OSCR

Effects of personalized transcranial magnetic stimulation on social cognitive network functional connectivity in schizophrenia spectrum disorders: A double-blind, randomized, sham-controlled target engagement trial.

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5 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 5 matches
  1. [1] § Methods › MRI processing and individualized connectivity metrics ↔ notebooks/01_Modsoccs_EA_FC_paper.Rmd, lines 773–816 · score 0.64 · right premotor, right TPJ, correlations, transformed, ROIs, Fisher
  2. [2] § Methods › Statistical analyses › Primary Analyses: ↔ notebooks/01_Modsoccs_EA_FC_paper.Rmd, lines 988–1052 · score 0.60 · post pre treatment, right IFC, right TPJ, DMPFC, connectivity
  3. [3] § Results › Primary Outcomes: ↔ notebooks/01_Modsoccs_EA_FC_paper.Rmd, lines 1141–1202 · score 0.55 · DMPFC left IFC, DMPFC right IFC, SE, connectivity
  4. [4] § Methods › Statistical analyses › Secondary Analyses: ↔ notebooks/02_Modsoccs_tolerability.Rmd, lines 12–76 · score 0.52 · adverse events, ANOVA, ITT, pain, Fisher, Tolerability
  5. [5] § Methods › Stimulation parameters ↔ notebooks/02_Modsoccs_tolerability.Rmd, lines 12–76 · score 0.52 · motor threshold, intensity, stimulation, active, sham, treatment

