_ModelFit_OscillatoryPowerSpectrum.R, the whole file · a weak match · score 0.61 · power spectrum, oscillatory power, peak frequency, fit, band, models
  5. [5] § Materials and methods › Electroencephalography parameters › Alpha-band oscillatory parameters and rhythmicity ↔ AdditionalScripts/SAF_ModelFit_OscillatoryPowerSpectrum.R, the whole file · a weak match · score 0.61 · oscillatory power, power spectrum, peak frequency, absolute, band, exploratory
  6. [6] § Materials and methods › Electroencephalography parameters › Intrinsic timescales ↔ tau_estimate.py, lines 167–234 · score 0.60 · exponential fit, curve fit, optimize, decay, autocorrelation, epoch
  7. [7] § Results › Early development of the intrinsic timescales ↔ 4_Tau_Development_LMMAnalysis.R, lines 71–128 · score 0.55 · random intercept, best model, slope, LMM, Squared, exploratory
  8. [8] § Materials and methods › Electroencephalography parameters › Alpha-band oscillatory parameters and rhythmicity ↔ 5_Tau_Correlations_AlphaProperties.R, lines 395–436 · score 0.54 · Lagged coherence, alpha band, metric, burst, properties
  9. [9] § Materials and methods › Electroencephalography parameters › Alpha-band oscillatory parameters and rhythmicity ↔ 5_Tau_Correlations_AlphaProperties.R, lines 40–128 · score 0.51 · alpha band, oscillatory power, R2, fit, peak, models

