OSCR

Hybrid spatial organization and evidence for magnitude-independent neural coding of linguistic information during sentence production.

Code ↔ Paper

3 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.

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  1. [1] § MATERIALS AND METHODS › Temporal warping ↔ code.zip/code/temporal warping/1 - simple linear stretch to median RT.R, lines 188–238 · score 0.64 · Temporal warping, speech onset, stimulus onset, median, outlier, epochs
  2. [2] § MATERIALS AND METHODS › Data collection and preprocessing ↔ code.zip/code/preprocessing/6_7_reference_hilbert_epoch.m, lines 237–255 · score 0.61 · spaced frequency bands, Hilbert, high gamma, 70 Hz, amplitude, 150 Hz
  3. [3] § MATERIALS AND METHODS › Temporal warping ↔ code.zip/code/temporal warping/1 - simple linear stretch to median RT.R, lines 188–238 · score 0.61 · median RT, speech onset, stimulus onset, epochs, temporally, warping

Paper

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The authors' code

R · 510 lines · 23 KB · CC-BY-4.0 · 2 matches

  1. ### Linearly interpolate the middle section (150ms post stim to 150ms pre speech) of all trials to have the same RT (i.e., median task RT)
  2. ### May 2023
  3. ### [email hidden]
  5. ###
  6. ### Readme
  7. ###
  10. ###
  11. ### Setup
  12. ###
  14. ### Packages
  15. library('dplyr') # for data organization, including bind_rows()
  17. # Clean up
  18. rm(list=ls())
  19. cat("\014")
  20. message("Begin stretching trials. ",Sys.time())
  22. ### Set path
  23. if(Sys.info()['sysname'] == 'Darwin'){ # Mac
  24. path = '/Users/adam/Dropbox/Research/ChickenSyntax/'
  25. n.cores.to.use = 4
  26. if(Sys.info()['nodename'] == 'FLINKERLABMBP06'){
  27. path = '/Users/am4611/Dropbox/Research/ChickenSyntax/'
  28. n.cores.to.use = 5
  29. }
  30. if(Sys.info()['nodename'] == 'FLINKERLABMS01'){
  31. path = '/Users/am4611/Dropbox/Research/ChickenSyntax/'
  32. n.cores.to.use = 15
  33. }
  34. }
  35. if(Sys.info()['sysname'] == 'Linux'){ # Ubuntu
  36. path = '/home/adam/Dropbox/Research/ChickenSyntax/'
  37. n.cores.to.use = 40
  38. }
  40. ### Lemmas
  41. # Function to convert between samples, sample labels, and times
  42. source(paste0(path,'/analysis/R/functions/time_convert.R'))
  43. # Function for rolling average smoothing
  44. source(paste0(path,'/analysis/R/functions/smoothing.R'))
  45. # Close any old parallel backends
  46. source(paste0(path,'/analysis/R/functions/unregister_dopar.R'))
  47. # Subset to just good picture naming trials
  48. source(paste0(path,'/analysis/R/functions/just_good_trial_functions.R'))
  49. # Plot time series
  50. source(paste0(path,'/analysis/R/functions/plot_time_series.R'))
  51. # Add colored text to plots
  52. source(paste0(path,'/analysis/R/functions/add_text_line_multiple_colors.R'))
  54. set.seed(seed = 404)
  56. # Data type:
  57. for(freq.band.loop in c('high_gamma','beta')){
  58. # freq.band.loop = c('high_gamma','beta')[1]
  59. freq.band.read.path <- list('high_gamma' = 'z_scored', 'beta' = 'z_scored_beta')[[freq.band.loop]]
  61. # Type of hilbert transform
  62. hilbert.type <- 'multiband'
  64. ## Metadata
  65. patients = c('Patient001',
  66. 'Patient002',
  67. 'Patient003', # slow PN RTs; check closely