Paper

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The authors' code

R Markdown · 1,204 lines · 49 KB · no license · 3 matches

  1. ---
  2. title: "Modsoccs EA Connectivity"
  3. output: html_notebook
  4. ---
  6. This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
  8. Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*.
  10. ```{r, message=FALSE, warning=FALSE, results=FALSE}
  11. library(splitstackshape)
  12. library(stringr)
  13. library(tidyr)
  14. library(dplyr)
  15. library(corrr)
  16. library(psych)
  17. library(tableone)
  18. library(corrplot)
  19. library(ggplot2)
  20. #library(effsize)
  22. ```
  24. ```{r, warning=FALSE, results=FALSE}
  25. # find ses 1 EA time series files # pattern glob2rx("sub*_ses-01_EA_2mm_noGSR_individualized_rois_meants.csv")
  26. setwd("/projects/loliver/ModSoCCS/data/processed")
  27. files_ea_ts1 <- list.files(path= ".", recursive=T, full.names=F, pattern="^sub.*_ses-01_EA_2mm_noGSR_individualized_allrois_meants\\.csv$")
  29. # confirm csvs aren't empty
  30. files_ea_ts1[file.size(files_ea_ts1) == 0]
  32. # create list of IDs
  33. ptlist1 <- paste("SPN20", substring(files_ea_ts1,5,7), substring(files_ea_ts1,8,11), sep = "_")
  35. # read in time series files
  36. ea_ts1 <- lapply(files_ea_ts1, read.csv, header=F)
  38. # transpose dfs
  39. ea_ts1 <- lapply(ea_ts1, t)
  41. # Name dfs with participant IDs
  42. names(ea_ts1) <- ptlist1
  44. # drop last 13 time points from each EA run for MRP and ZHP participants (not CMH) to align with CMH; not losing any task
  45. for (i in names(ea_ts1[15:46])) {
  46. ea_ts1[[i]] <- ea_ts1[[i]][c(1:690,704:1393),]
  47. }
  49. ```
  51. ```{r}
  52. # find circles onsets for each participant for ses 1
  53. setwd("/projects/loliver/ModSoCCS/data/processed")
  55. # pattern glob2rx("sub*_circles.1D")
  56. files_circles <- list.files(path= ".", recursive=T, full.names=F, pattern="^sub.*_circles\\.1D$")
  58. # keep only ses-01
  59. files_circles1 <- str_subset(files_circles, pattern="ses-01")
  61. # create list of IDs
  62. circ_ptlist1 <- paste("SPN20", substring(files_circles1,5,7), substring(files_circles1,8,11), sep = "_")
  64. # read in circles files
  65. circles1 <- lapply(files_circles1, read.csv, header=F, sep=" ", colClasses=c(NA, NA, "NULL"))
  66. names(circles1) <- circ_ptlist1
  68. # get circles onset times into same row, adding 552 s per run (690 TRs/run), dividing by 0.8 (as TRs are 0.8s) to get TR onsets
  69. circles_times1 <- list()
  71. for (i in names(circles1)) {
  72. circles_times1[[i]] <- round(((cbind(circles1[[i]][1,],(circles1[[i]][2,])+552))/0.8))
  73. }
  75. # remove circles from time series data (circles runs are 50 TRs or 40 s - this way we are removing 50 TRs - the onset + 49)
  76. ea_resid_ts1 <- list()
  78. for (i in names(ea_ts1)) {
  79. ea_resid_ts1[[i]] <- ea_ts1[[i]][-c(circles_times1[[i]][1,1]:(circles_times1[[i]][1,1]+49),circles_times1[[i]][1,2]:(circles_times1[[i]][1,2]+49),
  80. circles_times1[[i]][1,3]:(circles_times1[[i]][1,3]+49),circles_times1[[i]][1,4]:(circles_times1[[i]][1,4]+49)),]
  81. }
  84. # check dims
  85. #lapply(ea_resid_ts1, dim)
  87. # check for NAs
  88. #for (i in names(ea_resid_ts1)) {
  89. # print (i)
  90. # print(which(is.na(ea_resid_ts1[[i]])))
  91. #}
  93. ```
  95. ```{r, warning=FALSE, results=FALSE}
  96. # find ses 2 EA time series files
  97. setwd("/projects/loliver/ModSoCCS/data/processed")
  98. files_ea_ts2 <- list.files(path= ".", recursive=T, full.names=F, pattern="^sub.*_ses-02_EA_2mm_noGSR_individualized_allrois_meants\\.csv$")
  100. # confirm csvs aren't empty
  101. files_ea_ts2[file.size(files_ea_ts2) == 0]
  103. # create list of IDs
  104. ptlist2 <- paste("SPN20", substring(files_ea_ts2,5,7), substring(files_ea_ts2,8,11), sep = "_")
  106. # read in time series files
  107. ea_ts2 <- lapply(files_ea_ts2, read.csv, header=F)
  109. # transpose dfs
  110. ea_ts2 <- lapply(ea_ts2, t)
  112. # Name dfs with participant IDs
  113. names(ea_ts2) <- ptlist2
  115. # drop last 13 time points from each EA run for MRP and ZHP participants (not CMH) to align with CMH; not losing any task
  116. for (i in names(ea_ts2[15:46])) {
  117. ea_ts2[[i]] <- ea_ts2[[i]][c(1:690,704:1393),]
  118. }
  120. # keep only those with both baseline and longitudinal data
  121. #ea_ts2 <- ea_ts2[names(ea_ts2) %in% names(ea_ts1)]
  123. ```
  125. ```{r}
  126. # find circles onsets for each participant for ses 2
  127. setwd("/projects/loliver/ModSoCCS/data/processed")
  129. # pattern glob2rx("sub*_circles.1D")
  130. files_circles <- list.files(path= ".", recursive=T, full.names=F, pattern="^sub.*_circles\\.1D$")
  132. # keep only ses-02
  133. files_circles2 <- str_subset(files_circles, pattern="ses-02")
  135. # create list of IDs
  136. circ_ptlist2 <- paste("SPN20", substring(files_circles2,5,7), substring(files_circles2,8,11), sep = "_")
  138. # read in circles files
  139. circles2 <- lapply(files_circles2, read.csv, header=F, sep=" ", colClasses=c(NA, NA, "NULL"))
  140. names(circles2) <- circ_ptlist2
  142. # get circles onset times into same row, adding 552 s per run (690 TRs/run), dividing by 0.8 (as TRs are 0.8s) to get TR onsets
  143. circles_times2 <- list()
  145. for (i in names(circles2)) {
  146. circles_times2[[i]] <- round(((cbind(circles2[[i]][1,],(circles2[[i]][2,])+552))/0.8))
  147. }
  149. # remove circles from time series data (circles runs are 50 TRs or 40 s - this way we are removing 50 TRs - the onset + 49)
  150. ea_resid_ts2 <- list()
  152. for (i in names(ea_ts2)) {
  153. ea_resid_ts2[[i]] <- ea_ts2[[i]][-c(circles_times2[[i]][1,1]:(circles_times2[[i]][1,1]+49),circles_times2[[i]][1,2]:(circles_times2[[i]][1,2]+49),
  154. circles_times2[[i]][1,3]:(circles_times2[[i]][1,3]+49),circles_times2[[i]][1,4]:(circles_times2[[i]][1,4]+49)),]
  155. }
  158. # check dims
  159. #lapply(ea_resid_ts2, dim)
  161. # check for NAs
  162. #for (i in names(ea_resid_ts2)) {
  163. # print (i)
  164. # print(which(is.na(ea_resid_ts2[[i]])))
  165. #}
  167. ```
  169. ```{r}
  170. # read in Modsoccs behavioural data
  171. mod_data <- read.csv(file = "/projects/loliver/ModSoCCS/data/behavioural/ModSoCCS_data_09-10-2024.csv", header=T) %>%
  172. mutate(site = as.factor(site),
  173. demo_sex_birth = as.factor(demo_sex_birth),
  174. scid5_cat3_scz = as.factor(scid5_cat3_scz))
  176. # change sex at birth to female for ZHP0002 (randomized before this info was recorded in Redcap)
  177. mod_data[mod_data$record_id=="SPN20_ZHP_0002","demo_sex_birth"] <- "female"
  179. # data for those who started treatment (N=49 vs N=46 completers) - ITT table
  180. mod_data_ITT <- mod_data[mod_data$term_premature_yn == 0 | mod_data$record_id=="SPN20_MRP_0014" | mod_data$record_id=="SPN20_ZHP_0009" | mod_data$record_id=="SPN20_ZHP_0026",]
  182. # filter based on early termination (keep completers only)
  183. mod_data <- mod_data[mod_data$term_premature_yn == 0,]
  185. # read in days between ea mris and days since final treatment
  186. mod_mri_data <- read.csv(file = "/projects/loliver/ModSoCCS/data/behavioural/ModSoCCS_data_03-01-2023_days_between_ea_treat.csv", header=T)
  188. # add to mod_data
  189. mod_data <- merge(mod_data,mod_mri_data[,c(1,4:5)],by="record_id")
  191. # read in blinded groups
  192. mod_gr_data <- read.csv(file = "/projects/loliver/ModSoCCS/data/behavioural/Modsoccs_group_labels_blinded.csv", header=T)
  194. # add to mod_data
  195. mod_data <- merge(mod_data,mod_gr_data,by="record_id")
  196. mod_data$Label <- factor(mod_data$Label, levels= c("A","B","C"), labels = c("Sham","rTMS","iTBS"))
  198. # add to mod_data_ITT
  199. mod_data_ITT <- merge(mod_data_ITT,mod_gr_data,by="record_id")
  200. mod_data_ITT$Label <- factor(mod_data_ITT$Label, levels= c("A","B","C"), labels = c("Sham","rTMS","iTBS"))
  202. ```
  204. ```{r, warning=FALSE}
  205. # add mean EA to df
  206. mod_data_behav <- mod_data
  208. # find EA regressor files for ses 1
  209. setwd("/projects/loliver/ModSoCCS/data/processed")
  211. # pattern glob2rx("sub*_circles.1D")
  212. files_ea <- list.files(path= ".", recursive=T, full.names=F, pattern="^.*_EA\\.1D$")
  214. # keep only ses-01
  215. files_ea1 <- str_subset(files_ea, pattern="ses-01")
  217. # create list of IDs
  218. ea_ptlist1 <- paste("SPN20", substring(files_ea1,5,7), substring(files_ea1,8,11), sep = "_")
  220. # read in ea regressor files
  221. ea1 <- lapply(files_ea1, read.csv, header=F, sep="*")
  222. ea1 <- lapply(ea1, cSplit, splitCols=2:4, sep=":")
  223. names(ea1) <- ea_ptlist1
  225. # calculate mean EA and add to spins_behav_conn
  226. mod_data_behav$pre_scog_mean_ea <- NA
  228. for (i in names(ea1)) {
