OSCR

Cortical-limbic circuit dynamics of approach-avoidance conflict in humans.

Code ↔ Paper

10 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 10 matches
  1. [1] § Results › Behavioral Results ↔ analysis/behavior/figure_1_behave_plots.Rmd, lines 436–497 · score 0.69 · face validity, anger, boredom, disinterest, excitement, frustration
  2. [2] § Results › Theta coherence increases over the approach period and falls during avoidance ↔ analysis/coherence/create_sig_theta_coherence_csv.Rmd, lines 240–315 · score 0.66 · Pairwise Phase, connectivity metrics, theta coherence, 1.5 seconds, bins, Sig
  3. [3] § Results › Behavioral Results ↔ analysis/behavior/figure_1_behave_plots.Rmd, lines 285–339 · score 0.61 · risk tolerance, iEEG, turning distance, Variability, game, Figure 1
  4. [4] § Methods › Behavioral task ↔ R/clean_behavioral_data.R, lines 102–186 · score 0.60 · death animation, lost, corridor, caught, exit, game
  5. [5] § Results › Network circuit reorganization after decision to avoid ↔ analysis/attack/fig5_right_mfg_models_and_figures.Rmd, lines 252–283 · score 0.59 · strike trials, chase trials, right MFG, attack, locked, power
  6. [6] § Results › Network circuit reorganization after decision to avoid ↔ analysis/attack/fig5_right_mfg_models_and_figures.Rmd, lines 252–283 · score 0.56 · Strike trial, Chase trials, right MFG, stacks, Attack, locked
  7. [7] § Results › Pairwise synchrony during approach behavior correlates with choice to avoid ↔ analysis/turnaround_time_correlations/turntime_threshold_model_comparison.Rmd, lines 91–158 · score 0.56 · theta coherence thresholds, OFC ACC, HFA synchrony, OFC MFG, predictive, correlated
  8. [8] § Methods › Data preprocessing ↔ across_subject_analyses/scripts/average_tfr_functions.py, lines 49–157 · score 0.56 · trial onset, MNE, channels, preprocessing, epochs, log
  9. [9] § Results › Behavioral Results ↔ analysis/behavior/brms_behavioral_modeling.Rmd, lines 211–258 · score 0.56 · behavioral model, large reward, Male, sex, online, away
  10. [10] § Methods › Bayesian linear mixed effects models ↔ analysis/freq_power_analyses/all_roi_theta_app_av.Rmd, lines 334–460 · score 0.51 · standard deviation, brms, warmup, chains, fit, linear