Paper

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The authors' code

R · 684 lines · 30 KB · MIT · 3 matches

  1. # ITS Tau correlation with the oscillatory and rhythmic alpha variables ---------------------------
  3. ## Load the required packages -------------------------------------------------
  4. required_packages <- c(
  5. "tidyverse", # Load this first because it includes ggplot2 and dplyr
  6. "ggpubr",
  7. "psych",
  8. "reshape2",
  9. "flextable",
  10. "rempsyc",
  11. "ppcor",
  12. "boot")
  14. new_packages <- required_packages[!required_packages %in% installed.packages()[,"Package"]]
  16. if (length(new_packages) > 0) {
  17. install.packages(new_packages)
  18. }
  20. sapply(required_packages, library, character.only = TRUE)
  23. ## Functions and plot settings ------------------------------------------------
  25. ## Plot themes -----------------
  26. settheme = theme(strip.text.x = element_text(size = 9.5, face = "bold"), strip.text.y = element_text(size = 9.5, face = "bold")) +
  27. theme(axis.text.x = element_text(size = 10), axis.text.y = element_text(size = 10), title = element_text(size = 10), axis.title.x = element_text(size = 10, face = "bold"), axis.title.y = element_text(size = 10, face = "bold"))
  29. ## Partial correlation function for bootstrapping-----------------------
  30. partial_corr <- function(data, indices) {
  31. cormat <- data[indices, ] # Resample with replacement
  32. pcor_results <- ppcor::pcor.test(cormat$tau, cormat$voi, cormat[c("pernan", "rsq")], method = "spearman") # alpha oscillatory power also controls for R2
  33. return(pcor_results$estimate)}
  35. partial_corr_lag <- function(data, indices) {
  36. cormat <- data[indices, ] # Resample with replacement
  37. pcor_results <- ppcor::pcor.test(cormat$tau, cormat$voi, cormat[c("pernan")], method = "spearman")
  38. return(pcor_results$estimate)}
  40. #set seed for reproducibility
  41. set.seed(42)
  43. ## Load the data ---------------------------------------------------------------
  44. ## Data parameters -------------------------------------------------------------
  45. cohorts = c('exploratory', 'validation')
  47. # Where do you want to save the results?
  48. tablepath = "~/Library/CloudStorage/OneDrive-Personal/Documentos/Papers_msi/Thesis/Shared_Code_and_Data/"
  50. ## Tau data load, preparation, and analysis ------------------------------------
  51. for (cohort in cohorts) {
  52. if (cohort == "exploratory") {
  53. taudata <- read_csv("Desktop/Tau_data/tau_development_exploratory_v4.csv")%>%
  54. rename(sex = Gender, area = Area, channel_number = channel, channel = Label)
  56. } else {
  57. taudata <- read_csv("Desktop/Tau_data/tau_development_validation_v4.csv")%>%
  58. rename(sex = Gender, area = Area, channel_number = channel, channel = Label)}
  60. # Exclude 9999 events (social/high movement) and the epochs with low convergence
  61. taudata_space <- taudata%>%filter(pernan < .25, event != 9999)%>%
  62. group_by(subject, sex, ses_age, channel, area)%>% # Average the remaining epochs
  63. summarise(tau = mean(tauimp, na.rm = T),
  64. pernan = mean(is.na(tauorg), na.rm = T))%>%filter(pernan < .25)
  66. taudata_space <- dplyr::select(taudata_space, subject, sex, ses_age, channel, area, tau, pernan) #select only the relevant variables
  68. taudata <- taudata%>%filter(pernan < .25, event != 9999)%>% # Create a dataset with one value per electrode and area
  69. mutate(trials = mean(n()) / length(unique(channel)), .by = c(subject, ses_age))%>%
  70. group_by(subject, sex, ses_age, channel, area)%>%
  71. summarise(tau = mean(tauimp, na.rm = T),
  72. trials = mean(trials, na.rm = T),
  73. pernan = mean(pernan, na.rm = T))
  75. colnames(taudata) <- c("suj", "sex", "sesage", "elect", "area", "tau","trials", "pernan")
  76. colnames(taudata_space) <- c("suj", "sex", "sesage", "elect", "area", "tau","pernan")
  78. ## 1) Oscillatory alpha activity ----------------------------------------------
  79. ### Load the oscillatory alpha activity data --------------------------------
  80. pow <- read_csv("~/Library/CloudStorage/OneDrive-Personal/Documentos/Papers_msi/Thesis/Shared_Code_and_Data/pow_20Hz_6m_16m_ITS.csv")
  82. pow <- filter(pow, band == "Alpha" & rsquared > .949) # Select only the alpha band and the epochs with good R2
  85. fit_descriptives <- pow%>%filter(incluelect == 1, !is.na(trials), develop_f == 1, suj %in% taudata$suj)%>%
  86. group_by(sesage)%>%
  87. summarise(mtrials = mean(trials, na.rm = T),
  88. sdtrials = sd(trials, na.rm = T),
  89. mrsquared = mean(rsquared, na.rm = T),
  90. sdrsquared = sd(rsquared, na.rm = T))
  92. pow <- pow%>%dplyr::select(suj, sex, sesage, elect, area, oscpow, abspow, relpow, slope, offset, rsquared, peak, freq)%>%
  93. group_by(suj, sex, sesage, elect, area)%>%
  94. summarise(osc = mean(oscpow, na.rm= TRUE),
  95. peak = mean(peak, na.rm = TRUE),
  96. freq = mean(freq, na.rm = TRUE),
  97. rsq = mean(rsquared, na.rm = T))
  99. descriptives <- pow%>%filter(suj %in% taudata$suj) # model fit, power, and peak frequency descriptives
  100. descriptives <- dplyr::select(descriptives, suj, sex, sesage, area, osc, peak, freq, rsq)%>%
  101. group_by(suj, sex, sesage, area)%>%
  102. summarise(osc = mean(osc, na.rm = T),