  229. mod_data_behav[mod_data_behav$record_id==i,"pre_scog_mean_ea"] <- mean(c(ea1[[i]][[1,"V2_1"]],ea1[[i]][[1,"V3_1"]],ea1[[i]][[1,"V4_1"]],
  230. ea1[[i]][[2,"V2_1"]],ea1[[i]][[2,"V3_1"]],ea1[[i]][[2,"V4_1"]]), na.rm=T)
  231. }
  233. # fisher z transform EA values
  234. mod_data_behav$pre_scog_mean_ea <- fisherz(mod_data_behav$pre_scog_mean_ea)
  237. # same for ses 2
  238. files_ea2 <- str_subset(files_ea, pattern="ses-02")
  240. # create list of IDs
  241. ea_ptlist2 <- paste("SPN20", substring(files_ea2,5,7), substring(files_ea2,8,11), sep = "_")
  243. # read in ea regressor files
  244. ea2 <- lapply(files_ea2, read.csv, header=F, sep="*")
  245. ea2 <- lapply(ea2, cSplit, splitCols=2:4, sep=":")
  246. names(ea2) <- ea_ptlist2
  248. # calculate mean EA and add to spins_behav_conn
  249. mod_data_behav$post_scog_mean_ea <- NA
  251. for (i in names(ea2)) {
  252. mod_data_behav[mod_data_behav$record_id==i,"post_scog_mean_ea"] <- mean(c(ea2[[i]][[1,"V2_1"]],ea2[[i]][[1,"V3_1"]],ea2[[i]][[1,"V4_1"]],
  253. ea2[[i]][[2,"V2_1"]],ea2[[i]][[2,"V3_1"]],ea2[[i]][[2,"V4_1"]]), na.rm=T)
  254. }
  256. # fisher z transform EA values
  257. mod_data_behav$post_scog_mean_ea <- fisherz(mod_data_behav$post_scog_mean_ea)
  259. ```
  261. ```{r}
  262. # examine changes in behavioural metrics
  263. # generate difference scores
  264. mod_data_behav$diff_scog_er40_cr_columnpcr_value <- mod_data_behav$post_scog_er40_cr_columnpcr_value-mod_data_behav$pre_scog_er40_cr_columnpcr_value
  266. mod_data_behav$diff_scog_rmet_total <- mod_data_behav$post_scog_rmet_total-mod_data_behav$pre_scog_rmet_total
  268. mod_data_behav$diff_scog_tasit_p1_total <- mod_data_behav$post_scog_tasit_p1_total-mod_data_behav$pre_scog_tasit_p1_total
  270. mod_data_behav$diff_scog_tasit_p2_sin <- mod_data_behav$post_scog_tasit_p2_sin-mod_data_behav$pre_scog_tasit_p2_sin
  271. mod_data_behav$diff_scog_tasit_p2_sscar <- mod_data_behav$post_scog_tasit_p2_sscar-mod_data_behav$pre_scog_tasit_p2_sscar
  272. mod_data_behav$diff_scog_tasit_p2_psar <- mod_data_behav$post_scog_tasit_p2_psar-mod_data_behav$pre_scog_tasit_p2_psar
  274. mod_data_behav$diff_scog_tasit_p3_lie <- mod_data_behav$post_scog_tasit_p3_lie-mod_data_behav$pre_scog_tasit_p3_lie
  275. mod_data_behav$diff_scog_tasit_p3_sar <- mod_data_behav$post_scog_tasit_p3_sar-mod_data_behav$pre_scog_tasit_p3_sar
  277. mod_data_behav$diff_scog_mean_ea <- mod_data_behav$post_scog_mean_ea-mod_data_behav$pre_scog_mean_ea
  279. mod_data_behav$diff_np_domain_process_speed <- mod_data_behav$post_np_domain_tscore_process_speed-mod_data_behav$pre_np_domain_tscore_process_speed
  280. mod_data_behav$diff_np_domain_work_mem <- mod_data_behav$post_np_domain_tscore_work_mem-mod_data_behav$pre_np_domain_tscore_work_mem
  281. mod_data_behav$diff_np_domain_verbal_learning <- mod_data_behav$post_np_domain_tscore_verbal_learning-mod_data_behav$pre_np_domain_tscore_verbal_learning
  283. ```
  285. ```{r}
  286. # examine changes in clinical metrics
  287. # generate difference scores
  288. mod_data_behav$diff_bprs_total <- mod_data_behav$tx10_bprs_factor_total-mod_data_behav$pre_bprs_factor_total
  290. mod_data_behav$diff_cdss_total <- mod_data_behav$tx10_cdss_total-mod_data_behav$pre_cdss_total
  291. mod_data_behav$diff_cdss_depLV1 <- (mod_data_behav$tx10_cdss_001 + mod_data_behav$tx10_cdss_002 + mod_data_behav$tx10_cdss_003 + mod_data_behav$tx10_cdss_006 +
  292. mod_data_behav$tx10_cdss_008) - (mod_data_behav$pre_cdss_001 + mod_data_behav$pre_cdss_002 + mod_data_behav$pre_cdss_003 +
  293. mod_data_behav$pre_cdss_006 + mod_data_behav$pre_cdss_008)
  295. mod_data_behav$diff_sans_total <- mod_data_behav$post_sans_total_sc-mod_data_behav$pre_sans_total_sc
  297. ```
  299. ```{r}
  300. # exclude CMH0001 for EA metrics - didn't engage in EA task # Sham
  301. mod_data_behav[mod_data_behav$record_id=="SPN20_CMH_0001",c("pre_scog_mean_ea","post_scog_mean_ea","diff_scog_mean_ea")] <- NA
  303. # look at NAs per diff measure
  304. summary(mod_data_behav[,c(234,237:249,252,250)])
  306. ```
  308. ```{r, fig.height=9, fig.width=11}
  309. # check out distributions and check for outliers
  310. library(reshape2)
  312. summary(mod_data_behav)
  314. # melt data
  315. mod_data_behav_melt <- melt(mod_data_behav[,c(1,234,237:252)])
  317. # plots
  318. ggplot(data = mod_data_behav_melt, mapping = aes(x = value)) +
  319. geom_histogram(bins = 10) + facet_wrap(~variable, scales = 'free_x')
  321. # shapiro-wilks
  322. sapply(mod_data_behav[,c(237:252)], shapiro.test)
  324. ```
  326. ```{r}
  327. # check for outliers
  328. outfunc <- function(x) {abs(scale(x)) > 3}
  330. outlist_behav <- list()
  332. for (i in colnames(mod_data_behav[,c(237:252)])) {
  333. outlist_behav[[i]] <-
  334. as.vector(mod_data_behav[which(outfunc(mod_data_behav[[i]])),1])
  335. } # 1 to see IDs; i to see values
  337. print(outlist_behav)
  339. # remove outliers with 3 SD criteria - leave in as using non-parametric tests and these scores still seem reasonable/possible
  341. #for (i in names(outlist_behav)) {
  342. # mod_data_behav[mod_data_behav$record_id %in% outlist_behav[[i]], i] <- NA
  343. #}
  345. ```
  347. ```{r, fig.height=9, fig.width=11}
  348. # check out distributions after outlier removal (if remove)
  350. # melt data
  351. mod_data_behav_melt <- melt(mod_data_behav[,c(1,234,237:252)])
  353. # plots
  354. ggplot(data = mod_data_behav_melt, mapping = aes(x = value)) +
  355. geom_histogram(bins = 10) + facet_wrap(~variable, scales = 'free_x')
  357. # shapiro-wilks
  358. sapply(mod_data_behav[,c(237:252)], shapiro.test)
  360. ```
  362. ```{r, fig.height=9, fig.width=11}
  363. # check out T1 distributions
  365. # melt data
  366. mod_data_behav_times_melt <- melt(mod_data_behav[,c(1,10,125,124,138:141,147:148,235,121:123,107,90,105,100,112)])
  368. # plots
  369. ggplot(data = mod_data_behav_times_melt, mapping = aes(x = value)) +
  370. geom_histogram(bins = 10) + facet_wrap(~variable, scales = 'free_x')
  372. # shapiro-wilks
  373. sapply(mod_data_behav[,c(10,125,124,138:141,147:148,235,121:123,107,90,105,100,112)], shapiro.test)
  375. ```
  377. ```{r}
  378. # table to look at baseline behav diffs - Sham N=16, rTMS N=15, iTBS N=15
  379. table_one_beh <- CreateTableOne(strata="Label", testNonNormal = kruskal.test, data=mod_data_behav[,c(234,6,10,125,124,138:141,147:148,235,121:123,107,90,105,100,112)])
  380. table_one_beh_print <- print(table_one_beh)
  382. #write.csv(table_one_beh_print,file="/projects/loliver/ModSoCCS/results/modsoccs_table_beh_base_kw_09-07-2025.csv")
  384. ```
  386. ```{r}
  387. # table to look at pre-post behav diffs - Sham N=16, rTMS N=15, iTBS N=15
  388. table_one_beh_diff <- CreateTableOne(strata="Label", testNonNormal = kruskal.test, data=mod_data_behav[,c(234,237:249,252,250)])
  389. table_one_beh_diff_print <- print(table_one_beh_diff)
  391. #write.csv(table_one_beh_diff_print,file="/projects/loliver/ModSoCCS/results/modsoccs_table_beh_diff_kw_09-07-2025.csv")
  393. ```
  395. ```{r}
  396. # table to look at post behav diffs - Sham N=16, rTMS N=15, iTBS N=15
  397. table_one_beh_post <- CreateTableOne(strata="Label", testNonNormal = kruskal.test, data=mod_data_behav[,c(234,6,10,203,202,216:219,225:226,236,199:201,175,190,185)])
  398. table_one_beh_post_print <- print(table_one_beh_post)
  400. #write.csv(table_one_beh_post_print,file="/projects/loliver/ModSoCCS/results/modsoccs_table_beh_post_kw_09-07-2025.csv")
  402. ```
  404. ```{r Exploratory analyses behaviour}
  405. # exploratory analyses - linear models
  406. for (i in colnames(mod_data_behav[,237:252])) {
  407. print(i)
  408. assign(paste0("lm_",i), lm(get(i) ~ Label,
  409. data = mod_data_behav)) %>% summary() %>% print()
  410. }
  412. # with covariates
  413. for (i in colnames(mod_data_behav[,237:252])) {
  414. print(i)
  415. assign(paste0("lmcov_",i), lm(get(i) ~ Label + demo_sex_birth + demo_age_study_entry + site,
  416. data = mod_data_behav)) %>% summary() %>% print()
  417. }
  419. ```
  421. ```{r}
  422. # set up for treatment group comparisons
  423. mod_data_ShamTBS <- mod_data_behav[mod_data_behav$Label!="rTMS",]
  424. mod_data_ShamTBS$Label <- factor(mod_data_ShamTBS$Label, levels=c("iTBS","Sham"))
  426. mod_data_ShamTMS <- mod_data_behav[mod_data_behav$Label!="iTBS",]
  427. mod_data_ShamTMS$Label <- factor(mod_data_ShamTMS$Label, levels=c("rTMS","Sham"))
  429. mod_data_TBSTMS <- mod_data_behav[mod_data_behav$Label!="Sham",]
  430. mod_data_TBSTMS$Label <- factor(mod_data_TBSTMS$Label, levels=c("iTBS","rTMS"))
  432. ```
  434. ```{r}
  435. # effect sizes
  436. library(effsize)
  438. # ER40
  439. cliff.delta(mod_data_ShamTBS$diff_scog_er40_cr_columnpcr_value, mod_data_ShamTBS$Label, na.rm=T)
