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Remembered Event Features Shape Default Mode Network Engagement during Emotional Memory Recall.

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  1. ---
  2. title: "R Notebook"
  3. output: html_notebook
  4. ---
  6. This script contains univariate analyses at the network level + amygdala and hippocampus, and behavioral analyses, for the reframe-detail project.
  9. # Setup
  10. ```{r}
  11. # Libraries
  12. library(tidyverse)
  13. library(lme4)
  14. library(lmerTest)
  15. library(ez)
  16. library(ggsegSchaefer)
  17. library(ggseg3d)
  18. library(ggseg)
  19. library(sjPlot)
  20. library(kableExtra)
  21. library(corrplot)
  22. library(ggpubr)
  23. library(here)
  24. library(glue)
  25. library(officer)
  26. library(rvg)
  27. library(emmeans)
  30. # Calculations
  31. zscore <- function(x) (x-mean(x))/sd(x)
  32. se <- function(x) sqrt(var(x)/length(x)) #function to calculate SE
  33. ci <- function(x) (sqrt(var(x)/length(x))) * 1.96 #function to calculate 95% CI
  36. #Plotting theme
  37. fontsize <- 14
  38. linesize <- .5
  39. barcolors <- c("#f76c67","#67aaf7","#0bca81","#f6a84f","#9c67f7","#67f7f0","#7F7F7F")
  41. my_theme <- function() {
  42. theme(plot.title = element_text(hjust = 0.5, size=fontsize, margin=margin(0,0,20,0)),
  43. axis.line.x = element_line(color = "black", size = 1),
  44. axis.line.y = element_line(color = "black", size = 1),
  45. axis.text.x = element_text(size = .8*fontsize, hjust=1, vjust=1, angle = 45),
  46. axis.text.y = element_text(size = .8*fontsize),
  47. axis.title.y = element_text(size = 1*fontsize),
  48. axis.title.x = element_blank(),
  49. panel.background = element_blank(),
  50. legend.position="none",
  51. strip.text.x = element_text(size=fontsize),
  52. text = element_text(family="Helvetica")) # +
  53. # theme(plot.margin = margin(1, 1, 1, 1, "cm"))
  54. }
  55. ```
  57. ## Load data
  59. ### behavioral data
  60. ```{r}
  61. behav <- read.csv('/Volumes/memolab/Data/Behavioral_Online/reframe-details/detail_script_files/behav_data_allonsets.csv')
  63. behav <- behav %>%
  64. filter(period=="recall") # keep only one row per trial
  66. nina_behav <- read.csv('/Volumes/memolab/Data/Behavioral_Online/reframe-details/detail_script_files/onsets_all.csv')
  67. nina_behav <- nina_behav %>%
  68. filter(period=="recall")
  70. behav$eventDet = nina_behav$eventDetails.y # adding nina's add'l coding
  71. behav$visualDet = nina_behav$visualDetails.y
  74. # add in missing data from nina's coding
  75. missing = read.csv('/Volumes/memolab/Data/Behavioral_Online/reframe-details/detail_script_files/missing_coded.csv')
  76. behav <- behav %>%
  77. left_join(missing, by = c("subID", "videoName"))
  78. behav$eventDets = ifelse(is.na(behav$eventDet.x), behav$eventDet.y, behav$eventDet.x)
  79. behav$visualDets = ifelse(is.na(behav$visualDet.x), behav$visualDet.y, behav$visualDet.x)
  81. # remove unnecessary columns
  82. behav = behav %>% select(-eventDet.x, -visualDet.x, -eventDet.y, -visualDet.y)
  85. table(behav$subID,behav$run)
  86. nsub.behav <- length(unique(behav$subID))
  88. # Some video info is contained only for the first round. Make sure it's repeated for other rounds.
  89. behav.roundone <- behav %>%
  90. filter(regRound==1) %>%
  91. select("subID","videoName","recallValence","recallArousal","recallVividness",
  92. "encValence","encArousal","valenceChange","arousalChange","successful",
  93. "remembered","correctDetails", "eventDets", "visualDets")
  94. behav.left <- behav[,1:18]
  96. behav.all <- full_join(behav.left,behav.roundone,by=c("subID","videoName"),
  97. suffix=c("",".ignore"))
  100. # no event/visual detail column should have "NA" - replace with zero.
  102. behav.all$eventDets = ifelse(is.na(behav.all$eventDets), 0, behav.all$eventDets)
  103. behav.all$visualDets = ifelse(is.na(behav.all$visualDets), 0, behav.all$visualDets)
  104. ```
  106. ### activity data
  108. ```{r}
  109. subjects <- list.files(path='/Volumes/memolab/Data/Behavioral_Online/reframe-details/detail_script_files/redo/',full.names=TRUE, recursive = TRUE, pattern = 'block15s_timepoint-averages_schaefer')
  110. actdata = do.call(rbind, lapply(subjects, function(x) {read.csv(x, header = TRUE)}))
  113. tmp <- filter(actdata,ROI=="LH_HIPP")
  114. table(tmp$subID,tmp$run)
  115. nsub.act <- length(unique(actdata$subID))
  116. ```
  118. ### combine behavioral + activity
  119. ```{r}
  120. # first fix subID numbers to follow same format
  121. behav.all <- behav.all %>%
  122. mutate(subID.orig = subID,
  123. subID = paste0("sub-",subID.orig))
  125. alldata <- left_join(actdata,behav.all,by=c("subID","videoName","trial"),suffix=c(".act",".beh"))
  126. ```
  128. summarize trial numbers per condition:
  129. ```{r}
  130. tmp <- filter(alldata,ROI=="LH_HIPP" & subID=="sub-s002")
  131. table(tmp$conditionName,tmp$valenceCategory)
  132. ```
  134. ### cleaning
  136. #### Define factors
  137. ```{r}
  138. alldata$ROI <- as.factor(alldata$ROI)
  139. alldata$conditionName <- as.factor(alldata$conditionName)
  140. alldata$valenceCategory <- as.factor(alldata$valenceCategory)
  141. alldata$subID <- as.factor(alldata$subID)
  142. alldata$regRound.categ <- as.factor(alldata$regRound) # keep both integer & categorical versions
  143. alldata$videoName <- as.factor(alldata$videoName)
  144. alldata$periodLabel <- as.factor(alldata$periodLabel)
  145. ```
  147. #### Remove outliers
  149. Defined as values abs(z)>4, calculated within subject and ROI - as in Cooper & Ritchey 2020 parcel analysis
  151. Note that these time-points come from the residuals of a nuisance regressor-only model, which included run regressors that account for mean differences between runs.
  153. ```{r}
  154. ### add z score of activity estimates within region and subject and time-period
  155. alldata <- alldata %>%
  156. group_by(subID, ROI, periodLabel) %>%
  157. mutate(Z = zscore(regulationActivity)) %>%
  158. ungroup()
  160. # remove betas that are > 4SD
  161. tmp <- alldata %>%
  162. mutate(isOutlier = ifelse(abs(Z)>4,TRUE,FALSE))
  164. # Following Cooper & Ritchey, remove trials where mean for ANY ROI is an outlier
  165. outliers <- tmp %>%
  166. select(isOutlier,subID,trial) %>%
  167. filter(isOutlier) %>%
  168. unique()
  169. alldata <- left_join(alldata,outliers,by=c("subID","trial"))
  170. alldata$isOutlier[is.na(alldata$isOutlier)] <- FALSE
  172. # Summarize number of outliers
  173. table(alldata$subID,alldata$isOutlier)
  174. table(alldata$isOutlier)
  176. alldata$regulationActivity[alldata$isOutlier] <- NA #mark value as NA if outlier
  177. ```
  179. #### Trial filters
  180. Removing reappraisal trials that were initially collected for a separate study;
  181. filtering trials to remembered-only
  182. ```{r}
  183. alldata <- alldata %>%
  184. mutate(isComplete.emo = ifelse(is.na(valenceChange) | is.na(arousalChange),
  185. FALSE,TRUE))
  187. # Removing negative reappraisal (non-memory) trials
  188. alldata <- alldata %>%
  189. filter(conditionName == "remember")
  192. all_nr = alldata # capturing all trials, not just remembered
  194. alldata <- alldata %>% filter(remembered == 1) # only looking at remembered-only data!
  195. ```
  198. #### Mean centering predictors
  199. Within-participant
  200. ```{r}
  201. alldata <- alldata %>%
  202. group_by(subID,ROI,periodLabel) %>%
  203. mutate(regSuccess = scale(regSuccess,scale=FALSE),
  204. valenceChange = scale(valenceChange,scale=FALSE),
  205. arousalChange = scale(arousalChange,scale=FALSE),
  206. recallVividness = scale(recallVividness,scale=FALSE),
  207. eventDets = scale(eventDets, scale =FALSE),