Paper

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The authors' code

R Markdown · 497 lines · 19 KB · no license · 2 matches

  1. ---
  2. title: "Figure 1 Behavioral Data"
  3. output: html_document
  4. date: "2024-10-14"
  5. ---
  7. ```{r setup, include=FALSE}
  8. knitr::opts_chunk$set(
  9. echo <- FALSE, # don't print the code chunk
  10. warning <- FALSE, # don't print warnings
  11. message <- FALSE, # don't print messages
  12. fig.width <- 5, # set default width of figures
  13. fig.height <- 8, # set default height of figures
  14. fig.align <- "center", # always align figure in center
  15. fig.pos <- "H", # always plot figure at the exact location of the code chunk
  16. cache <- FALSE) # cache results
  18. ## libraries ##
  19. library(tidyverse)
  20. library(ggplot2)
  21. library(magrittr)
  22. library(ggthemr)
  23. library(grid)
  24. library(gtable)
  25. library(gridExtra)
  26. library(wesanderson)
  27. library(ggsci)
  28. library(zoo)
  29. library(kableExtra)
  30. library(lme4)
  31. library(RColorBrewer)
  32. library(doParallel)
  33. library(parallel)
  34. library(foreach)
  35. library(here)
  36. library(fs)
  37. library(ggcorrplot)
  38. library(viridis)
  39. library(lmtest)
  40. library(gt)
  41. library(survminer)
  42. library(survival)
  43. library(effectsize)
  44. library(scales)
  45. library(rcartocolor)
  46. library(brms)
  48. ## hand written functions ##
  49. source(path(here(), "R", 'mutate_cond.R'))
  50. source(path(here(), "R", "clean_behavioral_data.R"))
  51. source(path(here(), "R", "create_distance_df.R"))
  52. ## plotting helpers ##
  53. ggthemr("light")
  54. getPalette = colorRampPalette(brewer.pal(17, "Set1"))
  56. c25 <- c(
  57. "dodgerblue2", "#E31A1C", # red
  58. "green4",
  59. "#6A3D9A", # purple
  60. "#FF7F00", # orange
  61. "black", "gold1",
  62. "skyblue2", "#FB9A99", # lt pink
  63. "palegreen2",
  64. "#CAB2D6", # lt purple
  65. "#FDBF6F", # lt orange
  66. "gray70", "khaki2",
  67. "maroon", "orchid1", "deeppink1", "blue1", "steelblue4",
  68. "darkturquoise", "green1", "yellow4", "yellow3",
  69. "darkorange4", "brown"
  70. )
  72. # ## parallelization ##
  73. # nCores <- 2
  74. # registerDoParallel(nCores)
  76. ```
  79. ## Behavioral Plots (Figure 1)
  81. This script compares the behavior between iEEG and prolific participants and shows the normative behavior in the task.
  83. It requires:
  84. * `all_subs_complete_distance_df.csv` and `all_subs_complete_behavior_df.csv` which are outputs of `analysis/cleaning_ieeg_behavior/combine_ieeg_subs_behavior.Rmd`.
  85. * `cleaned_pilot_behavior.csv`, `cleaned_pilot_game_data.csv`, `cleaned_pilot_distance_data.csv`, and `cleaned_pilot_across_trial_data.csv` which are outputs of `analysis/behavior/normative_behavior.Rmd`.
  86. * `cleaned_pilot_behavior_newsample.csv`, `cleaned_pilot_game_data_newsample.csv`, `cleaned_pilot_distance_data_newsample.csv`, and `cleaned_pilot_across_trial_data_newsample.csv` which are outputs of `analysis/behavior/normative_behavior_newsample.Rmd`.
  88. It produces:
  89. * `figure1_dots_plot.png` in the `figures/behavior` folder
  90. * `figure1_last_away_plot.png` in the `figures/behavior` folder
  91. * `figure1_turnaround_reward_plot.png` in the `figures/behavior` folder
  92. * `figure1_experienced_emotions_plot.png` in the `figures/behavior` folder
  93. * `behave_turn_distance_reward_model.RData`in the `results` folder
  97. ```{r load-pilot-data}
  98. # load data #
  99. behave_data_pilot <- read_csv(path(here(), "munge", "prolific", "cleaned_pilot_behavior.csv"))
  100. game_data_clean_pilot <- read_csv(path(here(), "munge", "prolific", "cleaned_pilot_game_data.csv"))
  101. game_data_distance_pilot <- read_csv( path(here(), "munge", "prolific", "cleaned_pilot_distance_data.csv"))
  102. all_vars_df_pilot <- read_csv(path(here(), "munge", "prolific", "cleaned_pilot_across_trial_data.csv"))
  104. # add case #
  105. behave_data_pilot <- behave_data_pilot %>% mutate(case = "pilot")
  106. game_data_clean_pilot <- game_data_clean_pilot %>% mutate(case = "pilot")
  107. game_data_distance_pilot <- game_data_distance_pilot %>% mutate(case = "pilot")