  440. cliff.delta(mod_data_ShamTMS$diff_scog_er40_cr_columnpcr_value, mod_data_ShamTMS$Label, na.rm=T)
  442. # RMET
  443. cliff.delta(mod_data_ShamTBS$diff_scog_rmet_total, mod_data_ShamTBS$Label)
  444. cliff.delta(mod_data_ShamTMS$diff_scog_rmet_total, mod_data_ShamTMS$Label)
  446. # TASIT 1
  447. cliff.delta(mod_data_ShamTBS$diff_scog_tasit_p1_total, mod_data_ShamTBS$Label)
  448. cliff.delta(mod_data_ShamTMS$diff_scog_tasit_p1_total, mod_data_ShamTMS$Label)
  450. # TASIT 2
  451. cliff.delta(mod_data_ShamTBS$diff_scog_tasit_p2_sin, mod_data_ShamTBS$Label)
  452. cliff.delta(mod_data_ShamTMS$diff_scog_tasit_p2_sin, mod_data_ShamTMS$Label)
  454. cliff.delta(mod_data_ShamTBS$diff_scog_tasit_p2_sscar, mod_data_ShamTBS$Label)
  455. cliff.delta(mod_data_ShamTMS$diff_scog_tasit_p2_sscar, mod_data_ShamTMS$Label)
  457. cliff.delta(mod_data_ShamTBS$diff_scog_tasit_p2_psar, mod_data_ShamTBS$Label)
  458. cliff.delta(mod_data_ShamTMS$diff_scog_tasit_p2_psar, mod_data_ShamTMS$Label)
  460. # TASIT 3
  461. cliff.delta(mod_data_ShamTBS$diff_scog_tasit_p3_lie, mod_data_ShamTBS$Label)
  462. cliff.delta(mod_data_ShamTMS$diff_scog_tasit_p3_lie, mod_data_ShamTMS$Label)
  464. cliff.delta(mod_data_ShamTBS$diff_scog_tasit_p3_sar, mod_data_ShamTBS$Label)
  465. cliff.delta(mod_data_ShamTMS$diff_scog_tasit_p3_sar, mod_data_ShamTMS$Label)
  467. # mean EA
  468. cliff.delta(mod_data_ShamTBS$diff_scog_mean_ea, mod_data_ShamTBS$Label, na.rm=T)
  469. cliff.delta(mod_data_ShamTMS$diff_scog_mean_ea, mod_data_ShamTMS$Label, na.rm=T)
  471. # processing speed
  472. cliff.delta(mod_data_ShamTBS$diff_np_domain_process_speed, mod_data_ShamTBS$Label)
  473. cliff.delta(mod_data_ShamTMS$diff_np_domain_process_speed, mod_data_ShamTMS$Label)
  475. # working mem
  476. cliff.delta(mod_data_ShamTBS$diff_np_domain_work_mem, mod_data_ShamTBS$Label)
  477. cliff.delta(mod_data_ShamTMS$diff_np_domain_work_mem, mod_data_ShamTMS$Label)
  479. # verbal learning
  480. cliff.delta(mod_data_ShamTBS$diff_np_domain_verbal_learning, mod_data_ShamTBS$Label)
  481. cliff.delta(mod_data_ShamTMS$diff_np_domain_verbal_learning, mod_data_ShamTMS$Label)
  483. # BPRS total
  484. cliff.delta(mod_data_ShamTBS$diff_bprs_total, mod_data_ShamTBS$Label, na.rm=T)
  485. cliff.delta(mod_data_ShamTMS$diff_bprs_total, mod_data_ShamTMS$Label, na.rm=T)
  487. # CDSS total
  488. cliff.delta(mod_data_ShamTBS$diff_cdss_total, mod_data_ShamTBS$Label)
  489. cliff.delta(mod_data_ShamTMS$diff_cdss_total, mod_data_ShamTMS$Label)
  491. # SANS total
  492. cliff.delta(mod_data_ShamTBS$diff_sans_total, mod_data_ShamTBS$Label)
  493. cliff.delta(mod_data_ShamTMS$diff_sans_total, mod_data_ShamTMS$Label)
  495. ```
  497. ```{r Exploratory behavioural box plots, fig.height=5, fig.width=6}
  498. # box plots
  499. ggplot(mod_data_behav,aes(x=Label,y=diff_scog_er40_cr_columnpcr_value,fill=Label)) +
  500. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  501. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  502. geom_point(shape = 21)+
  503. ggtitle("ER40 Change by Treatment Group") +
  504. ylab("Post-Pre ER40") +
  505. xlab("Treatment Group")
  507. ggplot(mod_data_behav,aes(x=Label,y=diff_scog_rmet_total,fill=Label)) +
  508. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  509. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  510. geom_point(shape = 21)+
  511. ggtitle("RMET Change by Treatment Group") +
  512. ylab("Post-Pre RMET") +
  513. xlab("Treatment Group")
  515. ggplot(mod_data_behav,aes(x=Label,y=diff_scog_tasit_p1_total,fill=Label)) +
  516. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  517. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  518. geom_point(shape = 21)+
  519. ggtitle("TASIT 1 Change by Treatment Group") +
  520. ylab("Post-Pre TASIT 1") +
  521. xlab("Treatment Group")
  523. ggplot(mod_data_behav,aes(x=Label,y=diff_scog_tasit_p2_sscar,fill=Label)) +
  524. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  525. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  526. geom_point(shape = 21)+
  527. ggtitle("TASIT 2 Simple Sarcasm Change by Treatment Group") +
  528. ylab("Post-Pre TASIT 2") +
  529. xlab("Treatment Group")
  531. ggplot(mod_data_behav,aes(x=Label,y=diff_scog_tasit_p3_lie,fill=Label)) +
  532. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  533. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  534. geom_point(shape = 21)+
  535. ggtitle("TASIT 3 Lies Change by Treatment Group") +
  536. ylab("Post-Pre TASIT 3") +
  537. xlab("Treatment Group")
  539. ggplot(mod_data_behav,aes(x=Label,y=diff_scog_tasit_p3_sar,fill=Label)) +
  540. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  541. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  542. geom_point(shape = 21)+
  543. ggtitle("TASIT 3 Sarcasm Change by Treatment Group") +
  544. ylab("Post-Pre TASIT 3") +
  545. xlab("Treatment Group")
  547. ggplot(mod_data_behav,aes(x=Label,y=diff_np_domain_verbal_learning,fill=Label)) +
  548. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  549. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  550. geom_point(shape = 21)+
  551. ggtitle("Verbal Learning Change by Treatment Group") +
  552. ylab("Post-Pre Verbal Learning") +
  553. xlab("Treatment Group")
  555. ggplot(mod_data_behav,aes(x=Label,y=diff_bprs_total,fill=Label)) +
  556. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  557. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  558. geom_point(shape = 21)+
  559. ggtitle("BPRS Total Change by Treatment Group") +
  560. ylab("Post-Pre BPRS Total") +
  561. xlab("Treatment Group")
  563. ggplot(mod_data_behav,aes(x=Label,y=diff_cdss_total,fill=Label)) +
  564. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  565. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  566. geom_point(shape = 21)+
  567. ggtitle("CDSS Total Change by Treatment Group") +
  568. ylab("Post-Pre CDSS Total") +
  569. xlab("Treatment Group")
  571. ggplot(mod_data_behav,aes(x=Label,y=diff_cdss_depLV1,fill=Label)) +
  572. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  573. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  574. geom_point(shape = 21)+
  575. ggtitle("CDSS Dep LV1 Change by Treatment Group") +
  576. ylab("Post-Pre CDSS Dep LV1") +
  577. xlab("Treatment Group")
  579. ggplot(mod_data_behav,aes(x=Label,y=diff_sans_total,fill=Label)) +
  580. geom_boxplot()+ scale_fill_brewer(palette="BuPu") +
  581. geom_jitter(size=1.2,alpha=0.5) + #aes(colour = Label),
  582. geom_point(shape = 21)+
  583. ggtitle("SANS Total Change by Treatment Group") +
  584. ylab("Post-Pre SANS Total") +
  585. xlab("Treatment Group")
  587. ```
  589. ```{r}
  590. # change behav data to long format for line plots
  591. mod_behav_long1 <- mod_data_behav[,c(1,2,234,85:105,115:133,136:153,235)]
  592. mod_behav_long1$time <- rep("t1",nrow(mod_data_behav))
  593. colnames(mod_behav_long1)[4:62] <- sub("pre_", "", colnames(mod_behav_long1)[4:62])
  595. mod_behav_long2 <- mod_data_behav[,c(1,2,234,170:190,193:211,214:231,236)]
  596. mod_behav_long2$time <- rep("t2",nrow(mod_data_behav))
  597. colnames(mod_behav_long2)[4:19] <- sub("tx10_", "", colnames(mod_behav_long2)[4:19])
  598. colnames(mod_behav_long2)[20:62] <- sub("post_", "", colnames(mod_behav_long2)[20:62])
  600. mod_behav_long <- rbind(mod_behav_long1, mod_behav_long2)
  602. ```
  604. ```{r fig.height=4, fig.width=7}
  605. # line plots behav x time
  606. ggplot(mod_behav_long, aes(x=time, y=scog_er40_cr_columnpcr_value, colour=Label)) +
  607. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  608. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  609. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.6, size=1) +
  610. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  611. labs(x ="Time",
  612. y = "ER40 Total") +
  613. guides(fill=FALSE, color=FALSE) +
  614. facet_wrap(~Label) +
  615. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  616. theme_bw() +
  617. theme(strip.background = element_rect(fill="white")) +
  618. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  620. ggplot(mod_behav_long, aes(x=time, y=scog_rmet_total, colour=Label)) + #record_id
  621. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  622. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  623. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.6, size=1) +
  624. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  625. labs(x ="Time",
  626. y = "RMET Total") +
  627. guides(fill=FALSE, color=FALSE) +
  628. facet_wrap(~Label) +
  629. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  630. theme_bw() +
  631. theme(strip.background = element_rect(fill="white")) +