  208. visualDets = scale(visualDets, scale=FALSE),
  209. correctDetails = scale(correctDetails, scale=FALSE)) %>% # mean-center within subject
  210. ungroup()
  212. # double check that this worked as expected
  213. tmp <- alldata %>% filter(valenceCategory=="neg" & ROI=="LH_HIPP") %>% group_by(subID) %>% summarize(meansucc = mean(regSuccess,na.rm=TRUE))
  214. ```
  217. #### Getting ROI info
  218. ```{r}
  219. alldata %>%
  220. ungroup() %>%
  221. filter(subID=="sub-s005" & trialID==8) %>%
  222. mutate(ROIsplit = ROI) %>%
  223. separate(ROIsplit, into = c("hemisphere", "network", "netregion"), sep = "_", extra = "merge") %>%
  224. select(ROI,hemisphere,network,netregion) -> roi_info
  226. alldata <- left_join(alldata,roi_info,by=c("ROI"))
  227. tempdf = alldata %>%
  228. filter(subID=="sub-s005" & ROI=="DefaultC")
  229. ```
  232. save alldata for use in other analyses - specify a path accordingly
  233. ```{r}
  234. saveRDS(alldata, "alldata_reframe-detail.rds")
  235. saveRDS(mem.data, "mem.data_reframe-detail.rds")
  236. ```
  240. # Analysis: DMN subnetwork activity
  242. ## setup
  243. ```{r}
  244. rois <- levels(alldata$ROI)
  245. outDir <- '/Volumes/memolab/Data/Behavioral_Online/reframe-details/detail_script_files/reframe-detail_timepoint_results/'
  246. alphafdr <- .05
  247. ```
  249. get network-level data:
  250. ```{r}
  251. include_networks = c("DefaultA","DefaultB","DefaultC", "HIPP", "AMYG")
  253. # Summarize data
  254. alldata %>%
  255. # filter to include only some rows
  256. filter(network %in% include_networks) %>%
  257. # average over ROIs within a network
  258. group_by(subID,trial, cueWord.act, network, eventDets, visualDets, correctDetails, valenceCategory) %>%
  259. summarize(regulationActivity = mean(regulationActivity,na.rm=TRUE)) %>%
  260. ungroup() -> network.data
  261. ```
  263. create function for running model:
  264. ```{r}
  265. model.stats <- data.frame()
  268. # define function
  269. detail_emo_neuref <- function(curROI,model.stats) {
  271. # model
  272. roiData <- network.data %>% filter(network %in% curROI)
  273. roiData$valenceCategory <- factor(roiData$valenceCategory, levels=c("neu", "neg", "pos"))
  274. full.model <- lmer(regulationActivity ~ visualDets + eventDets +
  275. visualDets*valenceCategory +eventDets*valenceCategory + (1|subID), data = roiData)
  277. print(tab_model(full.model,title="detail x emotion, neutral reference"))
  280. stats <- data.frame(summary(full.model)$coefficients[2:nrow(summary(full.model)$coefficients),])
  281. stats <- mutate(stats,ROI=curROI)
  282. model.stats <- rbind(model.stats,stats)
  284. p <- plot_model(full.model, show.values = TRUE, show.p = FALSE,
  285. value.offset = .3, vline.color = "gray30",
  286. colors=c("lightskyblue","steelblue"), type = "std") + ylim(c(-.3,.3)) +
  287. ggtitle(curROI) +
  288. my_theme()
  289. print(p)
  291. return(model.stats)
  292. }
  293. ```
  295. ## run models
  296. ```{r}
  297. model.stats <- detail_emo_neuref("DefaultA",model.stats)
  298. model.stats <- detail_emo_neuref("DefaultB",model.stats)
  299. model.stats <- detail_emo_neuref("DefaultC",model.stats)
  300. model.stats <- detail_emo_neuref("HIPP",model.stats)
  301. model.stats <- detail_emo_neuref("AMYG",model.stats)
  302. ```
  305. ## Figure 2c (network activity by valence)
  306. ```{r}
  307. include_rois <- c("DefaultA","DefaultB","DefaultC")
  309. # Summarize data
  310. alldata %>%
  311. filter(network %in% include_rois) %>%
  312. mutate(network = factor(network,levels=include_rois)) %>%
  313. group_by(subID,ROI,network,periodLabel,valenceCategory) %>% # average across trials
  314. summarize(meanActivity = mean(regulationActivity,na.rm=TRUE)) %>%
  315. group_by(subID,network,periodLabel,valenceCategory) %>% # average across ROIs
  316. summarize(meanNet = mean(meanActivity)) -> subdata
  318. subdata %>%
  319. filter(valenceCategory=="neu") %>%
  320. mutate(meanNeutral = meanNet) %>%
  321. select(subID,network,periodLabel,meanNeutral) -> subdata.neu
  323. subdata %>%
  324. left_join(subdata.neu,by=c("subID","network","periodLabel")) %>%
  325. mutate(valenceDiff = meanNet - meanNeutral) -> subdata
  327. subdata %>%
  328. group_by(network,periodLabel,valenceCategory) %>% # then average across subjects
  329. summarize(meanAct = mean(meanNet),
  330. seAct = se(meanNet)) -> meandata
  332. network_names <- list(
  333. 'DMN-A'="Core midline",
  334. 'DMN-B'="Dorsomedial",
  335. 'DMN-C'="Medial temporal"
  336. )
  338. network_labeller <- function(variable,value){
  339. return(network_names[value])
  340. }
  342. subdata %>%
  343. ggplot(aes(y=meanNet,x=valenceCategory,color=valenceCategory,fill=valenceCategory)) +
  344. facet_grid(.~network, labeller = network_labeller) +
  345. # add individual data points
  346. geom_jitter(width = 0.15, size = 0.5, alpha = 0.75) +
  347. # add a boxplot
  348. geom_boxplot(width = .5, #0.25,
  349. fill = "white",
  350. alpha = 0.75,
  351. #fatten = 0, # removes the median line
  352. outlier.alpha = 0) +
  353. # add a point that represents the mean
  354. #stat_summary(fun = mean,
  355. # geom = "point",
  356. # size = 2) +
  357. ggtitle("Network activity by valence") +
  358. ylab("Network activity") + # for the y axis label
  359. xlab("Valence") +
  360. # Set fill colors for the bars and specify legend labels
  361. scale_fill_manual(values = c("neg" = "#e3401a", # Red for negative
  362. "neu" = "#1f78b4", # Blue for neutral
  363. "pos" = "#33a02c"), # Green for positive
  364. labels = c("neg" = "negative", "neu" = "neutral", "pos" = "positive"),
  365. name = "Valence") +
  366. scale_color_manual(values = c("neg" = "#e3401a", # Red for negative
  367. "neu" = "#1f78b4", # Blue for neutral
  368. "pos" = "#33a02c"), # Green for positive
  369. labels = c("neg" = "negative", "neu" = "neutral", "pos" = "positive"),
  370. name = "Valence") +
  371. my_theme() + theme(
  372. axis.title.y = element_text(size = 10),
  373. axis.title.x = element_text(size = 10),
  374. axis.text.y = element_text(size = 8),
  375. axis.text.x = element_text(size = 8),
  376. # strip.text.x = element_text(size = 8),
  377. axis.line.x = element_line(color = "black", size = .5),
  378. axis.line.y = element_line(color = "black", size = .5),
  379. axis.ticks = element_line(color = "black", size = .5),
  380. axis.ticks.length = unit(0.1, "cm"),
  381. plot.title = element_blank(),
  382. panel.background = element_blank(),
  383. strip.text.x = element_blank()
  386. ) -> p2
  388. p2
  390. # ggsave(filename = 'insert-path/network_valence.png',
  391. # plot = p2,
  392. # device = "png",
  393. # dpi = 300,
  394. # width = 4.2,
  395. # height = 2.7 ,
  396. # units = "in")
  398. ```
  400. ## results
  402. ### narrative details
  403. ```{r}
  404. # event.model
  405. event.model = model.stats %>% filter((row.names(model.stats) == "eventDets")|(row.names(model.stats) == "eventDets1")|(row.names(model.stats) == "eventDets2")|(row.names(model.stats) == "eventDets3")|(row.names(model.stats) == "eventDets4"))
  407. p.values <- event.model[,5]
  408. Pr.fdr <- p.adjust(p.values, method = "fdr")
  409. event.model <- cbind(event.model,Pr.fdr)
  410. event.model <- mutate(event.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  411. kbl(event.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  412. scroll_box(width="1200px",height="800px")
  413. ```
  415. ### perceptual details
  416. ```{r}
  417. # visual.model
  418. visual.model = model.stats %>% filter((row.names(model.stats) == "visualDets")|(row.names(model.stats) == "visualDets1")|(row.names(model.stats) == "visualDets2")|(row.names(model.stats) == "visualDets3")|(row.names(model.stats) == "visualDets4"))