  103. freq= mean(freq[peak==1], na.rm = T),
  104. rsq = mean(rsq, na.rm = T))
  106. descriptives <- melt(descriptives, id.vars = c("suj", "sex", "sesage", "area"))%>% # parse the descriptives to more standardized name
  107. group_by(sesage, sex, variable, area)%>%
  108. summarise(m = mean(as.double(value), na.rm = T),
  109. sd =sd(value,na.rm = T))
  111. descriptives$m <- round(descriptives$m, 2) # round descriptives to the second decimal point
  112. descriptives$sd <- round(descriptives$sd, 2)
  114. descriptives$stat <- paste(descriptives$m, " (", descriptives$sd, ")", sep ="") # create a statistic column with the mean and sd as m (sd)
  116. descriptives <- descriptives%>%dplyr::select(-m,-sd) # remove the mean and sd columns
  118. desctable <- dcast(descriptives, variable + sesage + sex ~ area) # reshape the descriptive table
  120. desctable <- nice_table(desctable, separate.header = F) # Formated table
  122. path2table = paste(tablepath, "oscpow_descriptives_", cohort, ".docx", sep="")
  123. save_as_docx(desctable,path = path2table) # save the table
  125. pow <- melt(pow, id.vars = c("suj", "sex", "sesage", "elect", "area", "peak"))
  127. varnames <- as.vector(unique(pow$variable))[1:2] # frequency and oscillatory power
  128. ses <- c(6,9,16)
  130. ### Within-participant correlations between alpha oscillatory activity and tau --------------------
  131. session = NA
  132. variable = NA
  133. rs = NA
  134. n = NA
  135. ci_min = NA
  136. ci_max = NA
  137. pval = NA
  140. sesidx = 0
  141. loopidx = 1
  142. for (age in ses){
  143. sesidx = sesidx + 1
  144. varidx = 0
  146. data1 <- taudata%>%filter(sesage == age)%>% # prepare tau data filtering only the relevant age
  147. group_by(suj, sex)%>%summarise(tau = mean(tau, na.rm = TRUE),
  148. trials = mean(trials, na.rm = TRUE),
  149. pernan = mean(trials, na.rm = TRUE))
  151. for (items in varnames) {
  152. varidx = varidx + 1
  154. if (varnames[varidx] != "freq"){ # we divide into frequency and oscillatory because frequency can only be computed in electrodes that had an oscillatory peak (i.e., peak == 1)
  155. data2 <- pow%>%filter(sesage == ses[sesidx] & (variable == varnames[varidx] | variable == "rsq"))%>% # select the variable of interest and the rsq
  156. group_by(suj, variable)%>%summarise(M = mean(value, na.rm = T))%>%dcast(suj ~ variable)
  157. colnames(data2) <- c("suj", "voi", "rsq") # rename the variable of interest to "voi" so it matches the general function below
  158. } else {
  159. data2 <- pow%>%filter(sesage == ses[sesidx] & (variable == varnames[varidx] | variable == "rsq") & peak == 1)%>%
  160. group_by(suj, variable)%>%summarise(M = mean(value, na.rm = T))%>%dcast(suj ~ variable)
  161. colnames(data2) <- c("suj", "voi", "rsq")
  163. }
  165. cormat <-merge(data1,data2, by = "suj") #merge tau and oscpow with tau data
  166. cor_results <- pcor.test(cormat$tau, cormat$voi, cormat[c("pernan", "rsq")], method = "spearman") #partial correlation controlling for rsq and percentage of nan electrodes
  168. boot_results <- boot(cormat, partial_corr, R = 5000) # bootstrap the partial correlation to create the confidence intervals
  169. ci <- boot.ci(boot_results, type = "perc") # save the CI
  171. #store the results
  172. session[loopidx] = age
  173. variable[loopidx] = items
  174. rs[loopidx] = cor_results$estimate
  175. n[loopidx] = cor_results$n
  176. ci_min[loopidx] = ci$percent[4]
  177. ci_max[loopidx] = ci$percent[5]
  178. pval[loopidx] = cor_results$p
  179. loopidx = loopidx + 1
  180. }
  181. }
  183. session <- c(session)
  184. variable <- c(variable)
  185. rs <- c(rs)
  186. n <- c(n)
  187. ci_min <- c(ci_min)
  188. ci_max <- c(ci_max)
  189. pval <- c(pval)
  191. pval <- p.adjust(pval, method = "fdr") # correct with FDR
  192. results_correlation <- data.frame(n, session, variable, rs, pval, ci_min, ci_max, stringsAsFactors = TRUE) # create data frame with the results
  195. results_correlation$significance[results_correlation$pval < .001] <- "***" # significance levels after FDR
  196. results_correlation$significance[results_correlation$pval < .01 & results_correlation$pval > .001] <- "**"
  197. results_correlation$significance[results_correlation$pval < .05 & results_correlation$pval > .01] <- "*"
  198. results_correlation$significance[results_correlation$pval < .1 & results_correlation$pval > .05] <- ""
  199. results_correlation$significance[results_correlation$pval > .1] <- ""
  201. results_correlation$session[results_correlation$session==6] <- "6-mo." #rename the ages
  202. results_correlation$session[results_correlation$session==9] <- "9-mo."
  203. results_correlation$session[results_correlation$session==16] <- "16-mo."
  205. results_correlation$session <- factor(results_correlation$session, levels = c("6-mo.", "9-mo.", "16-mo.")) #reorder the ages
  208. results_table <- dplyr::select(results_correlation, c("session", "n", "variable", "rs", "ci_min", "ci_max", "significance")) #select significant variables
  209. results_table$rs <- round(results_table$rs, 2) #round parameters
  210. results_table$ci_min <- round(results_table$ci_min,2)
  211. results_table$ci_max <- round(results_table$ci_max,2)
