OSCR

Altered frontal and occipital cortical microstructure in obsessive-compulsive disorder - a multisite mega-analysis.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Regional vulnerability index ↔ R/OBIC_all_R_code.R, lines 401–443 · score 0.84 · RVIpkg, subcortical volume, cortical thickness, accumbens, caudate, putamen
  2. [2] § Materials and methods › MRI acquisition and processing ↔ bash/extract_intensity_contrast_bash.sh, lines 1–39 · score 0.68 · FreeSurfer, 0.15 mm, rerun, nu, intensities, 60 %
  3. [3] § Results › Associations with demographic and clinical characteristics ↔ R/Comorbidity_symptom_dimensions.R, lines 179–225 · score 0.64 · symptom dimensions, clinical characteristics, hoarding, anxiety, depressive, onset
  4. [4] § Statistical analyses ↔ matlab/symptom_dimensions_in_OCD/PALM_symptom_dims_Anders.m, lines 7–23 · score 0.62 · tail approximation, PALM, Permutation, faster, TFCE, symptom
  5. [5] § Statistical analyses ↔ R/Comorbidity_symptom_dimensions.R, lines 179–225 · score 0.60 · ENIGMA OCD, hoarding, anxiety, depression, onset, comorbid
  6. [6] § Materials and methods › MRI acquisition and processing ↔ bash/unused_code/gwc_mean_sd.sh, the whole file · a weak match · score 0.60 · signal intensities, FreeSurfer, rerun, nu, map, volume
  7. [7] § Results › ICA-based decomposition of GWC ↔ bash/unused_code/gwc_mean_sd.sh, the whole file · a weak match · score 0.55 · pial surface, lower GWC, signal intensities, volume, ICA

Paper

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The authors' code

R · 551 lines · 21 KB · no license · 2 matches

  1. # Written by Anders Lillevik Thorsen, May 2022
  2. # This script compares GWC by comorbidity and symptom dimensions in OCD patients in the OBIC dataset
  4. library(tidyverse)
  5. library(lme4)
  6. library(skimr)
  7. library(ggplot2)
  8. library(haven)
  9. library(effectsize)
  10. library(parameters)
  11. library(corrplot)
  12. library(ggseg)
  13. library(psych)
  14. library(lmerTest)
  16. # Setup environment
  17. rm(list=ls()) # Clears variables
  18. options(scipen = 999) # Gives decimals rather than power for large number
  19. setwd("S:/Project/OBIC/R")
  21. # Load data
  22. #data <- read_sav(file="S:/Project/OBIC_dataset/Cortical_myelination_FINAL_08May22.sav")
  23. data <- read_sav(file="S:/Project/OBIC/dataset/Cortical_myelination_FINAL_20July22.sav")
  25. data$Age_sq = data$Age*data$Age
  27. # Plot GWC using ggseg
  30. # Compare OCD patients with and without anxiety comorbidity on ICA_3
  31. data_OCD <- subset(data, Group == 1 & GWC_anxiety < 2) # Select OCD patients only
  33. hist(data_OCD$Group)
  34. hist(data$GWC_anxiety)
  36. anxiety_comorb <- lmer(ICA_Rerun7z_3 ~ GWC_anxiety + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  37. summary(anxiety_comorb)
  39. anxiety_comorb <- lmer(ICA_Rerun7z_1 ~ GWC_anxiety + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  40. summary(anxiety_comorb)
  42. # With Pa
  43. test <- subset(data, GWC_anxiety != 999)
  44. hist(test$Group)
  45. hist(test$GWC_anxiety)
  47. anxiety_comorb <- lmer(ICA_Rerun7z_3 ~ as.factor(GWC_anxiety) + Sex + Age + SurfaceHoles + (1 | Site), data = test, REML = FALSE)
  48. summary(anxiety_comorb)
  50. # Compare OCD patients with and without depressive comorbidity on ICA_3
  51. data_OCD <- subset(data, Group == 1 & GWC_depressive < 2) # Select OCD patients only
  53. depressive_comorb <- lmer(ICA_Rerun7z_3 ~ GWC_depressive + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  54. summary(depressive_comorb)
  56. depressive_comorb <- lmer(ICA_Rerun7z_4 ~ GWC_depressive + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  57. summary(depressive_comorb)
  59. # Compare OCD patients with different symptom dimensions on ICA_3