  420. p.values <- visual.model[,5]
  421. Pr.fdr <- p.adjust(p.values, method = "fdr")
  422. visual.model <- cbind(visual.model,Pr.fdr)
  423. visual.model <- mutate(visual.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  424. kbl(visual.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  425. scroll_box(width="1200px",height="800px")
  426. ```
  428. ### positive valence
  429. ```{r}
  430. # pos.model
  431. pos.model = model.stats %>% filter((row.names(model.stats) == "valenceCategorypos")|(row.names(model.stats) == "valenceCategorypos1")|(row.names(model.stats) == "valenceCategorypos2")|(row.names(model.stats) == "valenceCategorypos3")|(row.names(model.stats) == "valenceCategorypos4"))
  432. p.values <- pos.model[,5]
  433. Pr.fdr <- p.adjust(p.values, method = "fdr")
  434. pos.model <- cbind(pos.model,Pr.fdr)
  435. pos.model <- mutate(pos.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  436. kbl(pos.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  437. scroll_box(width="1200px",height="800px")
  438. ```
  440. ### negative valence
  441. ```{r}
  442. # neg.model
  443. neg.model = model.stats %>% filter((row.names(model.stats) == "valenceCategoryneg")|(row.names(model.stats) == "valenceCategoryneg1")|(row.names(model.stats) == "valenceCategoryneg2")|(row.names(model.stats) == "valenceCategoryneg3")|(row.names(model.stats) == "valenceCategoryneg4"))
  444. p.values <- neg.model[,5]
  445. Pr.fdr <- p.adjust(p.values, method = "fdr")
  446. neg.model <- cbind(neg.model,Pr.fdr)
  447. neg.model <- mutate(neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  448. kbl(neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  449. scroll_box(width="1200px",height="800px")
  450. ```
  452. ### perceptual x positive
  453. ```{r}
  454. visual.pos.model = model.stats %>% filter((row.names(model.stats) == "visualDets:valenceCategorypos")|(row.names(model.stats) == "visualDets:valenceCategorypos1")|(row.names(model.stats) == "visualDets:valenceCategorypos2")|(row.names(model.stats) == "visualDets:valenceCategorypos3")|(row.names(model.stats) == "visualDets:valenceCategorypos4"))
  456. p.values <- visual.pos.model[,5]
  457. Pr.fdr <- p.adjust(p.values, method = "fdr")
  458. visual.pos.model <- cbind(visual.pos.model,Pr.fdr)
  459. visual.pos.model <- mutate(visual.pos.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  460. kbl(visual.pos.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  461. scroll_box(width="1200px",height="800px")
  462. ```
  464. ### perceptual x negative
  465. ```{r}
  466. # visual.neg.model
  467. visual.neg.model = model.stats %>% filter((row.names(model.stats) == "visualDets:valenceCategoryneg")|(row.names(model.stats) == "visualDets:valenceCategoryneg1")|(row.names(model.stats) == "visualDets:valenceCategoryneg2")|(row.names(model.stats) == "visualDets:valenceCategoryneg3")|(row.names(model.stats) == "visualDets:valenceCategoryneg4"))
  469. p.values <- visual.neg.model[,5]
  470. Pr.fdr <- p.adjust(p.values, method = "fdr")
  471. visual.neg.model <- cbind(visual.neg.model,Pr.fdr)
  472. visual.neg.model <- mutate(visual.neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  473. kbl(visual.neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  474. scroll_box(width="1200px",height="800px")
  475. ```
  477. ### narrative.x positive
  479. ```{r}
  480. event.pos.model = model.stats %>% filter((row.names(model.stats) == "eventDets:valenceCategorypos")|(row.names(model.stats) == "eventDets:valenceCategorypos1")|(row.names(model.stats) == "eventDets:valenceCategorypos2")|(row.names(model.stats) == "eventDets:valenceCategorypos3")|(row.names(model.stats) == "eventDets:valenceCategorypos4"))
  482. p.values <- event.pos.model[,5]
  483. Pr.fdr <- p.adjust(p.values, method = "fdr")
  484. event.pos.model <- cbind(event.pos.model,Pr.fdr)
  485. event.pos.model <- mutate(event.pos.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  486. kbl(event.pos.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  487. scroll_box(width="1200px",height="800px")
  488. ```
  490. ### narrative x negative
  492. ```{r}
  493. # event.neg.model
  494. event.neg.model = model.stats %>% filter((row.names(model.stats) == "eventDets:valenceCategoryneg")|(row.names(model.stats) == "eventDets:valenceCategoryneg1")|(row.names(model.stats) == "eventDets:valenceCategoryneg2")|(row.names(model.stats) == "eventDets:valenceCategoryneg3")|(row.names(model.stats) == "eventDets:valenceCategoryneg4"))
  496. p.values <- event.neg.model[,5]
  497. Pr.fdr <- p.adjust(p.values, method = "fdr")
  498. event.neg.model <- cbind(event.neg.model,Pr.fdr)
  499. event.neg.model <- mutate(event.neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  500. kbl(event.neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  501. scroll_box(width="1200px",height="800px")
  502. ```
  505. # Figure 2d (perceptual x medial temporal)
  507. ```{r}
  508. include_rois <- c("DefaultC")
  509. roiData <- network.data %>% filter(network %in% include_rois)
  510. roiData$valenceCategory <- factor(roiData$valenceCategory,
  511. levels = c("neu", "neg", "pos"))
  513. # model for DMN-C
  514. full.model.c <- lmer(regulationActivity ~ visualDets + eventDets +
  515. visualDets*valenceCategory + eventDets*valenceCategory +
  516. (1|subID), data = roiData)
  518. # plotting function
  519. plot_visualDets_noScatter <- function(cur_model, roiData, ylab) {
  521. visualDets_seq <- seq(-8, 12, length.out = 100)
  522. pred_data <- data.frame(
  523. visualDets = visualDets_seq,
  524. eventDets = mean(roiData$eventDets),
  525. valenceCategory = "neu"
  526. )
  528. pred_values <- predict(cur_model, newdata = pred_data, re.form = NA, se.fit = TRUE)
  530. pred_data <- pred_data %>%
  531. mutate(
  532. pred = pred_values$fit,
  533. se = pred_values$se.fit,
  534. lower = pred - 1.96 * se,
  535. upper = pred + 1.96 * se
  536. )
  538. p1 <- ggplot() +
  539. geom_point(data = roiData, aes(x = visualDets, y = regulationActivity), alpha = 0) +
  540. geom_line(data = pred_data, aes(x = visualDets, y = pred), color = "violet", size = 1) +
  541. geom_ribbon(data = pred_data, aes(x = visualDets, ymin = lower, ymax = upper),
  542. fill = "violet", alpha = 0.2) +
  543. labs(x = "Perceptual details recalled (mean-centered)", y = ylab) +
  544. theme_minimal(14) +
  545. theme(
  546. panel.grid.major.x = element_blank(),
  547. panel.grid.minor.x = element_blank(),
  548. panel.grid.major.y = element_blank(),
  549. panel.grid.minor.y = element_blank(),
  550. panel.background = element_blank(),
  551. axis.line.x = element_line(color = "black", size = .5),
  552. axis.line.y = element_line(color = "black", size = .5),
  553. axis.title.y = element_text(size = 10),
  554. axis.title.x = element_text(size = 10),
  555. axis.text.y = element_text(size = 8),
  556. axis.text.x = element_text(size = 8),
  557. plot.title = element_blank(),
  558. axis.ticks = element_line(color = "black", size = .5),
  559. axis.ticks.length = unit(0.1, "cm")
  560. ) +
  561. scale_x_continuous(limits = c(-8, 12), expand = c(0, 0)) +
  562. scale_y_continuous(limits = c(-.2, .4))
  564. ggMarginal(p1, type = "histogram", fill = "gray30", size = 10, margins = "x")
  565. }
  568. dw <- plot_visualDets_noScatter(full.model.c, roiData, "MT subnetwork activity")
  569. dw
  571. # UNCOMMENT + INSERT PATH TO EXPORT
  572. # ggsave(filename = 'insert-path',
  573. # plot = dw,
  574. # device = "png",
  575. # dpi = 300,
  576. # width = 3.5,
  577. # height = 2.25,
  578. # units = "in")
  579. ```