  68. 'Patient004',
  69. 'Patient005',
  70. 'Patient006', # slow PN RTs; check closely
  71. 'Patient007',
  72. 'Patient008', # slow PN RTs and generally weird
  73. 'Patient009',
  74. 'Patient010')
  76. ## Tasks
  77. tasks <- c('pn','sp','lp')
  79. ## Plot stuff:
  80. load(paste0(path,'analysis/R/color palettes/output/all palettes.RData'))
  81. text.size.big = 1.6
  82. text.size.med = 1.4
  83. text.size.small = 1.2
  84. zoom = 2
  86. # Elec MNI coords, etc.
  87. elec.info <-
  88. read.csv(paste0(path,
  89. '/analysis/R/brain plots/ecog/output/data/elec info/patients - combined/row_labels_and_localizations.csv'))
  90. elec.info$use.these.elecs <-
  91. as.numeric((! elec.info$region_clinical %in% c('Unknown',
  92. '',
  93. 'NaN',
  94. 'Left-Cerebral-White-Matter',
  95. 'Left-Inf-Lat-Vent',
  96. 'Right-Cerebral-White-Matter')) &
  97. (elec.info$bad_elec == 0) &
  98. (elec.info$visual_elec == 0) &
  99. (elec.info$active == 1))
  100. rownames(elec.info) <- elec.info$patient_elec
  101. use.these.elecs <- elec.info[elec.info$use.these.elecs == 1,]$patient_elec
  104. ## Get all trial info across patients in one dataframe to analyze RTs:
  105. # Define range of RTs per patient to use
  106. rt.quantile.range <- c(.025, .95) # .025 just to get rid of weird fast trials (e.g., second of two 'nurse' trials in a row?) and .8 because long tail, probably reflecing lots of different things, so get rid of a bunch. shouldn't matter long-run, still a decent amount of data for NMF clustering
  108. ### Load trial labels
  109. # Initialize storage
  110. trial.info <- list()
  111. for(task.loop in c(tasks,'all')){
  112. trial.info[[task.loop]] <- list()
  113. }; rm(task.loop)
  115. # Loop thru patients and load data
  116. for(patient in rev(patients)){
  117. # patient = patients[1]
  118. print(patient)
  120. # Load data
  121. trial.info[['all']][[patient]] <- read.csv(paste0(path,
  122. 'data/',patient,'/data/epoched/',patient,'_trial_labels_without_bad_trials.csv'))
  123. trial.info[['all']][[patient]]$patient <- patient
  124. trial.info[['all']][[patient]]$rt <- time.convert(trial.info[['all']][[patient]]$production_latency,
  125. "samples", "times")
  127. # Separate by task
  128. trial.info[['pn']][[patient]] <-
  129. just.good.pic.naming(trial.info[['all']][[patient]],
  130. .remove.outliers = TRUE,
  131. .rt.quantile.range = rt.quantile.range)
  132. trial.info[['sp']][[patient]] <-
  133. just.first.word.sentence.data(trial.info[['all']][[patient]],
  134. .remove.outliers = TRUE,
  135. .rt.quantile.range = rt.quantile.range)
  136. trial.info[['lp']][[patient]] <-
  137. just.first.word.list.data(trial.info[['all']][[patient]],
  138. .remove.outliers = TRUE,
  139. .rt.quantile.range = rt.quantile.range)
  140. }; rm(patient)
  142. # Clean up
  143. trial.info$all <- NULL
  144. trial.info[['pn']] <- data.frame(bind_rows(trial.info[['pn']]))
  145. trial.info[['sp']] <- data.frame(bind_rows(trial.info[['sp']]))
  146. trial.info[['lp']] <- data.frame(bind_rows(trial.info[['lp']][patients[patients != 'Patient006']]))
  148. ## Get median RT by tasks -- i.e., target RT for all adjusted trials
  149. half.median.rt.samples <- round(sapply(trial.info, function(x){median(x$production_latency)}) / 2)