  60. data_OCD <- subset(data, Group == 1) # Select OCD patients only
  62. hist(data_OCD$Agr_Check)
  63. hist(data$Contam_Clean)
  64. hist(data$Sym_Ordering)
  65. hist(data$Sex_Rel)
  66. hist(data$Hoarding)
  68. symptomdim_comorb <- lmer(ICA_Rerun7z_3 ~ Agr_Check + Contam_Clean + Sym_Ordering + Sex_Rel + Hoarding + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  69. summary(symptomdim_comorb)
  70. plot_model(symptomdim_comorb, title = "Component 3", type = "std", show.values = TRUE)
  71. effectsize(symptomdim_comorb)
  72. lme.dscore(symptomdim_comorb, data=data, type = "lme4")
  74. library(sjPlot)
  75. tab_model(symptomdim_comorb)
  77. symptomdim_comorb <- lmer(ICA_Rerun7z_7 ~ Agr_Check + Contam_Clean + Sym_Ordering + Sex_Rel + Hoarding + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  78. summary(symptomdim_comorb)
  80. library(performance)
  81. check_collinearity(symptomdim_comorb) # Multicolinearity of symptom dimensions not a problem
  83. # Compare medicated and unmedicated OCD
  84. data_medOCD_unnmedOCD <- subset(data, Medicated < 2) # Select OCD patients only
  86. hist(data_medOCD_unnmedOCD$Medicated)
  88. med_unmed <- lmer(ICA_Rerun7z_3 ~ Medicated + Sex + Age + SurfaceHoles + (1 | Site), data = data_medOCD_unnmedOCD, REML = FALSE)
  89. summary(med_unmed)
  91. med_unmed <- lmer(ICA_Rerun7z_4 ~ Medicated + Sex + Age + SurfaceHoles + (1 | Site), data = data_medOCD_unnmedOCD, REML = FALSE)
  92. summary(med_unmed)
  94. # Compared unmedicated OCD and HC
  95. data_unmedOCD_HC <- subset(data, Medicated != 1) # Select OCD patients only
  97. hist(data_unmedOCD_HC$Medicated)
  99. med_unmed <- lmer(ICA_Rerun7z_3 ~ Medicated + Sex + Age + SurfaceHoles + (1 | Site), data = data_unmedOCD_HC, REML = FALSE)
  100. summary(med_unmed)
  102. med_unmed <- lmer(ICA_Rerun7z_7 ~ Medicated + Sex + Age + SurfaceHoles + (1 | Site), data = data_unmedOCD_HC, REML = FALSE)
  103. summary(med_unmed)
  105. # Compared unmedicated OCD and HC
  106. data_medOCD_HC <- subset(data, Medicated != 0) # Select OCD patients only
  108. hist(data_medOCD_HC$Medicated)
  110. med_unmed <- lmer(ICA_Rerun7z_3 ~ Medicated + Sex + Age + SurfaceHoles + (1 | Site), data = data_medOCD_HC, REML = FALSE)
  111. summary(med_unmed)
  113. med_unmed <- lmer(ICA_Rerun7z_7 ~ Medicated + Sex + Age + SurfaceHoles + (1 | Site), data = data_medOCD_HC, REML = FALSE)
  114. summary(med_unmed)
  116. # Compared childhod and adult onset OCD
  117. data_onset <- subset(data, Onset_OCD <= 1 ) # Select OCD patients only
  119. hist(data_onset$Onset_OCD)
  121. onset_OCD <- lmer(ICA_Rerun7z_3 ~ Onset_OCD + Sex + Age + SurfaceHoles + (1 | Site), data = data_onset, REML = FALSE)
  122. summary(onset_OCD)
  124. onset_OCD <- lmer(ICA_Rerun7z_7 ~ Onset_OCD + Sex + Age + SurfaceHoles + (1 | Site), data = data_onset, REML = FALSE)
  125. summary(onset_OCD)
  127. # Relate RVI to ICA components in OCD
  128. data_OCD <- subset(data, data$Group == "OCD") # Select OCD patients only
  130. # Relate RVI to ICA_Rerun7z_3 with group
  131. lmer_OCD_RVI_mean <- lmer(ICA_Rerun7z_3 ~ RVI_mean + Group + Sex + SurfaceHoles + Age + (1 | Site), data = data, REML = FALSE)
  132. summary(lmer_OCD_RVI_mean)
  133. plot_model(lmer_OCD_RVI_mean, title = "Component 3", type = "std", show.values = TRUE)
  134. lme.dscore(lmer_OCD_RVI_mean, data=data, type = "lme4")
  136. lmer_OCD_RVI_mean <- lmer(ICA_Rerun7z_7 ~ RVI_mean + Group + Sex + SurfaceHoles + Age + (1 | Site), data = data, REML = FALSE)
  137. summary(lmer_OCD_RVI_mean)
  139. # Within OCD patients only
  140. lmer_OCD_RVI_cort <-lmer(ICA_Rerun7z_7 ~ RVI_cortical + Sex + SurfaceHoles + Age + (1 | Site), data = data_OCD, REML = FALSE)
  141. summary(lmer_OCD_RVI_cort)