  582. # Figure 3b (perceptual x amygdala)
  584. ```{r}
  585. plot_visualDets_noHist <- function(cur_model,roiData,ylab) {
  587. visualDets_seq <- seq(-8, 12, length.out = 100)
  588. pred_data <- data.frame(
  589. visualDets = visualDets_seq,
  590. eventDets = mean(roiData$eventDets), # Holding eventDets constant at its mean
  591. valenceCategory = "neu" # You can specify any level of valenceCategory here
  592. )
  594. # Get predictions and standard errors using the model
  595. pred_values <- predict(cur_model, newdata = pred_data, re.form = NA, se.fit = TRUE)
  597. # Combine predictions and standard errors into a data frame
  598. pred_data <- pred_data %>%
  599. mutate(
  600. pred = pred_values$fit,
  601. se = pred_values$se.fit,
  602. lower = pred - 1.96 * se, # Lower bound of 95% CI
  603. upper = pred + 1.96 * se # Upper bound of 95% CI
  604. )
  606. # Plot using ggplot2
  607. p1 <- ggplot() +
  608. # Individual data points
  609. geom_point(data=roiData, aes(x=visualDets,y=regulationActivity), alpha=0) +
  610. # Individual lines
  611. # geom_line(data=roiData, aes(x=visualDets,y=regulationActivity,group=subID),
  612. # stat="smooth",method = "lm", color="slateblue",size=1, alpha = 0.5) +
  613. # Line and ribbon for visualDets
  614. geom_line(data = pred_data, aes(x = visualDets, y = pred), color = "violet", size = 1) +
  615. geom_ribbon(data = pred_data, aes(x = visualDets, ymin = lower, ymax = upper), fill = "violet", alpha = 0.2) +
  616. labs(x = "Perceptual details recalled (mean-centered)", y = ylab) +
  617. theme_minimal(14) +
  618. theme(
  619. panel.grid.major.x = element_blank(),
  620. panel.grid.minor.x = element_blank(),
  621. panel.grid.major.y = element_blank(),
  622. panel.grid.minor.y = element_blank(),
  623. panel.background = element_blank(),
  624. axis.line.x = element_line(color = "black", size = .5),
  625. axis.line.y = element_line(color = "black", size = .5),
  626. axis.title.y = element_text(size = 10),
  627. axis.title.x = element_text(size = 10),
  628. axis.text.y = element_text(size = 8),
  629. axis.text.x = element_text(size = 8),
  630. plot.title = element_blank(),
  631. axis.ticks = element_line(color = "black", size = .5),
  632. axis.ticks.length = unit(0.1, "cm")
  633. ) +
  634. scale_x_continuous(limits = c(-8, 12), expand = c(0, 0)) +
  635. scale_y_continuous(limits = c(-.2, .4))
  638. p1
  640. return(p1)
  641. }
  644. include_rois <- c("AMYG")
  645. roiData <- network.data %>% filter(network %in% include_rois)
  646. roiData$valenceCategory <- factor(roiData$valenceCategory, levels=c("neu", "neg", "pos"))
  647. full.model <- lmer(regulationActivity ~ visualDets + eventDets +
  648. visualDets*valenceCategory + eventDets*valenceCategory +
  649. (1|subID), data = roiData)
  650. full.model.amy <- full.model
  652. amyg <- plot_visualDets_noHist(full.model, roiData, "Amygdala activity")
  653. amyg
  655. # UNCOMMENT + INSERT PATH TO EXPORT
  656. # ggsave(filename = 'insert-path',
  657. # plot = amyg,
  658. # device = "png",
  659. # dpi = 300,
  660. # width = 3.5,
  661. # height = 2.25,
  662. # units = "in")
  663. ```
  666. # Figure 2a + 3a (model plotting)
  668. set up models for plotting
  671. ```{r}
  672. include_rois <- c("HIPP")
  673. roiData <- network.data %>% filter(network %in% include_rois)
  674. roiData$valenceCategory <- factor(roiData$valenceCategory, levels=c("neu", "neg", "pos"))
  675. full.model <- lmer(regulationActivity ~ visualDets + eventDets +
  676. visualDets*valenceCategory + eventDets*valenceCategory +
  677. (1|subID), data = roiData)
  678. full.model.hipp <- full.model
  680. include_rois <- c("DefaultB")
  681. roiData <- network.data %>% filter(network %in% include_rois)
  682. roiData$valenceCategory <- factor(roiData$valenceCategory, levels=c("neu", "neg", "pos"))
  683. full.model.b <- lmer(regulationActivity ~ visualDets + eventDets +
  684. visualDets*valenceCategory + eventDets*valenceCategory +
  685. (1|subID), data = roiData)
  687. include_rois <- c("DefaultA")
  688. roiData <- network.data %>% filter(network %in% include_rois)
  689. roiData$valenceCategory <- factor(roiData$valenceCategory, levels=c("neu", "neg", "pos"))
  690. full.model.a <- lmer(regulationActivity ~ visualDets + eventDets +
  691. visualDets*valenceCategory + eventDets*valenceCategory +
  692. (1|subID), data = roiData)
  694. # full.model.c for Default C already made above
  695. # full.model.amy for Amygdala already made above
  696. ```
  699. plot:
  700. ```{r}
  701. p1 <- plot_models(full.model.a, full.model.b, full.model.c,
  702. # value.offset = .3, order.terms=c(1,4,2,3,5,6,7,8),
  703. vline.color = "gray30", colors=c("slateblue4","lightsteelblue","slateblue1"),
  704. #c("hotpink","lightpink","lightsalmon"),
  705. std.est = "std",
  706. m.labels = c("Core", "DM", "MTL"),
  707. show.values = FALSE, show.p = FALSE, p.shape = TRUE, p.threshold=.029,
  708. spacing=.7,
  709. axis.labels = rev(c('Perceptual Details', 'Narrative Details', 'Neg Valence','Pos Valence',
  710. 'Perceptual x Neg','Perceptual x Pos',
  711. 'Narrative x Neg','Narrative x Pos'))) +
  712. guides(shape=FALSE) +
  713. ylim(c(-.3,.3)) +
  714. theme_minimal(12) + theme(legend.title = element_blank())
  715. print(p1)
  717. p2 <- plot_models(full.model.amy, full.model.hipp,
  718. # value.offset = .3, order.terms=c(1,4,2,3,5,6,7,8),
  719. vline.color = "gray30", colors=c("hotpink","orange"),
  720. std.est = TRUE,
  721. m.labels = c("Amyg","Hipp"),
  722. show.values = FALSE, show.p = FALSE, p.shape = TRUE, p.threshold=.01,
  723. spacing=.7,
  724. axis.labels = rev(c('Perceptual Details', 'Narrative Details', 'Neg Valence','Pos Valence',
  725. 'Perceptual x Neg','Perceptual x Pos',
  726. 'Narrative x Neg','Narrative x Pos'))) +
  727. guides(shape=FALSE) +
  728. ylim(c(-.3,.3)) +
  729. theme_minimal(14) + theme(legend.title = element_blank())
  730. print(p2)
  732. ggarrange(p1,p2,ncol=2,legend="top")
  734. # UNCOMMENT + ADD PATH below for export
  736. # ggsave(filename = 'insert-path',
  737. # plot = p1,
  738. # device = "png",
  739. # dpi = 300,
  740. # width = 4,
  741. # height = 4,
  742. # units = "in")
  743. #
  744. #
  745. #
  746. # ggsave(filename = 'insert-path',
  747. # plot = p2,
  748. # device = "png",
  749. # dpi = 300,
  750. # width = 4,
  751. # height = 4,
  752. # units = "in")
  754. ```
  757. # Analysis: change reference category
  759. ```{r}
  760. model.stats <- data.frame()
  763. detail_emo_neuref <- function(curROI,model.stats) {
  765. # model
  766. roiData <- network.data %>% filter(network %in% curROI)
  767. roiData$valenceCategory <- factor(roiData$valenceCategory, levels=c("pos", "neu", "neg"))
  768. full.model <- lmer(regulationActivity ~ visualDets*valenceCategory+eventDets*valenceCategory+ (1|subID), data = roiData)
  769. print(tab_model(full.model,title="uni detail x emotion, ALT reference"))
  770. #,
  771. # file=paste0("outfiles/network_",curROI,"_rem-only.html")))
  773. # p1 <- plot_model(full.model, type = "pred",
  774. # terms = c("regSuccess [-2.5:2.5]","conditionName")) +
  775. # theme_minimal(10) + guides(color="none")
  777. stats <- data.frame(summary(full.model)$coefficients[2:nrow(summary(full.model)$coefficients),])
  778. stats <- mutate(stats,ROI=curROI)
  779. model.stats <- rbind(model.stats,stats)
  781. return(model.stats)
  782. }
  784. model.stats <- detail_emo_neuref("DefaultA",model.stats)
  785. model.stats <- detail_emo_neuref("DefaultB",model.stats)
  786. model.stats <- detail_emo_neuref("DefaultC",model.stats)
  787. model.stats <- detail_emo_neuref("HIPP",model.stats)
  788. model.stats <- detail_emo_neuref("AMYG",model.stats)