  108. all_vars_df_pilot <- all_vars_df_pilot %>% mutate(case = "pilot")
  110. ```
  113. ```{r load-newsample-data}
  115. # load data #
  116. behave_data_ns <- read_csv(path(here(), "munge", "prolific", "cleaned_pilot_behavior_newsample.csv"))
  117. game_data_clean_ns <- read_csv(path(here(), "munge", "prolific", "cleaned_pilot_game_data_newsample.csv"))
  118. game_data_distance_ns <- read_csv( path(here(), "munge", "prolific", "cleaned_pilot_distance_data_newsample.csv"))
  119. all_vars_df_ns <- read_csv(path(here(), "munge", "prolific", "cleaned_pilot_across_trial_data_newsample.csv"))
  121. # add case
  122. clinical_ids <- behave_data_ns %>% filter(case == "clinical") %>% pull(subject)
  123. all_vars_df_ns <- all_vars_df_ns %>% mutate(case = if_else(subject %in% clinical_ids, "clinical", "nonclinical"))
  125. ```
  128. ```{r load-ieeg-data}
  130. # load ieeg data
  131. all_subs_g_dist <- read_csv(path(here(), "munge", "all_subs_complete_distance_df.csv"))
  132. ieeg_clean_df <- read_csv(path(here(), "munge", "all_subs_complete_behavior_df.csv"))
  134. # add case
  135. all_subs_g_dist <- all_subs_g_dist %>% mutate(case = "ieeg")
  136. ieeg_clean_df <- ieeg_clean_df %>% mutate(case = "ieeg")
  138. ```
  140. ```{r ieeg_all_vars_df}
  143. ieeg_all_vars_df <- ieeg_clean_df %>%
  144. group_by(subject) %>%
  145. filter(!is.na(Score)) %>%
  146. filter(Trial != "ITI") %>%
  147. # calculate number of deaths
  148. mutate(death_check = as.numeric(c(diff(Lives) < 0, FALSE))) %>%
  149. mutate(total_deaths = sum(death_check)) %>%
  150. mutate(max_trial = max(trial_numeric)) %>%
  151. mutate(trial_in_block = trial_numeric %% 20) %>%
  152. mutate(trial_in_block = if_else(trial_in_block == 0, 20, trial_in_block)) %>%
  153. # new minigame
  154. mutate(lives_check = as.numeric(c(diff(Lives) > 0, FALSE))) %>%
  155. mutate(total_games = sum(lives_check) + 1) %>%
  156. group_by(subject, Trial) %>%
  157. mutate(dots_eaten = max(Eaten)) %>%
  158. mutate(max_score = max(Score, na.rm = T)) %>%
  159. filter(trial_length < 5) %>%
  160. mutate(chase_trial = any(Chase)) %>%
  161. mutate(attack_trial = any(Attack)) %>%
  162. mutate(trial_died = sum(death_check)) %>%
  163. mutate(last_trial_in_minigame = sum(lives_check)) %>% # if lose all lives, mark as last trial
  164. mutate(last_trial_in_minigame = if_else(trial_in_block == 20, 1, last_trial_in_minigame)) %>% # 20 is always last trial
  165. group_by(subject) %>%
  166. mutate(average_score = mean(max_score)) %>%
  167. mutate(max_time = max(Time)) %>%
  168. select(subject, Trial, trial_numeric, trial_in_block, TrialType, trial_length,
  169. trial_died, last_trial_in_minigame, Lives, dots_eaten,
  170. chase_trial, attack_trial,
  171. max_trial, total_deaths, average_score, max_time) %>%
  172. distinct()
  174. # get trials in minigame
  175. round <- 1
  176. game <- 1
  177. ieeg_all_vars_df$trial_in_minigame <- 0
  178. ieeg_all_vars_df$minigame <- 0
  179. for(idx in 1:nrow(ieeg_all_vars_df)){
  180. # add to df
  181. ieeg_all_vars_df$trial_in_minigame[idx] <- round
  182. ieeg_all_vars_df$minigame[idx] <- game
  183. if(ieeg_all_vars_df$last_trial_in_minigame[idx] == 1){
  184. round <- 1
  185. game <- game + 1
  186. } else {
  187. round <- round + 1
  188. }
  190. if(ieeg_all_vars_df$subject[idx + 1] != ieeg_all_vars_df$subject[idx] & idx != nrow(ieeg_all_vars_df)) {
  191. round <- 1
  192. game <- 1
  193. }
  195. }
  197. # max trials in minigame and such
  198. ieeg_all_vars_df <- ieeg_all_vars_df %>%
  199. group_by(subject) %>%
  200. mutate(longest_minigame = max(trial_in_minigame)) %>%
  201. mutate(longest_minigame_under20 = max(trial_in_minigame[trial_in_minigame < 20])) %>%
  202. mutate(number_of_minigames = max(minigame)) %>%
  203. mutate(block = ceiling(trial_numeric/20)) %>%
  204. group_by(subject, block) %>%
  205. mutate(block_deaths = sum(trial_died)) %>%
  206. mutate(average_dots_per_block = mean(dots_eaten))
  208. ```
  212. ```{r merge-samples}
  214. behave_data <- bind_rows(behave_data_pilot, behave_data_ns %>% mutate(comp_7 = as.logical(comp_7)))
  215. game_data_clean <- bind_rows(game_data_clean_pilot, game_data_clean_ns)