  143. lmer_OCD_RVI_subcort <-lmer(ICA_Rerun7z_7 ~ RVI_subcortical + Sex + SurfaceHoles + Age + (1 | Site), data = data_OCD, REML = FALSE)
  144. summary(lmer_OCD_RVI_subcort)
  146. lmer_OCD_RVI_mean <- lmer(ICA_Rerun7z_3 ~ RVI_mean + Sex + SurfaceHoles + Age + (1 | Site), data = data_OCD, REML = FALSE)
  147. summary(lmer_OCD_RVI_mean)
  149. lmer_YBOCS_RVI_mean <- lmer(RVI_mean ~ YBOCS_total + Sex + SurfaceHoles + Age + (1 | Site), data = data_OCD, REML = FALSE)
  150. summary(lmer_YBOCS_RVI_mean)
  152. symptomdim_RVI <- lmer(RVI_mean ~ Agr_Check + Contam_Clean + Sym_Ordering + Sex_Rel + Hoarding + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  153. summary(symptomdim_RVI)
  155. med_unmed_RVI <- lmer(RVI_mean ~ Medicated + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  156. summary(med_unmed_RVI)
  158. anxiety_RVI <- lmer(RVI_mean ~ GWC_anxiety + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  159. summary(anxiety_RVI)
  161. depressive_RVI <- lmer(RVI_mean ~ GWC_depressive + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  162. summary(depressive_RVI)
  165. data_OCD_depressive <- subset(data, Group == 1 & GWC_depressive < 2) # Select OCD patients only
  166. depressive_RVI <- lmer(RVI_mean ~ GWC_depressive + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD_depressive, REML = FALSE)
  167. summary(depressive_RVI)
  169. onset_OCD <- lmer(RVI_mean ~ Onset_OCD + Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  170. summary(onset_OCD)
  172. data_OCD <- subset(data, Group == 1 & GWC_depressive < 2 & GWC_anxiety < 2) # Select OCD patients only
  173. everything_OCD_RVI <- lmer(RVI_mean ~ Medicated + YBOCS_total + GWC_anxiety + GWC_depressive+ Sex + Age + SurfaceHoles + (1 | Site), data = data_OCD, REML = FALSE)
  174. summary(everything_OCD_RVI)
  176. summary(lmer(RVI_mean ~ Sex + SurfaceHoles + Age + (1 | Site), data = data_OCD, REML = FALSE))
  179. # Testing here
  180. cor(data$RVI_subcortical, data$RVI_mean)
  181. cor(data$RVI_cortical, data$RVI_mean)
  182. cor(data$RVI_subcortical, data$RVI_cortical)
  184. cor(data$RVI_cortical, data$lh_GWC_lateraloccipital)
  186. #### Status 12 May
  187. # Anxiety related to Comp1
  188. # Depressive related to Comp4, 5, 7
  189. # Multicolinearity of symptom dimensions not a problem
  190. # Sex_rel related to Comp3 (!)
  191. # Contam_Clean related to Comp2, 4, 5, 7
  192. # Hoarding related to Comp7
  193. # Med and unmed OCD differs in Comp1, 4
  194. # Unmed OCD and HC never differs
  195. # MedOCD and HC differs in Comp3 (!) and Comp1
  196. # Onset related to Comp1, 2, 4, 5
  197. # RVI has been coded according to Boedhoe, 2020 (largest ENIGMA-OCD study of cortical thickness and subcortical volume) to coded and used
  198. # RVI estimation run and RVI is significantly higher in OCD than HC. However, mean RVI is very close to 0 for both OCD and HC and Cohen's d is 0.17-0.20
  199. # Mean and subcortical RVI is related to Comp3 (and others) in OCD patients
  200. # RVI is not related to any clinical characteristic
  201. # Needs to confirm RVI estimation and investigate subcortal, cortical and mean RVI to GWC
  203. # Load ENIGMA group differences from RVIpkg, add OCD data from Boedhoe, 2020 Am. J. Psychiatry
  204. ENIGMA_subcortical <- RVIpkg::EP.Subcortical
  205. ENIGMA_cortical <- RVIpkg::EP.GM
  207. ENIGMA_cortical[nrow(ENIGMA_cortical) + 1,1] = "temporalpole" # Add temporal pole as this is missing IRVpkg
  208. #ENIGMA_subcortical<- ENIGMA_subcortical[-c(1), ] # Drop Ventricle as this is missing in OCD
  210. ENIGMA_subcortical$OCD <- c(.11, -0.05, 0.01, 0, 0.09, -0.09, -0.06, -0.03) # Missing Ventricle (row 1)