  792. # event.model
  793. event.model = model.stats %>% filter((row.names(model.stats) == "eventDets")|(row.names(model.stats) == "eventDets1")|(row.names(model.stats) == "eventDets2")|(row.names(model.stats) == "eventDets3")|(row.names(model.stats) == "eventDets4"))
  795. p.values <- event.model[,5]
  796. Pr.fdr <- p.adjust(p.values, method = "fdr")
  797. event.model <- cbind(event.model,Pr.fdr)
  798. event.model <- mutate(event.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  799. kbl(event.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  800. scroll_box(width="1200px",height="800px")
  803. # visual.model
  804. visual.model = model.stats %>% filter((row.names(model.stats) == "visualDets")|(row.names(model.stats) == "visualDets1")|(row.names(model.stats) == "visualDets2")|(row.names(model.stats) == "visualDets3")|(row.names(model.stats) == "visualDets4"))
  806. p.values <- visual.model[,5]
  807. Pr.fdr <- p.adjust(p.values, method = "fdr")
  808. visual.model <- cbind(visual.model,Pr.fdr)
  809. visual.model <- mutate(visual.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  810. kbl(visual.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  811. scroll_box(width="1200px",height="800px")
  814. # neg.model
  815. neg.model = model.stats %>% filter((row.names(model.stats) == "valenceCategoryneg")|(row.names(model.stats) == "valenceCategoryneg1")|(row.names(model.stats) == "valenceCategoryneg2")|(row.names(model.stats) == "valenceCategoryneg3")|(row.names(model.stats) == "valenceCategoryneg4"))
  816. p.values <- neg.model[,5]
  817. Pr.fdr <- p.adjust(p.values, method = "fdr")
  818. neg.model <- cbind(neg.model,Pr.fdr)
  819. neg.model <- mutate(neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  820. kbl(neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  821. scroll_box(width="1200px",height="800px")
  825. # neu.model
  826. neu.model = model.stats %>% filter((row.names(model.stats) == "valenceCategoryneu")|(row.names(model.stats) == "valenceCategoryneu1")|(row.names(model.stats) == "valenceCategoryneu2")|(row.names(model.stats) == "valenceCategoryneu3")|(row.names(model.stats) == "valenceCategoryneu4"))
  827. p.values <- neu.model[,5]
  828. Pr.fdr <- p.adjust(p.values, method = "fdr")
  829. neu.model <- cbind(neu.model,Pr.fdr)
  830. neu.model <- mutate(neu.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  831. kbl(neu.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  832. scroll_box(width="1200px",height="800px")
  836. # visual.neg.model
  837. visual.neg.model = model.stats %>% filter((row.names(model.stats) == "visualDets:valenceCategoryneg")|(row.names(model.stats) == "visualDets:valenceCategoryneg1")|(row.names(model.stats) == "visualDets:valenceCategoryneg2")|(row.names(model.stats) == "visualDets:valenceCategoryneg3")|(row.names(model.stats) == "visualDets:valenceCategoryneg4"))
  839. p.values <- visual.neg.model[,5]
  840. Pr.fdr <- p.adjust(p.values, method = "fdr")
  841. visual.neg.model <- cbind(visual.neg.model,Pr.fdr)
  842. visual.neg.model <- mutate(visual.neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  843. kbl(visual.neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  844. scroll_box(width="1200px",height="800px")
  847. # visual.neu.model
  848. visual.neu.model = model.stats %>% filter((row.names(model.stats) == "visualDets:valenceCategoryneu")|(row.names(model.stats) == "visualDets:valenceCategoryneu1")|(row.names(model.stats) == "visualDets:valenceCategoryneu2")|(row.names(model.stats) == "visualDets:valenceCategoryneu3")|(row.names(model.stats) == "visualDets:valenceCategoryneu4"))
  850. p.values <- visual.neu.model[,5]
  851. Pr.fdr <- p.adjust(p.values, method = "fdr")
  852. visual.neu.model <- cbind(visual.neu.model,Pr.fdr)
  853. visual.neu.model <- mutate(visual.neu.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  854. kbl(visual.neu.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  855. scroll_box(width="1200px",height="800px")
  858. # event.neg.model
  859. event.neg.model = model.stats %>% filter((row.names(model.stats) == "valenceCategoryneg:eventDets")|(row.names(model.stats) == "valenceCategoryneg:eventDets1")|(row.names(model.stats) == "valenceCategoryneg:eventDets2")|(row.names(model.stats) == "valenceCategoryneg:eventDets3")|(row.names(model.stats) == "valenceCategoryneg:eventDets4"))
  861. p.values <- event.neg.model[,5]
  862. Pr.fdr <- p.adjust(p.values, method = "fdr")
  863. event.neg.model <- cbind(event.neg.model,Pr.fdr)
  864. event.neg.model <- mutate(event.neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  865. kbl(event.neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  866. scroll_box(width="1200px",height="800px")
  869. # event.neu.model
  870. event.neu.model = model.stats %>% filter((row.names(model.stats) == "valenceCategoryneu:eventDets")|(row.names(model.stats) == "valenceCategoryneu:eventDets1")|(row.names(model.stats) == "valenceCategoryneu:eventDets2")|(row.names(model.stats) == "valenceCategoryneu:eventDets3")|(row.names(model.stats) == "valenceCategoryneu:eventDets4"))
  872. p.values <- event.neu.model[,5]
  873. Pr.fdr <- p.adjust(p.values, method = "fdr")
  874. event.neu.model <- cbind(event.neu.model,Pr.fdr)
  875. event..model <- mutate(event.neu.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  876. kbl(event.neu.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  877. scroll_box(width="1200px",height="800px")
  878. ```
  880. # Analysis: behavioral
  882. ## setup
  883. ```{r}
  884. allsubs <- unique(actdata$subID)
  886. behav.all %>%
  887. filter(subID %in% allsubs) %>%
  888. filter(conditionName=="remember") %>%
  889. filter(remembered==1) -> mem.data
  890. ```
  892. ## analysis
  895. ```{r}
  896. means_all = mem.data %>%
  897. group_by(valenceCategory) %>%
  898. summarise(
  899. mean_event = mean(eventDets),
  900. mean_visual = mean(visualDets),
  901. mean_total = mean(correctDetails)
  902. )
  904. means_all
  908. sd_all = mem.data %>% # for reporting SDs in paper
  909. group_by(valenceCategory) %>%
  910. summarise(
  911. sd_event = sd(eventDets),
  912. sd_visual = sd(visualDets),
  913. sd_total = sd(correctDetails)
  914. )
  917. sd_all
  920. # setting up for behavioral plot:
  923. means_individ = mem.data %>%
  924. group_by(valenceCategory, subID) %>%
  925. summarise(
  926. mean_event = mean(eventDets),
  927. mean_visual = mean(visualDets),
  928. mean_total = mean(correctDetails)
  929. )
  931. # pivot means_individ to long format
  932. means_individ_long <- means_individ %>%
  933. pivot_longer(cols = c(mean_event, mean_visual),
  934. names_to = "condition",
  935. values_to = "mean") %>%
  936. mutate(detail = ifelse(condition == "mean_event", "event", "visual"))
  939. # computing se from subject-level means
  940. se_individ_long <- means_individ_long %>%
  941. group_by(valenceCategory, detail) %>%
  942. summarise(se = sd(mean) / sqrt(n()), .groups = "drop")
  945. means_all_long = means_all %>%
  946. pivot_longer(cols = mean_event:mean_total,
  947. names_to = "detail",
  948. values_to = "mean") %>%
  949. mutate(detail = gsub("mean_", "", detail))
  952. behav_plot_data = merge(means_all_long, se_individ_long, by = c("valenceCategory", "detail"))
  953. behav_plot_nocorrectdets = behav_plot_data %>% filter(detail != "total")
  954. ```
  956. correct details
  957. ```{r}
  958. mem_Correct <-ezANOVA(data = mem.data,
  959. dv = .(correctDetails),
  960. wid = .(subID),
  961. within = .(valenceCategory),
  962. type = 3,
  963. detailed = TRUE)
  964. mem_Correct
  966. aov_model <- aov(correctDetails ~ valenceCategory + Error(subID/valenceCategory), data = mem.data)