  216. all_vars_df <- bind_rows(all_vars_df_pilot %>% select(-Trial),
  217. all_vars_df_ns %>% select(-Trial),
  218. ieeg_all_vars_df%>% select(-Trial))
  220. # distance df
  221. game_data_distance <- bind_rows(game_data_distance_pilot, game_data_distance_ns)
  222. good_cols <- colnames(all_subs_g_dist)[colnames(all_subs_g_dist) %in% colnames(game_data_distance)]
  223. game_data_distance <- bind_rows(game_data_distance %>% select(all_of(good_cols), -Trial),
  224. all_subs_g_dist %>% select(all_of(good_cols), -Trial))
  228. ```
  230. ## Dot Plot
  232. ```{r game-level-time, echo = F, fig.width=9, fig.height=4.5}
  234. ieeg_dot_df <- ieeg_all_vars_df %>%
  235. filter(block <= 12)
  237. prolific_dots_df <- bind_rows(all_vars_df_pilot %>% select(-Trial),
  238. all_vars_df_ns %>% select(-Trial)) %>%
  239. filter(block <= 12) %>%
  240. group_by(subject) %>%
  241. mutate(avg_sub_dots = mean(dots_eaten)) %>%
  242. distinct(subject, avg_sub_dots) %>%
  243. ungroup() %>%
  244. summarise(avg_dots = mean(avg_sub_dots), sd_dots = sd(avg_sub_dots))
  246. prolific_dots_df <- bind_rows(all_vars_df_pilot %>% select(-Trial),
  247. all_vars_df_ns %>% select(-Trial)) %>%
  248. filter(block <= 12) %>%
  249. group_by(subject) %>%
  250. mutate(avg_sub_dots = mean(dots_eaten)) %>%
  251. distinct(subject, avg_sub_dots) %>%
  252. ungroup() %>%
  253. summarise(avg_dots = mean(avg_sub_dots), sd_dots = sd(avg_sub_dots))
  258. dots_plot <- all_vars_df %>%
  259. filter(block <= 12) %>%
  260. ggplot(., aes(x = factor(block), y = average_dots_per_block, fill = 'f')) +
  261. geom_violin(alpha = .7, fill = "#FB6087") +
  262. geom_boxplot(notch = T, width =.2, fill = "#FB6087") +
  263. geom_point(data = ieeg_dot_df, aes(x = factor(block), y = average_dots_per_block), color = "#FB6087", fill = 'darkgrey', size = 2, shape = 23) +
  264. theme(panel.background = element_rect(fill = "white"),
  265. legend.position = "none",
  266. axis.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  267. axis.title = element_text(family = "Gill Sans", color = "#2D2327", size = 11),
  268. legend.title = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  269. legend.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  270. strip.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  271. plot.title = element_text(family = "Gill Sans", color = "#2D2327", size = 12, margin = margin(b = 5)),
  272. plot.subtitle = element_text(family = "Gill Sans", color = "#2D2327", size = 11, margin = margin(b = 0))) +
  273. labs(subtitle = "Participants balanced the rewards against the risks by\ncollecting most, but not all, of the reward on a given trial",
  274. x = "Block (each block consists of twenty trials)", y = "Average dots collected\n") +
  275. ggtitle("Average reward collected across blocks")
  278. ggsave(path(here(), "figures", "behavior", "figure1_dots_plot.png"),
  279. plot = dots_plot, width = 4.5, height = 4, dpi = 600)
  281. ```
  285. ## Turning Distance Plot
  287. ```{r last_away_min_dist, warning=F, fig.width=9, fig.height=4.5, echo = F}
  290. # last away #
  291. last_away_df_prolific <- game_data_distance %>%
  292. # filters #
  293. filter(case != "ieeg") %>%
  294. filter(number_of_runs > 0) %>%
  295. # distinct #
  296. select(trial_numeric, subject, last_away, case) %>%
  297. distinct() %>%
  298. filter(subject %in% sample(subject, 15))
  300. last_away_df_ieeg <- game_data_distance %>%
  301. # filters #
  302. filter(case == "ieeg") %>%
  303. filter(number_of_runs > 0) %>%
  304. # distinct #
  305. select(trial_numeric, subject, last_away, case) %>%
  306. distinct() %>%
  307. filter(subject %in% sample(subject, 4))
  309. last_away_df <- bind_rows(last_away_df_prolific, last_away_df_ieeg)
  311. last_away_plot <- last_away_df %>%
  312. mutate(ieeg = if_else(case == "ieeg", "iEEG", "Prolific")) %>%
  313. arrange(desc(case)) %>%
  314. mutate(subject = factor(subject)) %>%
  315. ggplot(., aes(x = subject, y = last_away)) +
  316. geom_jitter(alpha = .5, color = "grey", size = 1) +
  317. geom_boxplot(aes(fill = ieeg), notch = T, show.legend = F) +
  318. geom_vline(xintercept = 4.5, color = "darkgrey") +