  211. ENIGMA_cortical$OCD <- c(-0.0275,-0.0175,-0.0845,-0.0375,-0.024,-0.099,-0.14,-0.073,-0.06,-0.073,-0.102,-0.048,-0.093,-0.0955,-0.002,-0.0695,-0.0725,-0.0595,-0.044,0.018,0.0065,-0.064,-0.03,-0.099,-0.0345,-0.093,-0.0535,-0.0545,0.0035,-0.0245,-0.0135,-0.014,-0.065,0.0245)
  213. # Define names of all cortical ROIs
  214. ROI <- c("bankssts", "caudalanteriorcingulate", "caudalmiddlefrontal", "cuneus", "entorhinal", "fusiform", "inferiorparietal", "inferiortemporal", "isthmuscingulate", "lateraloccipital", "lateralorbitofrontal", "lingual", "medialorbitofrontal", "middletemporal", "parahippocampal", "paracentral", "parsopercularis", "parsorbitalis", "parstriangularis", "pericalcarine", "postcentral", "posteriorcingulate", "precentral", "precuneus", "rostralanteriorcingulate", "rostralmiddlefrontal", "superiorfrontal", "superiorparietal", "superiortemporal", "supramarginal", "frontalpole", "temporalpole", "transversetemporal", "insula")
  216. # Calculate mean of two hemispheres for cortical thickness
  217. for (region in 1:length(ROI)){
  218. lh_ROI <- paste0("lh_thickness", "_", ROI[region])
  219. rh_ROI <- paste0("rh_thickness", "_", ROI[region])
  221. bil_ROI_name <- paste0("bil_thickness", "_", ROI[region])
  222. bil_ROI_vals <- (data[[lh_ROI]]+data[[rh_ROI]])/2
  224. data[bil_ROI_name] <- bil_ROI_vals
  225. }
  227. # Define names of all subcortical ROIs
  228. data$lh_vol_lateralventricle <- data$LeftLateralVentricle
  229. data$rh_vol_lateralventricle <- data$RightLateralVentricle
  231. ROI <- c("lateralventricle","Thalamus","Caudate","Putamen","Pallidum","Hippocampus","Amygdala","Accumbensarea")
  233. # Calculate mean of two hemispheres for subcortical volume
  234. for (region in 1:length(ROI)){
  235. lh_ROI <- paste0("lh_vol", "_", ROI[region])
  236. rh_ROI <- paste0("rh_vol", "_", ROI[region])
  238. bil_ROI_name <- paste0("bil_vol", "_", ROI[region])
  239. bil_ROI_vals <- (data[[lh_ROI]]+data[[rh_ROI]])/2
  241. data[bil_ROI_name] <- bil_ROI_vals
  242. }
  244. # Test to check if corrected columns are selected for cortical thickness/subcortical volume
  245. cort_cols <- colnames(data[0,401:434])
  246. if(cort_cols[1] != "bil_thickness_bankssts" & cort_cols[34] != "bil_thickness_insula"){
  247. print("ERROR - you have selected the wrong columns")
  248. } else {
  249. print("ALL GOOD - you have selected the correct columns")
  250. }
  252. subcort_cols <- colnames(data[0,437:444])
  253. if(subcort_cols[1] != "bil_vol_lateralventricle" & subcort_cols[8] != "bil_vol_Accumbensarea"){
  254. print("ERROR - you have selected the wrong columns")
  255. } else {
  256. print("ALL GOOD - you have selected the correct columns")
  257. }
  259. # Run IRVpkg for cortical thickness
  260. library(RVIpkg)
  261. RVI_cortical <- RVI_func(ID='BIDS', DXcontrol='Group==0', covariates=c('Age','Sex'), resp.range=c(401:434),EP=ENIGMA_cortical$OCD, data=data)
  262. data$RVI_cortical <- RVI_cortical$RVI$RVI
  264. # Run IRVpkg for subcortical volume
  265. RVI_subcortical <- RVI_func(ID='BIDS', DXcontrol='Group==0', covariates=c('Age','Sex', 'EstimatedTotalIntraCranialVol'), resp.range=c(437:444),EP=ENIGMA_subcortical$OCD, data=data)
  266. data$RVI_subcortical <- RVI_subcortical$RVI$RVI
  268. # Calculate mean RVI across the cortex and subcortex
  269. data$RVI_mean <- (data$RVI_cortical+data$RVI_subcortical)/2
  272. describeBy(data$RVI_subcortical, group = data$Group) # M HC = 0.01, M OCD = 0.08
  273. describeBy(data$RVI_cortical, group = data$Group) # M HC = 0, M OCD = 0.03
  274. describeBy(data$RVI_mean, group = data$Group) # M HC = 0, M OCD = 0.05
  276. # Test Group difference in RVI
  277. lmer_RVI_cort <-lmer(RVI_cortical ~ Group + Sex + SurfaceHoles + Age +(1 | Site), data = data, REML = FALSE)
  278. summary(lmer_RVI_cort)
  279. plot_model(lmer_RVI_cort, title = "Cortical RVI", type = "std", show.values = TRUE)