  213. results_table$stats <- paste(results_table$rs, results_table$significance, " [", results_table$ci_min, " - ", results_table$ci_max, "]", sep ="") #create a single correlation value as r-significance [ci]
  214. results_table <- dplyr::select(results_table, c("session", "variable", "n", "stats")) # Keep only the relevant columns
  216. results_table <- dcast(results_table, session + n ~ variable, value.var = "stats") #format the table
  218. path2table = paste(tablepath, "tau_correlation_powvars_", cohort, ".docx", sep ="")
  219. save_as_docx(nice_table(results_table),path = path2table) #save a formated table as docx
  222. #### Within-participant correlation plots --------------------------------------
  223. powplot <- pow%>% filter(variable != 'rsq')%>%
  224. group_by(suj, sesage)%>%
  225. summarise(freq = mean(value[variable == "freq" & peak == 1], na.rm = TRUE),
  226. osc = mean(value[variable == "osc"], na.rm = TRUE))
  228. powplot <- melt(powplot, id.vars = c("suj", "sesage")) # reshape to long format
  230. tauplot <- taudata%>% # prepare tau data filtering only the relevant age
  231. group_by(suj, sesage)%>%summarise(tau = mean(tau, na.rm = TRUE),
  232. trials = mean(trials, na.rm = TRUE),
  233. pernan = mean(trials, na.rm = TRUE))
  235. plotdataset <- merge(powplot, tauplot, by = c("suj", "sesage"))
  237. plotdataset$sesage[plotdataset$sesage == 6] <- "6-mo."
  238. plotdataset$sesage[plotdataset$sesage == 9] <- "9-mo."
  239. plotdataset$sesage[plotdataset$sesage == 16] <- "16-mo."
  241. path2plot <- paste(tablepath, "individualcor_its_osc_", cohort, ".svg", sep ="")
  242. plot_osc = ggplot(filter(plotdataset, variable == "osc"), aes(y=tau, x=value)) +
  243. geom_smooth(method = "lm", se = T, color = "black", alpha = .30, fill = "grey75") + ggpubr::theme_pubr() +
  244. geom_point(aes(color = "lightpink")) +
  245. facet_grid(.~factor(sesage, levels = c("6-mo.", "9-mo.", "16-mo.")), scales = "free") +
  246. theme(legend.position = "none") +
  247. settheme +
  248. xlab("Oscillatory Power") +
  249. ylab("Tau (s)")
  251. plot_freq = ggplot(filter(plotdataset, variable == "freq"), aes(y=tau, x=value)) +
  252. geom_smooth(method = "lm", se = T, color = "black", alpha = .30, fill = "grey75") + ggpubr::theme_pubr() +
  253. geom_point(aes(color = "lightpink")) +
  254. facet_grid(.~factor(sesage, levels = c("6-mo.", "9-mo.", "16-mo.")), scales = "free") +
  255. theme(legend.position = "none") +
  256. settheme +
  257. xlab("Peak Frequency (Hz)") +
  258. ylab("Tau (s)")
  260. ggpubr::ggarrange(plot_osc, plot_freq, nrow = 2)
  261. ggsave(path2plot, width = 14.2, height = 11, units = "cm")
  263. ### Space correlations between alpha oscillatory actiivty and tau --------------
  264. varnames <- as.vector(unique(pow$variable))[1:2]
  265. ses <- c(6,9,16)
  267. session = NA
  268. variable = NA
  269. rs = NA
  270. n = NA
  271. ci_min = NA
  272. ci_max = NA
  273. pval = NA
  275. loopidx = 1
  276. for (sesi in ses){
  278. data1 <- taudata_space%>%filter(sesage == sesi)%>% #Now we reorder by electrode
  279. group_by(elect)%>%summarise(tau = mean(tau, na.rm = TRUE),
  280. pernan = mean(pernan, na.rm = TRUE))
  282. for (vari in varnames) {
  283. varidx = varidx + 1
  285. if (vari != "freq"){
  286. data2 <- pow%>%filter(sesage == sesi & (variable == vari | variable == "rsq"))%>%
  287. group_by(elect, variable)%>%summarise(M = mean(value, na.rm = T))%>%dcast(elect ~ variable)
  288. colnames(data2) <- c("elect", "voi", "rsq")
  289. } else {
  290. data2 <- pow%>%filter(sesage == sesi & (variable == vari | variable == "rsq") & peak == 1)%>%
  291. group_by(elect, variable)%>%summarise(M = mean(value, na.rm = T))%>%dcast(elect ~ variable)
  292. colnames(data2) <- c("elect", "voi", "rsq")
  294. }
  296. cormat <-merge(data1,data2, by = "elect")
  297. cor_results <- pcor.test(cormat$tau, cormat$voi, cormat[c("pernan", "rsq")], method = "spearman")
  299. boot_results <- boot(cormat, partial_corr, R = 5000)
  300. ci <- boot.ci(boot_results, type = "perc")
  302. session[loopidx] = sesi
  303. variable[loopidx] = vari
  304. rs[loopidx] = cor_results$estimate
  305. n[loopidx] = cor_results$n
  306. ci_min[loopidx] = ci$percent[4]
  307. ci_max[loopidx] = ci$percent[5]
  308. pval[loopidx] = cor_results$p
  309. loopidx = loopidx + 1
  311. }
  312. }
  314. session <- c(session)
  315. varaible <- c(variable)
  316. rs <- c(rs)
  317. n <- c(n)
  318. ci_min <- c(ci_min)
  319. ci_max <- c(ci_max)
  320. pval <- c(pval)
  322. pval <- p.adjust(pval, method = "fdr")
  323. results_correlation <- data.frame(n, session, variable, rs, pval, ci_min, ci_max, stringsAsFactors = TRUE)
  326. results_correlation$significance[results_correlation$pval < .001] <- "***"
  327. results_correlation$significance[results_correlation$pval < .01 & results_correlation$pval > .001] <- "**"
  328. results_correlation$significance[results_correlation$pval < .05 & results_correlation$pval > .01] <- "*"
  329. results_correlation$significance[results_correlation$pval < .1 & results_correlation$pval > .05] <- ""
  330. results_correlation$significance[results_correlation$pval > .1] <- ""
  332. results_correlation$session[results_correlation$session==6] <- "6-mo."
  333. results_correlation$session[results_correlation$session==9] <- "9-mo."