  319. ylab("Distance to ghost at turnaround (game units, max = 180)") + xlab("\n") +
  320. theme(panel.background = element_rect(fill = "white"),
  321. legend.position = "top",
  322. axis.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  323. axis.title = element_text(family = "Gill Sans", color = "#2D2327", size = 11),
  324. legend.title = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  325. legend.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  326. strip.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  327. plot.title = element_text(family = "Gill Sans", color = "#2D2327", size = 12, margin = margin(b = 5)),
  328. plot.subtitle = element_text(family = "Gill Sans", color = "#2D2327", size = 11, margin = margin(b = 0)),
  329. axis.ticks.x = element_blank(),
  330. axis.text.x = element_blank()) +
  331. scale_fill_manual(values = c("#B9DFD5", "#61BBA5")) +
  332. ggtitle("Variability in risk tolerance", subtitle = "Random sample of 20 participants shows across- and within- subject\nvariability in the degree of risk incurred on a given trial")
  334. last_away_plot
  336. ggsave(path(here(), "figures", "behavior", "figure1_last_away_plot.png"),
  337. plot = last_away_plot, width = 4.5, height = 4, dpi = 600)
  339. ```
  341. ## Turning Distance vs Last Reward Plot
  343. ```{r df-reward-turning-distance}
  345. turn_reward_df <- game_data_distance %>%
  346. select(reward_groups, last_away, trial_numeric, subject, case) %>%
  347. filter(last_away != 0) %>%
  348. mutate(large_reward = if_else(reward_groups %in% c(3, 4), "Large", "Small")) %>%
  349. select(-reward_groups) %>%
  350. distinct() %>%
  351. group_by(large_reward, subject) %>%
  352. mutate(avg_last_away = mean(last_away)) %>%
  353. ungroup() %>%
  354. mutate(last_away = scale(last_away)) %>%
  355. mutate(avg_last_away = scale(avg_last_away)) %>%
  356. mutate(ieeg = if_else(case == "ieeg", "iEEG", "Online"))
  358. ```
  360. ```{r}
  362. # Set the number of cores for parallel processing
  363. options(mc.cores = parallel::detectCores(), backend = "cmdstanr")
  365. # set the priors #
  366. priors <- c(
  367. prior(normal(0, 5), class = "Intercept"), # Prior for the intercept
  368. prior(normal(0, 2), class = "b"), # Prior for fixed effects
  369. prior(exponential(1), class = "sd"), # Prior for random effects standard deviations
  370. prior(lkj(2), class = "cor") # Prior for random effects correlations
  371. )
  374. # Fit the model
  375. behave_model <- brm(
  376. formula = last_away ~ large_reward + (1 + large_reward | subject),
  377. data = turn_reward_df,
  378. prior = priors,
  379. family = gaussian(),
  380. iter = 4000,
  381. warmup = 1000,
  382. chains = 4,
  383. control = list(adapt_delta = 0.99),
  384. seed = 1234
  385. )
  387. summary(behave_model)
  389. save(behave_model, file = path(here(), "results", "behave_turn_distance_reward_model.RData"))
  391. ```
  395. ```{r plot-reward-turning-dist, fig.height = 7, fig.width = 9}
  398. turn_reward_plot <- turn_reward_df %>%
  399. select(avg_last_away, subject, large_reward, ieeg) %>%
  400. distinct() %>%
  401. ggplot(., aes(x = large_reward, y = avg_last_away)) +
  402. geom_hline(yintercept = 0, color = "#2D2327", linetype = "dashed", size = 1) +
  403. geom_point(aes(color = ieeg)) +
  404. geom_line(aes(alpha = ieeg, group = subject, color = ieeg, size = ieeg)) +
  405. geom_boxplot(aes(fill = large_reward), notch = T, show.legend = F, color = "#2D2327", alpha = .9) +
  406. ylim(-1.25, 1.25) +
  407. coord_flip() +
  408. theme(panel.background = element_rect(fill = "white"),
  409. legend.position = c(.85, 1),
  410. legend.direction = 'horizontal',
  411. axis.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  412. axis.title = element_text(family = "Gill Sans", color = "#2D2327", size = 12),
  413. legend.title = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  414. legend.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  415. strip.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  416. plot.title = element_text(family = "Gill Sans", color = "#2D2327", size = 12, margin = margin(b = 5)),