  150. median.rt.samples <- 2 * half.median.rt.samples # double rounded halves so always even
  152. # Windows of data keep (i.e., not stretch) pre-stim and post-speech
  153. stimulus.samples.range <- c(time.convert(-500, "times", "samples"), time.convert(150, "times", "samples"))
  154. production.samples.range <- c(time.convert(-150, "times", "samples"), time.convert(1200, "times", "samples"))
  155. stimulus.samples <- stimulus.samples.range[1]:stimulus.samples.range[2]
  156. production.samples <- production.samples.range[1]:production.samples.range[2]
  157. stimulus.sample.labels <- time.convert(
  158. stimulus.samples,
  159. "samples", "sample.labels")
  160. production.sample.labels <- time.convert(
  161. production.samples,
  162. "samples", "sample.labels")
  166. ### Read in data from both time-locks
  167. stretch.data <- list()
  168. patient.trial.info <- list()
  169. elec.means.pre.post <- list()
  170. elec.mean.maxes.pre.post <- list()
  171. for(patient in rev(patients)){
  172. # patient = patients[1]
  174. # Initialize storage
  175. data <- list()
  176. keep.data.stimulus <- list()
  177. keep.data.production <- list()
  179. for(lock.loop in c('locked_to_stimulus_onset','locked_to_production_onset')){
  180. #lock.loop <- c('locked_to_production_onset','locked_to_stimulus_onset')[2]
  182. # Storage
  183. data[[lock.loop]] <- list()
  185. # Progress update
  186. message(patient,": Loading ",lock.loop," data. ",Sys.time())
  188. # Number of linguistic columns
  189. n.lx.cols = read.table(paste0(path,'data/',patient,'/data/epoched/',patient,'_n_linguistic_columns.txt'))[1,1]
  191. # Where data?
  192. data.dir <- paste0(path,'data/',
  193. patient,
  194. '/data/epoched/elec_data_without_bad_trials/',hilbert.type,'/',
  195. lock.loop,'/', # always train on data locked to stimulus onset since we care about early stages!
  196. freq.band.read.path,'/')
  198. # Read in data
  199. message('Attach...')
  200. attach(paste0(data.dir, patient,' elec data.RData')) # reads in list "all.data"
  201. data[[lock.loop]][['all']] <- all.data
  202. detach()
  203. message('...detach!')
  205. ## Limit trials to inner range of RTs
  206. data[[lock.loop]][['pn']] <- lapply(data[[lock.loop]][['all']], function(x){
  207. just.good.pic.naming(x, .remove.outliers = TRUE, .rt.quantile.range = rt.quantile.range)})
  208. data[[lock.loop]][['sp']] <- lapply(data[[lock.loop]][['all']], function(x){
  209. just.first.word.sentence.data(x, .remove.outliers = TRUE, .rt.quantile.range = rt.quantile.range)})
  210. data[[lock.loop]][['lp']] <- lapply(data[[lock.loop]][['all']], function(x){
  211. just.first.word.list.data(x, .remove.outliers = TRUE, .rt.quantile.range = rt.quantile.range)})
  213. # Clean up
  214. data[[lock.loop]][['all']] <- NULL
  215. }; rm(lock.loop)
  217. ## Set aside the "keep" data -- pre-stim and post-speech onsets (with a little extra from the middle)
  218. patient.trial.info[[patient]] <- list()
  219. for(task.loop in tasks){
  220. # task.loop = tasks[1]
  222. # Store the trial info (linguistic columns) for this patient
  223. patient.trial.info[[patient]][[task.loop]] <-
  224. data[['locked_to_production_onset']][[task.loop]][[1]][,1:n.lx.cols]