  967. emmeans_result <- emmeans(aov_model, ~ valenceCategory)
  968. pairs(emmeans_result, adjust = "tukey")
  970. ```
  973. narrative details
  974. ```{r}
  975. mem_Event <-ezANOVA(data = mem.data,
  976. dv = .(eventDets),
  977. wid = .(subID),
  978. within = .(valenceCategory),
  979. type = 3,
  980. detailed = TRUE)
  981. mem_Event
  983. # posthoc
  984. aov_model <- aov(eventDets ~ valenceCategory + Error(subID/valenceCategory), data = mem.data)
  985. emmeans_result <- emmeans(aov_model, ~ valenceCategory)
  986. pairs(emmeans_result, adjust = "tukey")
  987. ```
  990. perceptual details
  991. ```{r}
  992. mem_vis <-ezANOVA(data = mem.data,
  993. dv = .(visualDets),
  994. wid = .(subID),
  995. within = .(valenceCategory),
  996. type = 3,
  997. detailed = TRUE)
  998. mem_vis
  1001. # posthoc
  1002. aov_model <- aov(visualDets ~ valenceCategory + Error(subID/valenceCategory), data = mem.data)
  1003. emmeans_result <- emmeans(aov_model, ~ valenceCategory)
  1004. pairs(emmeans_result, adjust = "tukey")
  1005. ```
  1007. follow-up t-tests:
  1008. ```{r}
  1009. # Narrative details
  1010. valence_summary_event <- mem.data %>%
  1011. group_by(subID, valenceCategory) %>%
  1012. summarise(mean_event = mean(eventDets), .groups = "drop") %>%
  1013. tidyr::pivot_wider(names_from = valenceCategory, values_from = mean_event)
  1015. # Positive vs Negative
  1016. t.test(valence_summary_event$pos, valence_summary_event$neg, paired = TRUE)
  1018. # Positive vs Neutral
  1019. t.test(valence_summary_event$pos, valence_summary_event$neu, paired = TRUE)
  1021. # Negative vs Neutral
  1022. t.test(valence_summary_event$neg, valence_summary_event$neu, paired = TRUE)
  1023. ```
  1025. ```{r}
  1026. # Perceptual details
  1027. valence_summary_vis <- mem.data %>%
  1028. group_by(subID, valenceCategory) %>%
  1029. summarise(mean_vis = mean(visualDets), .groups = "drop") %>%
  1030. tidyr::pivot_wider(names_from = valenceCategory, values_from = mean_vis)
  1032. # Positive vs Negative
  1033. t.test(valence_summary_vis$pos, valence_summary_vis$neg, paired = TRUE)
  1035. # Positive vs Neutral
  1036. t.test(valence_summary_vis$pos, valence_summary_vis$neu, paired = TRUE)
  1038. # Negative vs Neutral
  1039. t.test(valence_summary_vis$neg, valence_summary_vis$neu, paired = TRUE)
  1040. ```
  1043. ```{r}
  1044. # Overall correct details
  1045. valence_summary_cor <- mem.data %>%
  1046. group_by(subID, valenceCategory) %>%
  1047. summarise(mean_correct = mean(correctDetails), .groups = "drop") %>%
  1048. tidyr::pivot_wider(names_from = valenceCategory, values_from = mean_correct)
  1050. valence_summary_cor
  1052. # Positive vs Negative
  1053. t.test(valence_summary_cor$pos, valence_summary_cor$neg, paired = TRUE)
  1055. # Positive vs Neutral
  1056. t.test(valence_summary_cor$pos, valence_summary_cor$neu, paired = TRUE)
  1058. # Negative vs Neutral
  1059. t.test(valence_summary_cor$neg, valence_summary_cor$neu, paired = TRUE)
  1060. ```
  1062. ## Figure 1d (detail counts by valence)
  1064. ```{r}
  1065. # behavcolors <- c("#f76c67","#67aaf7","#0bca81","#f6a84f","#9c67f7","#67f7f0","#7F7F7F")
  1067. ll = ggplot(data = behav_plot_nocorrectdets, aes(x = detail, y = mean, fill = valenceCategory)) +
  1068. geom_bar(stat = "identity", position = position_dodge(), alpha = 0.60, color = "black") +
  1070. # Set fill colors for the bars and specify legend labels
  1071. scale_fill_manual(values = c("neg" = "#e3401a", # Red for negative
  1072. "neu" = "#1f78b4", # Blue for neutral
  1073. "pos" = "#33a02c"), # Green for positive
  1074. labels = c("neg" = "negative", "neu" = "neutral", "pos" = "positive"),
  1075. name = "Valence") +
  1077. labs(x = "Detail type", y = "Mean details recalled") +
  1078. scale_y_continuous(limits = c(0, 8), expand = c(0, 0)) +
  1079. scale_x_discrete(labels = c("narrative", "perceptual")) +
  1081. theme(
  1082. panel.border = element_blank(),
  1083. panel.grid.major = element_blank(),
  1084. panel.grid.minor = element_blank(),
  1085. panel.background = element_blank(),
  1086. axis.title.x = element_text(size = 10),
  1087. axis.title.y = element_text(size = 10),
  1088. axis.text = element_text(size = 8),
  1089. legend.title = element_text(size = 8),
  1090. legend.text = element_text(size = 7),
  1091. legend.key.size = unit(0.4, "cm"),
  1092. axis.line = element_line(colour = "black")
  1093. ) +
  1094. geom_errorbar(data = behav_plot_nocorrectdets, stat = "identity", width = 0,
  1095. position = position_dodge(width = 0.9),
  1096. aes(x = detail, group = valenceCategory, ymax = mean + se, ymin = mean - se)) +
  1098. # Overlay individual data points
  1099. geom_point(data = means_individ_long, aes(x = detail, y = mean, color = valenceCategory),
  1100. position = position_jitterdodge(jitter.width = 0.1, dodge.width = 0.9),
  1101. alpha = 0.6, size = 1.5, show.legend = TRUE) +
  1103. # Set color mapping for the individual points and specify legend labels
  1104. scale_color_manual(values = c("neg" = "#e3401a", # Red for negative
  1105. "neu" = "#1f78b4", # Blue for neutral
  1106. "pos" = "#33a02c"), # Green for positive
  1107. labels = c("neg" = "negative", "neu" = "neutral", "pos" = "positive"),
  1108. name="Valence") # Ensure colors match the bars
  1110. ll
  1112. # UNCOMMENT and INSERT PATH to export
  1113. # ggsave(filename = 'insert-path',
  1114. # plot = ll,
  1115. # device = "png",
  1116. # dpi = 300,
  1117. # width = 3.1,
  1118. # height = 2.5,
  1119. # units = "in")
  1121. ```
  1124. # Analysis: anterior/posterior hipp
  1126. ## setup
  1127. ```{r}
  1128. # import activity data
  1129. subjects <- list.files(path='/Volumes/memolab/Data/Behavioral_Online/reframe-details/detail_script_files/hipp-recall-block15s/',full.names=TRUE, recursive = TRUE, pattern = 'block15s_timepoint-averages')
  1130. actdata2 = do.call(rbind, lapply(subjects, function(x) {read.csv(x, header = TRUE)}))
  1132. # join behavioral and activity data
  1133. alldata2 <- left_join(actdata2,behav.all,by=c("subID","videoName","trial"),suffix=c(".act",".beh"))
  1135. # define factors
  1136. alldata2$ROI <- as.factor(alldata2$ROI)
  1137. alldata2$conditionName <- as.factor(alldata2$conditionName)
  1138. alldata2$valenceCategory <- as.factor(alldata2$valenceCategory)
  1139. alldata2$subID <- as.factor(alldata2$subID)
  1140. alldata2$regRound.categ <- as.factor(alldata2$regRound) # keep both integer & categorical versions
  1141. alldata2$videoName <- as.factor(alldata2$videoName)
  1142. alldata2$periodLabel <- as.factor(alldata2$periodLabel)
  1143. ```
  1145. ```{r}
  1146. ### add z score of activity estimates within region and subject and time-period
  1147. alldata2 <- alldata2 %>%
  1148. group_by(subID, ROI, periodLabel) %>%
  1149. mutate(Z = zscore(regulationActivity)) %>%
  1150. ungroup()
  1152. # remove betas that are > 4SD
  1153. tmp <- alldata2 %>%
  1154. mutate(isOutlier = ifelse(abs(Z)>4,TRUE,FALSE))
  1156. # Following Cooper & Ritchey, remove trials where mean for ANY ROI is an outlier
  1157. outliers <- tmp %>%
  1158. select(isOutlier,subID,trial) %>%
  1159. filter(isOutlier) %>%
  1160. unique()
  1161. alldata2 <- left_join(alldata2,outliers,by=c("subID","trial"))
  1162. alldata2$isOutlier[is.na(alldata2$isOutlier)] <- FALSE
  1164. # Summarize number of outliers
  1165. table(alldata2$subID,alldata2$isOutlier)
  1166. table(alldata2$isOutlier)
  1168. alldata2$regulationActivity[alldata2$isOutlier] <- NA #mark value as NA if outlier
  1169. ```
  1171. ```{r}
  1172. # TRIAL FILTERS
  1173. alldata2 <- alldata2 %>%