  632. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  634. ggplot(mod_behav_long, aes(x=time, y=scog_mean_ea, colour=Label)) + #record_id
  635. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  636. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  637. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.6, size=1) +
  638. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  639. labs(x ="Time",
  640. y = "Mean EA") +
  641. guides(fill=FALSE, color=FALSE) +
  642. facet_wrap(~Label) +
  643. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  644. theme_bw() +
  645. theme(strip.background = element_rect(fill="white")) +
  646. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  648. ggplot(mod_behav_long, aes(x=time, y=scog_tasit_p3_sar, colour=Label)) + #record_id
  649. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  650. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  651. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.6, size=1) +
  652. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  653. labs(x ="Time",
  654. y = "TASIT 3 Sarcasm") +
  655. guides(fill=FALSE, color=FALSE) +
  656. facet_wrap(~Label) +
  657. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  658. theme_bw() +
  659. theme(strip.background = element_rect(fill="white")) +
  660. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  662. ggplot(mod_behav_long, aes(x=time, y=np_domain_tscore_verbal_learning, colour=Label)) + #record_id
  663. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  664. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  665. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.6, size=1) +
  666. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  667. labs(x ="Time",
  668. y = "Verbal Learning") +
  669. guides(fill=FALSE, color=FALSE) +
  670. facet_wrap(~Label) +
  671. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  672. theme_bw() +
  673. theme(strip.background = element_rect(fill="white")) +
  674. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  676. ggplot(mod_behav_long, aes(x=time, y=bprs_factor_total, colour=Label)) + #record_id
  677. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  678. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  679. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.6, size=1) +
  680. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  681. labs(x ="Time",
  682. y = "BPRS Total") +
  683. guides(fill=FALSE, color=FALSE) +
  684. facet_wrap(~Label) +
  685. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  686. theme_bw() +
  687. theme(strip.background = element_rect(fill="white")) +
  688. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  690. ggplot(mod_behav_long, aes(x=time, y=sans_total_sc, colour=Label)) + #record_id
  691. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  692. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  693. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.6, size=1) +
  694. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  695. labs(x ="Time",
  696. y = "SANS Total") +
  697. guides(fill=FALSE, color=FALSE) +
  698. facet_wrap(~Label) +
  699. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  700. theme_bw() +
  701. theme(strip.background = element_rect(fill="white")) +
  702. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  704. ggplot(mod_behav_long, aes(x=time, y=cdss_total, colour=Label)) + #record_id
  705. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  706. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  707. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.6, size=1) +
  708. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  709. labs(x ="Time",
  710. y = "CDSS Total") +
  711. guides(fill=FALSE, color=FALSE) +
  712. facet_wrap(~Label) +
  713. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  714. theme_bw() +
  715. theme(strip.background = element_rect(fill="white")) +
  716. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  718. ```
  720. ```{r}
  721. # sensitivity analyses
  722. # excluding those with 17 days since last treatment
  723. #mod_data_behav <- mod_data_behav[mod_data_behav$days_since_treat!=17,] # SPN20_CMH_0012 and SPN20_MRP_0010
  725. # excluding CMH0001 as didn't engage in EA task
  726. #mod_data_behav <- mod_data_behav[mod_data_behav$record_id!="SPN20_CMH_0001",]
  728. # excluding those with mean FD in either session > 0.5
  729. #mod_data_behav <- mod_data_behav[!(mod_data_behav$record_id %in% c("SPN20_CMH_0003","SPN20_MRP_0002","SPN20_ZHP_0002")),] # N=40 - Sham N=11, rTMS N=14, iTBS N=15
  731. ```
  733. ```{r}
  734. # make roi list
  735. rois <- c("dmpfc","precuneus","left_tpj","right_tpj","left_premotor","right_premotor","left_ips","right_ips")
  737. # generate ses 01 correlation matrices for each participant
  738. cor_ea1 <- lapply(ea_resid_ts1, cor)
  740. # fisher z transform corrs to normalize dist
  741. cor_ea1_z <- lapply(cor_ea1, fisherz)
  743. # add ROI names and replace inf values with 0
  744. for (i in names(cor_ea1_z)) {
  745. colnames(cor_ea1_z[[i]]) <- as.vector(rois)
  746. rownames(cor_ea1_z[[i]]) <- as.vector(rois)
  747. cor_ea1_z[[i]][is.infinite(cor_ea1_z[[i]])] <- 0
  748. }
  751. # generate df with dmpfc to other ROIs, and mean within network conn for each participant (across the four ROIs)
  752. ment_conn1 <- data.frame()
  753. for (i in names(cor_ea1_z)) {
  754. ment_conn1[i,"pre_dmpfc_precuneus"] <- cor_ea1_z[[i]][1,2]
  755. ment_conn1[i,"pre_dmpfc_left_tpj"] <- cor_ea1_z[[i]][1,3]
  756. ment_conn1[i,"pre_dmpfc_right_tpj"] <- cor_ea1_z[[i]][1,4]
  757. ment_conn1[i,"pre_dmpfc_ment_mean"] <- mean(cor_ea1_z[[i]][1,2:4])
  758. ment_conn1[i,"pre_mentalizing"] <- mean(cor_ea1_z[[i]][1:4,1:4][upper.tri(cor_ea1_z[[i]][1:4,1:4], diag=F)])
  759. ment_conn1[i,"pre_dmpfc_left_premotor"] <- cor_ea1_z[[i]][1,5]
  760. ment_conn1[i,"pre_dmpfc_right_premotor"] <- cor_ea1_z[[i]][1,6]
  761. ment_conn1[i,"pre_dmpfc_left_ips"] <- cor_ea1_z[[i]][1,7]
  762. ment_conn1[i,"pre_dmpfc_right_ips"] <- cor_ea1_z[[i]][1,8]
  763. ment_conn1[i,"pre_dmpfc_sim_mean"] <- mean(cor_ea1_z[[i]][1,5:8])
  764. ment_conn1[i,"pre_simulation"] <- mean(cor_ea1_z[[i]][5:8,5:8][upper.tri(cor_ea1_z[[i]][5:8,5:8], diag=F)])
  765. ment_conn1[i,"pre_ment_sim"] <- mean(cor_ea1_z[[i]][1:4,5:8])
  766. }
  768. ment_conn1$record_id <- rownames(ment_conn1)
  769. ment_conn1 <- ment_conn1[,c(13,1:12)]
  771. ```
  773. ```{r}
  774. # generate ses 02 correlation matrices for each participant
  775. cor_ea2 <- lapply(ea_resid_ts2, cor)
  777. # fisher z transform corrs to normalize dist
  778. cor_ea2_z <- lapply(cor_ea2, fisherz)
  780. # add ROI names and replace inf values with 0
  781. for (i in names(cor_ea2_z)) {
  782. colnames(cor_ea2_z[[i]]) <- as.vector(rois)
  783. rownames(cor_ea2_z[[i]]) <- as.vector(rois)
  784. cor_ea2_z[[i]][is.infinite(cor_ea2_z[[i]])] <- 0
  785. }
  787. # generate df with dmpfc to other ROIs, and mean within network conn for each participant (across the four ROIs)
  788. ment_conn2 <- data.frame()
  789. for (i in names(cor_ea2_z)) {
  790. ment_conn2[i,"post_dmpfc_precuneus"] <- cor_ea2_z[[i]][1,2]
  791. ment_conn2[i,"post_dmpfc_left_tpj"] <- cor_ea2_z[[i]][1,3]
  792. ment_conn2[i,"post_dmpfc_right_tpj"] <- cor_ea2_z[[i]][1,4]
  793. ment_conn2[i,"post_dmpfc_ment_mean"] <- mean(cor_ea2_z[[i]][1,2:4])
  794. ment_conn2[i,"post_mentalizing"] <- mean(cor_ea2_z[[i]][1:4,1:4][upper.tri(cor_ea2_z[[i]][1:4,1:4], diag=F)])
  795. ment_conn2[i,"post_dmpfc_left_premotor"] <- cor_ea2_z[[i]][1,5]
  796. ment_conn2[i,"post_dmpfc_right_premotor"] <- cor_ea2_z[[i]][1,6]
  797. ment_conn2[i,"post_dmpfc_left_ips"] <- cor_ea2_z[[i]][1,7]
  798. ment_conn2[i,"post_dmpfc_right_ips"] <- cor_ea2_z[[i]][1,8]
  799. ment_conn2[i,"post_dmpfc_sim_mean"] <- mean(cor_ea2_z[[i]][1,5:8])
  800. ment_conn2[i,"post_simulation"] <- mean(cor_ea2_z[[i]][5:8,5:8][upper.tri(cor_ea2_z[[i]][5:8,5:8], diag=F)])
  801. ment_conn2[i,"post_ment_sim"] <- mean(cor_ea2_z[[i]][1:4,5:8])
  802. }
  804. ment_conn2$record_id <- rownames(ment_conn2)
  805. ment_conn2 <- ment_conn2[,c(13,1:12)]
  807. # merge pre and post conn data
  808. ment_conn <- merge(ment_conn1, ment_conn2, by="record_id")
  810. # merge behav and conn data
  811. mod_data_conn <- merge(mod_data_behav, ment_conn, by="record_id")
  813. # write out csv
  814. #write.csv(mod_data_conn, '/projects/loliver/ModSoCCS/data/behavioural/modsoccs_behav_conn_data_07-22-2025.csv', row.names = FALSE)
  816. ```
  818. ```{r}
  819. # generate conn difference scores (post-pre)
  820. mod_data_conn$diff_dmpfc_precuneus <- mod_data_conn$post_dmpfc_precuneus-mod_data_conn$pre_dmpfc_precuneus
  821. mod_data_conn$diff_dmpfc_left_tpj <- mod_data_conn$post_dmpfc_left_tpj-mod_data_conn$pre_dmpfc_left_tpj