  226. # Get the "keep" data
  227. keep.data.stimulus[[task.loop]] <-
  228. lapply(data[['locked_to_stimulus_onset']][[task.loop]], function(x){
  229. x <- x[,stimulus.sample.labels]
  230. colnames(x) <- paste0('stim.',colnames(x)) # make unique column names to avoid duplicates
  231. return(x)
  232. })
  233. keep.data.production[[task.loop]] <-
  234. lapply(data[['locked_to_production_onset']][[task.loop]], function(x){
  235. x <- x[,production.sample.labels]
  236. colnames(x) <- paste0('prod.',colnames(x)) # make unique column names to avoid duplicates
  237. return(x)
  238. })
  240. # Remove the linguistic trial data from the data to be stretched
  241. for(lock.loop in c('locked_to_stimulus_onset','locked_to_production_onset')){
  242. data[[lock.loop]][[task.loop]] <-
  243. lapply(data[[lock.loop]][[task.loop]], function(x){
  244. x[,-c(1:n.lx.cols)]
  245. })
  246. }; rm(lock.loop)
  248. # Remove the "keep" data (and any even earlier/later than stimulus/production) from the data to be stretched
  249. data[['locked_to_stimulus_onset']][[task.loop]] <-
  250. lapply(data[['locked_to_stimulus_onset']][[task.loop]], function(x){
  251. x[,-which(time.convert(colnames(x), "sample.labels", "samples") <= max(stimulus.samples))]
  252. })
  253. data[['locked_to_production_onset']][[task.loop]] <-
  254. lapply(data[['locked_to_production_onset']][[task.loop]], function(x){
  255. x[,-which(time.convert(colnames(x), "sample.labels", "samples") >= min(production.samples))]
  256. })
  258. }; rm(task.loop)
  261. ### Set up metadata for stretching data
  262. # Electrodes
  263. elecs <- names(data$locked_to_stimulus_onset$pn)
  265. # Target number of samples
  266. stim.n.samples.out <- half.median.rt.samples - stimulus.samples.range[2]
  267. prod.n.samples.out <- half.median.rt.samples - (-production.samples.range[1])
  269. # Save names for the final, stretched sample labels
  270. stretch.sample.labels <- list()
  271. for(task.loop in tasks){ # task.loop = tasks[1]
  272. stretch.sample.labels[[task.loop]] <- list()
  273. # Get samples
  274. stretch.sample.labels[[task.loop]][['locked_to_stimulus_onset']] <- c(
  275. stimulus.samples.range[1]:(median.rt.samples[[task.loop]] + production.samples.range[2] + 1)
  276. )
  277. stretch.sample.labels[[task.loop]][['locked_to_production_onset']] <- c(
  278. stretch.sample.labels[[task.loop]][['locked_to_stimulus_onset']] - median.rt.samples[[task.loop]]
  279. )
  280. # Convert to sample labels
  281. stretch.sample.labels[[task.loop]][['locked_to_stimulus_onset']] <-
  282. time.convert(stretch.sample.labels[[task.loop]][['locked_to_stimulus_onset']],
  283. "samples", "sample.labels")
  284. stretch.sample.labels[[task.loop]][['locked_to_production_onset']] <-
  285. time.convert(stretch.sample.labels[[task.loop]][['locked_to_production_onset']],
  286. "samples", "sample.labels")
  287. }; rm(task.loop)
  290. ### For each trial, get the "stretch" data as a single list entry -- but *just* the data from 150ms post-stim to 150ms pre-production
  291. # Take the first half from the stim-locked data and second half from the prod-locked data to cover cases where the RT is longer than the epoch
  292. stretch.data[[patient]] <- list()