  417. plot.subtitle = element_text(family = "Gill Sans", color = "#2D2327", size = 11, margin = margin(b = 0))) +
  418. scale_fill_manual(values = c("#31ABED", "#A1D9F7")) +
  419. scale_color_manual(values = c("black", "darkgrey")) +
  420. scale_alpha_manual(values = c(1, 0.75), guide = F) +
  421. scale_size_manual(values = c(1, .25), guide = F) +
  422. labs(x = "Last Reward", y = "\nDistance to ghost at turnaround, scaled (a.u.)", color = "", title = "Turnaround Distance by Reward Groups", subtitle =
  423. "Participants were willing to get closer to the ghost when the\nlast reward was large")
  425. turn_reward_plot
  429. ggsave(path(here(), "figures", "behavior", "figure1_turnaround_reward_plot.png"),
  430. plot = turn_reward_plot, width = 5, height = 3.9, dpi = 600)
  432. ```
  436. ## Face Validity Plot
  439. ```{r plot-phenom}
  441. expected_options <- c("Stress", "Hope", "Bordeom", "Anxiety", "Excitement", "Disinterest", "Anger", "Suspense", "Frustration", "Other")
  442. reported_options <- behave_data %>%
  443. select(subject, starts_with("pilot_5")) %>%
  444. pivot_longer(cols = starts_with("pilot"), values_to = "experienced", names_to = "emotion") %>%
  445. pull(experienced) %>%
  446. unique()
  447. other_options <- reported_options[(!reported_options %in% expected_options) & !grepl(",", reported_options)]
  449. phenom_df <- behave_data %>%
  450. select(subject, starts_with("pilot_5")) %>%
  451. pivot_longer(cols = starts_with("pilot"), values_to = "experienced", names_to = "emotion") %>%
  452. group_by(subject) %>%
  453. mutate(stress = if_else(any(grepl("Stress", experienced)), 1, 0)) %>%
  454. mutate(hope = if_else(any(grepl("Hope", experienced)), 1, 0)) %>%
  455. mutate(boredom = if_else(any(grepl("Boredom", experienced)), 1, 0)) %>%
  456. mutate(anxiety = if_else(any(grepl("Anxiety", experienced)), 1, 0)) %>%
  457. mutate(excited = if_else(any(grepl("Excitement", experienced)), 1, 0)) %>%
  458. mutate(disinterest = if_else(any(grepl("Disinterest", experienced)), 1, 0)) %>%
  459. mutate(anger = if_else(any(grepl("Anger", experienced)), 1, 0)) %>%
  460. mutate(suspense = if_else(any(grepl("Suspense", experienced)), 1, 0)) %>%
  461. mutate(frustration = if_else(any(grepl("Frustration", experienced)), 1, 0)) %>%
  462. mutate(fear = if_else(any(grepl("fear", experienced, ignore.case = T)), 1, 0)) %>%
  463. mutate(nostalgia = if_else(any(grepl("nostalgia", experienced, ignore.case = T)), 1, 0)) %>%
  464. select(-emotion, -experienced) %>%
  465. distinct() %>%
  466. ungroup()
  469. experienced_plot <- phenom_df %>%
  470. pivot_longer(cols = -subject, values_to = "experienced", names_to = "emotion") %>%
  471. group_by(emotion) %>%
  472. summarize(count = sum(experienced)) %>%
  473. mutate(count = 100 * count / length(unique(behave_data$subject))) %>%
  474. ggplot(., aes(x = emotion, y = count, fill = emotion)) +
  475. geom_col(alpha = .8, color = "black") +
  476. xlab("") +
  477. ylab("Percent of Online Participants (n = 191)") +
  478. theme(panel.background = element_rect(fill = "white"),
  479. legend.position = "none",
  480. plot.margin = margin(t = 1, r = 1, b = 1, l = 1),
  481. axis.title = element_text(family = "Gill Sans", color = "#2D2327", size = 11),
  482. legend.title = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  483. legend.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  484. strip.text = element_text(family = "Gill Sans", color = "#2D2327", size = 10),
  485. plot.subtitle = element_text(family = "Gill Sans", color = "#2D2327", size = 11, margin = margin(b = 0)),
  486. plot.title = element_text(family = "Gill Sans", color = "#2D2327", size = 12, margin = margin(b = 5)),
  487. axis.text = element_text(family = "Gill Sans", color = '#2D2327', size = 11, angle = 45, vjust = 1, hjust = 1)) +
  488. ggtitle("Emotional experience during the task", subtitle = "Responses to: 'What did you experience when the ghost was\nclose to catching you?'") +
  489. scale_fill_carto_d(palette = "Prism")
  491. experienced_plot
  494. ggsave(path(here(), "figures", "behavior", "figure1_experienced_emotions_plot.png"),
  495. plot = experienced_plot, width = 4.5, height = 3.9, dpi = 600)
  497. ```