  822. mod_data_conn$diff_dmpfc_right_tpj <- mod_data_conn$post_dmpfc_right_tpj-mod_data_conn$pre_dmpfc_right_tpj
  823. mod_data_conn$diff_dmpfc_ment_mean <- mod_data_conn$post_dmpfc_ment_mean-mod_data_conn$pre_dmpfc_ment_mean
  824. mod_data_conn$diff_mentalizing <- mod_data_conn$post_mentalizing-mod_data_conn$pre_mentalizing
  826. mod_data_conn$diff_dmpfc_left_premotor <- mod_data_conn$post_dmpfc_left_premotor-mod_data_conn$pre_dmpfc_left_premotor
  827. mod_data_conn$diff_dmpfc_right_premotor <- mod_data_conn$post_dmpfc_right_premotor-mod_data_conn$pre_dmpfc_right_premotor
  828. mod_data_conn$diff_dmpfc_left_ips <- mod_data_conn$post_dmpfc_left_ips-mod_data_conn$pre_dmpfc_left_ips
  829. mod_data_conn$diff_dmpfc_right_ips <- mod_data_conn$post_dmpfc_right_ips-mod_data_conn$pre_dmpfc_right_ips
  830. mod_data_conn$diff_dmpfc_sim_mean <- mod_data_conn$post_dmpfc_sim_mean-mod_data_conn$pre_dmpfc_sim_mean
  831. mod_data_conn$diff_simulation <- mod_data_conn$post_simulation-mod_data_conn$pre_simulation
  833. mod_data_conn$diff_ment_sim <- mod_data_conn$post_ment_sim-mod_data_conn$pre_ment_sim
  835. ```
  837. ```{r, fig.height=8, fig.width=11}
  838. # check out distributions and check for outliers
  839. library(reshape2)
  841. # melt conn data
  842. mod_data_melt <- melt(mod_data_conn[,c(1,234,277:288)])
  844. # plots
  845. ggplot(data = mod_data_melt, mapping = aes(x = value)) +
  846. geom_histogram(bins = 10) + facet_wrap(~variable, scales = 'free_x')
  848. # shapiro-wilks
  849. sapply(mod_data_conn[,c(277:288)], shapiro.test)
  851. ```
  853. ```{r}
  854. # check for outliers
  855. outfunc <- function(x) {abs(scale(x)) > 3}
  857. outlist <- list()
  859. for (i in colnames(mod_data_conn[,c(277:288)])) {
  860. outlist[[i]] <-
  861. as.vector(mod_data_conn[which(outfunc(mod_data_conn[[i]])),1])
  862. } # 1 to see IDs; i to see values
  864. print(outlist)
  867. # remove outliers with 3 SD criteria
  868. for (i in names(outlist)) {
  869. mod_data_conn[mod_data_conn$record_id %in% outlist[[i]], i] <- NA
  870. }
  872. # change pre and post values to NAs too, for conn table and spaghetti plots
  873. mod_data_conn[mod_data_conn$record_id == "SPN20_MRP_0002", c("pre_dmpfc_left_tpj","post_dmpfc_left_tpj","pre_dmpfc_right_tpj","post_dmpfc_right_tpj","pre_dmpfc_ment_mean","post_dmpfc_ment_mean","pre_mentalizing","post_mentalizing","pre_dmpfc_right_ips","post_dmpfc_right_ips")] <- NA
  875. ment_conn1[ment_conn1$record_id == "SPN20_MRP_0002", c("pre_dmpfc_left_tpj","pre_dmpfc_right_tpj","pre_dmpfc_ment_mean","pre_mentalizing","pre_dmpfc_right_ips")] <- NA
  877. ment_conn2[ment_conn2$record_id == "SPN20_MRP_0002", c("post_dmpfc_left_tpj","post_dmpfc_right_tpj","post_dmpfc_ment_mean","post_mentalizing","post_dmpfc_right_ips")] <- NA
  879. ```
  881. ```{r, fig.height=8, fig.width=11}
  882. # check out distributions after outlier removal
  884. # melt conn data
  885. mod_data_melt <- melt(mod_data_conn[,c(1,234,277:288)])
  887. # plots
  888. ggplot(data = mod_data_melt, mapping = aes(x = value)) +
  889. geom_histogram(bins = 10) + facet_wrap(~variable, scales = 'free_x')
  891. # shapiro-wilks
  892. sapply(mod_data_conn[,c(277:288)], shapiro.test)
  894. ```
  896. ```{r Primary analyses FC - change}
  897. # primary analyses - linear models
  898. for (i in colnames(mod_data_conn[,277:288])) {
  899. print(i)
  900. assign(paste0("lm_",i), lm(get(i) ~ Label + days_since_treat,
  901. data = mod_data_conn)) %>% summary() %>% print()
  902. }
  904. # with covariates
  905. for (i in colnames(mod_data_conn[,277:288])) {
  906. print(i)
  907. assign(paste0("lmcov_",i), lm(get(i) ~ Label + demo_sex_birth + demo_age_study_entry + site + days_since_treat,
  908. data = mod_data_conn)) %>% summary() %>% print()
  909. }
  911. ```
  913. ```{r}
  914. # post hoc and effect size comparisons
  915. library(emmeans)
  916. library(effectsize)
  918. # DMPFC-precuneus
  919. # post-hoc comparisons
  920. em_dmpfc_precuneus <- emmeans(lmcov_diff_dmpfc_precuneus, ~ Label)
  922. # Compare each treatment to sham only
  923. pw_dmpfc_precuneus <- contrast(em_dmpfc_precuneus, method = "trt.vs.ctrl", ref = "Sham", adjust = "fdr")
  925. # effect size
  926. eff_size(em_dmpfc_precuneus, sigma = sigma(lmcov_diff_dmpfc_precuneus), edf = df.residual(lmcov_diff_dmpfc_precuneus), type = "d")
  929. # DMPFC-left TPJ
  930. # post-hoc comparisons
  931. em_dmpfc_left_tpj <- emmeans(lmcov_diff_dmpfc_left_tpj, ~ Label)
  933. # Compare each treatment to sham only
  934. pw_dmpfc_left_tpj <- contrast(em_dmpfc_left_tpj, method = "trt.vs.ctrl", ref = "Sham", adjust = "fdr")
  935. print(pw_dmpfc_left_tpj)
  937. # effect size
  938. eff_size(em_dmpfc_left_tpj, sigma = sigma(lmcov_diff_dmpfc_left_tpj), edf = df.residual(lmcov_diff_dmpfc_left_tpj), type = "d")
  941. # DMPFC-right TPJ
  942. # post-hoc comparisons
  943. em_dmpfc_right_tpj <- emmeans(lmcov_diff_dmpfc_right_tpj, ~ Label)
  945. # Compare each treatment to sham only
  946. pw_dmpfc_right_tpj <- contrast(em_dmpfc_right_tpj, method = "trt.vs.ctrl", ref = "Sham", adjust = "fdr")
  947. print(pw_dmpfc_right_tpj)
  949. # effect size
  950. eff_size(em_dmpfc_right_tpj, sigma = sigma(lmcov_diff_dmpfc_right_tpj), edf = df.residual(lmcov_diff_dmpfc_right_tpj), type = "d")
  953. # DMPFC-mentalizing mean
  954. em_dmpfc_ment_mean <- emmeans(lmcov_diff_dmpfc_ment_mean, ~ Label)
  956. pw_dmpfc_ment_mean <- contrast(em_dmpfc_ment_mean, method = "trt.vs.ctrl", ref = "Sham", adjust = "fdr")
  958. eff_size(em_dmpfc_ment_mean, sigma = sigma(lmcov_diff_dmpfc_ment_mean), edf = df.residual(lmcov_diff_dmpfc_ment_mean), type = "d")
  961. # mentalizing mean
  962. em_mentalizing <- emmeans(lmcov_diff_mentalizing, ~ Label)
  964. pw_mentalizing <- contrast(em_mentalizing, method = "trt.vs.ctrl", ref = "Sham", adjust = "fdr")
  966. eff_size(em_mentalizing, sigma = sigma(lmcov_diff_mentalizing), edf = df.residual(lmcov_diff_mentalizing), type = "d")
  969. # DMPFC-left premotor
  970. em_dmpfc_left_premotor <- emmeans(lmcov_diff_dmpfc_left_premotor, ~ Label)
  972. pw_dmpfc_left_premotor <- contrast(em_dmpfc_left_premotor, method = "trt.vs.ctrl", ref = "Sham", adjust = "fdr")
  973. print(pw_dmpfc_left_premotor)
  975. eff_size(em_dmpfc_left_premotor, sigma = sigma(lmcov_diff_dmpfc_left_premotor), edf = df.residual(lmcov_diff_dmpfc_left_premotor), type = "d")
  978. # DMPFC-right premotor
  979. em_dmpfc_right_premotor <- emmeans(lmcov_diff_dmpfc_right_premotor, ~ Label)
  981. pw_dmpfc_right_premotor <- contrast(em_dmpfc_right_premotor, method = "trt.vs.ctrl", ref = "Sham", adjust = "fdr")
  982. print(pw_dmpfc_right_premotor)
  984. eff_size(em_dmpfc_right_premotor, sigma = sigma(lmcov_diff_dmpfc_right_premotor), edf = df.residual(lmcov_diff_dmpfc_right_premotor), type = "d")
  986. ```
  988. ```{r Primary FC box plots, fig.height=4, fig.width=4}
  989. # box plots for change in conn by treatment group
  991. ggplot(mod_data_conn,aes(x=Label,y=diff_dmpfc_left_tpj,fill=Label)) +
  992. geom_boxplot() + scale_fill_brewer(palette="BuPu") +
  993. geom_jitter(size=1.4,alpha=0.5) + #aes(colour = Label),
  994. #geom_point(shape = 21)+
  995. ggtitle("DMPFC-left TPJ Connectivity Change") +
  996. ylab("Post - Pre-treatment Connectivity") +
  997. xlab("Treatment Group") +
  998. theme_bw() +
  999. theme(legend.position = "none",
  1000. axis.text=element_text(size=12),
  1001. axis.title=element_text(size=14)) #face="bold"
  1003. ggplot(mod_data_conn,aes(x=Label,y=diff_dmpfc_right_tpj,fill=Label)) +
  1004. geom_boxplot() + scale_fill_brewer(palette="BuPu") +
  1005. geom_jitter(size=1.4,alpha=0.5) + #aes(colour = Label),
  1006. #geom_point(shape = 21)+
  1007. ggtitle("DMPFC-right TPJ Connectivity Change") +
  1008. ylab("Post - Pre-treatment Connectivity") +
  1009. xlab("Treatment Group") +
  1010. theme_bw() +
  1011. theme(legend.position = "none",
  1012. axis.text=element_text(size=12),
  1013. axis.title=element_text(size=14)) #face="bold"
  1015. ggplot(mod_data_conn,aes(x=Label,y=diff_dmpfc_left_premotor,fill=Label)) +
  1016. geom_boxplot() + scale_fill_brewer(palette="BuPu") +
  1017. geom_jitter(size=1.4,alpha=0.5) + #aes(colour = Label),
  1018. #geom_point(shape = 21)+
  1019. ggtitle("DMPFC-left IFC Connectivity Change") +
  1020. ylab("Post - Pre-treatment Connectivity") +
  1021. xlab("Treatment Group") +
  1022. theme_bw() +
  1023. theme(legend.position = "none",
  1024. axis.text=element_text(size=12),
  1025. axis.title=element_text(size=14)) #face="bold"
  1027. ggplot(mod_data_conn,aes(x=Label,y=diff_dmpfc_right_premotor,fill=Label)) +
  1028. geom_boxplot() + scale_fill_brewer(palette="BuPu") +