  293. for(task.loop in tasks){
  294. # task.loop = tasks[3]
  296. message(patient,': Beginning stretching of ',task.loop,' data. ',Sys.time())
  298. # Loop thru elecs
  299. stretch.data[[patient]][[task.loop]] <- list()
  300. for(elec.loop in elecs){
  301. # elec.loop = elecs[1]
  303. .n.trials <- nrow(patient.trial.info[[patient]][[task.loop]])
  304. if(.n.trials == 0){
  305. stretch.data[[patient]][[task.loop]][[elec.loop]]
  306. }else{
  308. # Loop thru trials and interpolate
  309. stretch.data[[patient]][[task.loop]][[elec.loop]] <- list()
  310. for(trial.loop in 1:nrow(patient.trial.info[[patient]][[task.loop]])){
  311. # trial.loop = 1
  313. # Get all the samples from 1 to production onset
  314. .rt.samples <- patient.trial.info[[patient]][[task.loop]]$production_latency[trial.loop]
  315. .all.rt.samples <- (stimulus.samples.range[2] + 1) : (.rt.samples + production.samples.range[1] - 1)
  316. .stim.locked.samples <-
  317. time.convert(.all.rt.samples[c(1:floor(length(.all.rt.samples) / 2))],
  318. "samples", "sample.labels")
  319. .prod.locked.samples <-
  320. time.convert(.all.rt.samples[-c(1:floor(length(.all.rt.samples) / 2))] - .rt.samples,
  321. "samples", "sample.labels")
  323. # Verify that the value at t=0 prod-locked is the same as the value at the RT stim-locked
  324. if(! all(round(data[['locked_to_stimulus_onset']][[task.loop]][[elec.loop]][
  325. trial.loop, c(.stim.locked.samples[length(.stim.locked.samples)],
  326. time.convert(time.convert(.stim.locked.samples[length(.stim.locked.samples)],
  327. "sample.labels", "samples") + 1,
  328. "samples", "sample.labels"))], 6) ==
  329. round(data[['locked_to_production_onset']][[task.loop]][[elec.loop]][
  330. trial.loop, c(time.convert(time.convert(.prod.locked.samples[1],
  331. "sample.labels", "samples") - 1,
  332. "samples", "sample.labels"),
  333. .prod.locked.samples[1])], 6))){
  334. message('WARNING!!! Stim- and prod-locked data not the same!')
  335. }
  337. # Interpolate
  338. .stim.locked.interpolated.data <-
  339. approx(x = time.convert(.stim.locked.samples, "sample.labels", "samples"),
  340. y = unlist(data[['locked_to_stimulus_onset']][[task.loop]][[elec.loop]][trial.loop, .stim.locked.samples]),
  341. n = stim.n.samples.out[[task.loop]])$y
  342. .prod.locked.interpolated.data <-
  343. approx(x = time.convert(.prod.locked.samples, "sample.labels", "samples"),
  344. y = unlist(data[['locked_to_production_onset']][[task.loop]][[elec.loop]][trial.loop, .prod.locked.samples]),
  345. n = prod.n.samples.out[[task.loop]])$y
  347. # Turn into a dataframe
  348. .stim.locked.interpolated.data <- data.frame(matrix(.stim.locked.interpolated.data, nrow = 1))
  349. .prod.locked.interpolated.data <- data.frame(matrix(.prod.locked.interpolated.data, nrow = 1))
  350. colnames(.stim.locked.interpolated.data) <- paste0('stim.', 1:stim.n.samples.out[[task.loop]])
  351. colnames(.prod.locked.interpolated.data) <- paste0('prod.', 1:prod.n.samples.out[[task.loop]])
  353. stretch.data[[patient]][[task.loop]][[elec.loop]][[trial.loop]] <-
  354. cbind(.stim.locked.interpolated.data, .prod.locked.interpolated.data)
  356. rm(.stim.locked.interpolated.data, .prod.locked.interpolated.data, .rt.samples, .all.rt.samples, .stim.locked.samples, .prod.locked.samples)