figure_1_behave_plots.Rmd at commit fff4ed4, no license · at the source

Overview

Authors: Brooke R Staveland1,2, Julia Oberschulte3, Barbara Berger1,2,4, Tamas Minarik1,2, Olivia Kim-McManus5,6, Jon T Willie7,8, Peter Brunner7,8, Mohammad Dastjerdi9, Jack J Lin10,11, Elizabeth L Johnson12, Michelle Paff13, Ming Hsu1,14, Robert T Knight1,2
ORCID iDs: Brooke R Staveland, Barbara Berger, Olivia Kim-McManus, Jon T Willie, Peter Brunner, Mohammad Dastjerdi, Jack J Lin, Elizabeth L Johnson, Michelle Paff, Ming Hsu, Robert T Knight
14 affiliations
  1. Helen Wills Neuroscience Institute, University of California, Berkeley, CA USA
  2. Departments of Psychology and Neuroscience, University of California, Berkeley, CA USA
  3. Department of Psychology, Harvard University, Cambridge, MA USA
  4. School of Psychological Sciences, Victoria University of Wellington, Wellington, New Zealand
  5. Department of Neurosciences, University of California, San Diego, CA USA
  6. Division of Neurology, Rady Children’s Hospital, San Diego, CA USA
  7. Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO USA
  8. National Center for Adaptive Neurotechnologies, Albany, NY USA
  9. Department of Neurology, Loma Linda University, Loma Linda, CA USA
  10. Department of Neurology, University of California, Davis, CA USA
  11. Center for Mind and Brain, University of California, Davis, CA USA
  12. Departments of Medical Social Sciences, Pediatrics, and Psychology, Northwestern University, Evanston, IL USA
  13. Department of Neurological Surgery, University of California, Irvine, CA USA
  14. Haas School of Business, University of California, Berkeley, CA USA
Institutions: University of California, Berkeley (United States); Harvard University (United States); Victoria University of Wellington (New Zealand); University of California San Diego (United States); Rady Children's Hospital-San Diego (United States); Washington University in St. Louis (United States); Loma Linda University (United States); University of California, Davis (United States); Northwestern University (United States); University of California, Irvine (United States)
Journal: Nature communications, volume 17, issue 1, article 3867
Dates: received 18 March 2025; accepted 24 February 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70287-5 · PMID 41820348 · PMCID PMC13125241 · OpenAlex W7135061359
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), epilepsy (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Machine learning, Physiology & signal measures
Keywords: Cognitive control, Motivation, Neural circuits, Limbic system, Prefrontal cortex
MeSH: Avoidance Learning*, Conflict, Psychological*, Limbic System*, Prefrontal Cortex*, Adult, Amygdala, Decision Making, Electroencephalography, Epilepsy, Female, Gyrus Cinguli, Hippocampus, Humans, Male, Theta Rhythm, Young Adult (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (R01 NS-021135); U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (R01 NS-021135); National Science Foundation (GRFP)
Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Choosing to approach or avoid is common in everyday life and excessive avoidance is a cardinal feature of anxiety disorders. We use intracranial EEG to define a prefrontal-limbic circuit supporting approach and avoidance. Presurgical epilepsy patients (n = 20) performed an approach-avoidance conflict decision-making task inspired by the arcade game Pac-Man, where patients trade off rewards against losses from ghost attack. During approach, theta power increases across a limbic circuit including the hippocampus, amygdala, orbitofrontal cortex and anterior cingulate cortex, which drops during avoidance. Theta connectivity between this circuit and lateral prefrontal cortex increases during approach and falls during avoidance. Network connectivity tracks how long patients approach, with enhanced synchronicity extending approach times. During imminent threat, the system switches to sustained increase in high-frequency activity in the lateral prefrontal cortex. The results provide evidence of a distributed prefrontal-limbic circuit, mediated by theta oscillations and high frequency activity, underlying approach-avoidance conflict in humans.