  280. lme.dscore(lmer_RVI_cort, data=data, type = "lme4")
  282. lmer_RVI_subcort <-lmer(RVI_subcortical ~ Group + Sex + SurfaceHoles + Age +(1 | Site), data = data, REML = FALSE)
  283. summary(lmer_RVI_subcort)
  284. plot_model(lmer_RVI_subcort, title = "Subcortical RVI", type = "std", show.values = TRUE)
  285. lme.dscore(lmer_RVI_subcort, data=data, type = "lme4")
  287. lmer_RVI_mean <-lmer(RVI_mean ~ Group + Sex + SurfaceHoles + Age +(1 | Site), data = data, REML = FALSE)
  288. summary(lmer_RVI_mean)
  289. plot_model(lmer_RVI_mean, title = "Mean cortical and subcortical RVI", type = "std", show.values = TRUE)
  290. lme.dscore(lmer_RVI_mean, data=data, type = "lme4")
  292. lmer_RVI_mean <-lmer(ICA_Rerun7z_3 ~ RVI_mean + Sex + SurfaceHoles + Age + (1 | Site), data = data, REML = FALSE)
  293. summary(lmer_RVI_mean)
  295. lmer_RVI_mean <-lmer(ICA_Rerun7z_3 ~ Group + RVI_mean + Sex + SurfaceHoles + Age + (1 | Site), data = data, REML = FALSE)
  296. summary(lmer_RVI_mean)
  298. boxplot(subset(data$RVI_mean, data$Group == 1))
  299. boxplot(subset(data$RVI_mean, data$Group == 0))
  300. boxplot(data$RVI_mean ~ data$Group)
  304. # Calculate mean GWC across cortex
  305. library(fame)
  308. rowMeans(data)
  310. data$lh_mean_GWC <- rowMeans(data[, 118:151])
  311. data$rh_mean_GWC <- rowMeans(data[, 153:186])
  312. data$bil_mean_GWC <- rowMeans(data[, 449:450])
  314. lmer_RVI_cort <-lmer(bil_mean_GWC~ RVI_cortical*Group+ Sex + SurfaceHoles + Age +(1 | Site), data = data, REML = FALSE)
  315. summary(lmer_RVI_cort)
  317. lmer_mean_GWC <-lmer(bil_mean_GWC ~ Group+ Sex + SurfaceHoles + Age +(1 | Site), data = data, REML = FALSE)
  318. summary(lmer_mean_GWC)
  320. hist(data$RVI_mean)
  322. library(ggplot2)
  323. RVI_cort_plot <- ggplot(data, aes(x = as.factor(Group), y = bil_mean_GWC)) +
  324. geom_violin()
  325. RVI_cort_plot
  326. RVI_cort_plot + geom_boxplot(width = 0.3)
  328. RVI_mean_plot <- ggplot(data, aes(x = as.factor(Group), y = RVI_mean)) +
  329. geom_violin()
  330. RVI_mean_plot
  331. RVI_mean_plot + geom_boxplot(width = 0.3)
  333. library(psych)
  334. describeBy(data$RVI_mean, group = data$Group)
  335. a <- cohen.d(data$ICA_Rerun7z_3, group=as.factor(data$Group))
  336. a$cohen.d
  338. describeBy(data$ICA_Rerun7z_3, group = data$Group)
  339. a <- cohen.d(data$ICA_Rerun7z_3, group=as.factor(data$Group))
  340. a$cohen.d
  341. hist(data$ICA_Rerun7z_3)
  343. a <- cohen.d(data$RVI_mean, group=as.factor(data$Group))
  344. a$cohen.d
  346. # ICV = data$EstimatedTotalIntraCranialVol
  347. which( colnames(data) == "lh_GWC_bankssts")
  350. # Delete below here
  351. data$RVI_BIDS <- RVI_cortical$RVI$BIDS
  352. data$BIDS[603]
  353. data$RVI_BIDS[603]
  355. ROI <- data[0,356:360]
  356. ROI <- colnames(ROI)
  361. # Testing below here
  362. ICA_z_1 <- lmer(ICA_Rerun7z_1 ~ Group + Sex + (1 | Site) + Age + Age_sq, data = OBIC, REML = FALSE)
  363. summary(ICA_z_1)
  365. ICA_z_2 <- lmer(ICA_Rerun7z_2 ~ Group + Sex + (1 | Site) + Age + Age_sq, data = OBIC, REML = FALSE)
  366. summary(ICA_z_2)
  368. ICA_z_3 <- lmer(ICA_Rerun7z_3 ~ Group + Sex + SurfaceHoles + Age + (1 | Site), data = data, REML = FALSE)
  369. summary(ICA_z_3)
  370. plot_model(ICA_z_3, title = "Component 3", type = "std", show.values = TRUE)
  372. library(EMAtools)
  373. lme.dscore(ICA_z_3, data=data, type = "lme4")
  375. ICA_z_3_Age2 <- lmer(ICA_Rerun7z_3 ~ Group + Sex + SurfaceHoles + (1 | Site) + Age + Age_sq, data = OBIC, REML = FALSE)
  376. summary(ICA_z_3_Age2)
  378. anova(ICA_z_3, ICA_z_3_Age2)
  380. ICA_5 <- lmer(ICA7_5 ~ Group + Sex + SurfaceHoles + Age + (1 | Site), data = data, REML = FALSE)
  381. summary(ICA_5)