  334. results_correlation$session[results_correlation$session==16] <- "16-mo."
  336. results_correlation$session <- factor(results_correlation$session, levels = c("6-mo.", "9-mo.", "16-mo."))
  339. results_table <- dplyr::select(results_correlation, c("session", "n", "variable", "rs", "ci_min", "ci_max", "significance"))
  340. results_table$rs <- round(results_table$rs, 2)
  341. results_table$ci_min <- round(results_table$ci_min,2)
  342. results_table$ci_max <- round(results_table$ci_max,2)
  344. results_table$stats <- paste(results_table$rs, results_table$significance, " [", results_table$ci_min, " - ", results_table$ci_max, "]", sep ="")
  345. results_table <- dplyr::select(results_table, c("session", "variable", "n", "stats"))
  347. results_table <- dcast(results_table, session + n ~ variable, value.var = "stats")
  349. path2table = paste(tablepath, "tau_correlation_powvars_space_", cohort, ".docx", sep ="")
  350. save_as_docx(nice_table(results_table),path = path2table)
  353. #### Space correlation plots --------------------------------------------------
  354. powplot <- pow%>% filter(variable != 'rsq')%>%
  355. group_by(sesage, elect, area)%>%
  356. summarise(freq = mean(value[variable == "freq" & peak == 1], na.rm = TRUE),
  357. osc = mean(value[variable == "osc"], na.rm = TRUE))
  359. powplot <- melt(powplot, id.vars = c("sesage", "elect", "area")) # reshape to long format
  361. tauplot <- taudata_space%>%
  362. group_by(sesage, elect, area)%>%summarise(tau = mean(tau, na.rm = TRUE))
  364. plotdataset <- merge(powplot, tauplot, by = c("sesage", "elect", "area"))
  366. plotdataset$sesage[plotdataset$sesage == 6] <- "6-mo."
  367. plotdataset$sesage[plotdataset$sesage == 9] <- "9-mo."
  368. plotdataset$sesage[plotdataset$sesage == 16] <- "16-mo."
  370. plotdataset$sesage <- factor(plotdataset$sesage, levels = c("6-mo.", "9-mo.", "16-mo."))
  372. path2plot <- paste(tablepath, "spatial_stability_its_osc_", cohort, ".svg", sep ="")
  373. plot_osc = ggplot(filter(plotdataset, variable == "osc"), aes(y=tau, x=value)) + geom_point(aes(color = area)) +
  374. geom_smooth(method = "lm", color = "black", se = T, alpha = .30, fill = "grey75") +
  375. scale_color_manual(values =c("#C4961A","#D16103", "#C3D7A4", "#52854C", "#4E84C4", "#293352")) +
  376. theme_pubr() + facet_grid(.~factor(sesage, levels = c("6-mo.", "9-mo.", "16-mo.")), scales = "free") +
  377. settheme +
  378. labs(x = "Oscillatory Power",
  379. y = "Tau (s)",
  380. color = "Area")
  382. plot_freq = ggplot(filter(plotdataset, variable == "freq"), aes(y=tau, x=value)) + geom_point(aes(color = area)) +
  383. geom_smooth(method = "lm", color = "black", se = T, alpha = .30, fill = "grey75") +
  384. scale_color_manual(values =c("#C4961A","#D16103", "#C3D7A4", "#52854C", "#4E84C4", "#293352")) +
  385. theme_pubr() + facet_grid(.~factor(sesage, levels = c("6-mo.", "9-mo.", "16-mo.")), scales = "free") +
  386. settheme +
  387. labs(x = "Frequency (Hz)",
  388. y = "Tau (s)",
  389. color = "Area")
  391. ggarrange(plot_osc, plot_freq, nrow = 2, common.legend = T, legend = "bottom")
  392. ggsave(path2plot, width = 16, height = 12, units = "cm")
  395. ## 2) Lagged coherence correlations --------------------------------------------
  396. #Load the raw lagged coherence data and compute the metrics of interest
  397. #pow <- read_csv("~/Library/CloudStorage/OneDrive-Personal/Documentos/Papers_msi/Thesis/Shared_Code_and_Data/pow_burst_rythm_6m_36m_ITS.csv") #Load the lagged coherence data
  398. #pow <- filter(pow, band == "Alpha") # We keep only the alpha band
  399. #pow <- pow%>%dplyr::select(suj, sex, sesage, elect, incluelect, area, lag, lag_pow)%>% # Relevant variables
  400. #group_by(suj, sex, sesage, elect, area)%>%
  401. #summarise(rhythmlag = mean(lag_pow[lag>2.5&lag<3.5], na.rm= TRUE), # Burst lags
  402. #burstlag = mean(lag_pow[lag<2.5], na.rm = TRUE)) # Rhythm lags
  404. ### Save for future use
  405. #write.csv(pow, "~/Library/CloudStorage/OneDrive-Personal/Documentos/Papers_msi/Thesis/Shared_Code_and_Data/lagged_coherence_alpha_6_16m_metrics.csv", row.names = F)
  407. # Load the dataset with the lagged coherence data
  408. pow <- read_csv("~/Library/CloudStorage/OneDrive-Personal/Documentos/Papers_msi/Thesis/Shared_Code_and_Data/lagged_coherence_alpha_6_16m_metrics.csv")
  410. descriptives <- pow%>%filter(suj %in% taudata$suj)
  412. descriptives <- melt(descriptives, id.vars = c("suj", "sex", "sesage", "elect", "area"))%>% #Descriptive table
  413. group_by(sesage, sex, variable, area)%>%
  414. summarise(m = mean(as.double(value), na.rm = T),
  415. sd =sd(value,na.rm = T))
  417. descriptives$m <- round(descriptives$m, 2)
  418. descriptives$sd <- round(descriptives$sd, 2)
  420. descriptives$stat <- paste(descriptives$m, " (", descriptives$sd, ")", sep ="")
  422. descriptives <- descriptives%>%dplyr::select(-m,-sd)
  424. desctable <- dcast(descriptives, variable + sesage + sex ~ area)
  427. desctable <- nice_table(desctable, separate.header = F)
  429. path2table = paste(tablepath, "lagcoh_descriptives_", cohort, ".docx", sep="")
  430. save_as_docx(desctable,path = path2table)
  433. pow <- melt(pow, id.vars = c("suj", "sex", "sesage", "elect", "area"))
  435. varnames <- as.vector(unique(pow$variable))
  436. ses <- c(6,9,16)