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

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Zenodo 17727554

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Zenodo 17727552

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morrocwi/rrhm-open-lab

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Zenodo 22255211

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bstavel/staveland_et_al_pacman_statistics_and_behavior

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bstavel/staveland_et_al_pacman_neural_analyses

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Code availability

Original code is deposited on Zenodo and for public download. Original code is comprised of two GitHub repos: Staveland_et_al_Pacman_Neural_Analyses (10.5281/zenodo.17727554) and Staveland_et_al_Pacman_Statistics_and_Behavior (10.5281/zenodo.17727552).

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

Tracing map

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What the map holds:

  • 7 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 246 scripts, each with its path and the digest of its content;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability

The cleaned, minimally-processed, patient-level data generated in this study have been deposited in the Zenodo database under accession (https://zenodo.org/records/17726565). The data used to generate each figure in this study are provided in the Source Data file. Source data are provided with this paper.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 16 MeSH terms, 3 funders, 74 references.

Cite

This paper

Staveland, B. R., Oberschulte, J., Berger, B., Minarik, T., Kim-McManus, O., Willie, J. T., Brunner, P., Dastjerdi, M., Lin, J. J., Johnson, E. L., Paff, M., Hsu, M., & Knight, R. T. (2026). Cortical-limbic circuit dynamics of approach-avoidance conflict in humans. Nature communications, 17(1), 3867. https://doi.org/10.1038/s41467-026-70287-5

BibTeX

@article{staveland2026cortical,
author = {Staveland, Brooke R and Oberschulte, Julia and Berger, Barbara and Minarik, Tamas and Kim-McManus, Olivia and Willie, Jon T and Brunner, Peter and Dastjerdi, Mohammad and Lin, Jack J and Johnson, Elizabeth L and Paff, Michelle and Hsu, Ming and Knight, Robert T},
title = {{Cortical-limbic circuit dynamics of approach-avoidance conflict in humans}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3867},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70287-5},
url = {https://doi.org/10.1038/s41467-026-70287-5},
pmid = {41820348},
pmcid = {PMC13125241}
}

RIS

TY - JOUR
AU - Staveland, Brooke R
AU - Oberschulte, Julia
AU - Berger, Barbara
AU - Minarik, Tamas
AU - Kim-McManus, Olivia
AU - Willie, Jon T
AU - Brunner, Peter
AU - Dastjerdi, Mohammad
AU - Lin, Jack J
AU - Johnson, Elizabeth L
AU - Paff, Michelle
AU - Hsu, Ming
AU - Knight, Robert T
TI - Cortical-limbic circuit dynamics of approach-avoidance conflict in humans
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/12
VL - 17
IS - 1
SP - 3867
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70287-5
UR - https://doi.org/10.1038/s41467-026-70287-5
LA - en
ER -

CSL-JSON

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