  383. ICA_z_4 <- lmer(ICA_Rerun7z_4 ~ Group + Sex + (1 | Site) + Age + Age_sq, data = OBIC, REML = FALSE)
  384. summary(ICA_z_4)
  386. ICA_z_5 <- lmer(ICA_Rerun7z_5 ~ Group + Sex + (1 | Site) + Age + Age_sq, data = OBIC, REML = FALSE)
  387. summary(ICA_z_5)
  389. ICA_z_6 <- lmer(ICA_Rerun7z_6 ~ Group + Sex + (1 | Site) + Age + Age_sq, data = OBIC, REML = FALSE)
  390. summary(ICA_z_6)
  392. ICA_z_7 <- lmer(ICA_Rerun7z_7 ~ Group + Sex + (1 | Site) + Age + Age_sq, data = OBIC, REML = FALSE)
  393. summary(ICA_z_7)
  395. cor(data$ICA7_5, data$ICA_Rerun7z_3, method = "kendall")
  397. p <- c(0.3237,0.38410,0.00837,0.727035,0.212325,0.976548,0.373037)
  399. fdrp <- p.adjust(p, method="fdr")
  401. data_count_Bergen <- subset(data, Site == 9 & Group == 1)
  402. print(data_count_Bergen$BIDS)
  404. #Test group differences for the seven components"
  405. library(tidyverse)
  407. #data <- data
  408. data$r<-0
  409. ROI.names_LME_OBIC<-data %>%
  410. select(contains("*ICA"), (contains("_Rerun")), -(contains("Medicated")))
  411. region_OBIC_LME<-names(ROI.names_LME_OBIC)
  413. stats_LME_ICA<-data.frame(t=matrix(0,7,1), p=matrix(0,7,1))
  414. row.names(stats_LME_ICA)<-region_OBIC_LME
  416. for(region in region_OBIC_LME){
  417. #add the ICA value
  418. #data$r <- ROI.names_LME_OBIC[,region]
  419. data$r<- data[[paste0(ROI.names_LME_OBIC[,region])]
  420. print(region)
  423. #run the LMER
  424. ICA.test <- lmer(r ~ Group + Sex + (1 | Site) + Age + Age_sq, data = data, REML = FALSE)
  425. s=summary(ICA.test)
  427. #Add values to table
  428. stats_LME_ICA[region,"t"]<-s$coefficients[2,4]
  429. stats_LME_ICA[region,"p"]<-s$coefficients[2,5]
  431. }
  434. # altered code
  436. for(region in 1:length(region_OBIC_LME)){
  437. #add the ICA value
  438. data$r <- data[region_OBIC_LME[region]]
  439. print(region)
  442. #run the LMER
  443. ICA.test <- lmer(r ~ Group + Sex + (1 | Site) + Age + Age_sq, data = data, REML = FALSE)
  444. s=summary(ICA.test)
  446. #Add values to table
  447. stats_LME_ICA[region,"t"]<-s$coefficients[2,4]
  448. stats_LME_ICA[region,"p"]<-s$coefficients[2,5]
  450. }
  453. # This works by 12 May
  454. for(region in 1:length(region_OBIC_LME)){
  455. #add the ICA value
  456. #data$r <- data[region_OBIC_LME[region]]
  458. data$r <- data[[paste0("ICA_Rerun7z_", region)]]
  460. #run the LMER
  461. ICA.test <- lmer(r ~ Group + Sex + Age + SurfaceHoles + (1 | Site), data = data, REML = FALSE)
  462. s=summary(ICA.test)
  464. #Add values to table
  465. stats_LME_ICA[region,"t"]<-s$coefficients[2,4]
  466. stats_LME_ICA[region,"p"]<-s$coefficients[2,5]
  468. }
  470. # do fdr correction
  471. p_fdr_LME<-p.adjust(stats_LME_ICA$p, method="fdr")
  472. stats_LME_ICA$p_fdr<-p_fdr_LME
  475. # Test if new and old datasets match
  476. Cortical_myelination_with_ICA_ROI_Bruk_denne <- read_sav("Anders/Cortical_myelination_with_ICA_ROI_Bruk_denne.sav")
  478. Cortical_myelination_with_ICA_ROI_Bruk_denne <- Cortical_myelination_with_ICA_ROI_Bruk_denne[order(Cortical_myelination_with_ICA_ROI_Bruk_denne$BIDS),]
  479. cor(data$Group, Cortical_myelination_with_ICA_ROI_Bruk_denne$Group)
  480. cor(data$ICA_Rerun7z_3, Cortical_myelination_with_ICA_ROI_Bruk_denne$ICA_Rerun7z_3)
  482. # Dataet matcher bortsett fra Group (r=0.9902)
  483. # Finn ut hvilke caser som har skiftet Group
  485. for(n in 1:nrow(data)){
  486. if(data$Group[n] == Cortical_myelination_with_ICA_ROI_Bruk_denne$Group[n]){
  487. data$same_group[n] = 1
  488. Cortical_myelination_with_ICA_ROI_Bruk_denne$same_group[n] = 1
  489. } else {
  490. data$same_group[n] = 0
  491. Cortical_myelination_with_ICA_ROI_Bruk_denne$same_group[n] = 0
  492. }
  493. }
  495. data$same_group
  496. Cortical_myelination_with_ICA_ROI_Bruk_denne$same_group