  439. #### Within-subject correlations between lagged coherence and tau -------------
  440. session = NA
  441. variable = NA
  442. rs = NA
  443. n = NA
  444. ci_min = NA
  445. ci_max = NA
  446. pval = NA
  448. loopidx = 1
  449. for (sesi in ses){
  451. data1 <- taudata%>%filter(sesage == sesi)%>%
  452. group_by(suj)%>%summarise(tau = mean(tau, na.rm = TRUE),
  453. pernan = mean(pernan, na.rm = TRUE))
  455. for (vari in varnames) {
  457. if (vari != "freq"){
  458. data2 <- pow%>%filter(sesage == sesi & variable == vari)%>%
  459. group_by(suj)%>%summarise(voi = mean(value, na.rm = TRUE))}
  460. else {
  461. data2 <- filter(pow, peak == 1)
  462. data2 <- data2%>%filter(sesage == sesi & variable == vari)%>%
  463. group_by(suj)%>%summarise(voi = mean(value, na.rm = TRUE))
  464. }
  466. cormat <-merge(data1,data2, by = "suj")
  468. cor_results <- pcor.test(cormat$tau, cormat$voi, cormat[c("pernan")], method = "spearman")
  471. boot_results <- boot(cormat, partial_corr_lag, R = 5000) # Different correlation function because we cannot control for R2
  472. ci <- boot.ci(boot_results, type = "perc")
  474. session[loopidx] = sesi
  475. variable[loopidx] = vari
  476. rs[loopidx] = cor_results$estimate
  477. n[loopidx] = cor_results$n
  478. ci_min[loopidx] = ci$percent[4]
  479. ci_max[loopidx] = ci$percent[5]
  480. pval[loopidx] = cor_results$p
  481. loopidx = loopidx + 1
  482. }
  483. }
  485. session <- c(session)
  486. varaible <- c(variable)
  487. rs <- c(rs)
  488. n <- c(n)
  489. ci_min <- c(ci_min)
  490. ci_max <- c(ci_max)
  491. pval <- c(pval)
  493. pval <- p.adjust(pval, method = "fdr")
  494. results_correlation <- data.frame(n, session, variable, rs, pval, ci_min, ci_max, stringsAsFactors = TRUE)
  497. results_correlation$significance[results_correlation$pval < .001] <- "***"
  498. results_correlation$significance[results_correlation$pval < .01 & results_correlation$pval > .001] <- "**"
  499. results_correlation$significance[results_correlation$pval < .05 & results_correlation$pval > .01] <- "*"
  500. results_correlation$significance[results_correlation$pval < .1 & results_correlation$pval > .05] <- ""
  501. results_correlation$significance[results_correlation$pval > .1] <- ""
  503. results_correlation$session[results_correlation$session==6] <- "6-mo."
  504. results_correlation$session[results_correlation$session==9] <- "9-mo."
  505. results_correlation$session[results_correlation$session==16] <- "16-mo."
  507. results_correlation$session <- factor(results_correlation$session, levels = c("6-mo.", "9-mo.", "16-mo."))
  510. results_table <- dplyr::select(results_correlation, c("session", "n", "variable", "rs", "ci_min", "ci_max", "significance"))
  511. results_table$rs <- round(results_table$rs, 2)
  512. results_table$ci_min <- round(results_table$ci_min,2)
  513. results_table$ci_max <- round(results_table$ci_max,2)
  515. results_table$stats <- paste(results_table$rs, results_table$significance, " [", results_table$ci_min, " - ", results_table$ci_max, "]", sep ="")
  516. results_table <- dplyr::select(results_table, c("session", "variable", "n", "stats"))
  518. results_table <- dcast(results_table, session + n ~ variable, value.var = "stats")
  520. path2table = paste(tablepath, "tau_correlation_brythm_", cohort, ".docx", sep ="")
  521. save_as_docx(nice_table(results_table),path = path2table)
  523. #### Within participants correlation plots ------------------------------------
  525. powplot <- pow%>%group_by(suj, sesage, variable)%>%
  526. summarise(value = mean(value, na.rm = TRUE))
  528. tauplot <- taudata%>% # prepare tau data filtering only the relevant age
  529. group_by(suj, sesage)%>%summarise(tau = mean(tau, na.rm = TRUE))
  531. plotdataset <- merge(powplot, tauplot, by = c("suj", "sesage"))
  533. plotdataset <- filter(plotdataset, variable == "rhythmlag" | variable == "burstlag")
  535. plotdataset$sesage[plotdataset$sesage == 6] <- "6-mo."
  536. plotdataset$sesage[plotdataset$sesage == 9] <- "9-mo."
  537. plotdataset$sesage[plotdataset$sesage == 16] <- "16-mo."
  539. plotdataset$sesage <- factor(plotdataset$sesage, levels = c("6-mo.", "9-mo.", "16-mo."))
  541. path2plot <- paste(tablepath, "individualcor_its_lcoh_", cohort, ".svg", sep ="")
  542. plot_osc = ggplot(filter(plotdataset, variable == "burstlag"), aes(y=tau, x=value)) +
  543. geom_smooth(method = "lm", se = T, color = "black", alpha = .30, fill = "grey75") + ggpubr::theme_pubr() +
  544. geom_point(aes(color = "lightpink")) +
  545. facet_grid(.~factor(sesage, levels = c("6-mo.", "9-mo.", "16-mo.")), scales = "free") +
  546. theme(legend.position = "none") +
  547. settheme +
  548. xlab("Lagged Coh. Burst") +
  549. ylab("Tau (s)")
  551. plot_freq = ggplot(filter(plotdataset, variable == "rhythmlag"), aes(y=tau, x=value)) +
  552. geom_smooth(method = "lm", se = T, color = "black", alpha = .30, fill = "grey75") + ggpubr::theme_pubr() +
  553. geom_point(aes(color = "lightpink")) +
  554. facet_grid(.~factor(sesage, levels = c("6-mo.", "9-mo.", "16-mo.")), scales = "free") +
  555. theme(legend.position = "none") +
  556. settheme +
  557. xlab("Lagged Coh. Rhythm") +
  558. ylab("Tau (s)")
  560. ggpubr::ggarrange(plot_osc, plot_freq, nrow = 2)