  357. }; rm(trial.loop)
  359. # Recombine trials into dataframe
  360. stretch.data[[patient]][[task.loop]][[elec.loop]] <- bind_rows(stretch.data[[patient]][[task.loop]][[elec.loop]])
  362. # Add stim-locked prod-locked data back in
  363. stretch.data[[patient]][[task.loop]][[elec.loop]] <- cbind(
  364. keep.data.stimulus[[task.loop]][[elec.loop]],
  365. stretch.data[[patient]][[task.loop]][[elec.loop]],
  366. keep.data.production[[task.loop]][[elec.loop]]
  367. )
  368. colnames(stretch.data[[patient]][[task.loop]][[elec.loop]]) <- stretch.sample.labels[[task.loop]][['locked_to_production_onset']]
  369. } # if(.n.trials > 0){
  371. }; rm(elec.loop)
  372. }; rm(task.loop)
  375. ### Visual check
  376. ## Plot random trials
  377. save.fig.path <- paste0(path,
  378. 'analysis/R/warp time series to standard RT/simple linear stretch to median RT/output - ',
  379. freq.band.loop,
  380. '/figures/',patient,'/')
  381. save.fig.path.trial <- paste0(save.fig.path, 'randomly sampled trials/')
  382. save.fig.path.mean <- paste0(save.fig.path, 'elec means/')
  383. dir.create(save.fig.path.trial, showWarnings = FALSE, recursive = TRUE)
  384. dir.create(save.fig.path.mean, showWarnings = FALSE, recursive = TRUE)
  386. elec.means.pre.post[[patient]] <- list()
  387. elec.mean.maxes.pre.post[[patient]] <- list()
  388. for(task.loop in tasks){
  389. # task.loop = tasks[1]
  391. elec.means.pre.post[[patient]][[task.loop]] <- list()
  392. elec.mean.maxes.pre.post[[patient]][[task.loop]] <- list()
  393. for(elec.loop in elecs){
  394. # elec.loop = elecs[3]
  396. ## Compare difference between [mean unstretched signal] & [mean stretched signal] as a function of signal:
  397. # low signal (noise) the should get lower (noise reduction)
  398. # high signal should get higher if this stretching approach isn't muddying things
  400. .n.trials <- nrow(patient.trial.info[[patient]][[task.loop]])
  401. if(.n.trials > 0){ # if they completed any trials this task (Patient006 didn't do listing block)
  403. # Samples to compare
  404. samples.to.compare <-
  405. time.convert((-(median.rt.samples[task.loop] - stimulus.samples.range[2] - 1)):
  406. (production.samples.range[1]),
  407. "samples", "sample.labels")
  409. # Stretched data
  410. .stretch.mean <- colMeans(stretch.data[[patient]][[task.loop]][[elec.loop]])
  411. .stretch.peak.time <-
  412. time.convert(names(which.max(abs(.stretch.mean))),
  413. "sample.labels", "times")
  415. # Means
  416. elec.means.pre.post[[patient]][[task.loop]][[elec.loop]] <-
  417. data.frame('sample.label' = samples.to.compare,
  418. 'elec' = elec.loop,
  419. 'task' = task.loop,
  420. 'localization' = elec.info[elec.loop, 'region_clinical'],
  421. 'stretch.mean' = abs(.stretch.mean[samples.to.compare]),
  422. row.names = NULL)
  424. # Maxes
  425. elec.mean.maxes.pre.post[[patient]][[task.loop]][[elec.loop]] <-
  426. data.frame('patient' = patient,
  427. 'elec' = elec.loop,
  428. 'task' = task.loop,
  429. 'localization' = elec.info[elec.loop, 'region_clinical'],
  430. 'stretch.max' = max(abs(.stretch.mean[samples.to.compare]), na.rm = TRUE),
  431. 'stretch.peak.time' = .stretch.peak.time,