  1174. mutate(isComplete.emo = ifelse(is.na(valenceChange) | is.na(arousalChange),
  1175. FALSE,TRUE))
  1177. # Removing negative reappraisal trials
  1178. alldata2 <- alldata2 %>%
  1179. filter(conditionName == "remember")
  1181. alldata2 <- alldata2 %>%
  1182. filter(remembered == "1") # remembered-only
  1183. ```
  1186. ```{r}
  1187. alldata2 <- alldata2 %>%
  1188. group_by(subID,ROI,periodLabel) %>%
  1189. mutate(regSuccess = scale(regSuccess,scale=FALSE),
  1190. valenceChange = scale(valenceChange,scale=FALSE),
  1191. arousalChange = scale(arousalChange,scale=FALSE),
  1192. recallVividness = scale(recallVividness,scale=FALSE),
  1193. eventDets = scale(eventDets, scale =FALSE),
  1194. visualDets = scale(visualDets, scale=FALSE),
  1195. correctDetails = scale(correctDetails, scale=FALSE)) %>% # mean-center within subject
  1196. ungroup()
  1198. # double check that this worked as expected
  1199. tmp <- alldata2 %>% filter(valenceCategory=="neg" & ROI=="AntHipp_L") %>% group_by(subID) %>% summarize(meansucc = mean(regSuccess,na.rm=TRUE))
  1201. ```
  1203. ## analysis
  1204. ```{r}
  1205. alldata2$ROI = as.factor(alldata2$ROI)
  1206. rois <- levels(alldata2$ROI)
  1207. outDir <- '/Volumes/memolab/Data/Behavioral_Online/reframe-details/detail_script_files/reframe-detail_timepoint_results/'
  1208. alphafdr <- .05
  1210. curFile = paste0(outDir,'lme_hippocampal_rem.csv')
  1211. ### set up data frame for mixed effect model results:
  1212. mixed.data <- data.frame(array(0,c(length(rois)*8,8)))
  1213. colnames(mixed.data) <- c('ROI','Period','Predictor','Beta','SE','T','Df','Pvalue')
  1214. row = 0
  1216. model.data <- alldata2
  1217. periodLabel <- as.character(model.data$periodLabel[1])
  1220. # ******** run linear mixed effects model within each ROI and store results **********
  1221. for (r in 1:length(rois)) {
  1223. thisROI <- as.character(rois[r])
  1225. roiData <- filter(model.data, ROI == thisROI)
  1226. roiData$regSuccess <- scale(roiData$regSuccess, scale = FALSE)
  1228. # run lmer and get fixed effect stats
  1229. roiData$valenceCategory <- factor(roiData$valenceCategory, levels=c("neu", "pos", "neg"))
  1230. full.model <- suppressMessages(lmer(regulationActivity ~ valenceCategory*eventDets + valenceCategory*visualDets+
  1231. (1|subID), data = roiData))
  1232. stats <- summary(full.model)$coefficients
  1233. nIV <- nrow(stats) - 1 # number of fixed effects, discounting intercept
  1235. ### add fixed effects and p values to data frame
  1236. mixed.data$ROI[(row+1):(row+nIV)] <- thisROI
  1237. mixed.data$Phase[(row+1):(row+nIV)] <- periodLabel
  1238. mixed.data$Predictor[(row+1):(row+nIV)] <- row.names(stats)[2:(nIV+1)]
  1239. mixed.data[(row+1):(row+nIV),c("Beta","SE","T","Df","Pvalue")] <- stats[2:(nIV+1),
  1240. c("Estimate","Std. Error","t value","df","Pr(>|t|)")]
  1241. row = row + nIV
  1243. }
  1244. ```
  1246. ```{r}
  1247. # event.model
  1248. event.model = mixed.data %>% filter(mixed.data$Predictor == "eventDets")
  1250. p.values <- event.model[,8]
  1251. Pr.fdr <- p.adjust(p.values, method = "fdr")
  1252. event.model <- cbind(event.model,Pr.fdr)
  1253. event.model <- mutate(event.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  1254. kbl(event.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  1255. scroll_box(width="1200px",height="800px")
  1258. # visual.model
  1259. visual.model = mixed.data %>% filter(mixed.data$Predictor == "visualDets")
  1261. p.values <- visual.model[,8]
  1262. Pr.fdr <- p.adjust(p.values, method = "fdr")
  1263. visual.model <- cbind(visual.model,Pr.fdr)
  1264. visual.model <- mutate(visual.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  1265. kbl(visual.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  1266. scroll_box(width="1200px",height="800px")
  1269. # pos.model
  1270. pos.model = mixed.data %>% filter(mixed.data$Predictor == "valenceCategorypos")
  1271. p.values <- pos.model[,8]
  1272. Pr.fdr <- p.adjust(p.values, method = "fdr")
  1273. pos.model <- cbind(pos.model,Pr.fdr)
  1274. pos.model <- mutate(pos.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  1275. kbl(pos.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  1276. scroll_box(width="1200px",height="800px")
  1278. # neg.model
  1279. neg.model = mixed.data %>% filter(mixed.data$Predictor == "valenceCategoryneg")
  1280. p.values <- neg.model[,8]
  1281. Pr.fdr <- p.adjust(p.values, method = "fdr")
  1282. neg.model <- cbind(neg.model,Pr.fdr)
  1283. neg.model <- mutate(neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  1284. kbl(neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  1285. scroll_box(width="1200px",height="800px")
  1287. # visual.pos.model
  1288. visual.pos.model = mixed.data %>% filter(mixed.data$Predictor == "valenceCategorypos:visualDets")
  1290. p.values <- visual.pos.model[,8]
  1291. Pr.fdr <- p.adjust(p.values, method = "fdr")
  1292. visual.pos.model <- cbind(visual.pos.model,Pr.fdr)
  1293. visual.pos.model <- mutate(visual.pos.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  1294. kbl(visual.pos.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  1295. scroll_box(width="1200px",height="800px")
  1298. # visual.neg.model
  1299. visual.neg.model =mixed.data %>% filter(mixed.data$Predictor == "valenceCategoryneg:visualDets")
  1301. p.values <- visual.neg.model[,8]
  1302. Pr.fdr <- p.adjust(p.values, method = "fdr")
  1303. visual.neg.model <- cbind(visual.neg.model,Pr.fdr)
  1304. visual.neg.model <- mutate(visual.neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  1305. kbl(visual.neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  1306. scroll_box(width="1200px",height="800px")
  1308. # event.pos.model
  1310. event.pos.model =mixed.data %>% filter(mixed.data$Predictor == "valenceCategorypos:eventDets")
  1312. p.values <- event.pos.model[,8]
  1313. Pr.fdr <- p.adjust(p.values, method = "fdr")
  1314. event.pos.model <- cbind(event.pos.model,Pr.fdr)
  1315. event.pos.model <- mutate(event.pos.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  1316. kbl(event.pos.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  1317. scroll_box(width="1200px",height="800px")
  1319. # event.neg.model
  1320. event.neg.model = mixed.data %>% filter(mixed.data$Predictor == "valenceCategoryneg:eventDets")
  1322. p.values <- event.neg.model[,8]
  1323. Pr.fdr <- p.adjust(p.values, method = "fdr")
  1324. event.neg.model <- cbind(event.neg.model,Pr.fdr)
  1325. event.neg.model <- mutate(event.neg.model,sig = ifelse(Pr.fdr<.05,TRUE,FALSE))
  1326. kbl(event.neg.model,digits=3) %>% kable_styling(bootstrap_options = c("striped", "hover")) %>%
  1327. scroll_box(width="1200px",height="800px")
  1328. ```
  1330. ## Figure 3c (perceptual x aHipp)
  1332. ```{r}
  1333. alldata2 %>% filter(ROI != "PostHipp_L") %>%
  1334. filter(ROI != "PostHipp_R") %>%
  1335. filter(ROI != "AntHipp_R") %>%
  1336. group_by(trial, subID) %>%
  1337. summarise(eventDets, visualDets, valenceCategory, regulationActivity = mean(regulationActivity)) -> anterioract_data
  1339. anterioract_data$valenceCategory <- factor(anterioract_data$valenceCategory, levels=c("neu", "pos", "neg"))
  1340. full.model.hpca <- suppressMessages(lmer(regulationActivity ~ eventDets*valenceCategory + visualDets*valenceCategory + (1|subID), data = anterioract_data))
  1342. anth = plot_visualDets_noHist(full.model.hpca, anterioract_data, "Left aHipp activity")
  1343. anth
  1345. # UNCOMMENT AND INSERT PATH to export
  1346. # ggsave(filename = 'insert-path',
  1347. # plot = anth,
  1348. # device = "png",
  1349. # dpi = 300,
  1350. # width = 3.5,
  1351. # height = 2.25,
  1352. # units = "in")
  1353. ```