  561. ggsave(path2plot, width = 14.2, height = 11, units = "cm")
  564. #### Space correlations between lagged coherence and tau ----------------------
  566. varnames <- as.vector(unique(pow$variable))
  567. ses <- c(6,9,16)
  570. session = NA
  571. variable = NA
  572. rs = NA
  573. n = NA
  574. ci_min = NA
  575. ci_max = NA
  576. pval = NA
  578. loopidx = 1
  579. for (sesi in ses){
  581. data1 <- taudata_space%>%filter(sesage == sesi)%>%
  582. group_by(elect)%>%summarise(tau = mean(tau, na.rm = TRUE),
  583. pernan = mean(pernan, na.rm = TRUE))
  585. for (vari in varnames) {
  587. data2 <- pow%>%filter(sesage == sesi & variable == vari)%>%
  588. group_by(elect)%>%summarise(voi = mean(value, na.rm = TRUE))
  590. cormat <-merge(data1,data2, by = "elect")
  592. cor_results <- pcor.test(cormat$tau, cormat$voi, cormat[c("pernan")], method = "spearman")
  594. boot_results <- boot(cormat, partial_corr_lag, R = 5000)
  595. ci <- boot.ci(boot_results, type = "perc")
  597. session[loopidx] = sesi
  598. variable[loopidx] = vari
  599. rs[loopidx] = cor_results$estimate
  600. n[loopidx] = cor_results$n
  601. ci_min[loopidx] = ci$percent[4]
  602. ci_max[loopidx] = ci$percent[5]
  603. pval[loopidx] = cor_results$p
  604. loopidx = loopidx + 1
  606. }
  607. }
  609. session <- c(session)
  610. varaible <- c(variable)
  611. rs <- c(rs)
  612. n <- c(n)
  613. ci_min <- c(ci_min)
  614. ci_max <- c(ci_max)
  615. pval <- c(pval)
  617. pval <- p.adjust(pval, method = "fdr")
  618. results_correlation <- data.frame(n, session, variable, rs, pval, ci_min, ci_max, stringsAsFactors = TRUE)
  621. results_correlation$significance[results_correlation$pval < .001] <- "***"
  622. results_correlation$significance[results_correlation$pval < .01 & results_correlation$pval > .001] <- "**"
  623. results_correlation$significance[results_correlation$pval < .05 & results_correlation$pval > .01] <- "*"
  624. results_correlation$significance[results_correlation$pval < .1 & results_correlation$pval > .05] <- ""
  625. results_correlation$significance[results_correlation$pval > .1] <- ""
  627. results_correlation$session[results_correlation$session==6] <- "6-mo."
  628. results_correlation$session[results_correlation$session==9] <- "9-mo."
  629. results_correlation$session[results_correlation$session==16] <- "16-mo."
  631. results_correlation$session <- factor(results_correlation$session, levels = c("6-mo.", "9-mo.", "16-mo."))
  634. results_table <- dplyr::select(results_correlation, c("session", "n", "variable", "rs", "ci_min", "ci_max", "significance"))
  635. results_table$rs <- round(results_table$rs, 2)
  636. results_table$ci_min <- round(results_table$ci_min,2)
  637. results_table$ci_max <- round(results_table$ci_max,2)
  639. results_table$stats <- paste(results_table$rs, results_table$significance, " [", results_table$ci_min, " - ", results_table$ci_max, "]", sep ="")
  640. results_table <- dplyr::select(results_table, c("session", "variable", "n", "stats"))
  642. results_table <- dcast(results_table, session + n ~ variable, value.var = "stats")
  644. path2table = paste(tablepath, "tau_correlation_brythm_space_", cohort, ".docx", sep ="")
  645. save_as_docx(nice_table(results_table),path = path2table)
  647. #### Space correlation plots --------------------------------------------------
  648. plotdataset <- merge(pow, taudata_space, by = c("suj", "sex", "sesage", "elect", "area"))
  650. plotdataset <- plotdataset%>%group_by(elect, sesage, area, variable)%>%
  651. summarise(value = mean(value, na.rm = TRUE),
  652. tau = mean(tau, na.rm = TRUE))
  654. plotdataset <- filter(plotdataset, variable == "rhythmlag" | variable == "burstlag")
  657. plotdataset$sesage[plotdataset$sesage == 6] <- "6-mo."
  658. plotdataset$sesage[plotdataset$sesage == 9] <- "9-mo."
  659. plotdataset$sesage[plotdataset$sesage == 16] <- "16-mo."
  661. plotdataset$sesage <- factor(plotdataset$sesage, levels = c("6-mo.", "9-mo.", "16-mo."))
  663. path2plot <- paste(tablepath, "spatial_stability_its_burstlag_", cohort, ".svg", sep ="")
  664. plot_osc = ggplot(filter(plotdataset, variable == "burstlag"), aes(y=tau, x=value)) + geom_point(aes(color = area)) +
  665. geom_smooth(method = "lm", color = "black", se = T, alpha = .30, fill = "grey75") +
  666. scale_color_manual(values =c("#C4961A","#D16103", "#C3D7A4", "#52854C", "#4E84C4", "#293352")) +
  667. theme_pubr() + facet_grid(.~factor(sesage, levels = c("6-mo.", "9-mo.", "16-mo.")), scales = "free") +
  668. settheme +
  669. labs(x = "Lagged Coh. Burst",
  670. y = "Tau (s)",
  671. color = "Area")
  673. plot_freq = ggplot(filter(plotdataset, variable == "rhythmlag"), aes(y=tau, x=value)) + geom_point(aes(color = area)) +
  674. geom_smooth(method = "lm", color = "black", se = T, alpha = .30, fill = "grey75") +
  675. scale_color_manual(values =c("#C4961A","#D16103", "#C3D7A4", "#52854C", "#4E84C4", "#293352")) +
  676. theme_pubr() + facet_grid(.~factor(sesage, levels = c("6-mo.", "9-mo.", "16-mo.")), scales = "free") +
  677. settheme +
  678. labs(x = "Lagged Coh. Rhythm",
  679. y = "Tau (s)",
  680. color = "Area")
  682. ggarrange(plot_osc, plot_freq, nrow = 2, common.legend = T, legend = "bottom")
  683. ggsave(path2plot, width = 16, height = 12, units = "cm")
  684. }