  432. row.names = NULL)
  434. for(lock.loop in c('locked_to_production_onset','locked_to_stimulus_onset')){
  435. # lock.loop in c('locked_to_production_onset','locked_to_stimulus_onset')[1]
  437. # Redefine samples to compare if stim-locked
  438. if(lock.loop == 'locked_to_stimulus_onset'){
  439. samples.to.compare <-
  440. time.convert((stimulus.samples.range[2]):
  441. (median.rt.samples[task.loop] + production.samples.range[1] - 1),
  442. "samples", "sample.labels")
  443. } # if(lock.loop == 'locked_to_stimulus_onset'){
  445. # Get mean of OG data locked to lock.loop
  446. .original.mean <- colMeans(data[[lock.loop]][[task.loop]][[elec.loop]])
  447. .original.peak.time <-
  448. time.convert(names(which.max(abs(.original.mean))),
  449. "sample.labels", "times")
  451. # Store original mean and peak
  452. elec.means.pre.post[[patient]][[task.loop]][[elec.loop]][,paste0('original.mean_',lock.loop)] <-
  453. abs(.original.mean[samples.to.compare])
  454. elec.mean.maxes.pre.post[[patient]][[task.loop]][[elec.loop]][,paste0('original.max_',lock.loop)] <-
  455. max(abs(.original.mean[samples.to.compare]), na.rm = TRUE)
  456. elec.mean.maxes.pre.post[[patient]][[task.loop]][[elec.loop]][,paste0('original.peak.time_',lock.loop)] <- .original.peak.time
  458. rm(.original.mean)
  459. }; rm(lock.loop)
  460. } # if(.n.trials > 0)
  461. }; rm(elec.loop)
  462. }; rm(task.loop)
  464. ### Clean up
  465. elec.means.pre.post[[patient]] <-
  466. bind_rows(lapply(elec.means.pre.post[[patient]], function(x){bind_rows(x)}))
  467. elec.mean.maxes.pre.post[[patient]] <-
  468. lapply(elec.mean.maxes.pre.post[[patient]], bind_rows)
  470. ### Remove the "keep" data from the "adjust" data
  471. rm(data, keep.data.stimulus, keep.data.production)
  472. }; rm(patient)
  475. ###
  476. ### Save data
  477. ###
  479. ### Save all data
  480. save.these <- c(
  481. 'patient.trial.info',
  482. 'median.rt.samples',
  483. 'stretch.data',
  484. 'stretch.sample.labels',
  485. 'elec.mean.maxes.pre.post'
  486. )
  488. save.data.path <- paste0(path,'analysis/R/warp time series to standard RT/simple linear stretch to median RT/output - ',
  489. freq.band.loop,
  490. '/data/')
  491. dir.create(save.data.path, showWarnings = FALSE, recursive = TRUE)
  492. save(list = save.these,
  493. file = paste0(save.data.path,'elec data with RTs stretched to global median by task - bad trials and .025 to .95 RT outliers excluded.RData'))
  495. ### Save just median.rt.samples
  496. save.data.path <- paste0(path,'analysis/R/warp time series to standard RT/simple linear stretch to median RT/output/data/')
  497. dir.create(save.data.path, showWarnings = FALSE, recursive = TRUE)
  498. save(median.rt.samples,
  499. file = paste0(save.data.path,'median RT samples.RData'))
  501. ### Save just stretch sample labels
  502. save.data.path <- paste0(path,'analysis/R/warp time series to standard RT/simple linear stretch to median RT/output/data/')
  503. dir.create(save.data.path, showWarnings = FALSE, recursive = TRUE)
  504. save(stretch.sample.labels,
  505. file = paste0(save.data.path,'warped sample labels.RData'))
  507. } # freq.band.loop
  509. # Finish!
  510. message('Script completed successfully. ',Sys.time())