  1029. geom_jitter(size=1.4,alpha=0.5) + #aes(colour = Label),
  1030. #geom_point(shape = 21)+
  1031. ggtitle("DMPFC-right IFC Connectivity Change") +
  1032. ylab("Post - Pre-treatment Connectivity") +
  1033. xlab("Treatment Group") +
  1034. theme_bw() +
  1035. theme(legend.position = "none",
  1036. axis.text=element_text(size=12),
  1037. axis.title=element_text(size=14)) #face="bold"
  1039. # old
  1040. ggplot(mod_data_conn,aes(x=Label,y=diff_dmpfc_right_premotor,fill=Label)) +
  1041. geom_boxplot(alpha = 0.8) + scale_fill_brewer(palette="BuPu") +
  1042. geom_dotplot(binaxis = 'y', stackdir = 'center', alpha = 1, dotsize=.5) +
  1043. #geom_point(shape = 21)+
  1044. ggtitle("DMPFC-right Premotor Connectivity Change") +
  1045. ylab("Post - Pre-treatment Connectivity") +
  1046. xlab("Treatment Group") +
  1047. theme_bw() +
  1048. theme(legend.position = "none",
  1049. axis.text=element_text(size=12),
  1050. axis.title=element_text(size=14))
  1052. ```
  1054. ```{r}
  1055. # generate Table 1 ITT - demographics
  1056. mod_demo_ITT <- mod_data_ITT[,c(1:2,232,6,10,13:21,30:37,90,105,100,107,112)]
  1057. mod_demo_ITT$demo_race_white <- as.factor(rowSums(mod_demo_ITT[,c("demo_race_ca___1_white","demo_race_us___1_white")]))
  1058. mod_demo_ITT$demo_race_black <- as.factor(rowSums(mod_demo_ITT[,c("demo_race_ca___2_black","demo_race_us___2_black")]))
  1059. mod_demo_ITT$demo_race_asian <- as.factor(rowSums(mod_demo_ITT[,c("demo_race_ca___3_asian","demo_race_us___3_asian")]))
  1060. mod_demo_ITT$demo_race_native <- as.factor(rowSums(mod_demo_ITT[,c("demo_race_ca___4_native","demo_race_us___4_native")]))
  1061. mod_demo_ITT$demo_race_mixed <- as.factor(rowSums(mod_demo_ITT[,c("demo_race_ca___7_mixed","demo_race_us___7_mixed")]))
  1062. mod_demo_ITT$demo_race_other <- as.factor(rowSums(mod_demo_ITT[,c("demo_race_ca___8_other","demo_race_us___6_other")]))
  1063. mod_demo_ITT$demo_race_unknown <- as.factor(rowSums(mod_demo_ITT[,c("demo_race_ca___9_unknown","demo_race_us___8_unknown")]))
  1064. mod_demo_ITT <- mod_demo_ITT[,c(1:5,28:34,23:27)]
  1066. # for those who endorsed mixed race, change other endorsed races to 0 so sum equals total N (just for this table)
  1067. mod_demo_ITT[mod_demo_ITT$demo_race_mixed!=0,c("demo_race_white","demo_race_black","demo_race_asian","demo_race_native")] <- 0
  1069. table_one_ITT <- CreateTableOne(strata="Label", testNonNormal = kruskal.test, testExact = fisher.test, data=mod_demo_ITT[,c(3:17)])
  1070. table_one_ITT_print <- print(table_one_ITT)
  1072. #write.csv(table_one_ITT_print,file="/projects/loliver/ModSoCCS/results/modsoccs_ITT_table1_demo_kw_09-07-2025.csv")
  1074. # write out mod_demo_ITT
  1075. #write.csv(mod_demo_ITT,file="/projects/loliver/ModSoCCS/results/mod_demo_ITT.csv", row.names = F)
  1077. ```
  1079. ```{r}
  1080. # generate Table 2 - connectivity (use for baseline)
  1081. mod_conn <- mod_data_conn[,c("record_id","site","Label","days_since_treat","pre_dmpfc_left_tpj","post_dmpfc_left_tpj","pre_dmpfc_right_tpj","post_dmpfc_right_tpj","pre_dmpfc_left_premotor","post_dmpfc_left_premotor","pre_dmpfc_right_premotor","post_dmpfc_right_premotor")]
  1083. table_two <- CreateTableOne(strata="Label", data=mod_conn[,c(3:12)])
  1084. table_two_print <- print(table_two)
  1086. #write.csv(table_two_print,file="/projects/loliver/ModSoCCS/results/modsoccs_table2_conn_09-07-2025.csv") # probably use this as it's reflected in the main analyses
  1088. #write.csv(table_two_print,file="/projects/loliver/ModSoCCS/results/modsoccs_table2_conn_woutlier_09-07-2025.csv") # all NS before and after outlier removal
  1090. ```
  1092. ```{r}
  1093. # add diff vars and generate table with those values
  1094. mod_conn$diff_dmpfc_left_tpj <- mod_conn$post_dmpfc_left_tpj - mod_conn$pre_dmpfc_left_tpj
  1095. mod_conn$diff_dmpfc_right_tpj <- mod_conn$post_dmpfc_right_tpj - mod_conn$pre_dmpfc_right_tpj
  1097. table_two_diff <- CreateTableOne(strata="Label", data=mod_conn[,c(3,13:14)])
  1098. table_two_diff_print <- print(table_two_diff)
  1100. ```
  1102. ```{r}
  1103. # merge data to plot connectivity data by time
  1104. ment_conn_long1 <- ment_conn1[ment_conn1$record_id %in% mod_data_conn$record_id,]
  1105. ment_conn_long1$time <- rep("t1",nrow(mod_data_behav))
  1106. colnames(ment_conn_long1)[2:13] <- sub("pre_", "", colnames(ment_conn_long1)[2:13])
  1107. ment_conn_long1 <- merge(ment_conn_long1, mod_data[,c("record_id","site","Label")], by="record_id")
  1109. ment_conn_long2 <- ment_conn2[ment_conn2$record_id %in% mod_data_conn$record_id,]
  1110. ment_conn_long2$time <- rep("t2",nrow(mod_data_behav))
  1111. colnames(ment_conn_long2)[2:13] <- sub("post_", "", colnames(ment_conn_long2)[2:13])
  1112. ment_conn_long2 <- merge(ment_conn_long2, mod_data[,c("record_id","site","Label")], by="record_id")
  1114. ment_conn_long <- rbind(ment_conn_long1,ment_conn_long2)
  1116. ```
  1118. ```{r}
  1119. # check out distributions by timepoint
  1120. # melt conn data
  1121. ment_conn_melt <- melt(ment_conn_long)
  1123. # T1
  1124. # plot
  1125. ggplot(data = ment_conn_melt[ment_conn_melt$time=="t1",], mapping = aes(x = value)) +
  1126. geom_histogram(bins = 10) + facet_wrap(~variable, scales = 'free_x')
  1128. # shapiro-wilks
  1129. sapply(ment_conn_long[ment_conn_long$time=="t1",c(2:13)], shapiro.test)
  1131. # T2
  1132. # plot
  1133. ggplot(data = ment_conn_melt[ment_conn_melt$time=="t2",], mapping = aes(x = value)) +
  1134. geom_histogram(bins = 10) + facet_wrap(~variable, scales = 'free_x')
  1136. # shapiro-wilks
  1137. sapply(ment_conn_long[ment_conn_long$time=="t2",c(2:13)], shapiro.test)
  1139. ```
  1141. ```{r Primary FC spaghetti plots, fig.height=4, fig.width=6}
  1142. # spaghetti plot DMPFC-left TPJ conn x time
  1143. ggplot(ment_conn_long, aes(x=time, y=dmpfc_left_tpj, colour=Label)) + #record_id
  1144. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  1145. geom_line(aes(group=record_id), alpha = 0.5) + # color=Label #color=rep(ment_conn_diff$colour,2)
  1146. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.3, size=1) +
  1147. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  1148. labs(x ="Time",
  1149. y = "DMPFC-left TPJ Connectivity") +
  1150. guides(fill=FALSE, color=FALSE) +
  1151. facet_wrap(~Label) +
  1152. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  1153. theme_bw() +
  1154. theme(strip.background = element_rect(fill="white")) +
  1155. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  1157. # DMPFC-right TPJ conn x time
  1158. ggplot(ment_conn_long, aes(x=time, y=dmpfc_right_tpj, colour=Label)) + #record_id
  1159. geom_point(aes(colour=Label)) + # aes(shape = PlotLabel)
  1160. geom_line(aes(group=record_id), alpha = 0.5) +
  1161. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.3, size=1) +
  1162. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  1163. labs(x ="Time",
  1164. y = "DMPFC-right TPJ Connectivity") +
  1165. guides(fill=FALSE, color=FALSE) +
  1166. facet_wrap(~Label) +
  1167. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  1168. theme_bw() +
  1169. theme(strip.background = element_rect(fill="white")) +
  1170. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  1172. # DMPFC-left premotor
  1173. ggplot(ment_conn_long, aes(x=time, y=dmpfc_left_premotor, colour=Label)) + #record_id
  1174. geom_point(aes(colour=Label)) +
  1175. geom_line(aes(group=record_id), alpha = 0.5) +
  1176. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.3, size=1) +
  1177. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  1178. labs(x ="Time",
  1179. y = "DMPFC-left IFC Connectivity") +
  1180. guides(fill=FALSE, color=FALSE) +
  1181. facet_wrap(~Label) +
  1182. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  1183. theme_bw() +
  1184. theme(strip.background = element_rect(fill="white")) +
  1185. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  1187. # DMPFC-right premotor
  1188. ggplot(ment_conn_long, aes(x=time, y=dmpfc_right_premotor, colour=Label)) + #record_id
  1189. geom_point(aes(colour=Label)) +
  1190. geom_line(aes(group=record_id), alpha = 0.5) +
  1191. stat_smooth(aes(group = Label, colour=Label), method = "lm", se = TRUE, fill = "lightgrey", alpha = 0.3, size=1) +
  1192. stat_summary(aes(group = Label, colour=Label), geom = "point", fun = mean) +
  1193. labs(x ="Time",
  1194. y = "DMPFC-right IFC Connectivity") +
  1195. guides(fill=FALSE, color=FALSE) +
  1196. facet_wrap(~Label) +
  1197. scale_x_discrete(expand=c(0.1, 0.1)) + # add space between time points
  1198. theme_bw() +
  1199. theme(strip.background = element_rect(fill="white")) +
  1200. theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(),text=element_text(size=15))
  1202. ```