5_Tau_Correlations_AlphaProperties.R at commit 03333a8, under MIT · at the source

Overview

Authors: Anna Truzzi1, Josué Rico-Picó2, Maria Rosario Rueda3,4, Rhodri Cusack5,6
ORCID iDs: Anna Truzzi
  1. School of Psychology, Queen’s University Belfast, David Keir Building, 18-30 Malone Road, BELFAST, BT9 5BN, United Kingdom
  2. Department of Biobehavioural Sciences, Teachers College, Columbia University, Building 528, 525 W 120th St Suite 1159, New York, NY 10027, United States
  3. Mind, Brain and Behaviour Research Center, University of Granada, Campus de Cartuja, s/n 18071 Granada, Spain
  4. Department of Experimental Psychology, University of Granada, Campus Universitario de Cartuja, s/n18071 Granada, Spain
  5. School of Psychology, Trinity College Dublin, Pearse St, Dublin, 2, Ireland
  6. Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
Institutions: Queen's University Belfast (United Kingdom); Columbia University (United States); Universidad de Granada (Spain); Centro de Investigación Mente, Cerebro y Comportamiento (Spain); Trinity College Dublin (Ireland)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 6, article bhag077
Dates: received 2 February 2026; accepted 18 May 2026; published online 24 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/cercor/bhag077 · PMID 42341187 · PMCID PMC13293257 · OpenAlex W4412034202
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/c462j_v1
Status: code verified
Categories: EEG (modality), human (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Spectral & time-frequency, Connectivity, Preprocessing
Keywords: brain timescales, developmental trajectory, infant, longitudinal, oscillatory, resting state EEG
MeSH: Alpha Rhythm*, Brain*, Child Development*, Adult, Brain Mapping, Electroencephalography, Female, Humans, Infant, Longitudinal Studies, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Child and Animal Learning Development (Developmental and Educational Psychology, Psychology), according to OpenAlex
Funding: ERC Advanced (ERC - 2017-ADG); FOUNDCOG (787981); MCIN/AEI (PID2020-113996GB-I00, PSI2017-82670-P); Fundación Tatiana in Neuroscience
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Adult brain regions differ in the intrinsic timescales (INT) over which they integrate information. This spatial organization appears to emerge gradually: infants’ brain activity recorded during sleep with functional magnetic resonance imaging (fMRI) shows overall longer INT and a different spatial structure. However, since fMRI is sensitive to hemodynamic confounds and is affected by arousal state, these factors may have accounted for observed age-related differences. Here, we used electroencephalography (EEG) to investigate for the first time how INT develop in infancy in a longitudinal sample from 6 to 16-months-old (exploratory cohort, n = 45; validation cohort, n = 45) and adults (n = 10). Infants were awake and engaged in baseline visual protocol, and adults were recorded under comparable (and distinct) conditions. Infant intrinsic timescales shortened from 6 to 16-months but remained longer than those of adults at all ages. Finally, INT correlated with alpha lagged coherence, a metric of self-predictability and, to lesser extent, with alpha peak frequency, suggesting that alpha oscillatory activity may contribute to the emergence of INT. Identifying the mechanisms underlying longer INT early in infancy—a finding replicated across fMRI and EEG—is a crucial step toward understanding the neural computations that allow infants to extract and learn patterns from their environment.

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 9 matches between paragraphs and lines of code.

AnnaTruzzi/longitudinal_EEG_timescales

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 03333a87aa28e8d7454512295f706f80a79f11d7, 22 January 2026
Languages: R (9), Python (6), MATLAB (1)
Size: 32 files, 16 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), ggpubr (6 files), Matplotlib (6 files), NumPy (6 files), pandas (6 files), ggplot2 (5 files), reshape2 (5 files), SciPy (5 files), MNE-Python (4 files), seaborn (3 files), statsmodels (3 files), lme4 (2 files), lmerTest (2 files), psych (2 files), easystats (1 file), EEGLAB (1 file), FieldTrip (1 file), multcomp (1 file), Pingouin (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
18 files
At the source: github.com/AnnaTruzzi/longitudinal_EEG_timescales

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 16 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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

Data availability

Raw data are confidential because they contain sensitive information about infants. The anonymized data reporting the estimated intrinsic timescales together with the code used to process and analyze the data are published in a public GitHub repository (https://github.com/AnnaTruzzi/longitudinal_EEG_timescales).

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 13 MeSH terms, 4 funders, 55 references.

Cite

This paper

Truzzi, A., Rico-Picó, J., Rueda, M. R., & Cusack, R. (2026). The longitudinal development of intrinsic timescales in infancy and their relation to alpha brain rhythm. Cerebral cortex (New York, N.Y. : 1991), 36(6), bhag077. https://doi.org/10.1093/cercor/bhag077

BibTeX

@article{truzzi2026longitudinal,
author = {Truzzi, Anna and Rico-Picó, Josué and Rueda, Maria Rosario and Cusack, Rhodri},
title = {{The longitudinal development of intrinsic timescales in infancy and their relation to alpha brain rhythm}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = jun,
volume = {36},
number = {6},
pages = {bhag077},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag077},
url = {https://doi.org/10.1093/cercor/bhag077},
pmid = {42341187},
pmcid = {PMC13293257}
}

RIS

TY - JOUR
AU - Truzzi, Anna
AU - Rico-Picó, Josué
AU - Rueda, Maria Rosario
AU - Cusack, Rhodri
TI - The longitudinal development of intrinsic timescales in infancy and their relation to alpha brain rhythm
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/06/01
VL - 36
IS - 6
SP - bhag077
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag077
UR - https://doi.org/10.1093/cercor/bhag077
LA - en
ER -

CSL-JSON

{
"id": "10.1093/cercor/bhag077",
"type": "article-journal",
"title": "The longitudinal development of intrinsic timescales in infancy and their relation to alpha brain rhythm",
"container-title": "Cerebral cortex (New York, N.Y. : 1991)",
"author": [
{
"family": "Truzzi",
"given": "Anna"
},
{
"family": "Rico-Picó",
"given": "Josué"
},
{
"family": "Rueda",
"given": "Maria Rosario"
},
{
"family": "Cusack",
"given": "Rhodri"
}
],
"container-title-short": "Cereb Cortex",
"volume": "36",
"issue": "6",
"page": "bhag077",
"DOI": "10.1093/cercor/bhag077",
"PMID": "42341187",
"PMCID": "PMC13293257",
"ISSN": "1047-3211",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/cercor/bhag077",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language.
Journal: PloS one
In common: multcomp, psych, EEGLAB, 6 other tools, EEG, 2 references

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