1 - simple linear stretch to median RT.R, under CC-BY-4.0 · at the source

Overview

Authors: Adam M Morgan1, Orrin Devinsky1, Werner K Doyle2, Patricia Dugan1, Daniel Friedman1, Adeen Flinker1,3
ORCID iDs: Adam M Morgan, Orrin Devinsky, Werner K Doyle, Patricia Dugan, Daniel Friedman, Adeen Flinker
  1. Department of Neurology, NYU Grossman School of Medicine, New York, NY 10016, USA
  2. Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY 10016, USA
  3. Department of Biomedical Engineering, NYU Tandon School of Engineering, New York, NY 11201, USA
Institutions: New York University (United States)
Journal: Science advances, volume 12, issue 32, article eaec0518
Dates: received 5 September 2025; accepted 1 July 2026; published online 5 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.aec0518 · PMID 42555736 · PMCID PMC13440437 · OpenAlex W7172481444
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), systems (subfield)
Methods: Statistics, Spectral & time-frequency, Preprocessing, Machine learning, fMRI & imaging
MeSH: Language*, Linguistics*, Nerve Net*, Brain Mapping, Electrocorticography, Humans, Semantics (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS109367, R01 NS115929); NIDCD NIH HHS (F32 DC019533, R01 DC018805)
Citations: not cited yet (Europe PMC); 152 references in the paper

Abstract

Humans are the only species with the ability to systematically combine words to convey an unbounded number of complex meanings. This process is guided by combinatorial processes whose underlying neural mechanisms remain obscured by inherent limitations of noninvasive brain measures and a near-total focus on comprehension paradigms. Here, we address these limitations with high-resolution neurosurgical recordings (electrocorticography) and a controlled sentence production experiment. We uncover distinct cortical networks encoding word-level and higher-order information. These networks exhibited a hybrid spatial organization: broadly distributed across traditional language areas but with focal concentrations of sensitivity to semantic and structural contrasts in canonical language regions. In contrast to previous comprehension-based findings, we find that these networks are largely nonoverlapping. Most notably, higher-order linguistic information showed an unexpected dissociation from local activity magnitude. This result establishes an operational dissociation between activity magnitude and information content, pointing toward a potentially distinct neural coding scheme for higher-order language, with important implications for the neurobiology of language.

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

Repository

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Zenodo 20543385

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 5 files
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (22 files), caret (14 files), BayesFactor (2 files), lme4 (2 files), lmerTest (2 files), reticulate (2 files), EEGLAB (1 file), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
41 files
At the source: doi.org/10.5281/zenodo.20543385

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

Tracing map

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Data

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

Data, code, and materials availability

All data, and code needed to evaluate and reproduce the results in the paper are present in the paper, the Supplementary Materials, and/or on Zenodo under the following DOI: https://doi.org/10.5281/zenodo.20543385. This study did not generate new materials.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 MeSH terms, 2 funders, 126 references.

Cite

This paper

Morgan, A. M., Devinsky, O., Doyle, W. K., Dugan, P., Friedman, D., & Flinker, A. (2026). Hybrid spatial organization and evidence for magnitude-independent neural coding of linguistic information during sentence production. Science advances, 12(32), eaec0518. https://doi.org/10.1126/sciadv.aec0518

BibTeX

@article{morgan2026hybrid,
author = {Morgan, Adam M and Devinsky, Orrin and Doyle, Werner K and Dugan, Patricia and Friedman, Daniel and Flinker, Adeen},
title = {{Hybrid spatial organization and evidence for magnitude-independent neural coding of linguistic information during sentence production}},
journal = {Science advances},
year = {2026},
month = aug,
volume = {12},
number = {32},
pages = {eaec0518},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aec0518},
url = {https://doi.org/10.1126/sciadv.aec0518},
pmid = {42555736},
pmcid = {PMC13440437}
}

RIS

TY - JOUR
AU - Morgan, Adam M
AU - Devinsky, Orrin
AU - Doyle, Werner K
AU - Dugan, Patricia
AU - Friedman, Daniel
AU - Flinker, Adeen
TI - Hybrid spatial organization and evidence for magnitude-independent neural coding of linguistic information during sentence production
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/08/05
VL - 12
IS - 32
SP - eaec0518
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aec0518
UR - https://doi.org/10.1126/sciadv.aec0518
LA - en
ER -

CSL-JSON

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"author": [
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"PMCID": "PMC13440437",
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"publisher": "American Association for the Advancement of Science",
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"date-parts": [
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}
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