01_Modsoccs_EA_FC_paper.Rmd at commit 7f39509, no license · at the source

Overview

Authors: Lindsay D. Oliver1,2, Daniel M. Blumberger1,2, Zhi-De Deng3, Colin Hawco1,2, Erin W. Dickie1,2, Julia Gallucci1,4, Jerrold Jeyachandra1, Salim Mansour1, Stephanie M. Hare5, James M. Gold5, George Foussias1,2, Miklos Argyelan6,7,8, Zafiris J. Daskalakis9, Robert W. Buchanan5, Anil K. Malhotra6,7,8, Aristotle N. Voineskos1,2
ORCID iDs: Lindsay D. Oliver, Stephanie M. Hare, George Foussias, Miklos Argyelan, Robert W. Buchanan, Aristotle N. Voineskos
  1. Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
  2. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
  3. Computational Neurostimulation Research Program, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
  4. Institute of Medical Science, University of Toronto, Toronto, ON, Canada
  5. Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
  6. Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, NY, USA
  7. The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry, Hempstead, NY, USA
  8. Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
  9. Department of Psychiatry, University of California San Diego, San Diego, CA, USA
Institutions: Centre for Addiction and Mental Health (Canada); University of Toronto (Canada); Campbell Family Mental Health Research Institute (Canada); National Institute of Mental Health (United States); University of Maryland, Baltimore (United States); Feinstein Institute for Medical Research (United States); Hofstra University (United States); Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (United States); Zucker Hillside Hospital (United States); University of California San Diego (United States)
Journal: Brain stimulation, volume 19, issue 5, article 103172
Dates: published online 29 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.brs.2026.103172 · PMID 42526755 · PMCID PMC13551969 · OpenAlex W7171616946
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), other (modality), human (organism), schizophrenia / psychosis (population)
Methods: Connectivity, Statistics, fMRI & imaging
Keywords: rTMS, Schizophrenia spectrum disorders, Social cognition, fMRI, Individualized targeting, Electric field modelling, Functional connectivity mapping
MeSH: Nerve Net*, Prefrontal Cortex*, Schizophrenia*, Social Cognition*, Transcranial Magnetic Stimulation*, Adult, Double-Blind Method, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: Brain and Behavior Research Foundation; Wellcome Trust; Intramural Research Program (ZIAMH002955); National Institute of Mental Health (R61 MH120188); Intramural NIH HHS (ZIA MH002955); NIMH NIH HHS (R61 MH120188)
Citations: not cited yet (Europe PMC); 98 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

loliver4/ModSoCCS

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7f39509966bfc8b1e3e3a78bf372ba125dc282ee, 9 January 2026
Languages: Shell (7), Python (3), R (3)
Size: 22 files, 13 scripts
Software Heritage: not archived
Found in: the text, “Exploratory Analyses:”
Holds: README, environment (requirements.txt), documentation, 3 notebooks
Not found: license file, CITATION.cff, tests, continuous integration
Tools: Connectome Workbench (3 files), AFNI (2 files), pandas (2 files), tidyverse (2 files), easystats (1 file), emmeans (1 file), ggplot2 (1 file), Nipype (1 file), NumPy (1 file), psych (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
14 files
At the source: github.com/loliver4/ModSoCCS

Tracing map

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

What the map holds:

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

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

Data

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Elsevier BV

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 7 keywords, 12 MeSH terms, 6 funders, 92 references.

Cite

This paper

Oliver, L. D., Blumberger, D. M., Deng, Z.-D., Hawco, C., Dickie, E. W., Gallucci, J., Jeyachandra, J., Mansour, S., Hare, S. M., Gold, J. M., Foussias, G., Argyelan, M., Daskalakis, Z. J., Buchanan, R. W., Malhotra, A. K., & Voineskos, A. N. (2026). Effects of personalized transcranial magnetic stimulation on social cognitive network functional connectivity in schizophrenia spectrum disorders: A double-blind, randomized, sham-controlled target engagement trial. Brain stimulation, 19(5), 103172. https://doi.org/10.1016/j.brs.2026.103172

BibTeX

@article{oliver2026effects,
author = {Oliver, Lindsay D. and Blumberger, Daniel M. and Deng, Zhi-De and Hawco, Colin and Dickie, Erin W. and Gallucci, Julia and Jeyachandra, Jerrold and Mansour, Salim and Hare, Stephanie M. and Gold, James M. and Foussias, George and Argyelan, Miklos and Daskalakis, Zafiris J. and Buchanan, Robert W. and Malhotra, Anil K. and Voineskos, Aristotle N.},
title = {{Effects of personalized transcranial magnetic stimulation on social cognitive network functional connectivity in schizophrenia spectrum disorders: A double-blind, randomized, sham-controlled target engagement trial}},
journal = {Brain stimulation},
year = {2026},
month = jul,
volume = {19},
number = {5},
pages = {103172},
publisher = {Elsevier BV},
issn = {1935-861X},
doi = {10.1016/j.brs.2026.103172},
url = {https://doi.org/10.1016/j.brs.2026.103172},
pmid = {42526755},
pmcid = {PMC13551969}
}

RIS

TY - JOUR
AU - Oliver, Lindsay D.
AU - Blumberger, Daniel M.
AU - Deng, Zhi-De
AU - Hawco, Colin
AU - Dickie, Erin W.
AU - Gallucci, Julia
AU - Jeyachandra, Jerrold
AU - Mansour, Salim
AU - Hare, Stephanie M.
AU - Gold, James M.
AU - Foussias, George
AU - Argyelan, Miklos
AU - Daskalakis, Zafiris J.
AU - Buchanan, Robert W.
AU - Malhotra, Anil K.
AU - Voineskos, Aristotle N.
TI - Effects of personalized transcranial magnetic stimulation on social cognitive network functional connectivity in schizophrenia spectrum disorders: A double-blind, randomized, sham-controlled target engagement trial
T2 - Brain stimulation
J2 - Brain Stimul
PY - 2026
DA - 2026/07/29
VL - 19
IS - 5
SP - 103172
SN - 1935-861X
PB - Elsevier BV
DO - 10.1016/j.brs.2026.103172
UR - https://doi.org/10.1016/j.brs.2026.103172
LA - en
ER -

CSL-JSON

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