  498. subjects <- print(subset(data$BIDS, data$same_group == 0))
  499. # Returns "sub-09subject00066_T1w" "sub-09subject00067_T1w" "sub-09subject00068_T1w" "sub-09subject00069_T1w"
  502. Cortical_myelination_with_ICA_ROI_Bruk_denne$Group
  506. data$lh_GWC_bankssts[1]
  507. Cortical_myelination_with_ICA_ROI_Bruk_denne$lh_GWC_bankssts[1]
  509. ICA_z_3 <- lmer(ICA_Rerun7z_3 ~ Group + Sex + (1 | site) + Age, data = Cortical_myelination_with_ICA_ROI_Bruk_denne, REML = FALSE)
  510. summary(ICA_z_3)
  513. # Count cases
  514. data_OCD <- subset(data, data$Group == 1)
  515. summary(as.factor(data_OCD$Onset_OCD))
  516. hist(data$GWC_anxiety)
  518. group_by(data$Age, group = data$Group) # M HC = 0.01, M OCD = 0.08
  520. grouped_data <- data %>% group_by(Group)
  521. summary(grouped_data)
  523. hist(data$Age)
  524. qqnorm(y = data$Age)
  525. describeBy(data$Age, group = data$Group)
  526. summary(data$Age)
  527. summary(lmer(Age ~ Group + (1 | Site), data = data, REML = FALSE))
  529. describeBy(data$Education, group = data$Group)
  530. summary(data$Education)
  531. hist(data$Education)
  532. qqnorm(y = data$Education); qqline(y = data$Education, col = 10)
  533. summary(lmer(Education ~ Group + Age + Sex + (1 | Site), data = data, REML = FALSE))
  535. data %>% count(Sex, Group)
  537. summary()
  539. summary(glmer(Sex ~ Group + (1 | Site), data = data, family = binomial))
  541. summary(glmer(Group ~ Sex + (1 | Site), data = data, family = binomial))
  543. summary(as.factor(data$Group))
  544. data %>% count(Medicated, Group)
  545. data %>% count(Agr_Check, Group)
  546. data %>% count(Contam_Clean, Group)
  547. data %>% count(Sym_Ordering, Group)
  548. data %>% count(Sex_Rel, Group)
  549. data %>% count(Hoarding, Group)

Comorbidity_symptom_dimensions.R at commit 49c7e67, no license · at the source

Overview

Authors: Anders Lillevik Thorsen1,2,3, Vilde Brecke1,2, Dag Alnæs4,5, David Mataix-Cols6,7, Jun Soo Kwon8, Jose M Menchon9, Yoshinari Abe10, Yuki Sakai10,11, Mary L Phillips12, Bjarne Hansen1,2, Marcelo Hoexter13, Janardhan Reddy14, Francesco Benedetti15, Brian P Brennan16, Yuqi Cheng17, Damiaan Denys18,19, Yoshiyuki Hirano20, Kathrin Koch21,22, Tomohiro Nakao23, Erika L Nurmi24
and 12 other authorsHelen Blair Simpson25,26, Fabrizio Piras27, David F Tolin28,29, Emily R Stern30,31, Zhen Wang32,33, Jan Buitelaar34,35, Pedro Morgado36,37,38, Jan C Beucke6,39,40, Christine Lochner41, Dan J Stein42, Odile A van den Heuvel1,3,43,44, Olga Therese Ousdal1,45,46
ORCID iDs: Anders Lillevik Thorsen, Vilde Brecke, Dag Alnæs, David Mataix-Cols, Jun Soo Kwon, Jose M Menchon, Yoshinari Abe, Mary L Phillips, Marcelo Hoexter, Francesco Benedetti, Damiaan Denys, Yoshiyuki Hirano, Kathrin Koch, Erika L Nurmi, Fabrizio Piras, Zhen Wang, Pedro Morgado, Christine Lochner, Dan J Stein, Odile A van den Heuvel, Olga Therese Ousdal
46 affiliations
  1. Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
  2. Centre for Crisis Psychology, University of Bergen, Bergen, Norway
  3. Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, De Boelelaan, 1117 Netherlands
  4. NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
  5. Department of Psychology, Pedagogy and Law, Kristiania University College, Oslo, Norway
  6. Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
  7. Department of Clinical Sciences, Lund University, Lund, Sweden
  8. Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
  9. Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
  10. Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
  11. Department of Neural Computation for Decision-Making, Advanced Telecommunications Research Institute International Brain Information Communication Research Laboratory Group, Kyoto, Japan