reframe-detail-main-paper.Rmd at commit 0531848, no license · at the source

Overview

Authors: Nina Curko1, Rosalie Samide1, Valentina Krenz1, Elizabeth A Kensinger1, Maureen Ritchey1,2
ORCID iDs: Nina Curko, Maureen Ritchey
  1. Department of Psychology and Neuroscience, Boston College, Chestnut Hill, Massachusetts 02467
  2. Department of Psychology, University of California, Los Angeles, California 90095
Institutions: Boston College (United States); University of California, Los Angeles (United States)
Journal: eNeuro, volume 13, issue 11, pages ENEURO.0146-26.2026
Dates: received 4 May 2026; accepted 14 September 2026; published online 6 November 2026; in print November 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0146-26.2026 · PMID 42888042 · PMCID PMC13750827 · OpenAlex W7222583887
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.05.05.721961
Status: code verified (how the code is found and verified)
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Statistics, Preprocessing, Connectivity, fMRI & imaging, Machine learning
Keywords: default mode network, emotional memory, episodic memory, fMRI
MeSH: Brain*, Default Mode Network*, Emotions*, Memory, Episodic*, Mental Recall*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Memory Processes and Influences (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (1625552, 2047415); National Alliance for Research on Schizophrenia and Depression
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Episodic memory involves reconstruction of past events through integration of multiple types of information, including perceptual details, narrative content, and emotional tone. The default mode network (DMN) is a set of regions thought to support episodic retrieval, yet it remains unclear how distinct subnetworks contribute to recall of these different memory features. Here, we examined how DMN subnetworks support and represent memory for naturalistic emotional events. Male and female human participants encoded short news videos that varied in emotional valence and recalled them in response to neutral cues during functional MRI scanning. Videos were again recalled 1 d later and memories were scored for perceptual and narrative details. Activity in the dorsomedial subnetwork was related to the emotional valence of the memory, while activity in the medial temporal subnetwork was associated with the number of perceptual details recalled. Multivariate pattern analyses further revealed that the medial temporal subnetwork exhibited greater pattern stability across recalls when recalling more perceptual details, while stability in the dorsomedial and core subnetworks was tied to emotional remembering. Our findings suggest that the dorsomedial subnetwork provides an affective frame for a memory, while the medial temporal subnetwork contributes perceptual specificity. These results demonstrate that the contents of memory retrieval shape network engagement during emotional recall, providing insight into how the brain reconstructs complex real-world experiences.

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

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memobc/reframe-detail-paper

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State: the link answers, verified on 12 November 2026
Evidence: files inventoried
Commit: 0531848de1a73e2b7113376d1bf0be3e83243b74, 30 October 2026
Languages: R (6)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: the text, “Statistical analysis”
Holds: README, 6 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), lme4 (5 files), ggpubr (4 files), ggseg (4 files), lmerTest (4 files), ggplot2 (3 files), emmeans (2 files), patchwork (2 files), cowplot (1 file)
Availability: 1 check, the latest on 12 November 2026: the link answers
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7 files
At the source: github.com/memobc/reframe-detail-paper

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Version 1, 12 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 12 MeSH terms, 2 funders, 66 references.

Cite

This paper

Curko, N., Samide, R., Krenz, V., Kensinger, E. A., & Ritchey, M. (2026). Remembered Event Features Shape Default Mode Network Engagement during Emotional Memory Recall. eNeuro, 13(11), ENEURO.0146-26.2026. https://doi.org/10.1523/eneuro.0146-26.2026

BibTeX

@article{curko2026remembered,
author = {Curko, Nina and Samide, Rosalie and Krenz, Valentina and Kensinger, Elizabeth A and Ritchey, Maureen},
title = {{Remembered Event Features Shape Default Mode Network Engagement during Emotional Memory Recall}},
journal = {eNeuro},
year = {2026},
month = nov,
volume = {13},
number = {11},
pages = {ENEURO.0146--26.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0146-26.2026},
url = {https://doi.org/10.1523/eneuro.0146-26.2026},
pmid = {42888042},
pmcid = {PMC13750827}
}

RIS

TY - JOUR
AU - Curko, Nina
AU - Samide, Rosalie
AU - Krenz, Valentina
AU - Kensinger, Elizabeth A
AU - Ritchey, Maureen
TI - Remembered Event Features Shape Default Mode Network Engagement during Emotional Memory Recall
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/11/10
VL - 13
IS - 11
SP - ENEURO.0146
EP - 26.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0146-26.2026
UR - https://doi.org/10.1523/eneuro.0146-26.2026
LA - en
ER -

CSL-JSON

{
"id": "10.1523/eneuro.0146-26.2026",
"type": "article-journal",
"title": "Remembered Event Features Shape Default Mode Network Engagement during Emotional Memory Recall",
"container-title": "eNeuro",
"author": [
{
"family": "Curko",
"given": "Nina"
},
{
"family": "Samide",
"given": "Rosalie"
},
{
"family": "Krenz",
"given": "Valentina"
},
{
"family": "Kensinger",
"given": "Elizabeth A"
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{
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"given": "Maureen"
}
],
"container-title-short": "eNeuro",
"volume": "13",
"issue": "11",
"page": "ENEURO.0146-26.2026",
"DOI": "10.1523/eneuro.0146-26.2026",
"PMID": "42888042",
"PMCID": "PMC13750827",
"ISSN": "2373-2822",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/eneuro.0146-26.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
11,
10
]
]
}
}

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Journal: Nature communications
In common: emmeans, lmerTest, lme4, 2 other tools, fMRI, 10 references
[5] doi:10.1038/s44271-026-00481-0 [code]
Natural language processing captures memory content associated with shared neural patterns at encoding and retrieval.
Journal: Communications psychology
In common: ggseg, emmeans, lmerTest, 3 other tools, cognitive, 7 references
[6] doi:10.1038/s41467-023-36805-5 [code]
Flexible reuse of cortico-hippocampal representations during encoding and recall of naturalistic events.
Journal: Nature communications
In common: cognitive, 11 references
[7] doi:10.1038/s41467-022-33517-0 [code]
Schemas provide a scaffold for neocortical integration of new memories over time.
Journal: Nature communications
In common: emmeans, lmerTest, lme4, 2 other tools, 7 references
[8] doi:10.1002/hbm.70475 [code]
The Development of Hippocampal-Cortical Functional Connectivity in Infants and Toddlers.
Journal: Human brain mapping
In common: emmeans, cowplot, ggplot2, 1 other tool, fMRI, 8 references
[9] doi:10.1162/imag.a.1321 [code]
Phase similarity between similar objects indicates representational merging across retrieval training but not sleep.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: emmeans, lmerTest, lme4, 2 other tools, cognitive, 6 references
[10] doi:10.1162/imag.a.1313 [code]
Functional specialization of angular gyrus and precuneus subregions for perspective-guided autobiographical memory retrieval.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: emmeans, lmerTest, lme4, 4 other tools, cognitive, 4 references

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