  12. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, USA
  13. Institute of Psychiatry, Hospital das Clínicas, University of São Paulo, São Paulo, Brasil
  14. Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
  15. Department of Psychiatry and Clinical Psychobiology, Scientific Institute Ospedale, Milan, Italy
  16. McLean Hospital, Harvard Medical School, Belmont, Massachusetts USA
  17. Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
  18. Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, Netherlands
  19. Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
  20. Research Center for Child Mental Development, Chiba University, Chiba, Japan
  21. Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
  22. TUM-Neuroimaging Center (TUM-NIC) of Klinikum rechts der Isar, Technische Universität München, Munich, Germany
  23. Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
  24. Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA USA
  25. Columbia University Irving Medical Center, Columbia University, New York, NY USA
  26. Center for OCD and Related Disorders, New York State Psychiatric Institute, New York, NY USA
  27. Laboratory of Neuropsychiatry, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
  28. Institute of Living/Hartford Hospital, Hartford, Connecticut USA
  29. Yale University School of Medicine, New Haven, Connecticut USA
  30. Department of Psychiatry, New York University School of Medicine, New York, NY USA
  31. Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
  32. Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
  33. Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
  34. Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
  35. Karakter Child and Adolescent Psychiatry University Center, Nijmegen, The Netherlands
  36. Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
  37. ICVS/3B’s, PT Government Associate Laboratory, Braga/Guimarães, Portugal
  38. Clinical Academic Center-Braga, Braga, Portugal
  39. Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
  40. Institute for Systems Medicine & Department of Human Medicine, MSH Medical School Hamburg, Hamburg, Germany
  41. SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
  42. SAMRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
  43. Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, De Boelelaan 1117, Amsterdam, Netherlands
  44. Amsterdam Neuroscience, Compulsivity, Impulsivity and Attention program, Amsterdam, Netherlands
  45. Department of Biomedicine, University of Bergen, Bergen, Norway
  46. Department of Radiology, Haukeland University Hospital, Bergen, Norway
Institutions: Haukeland University Hospital (Norway); Amsterdam University Medical Centers (Netherlands); Senter for Krisepsykologi (Norway); University of Bergen (Norway); Vrije Universiteit Amsterdam (Netherlands); Oslo University Hospital (Norway); University of Oslo (Norway); Høyskolen Kristiania (Norway); Lund University (Sweden); Region Stockholm (Sweden); Karolinska Institutet (Sweden); Stockholm Health Care Services (Sweden); Seoul National University (South Korea); Bellvitge University Hospital (Spain); Institut d'Investigació Biomédica de Bellvitge (Spain); Kyoto Prefectural University of Medicine (Japan); Advanced Telecommunications Research Institute International (Japan); University of Pittsburgh (United States); Universidade de São Paulo (Brazil); Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (Brazil); National Institute of Mental Health and Neurosciences (India); Harvard University (United States); McLean Hospital (United States); First Affiliated Hospital of Kunming Medical University (China); Netherlands Institute for Neuroscience (Netherlands); Royal Netherlands Academy of Arts and Sciences (Netherlands); Amsterdam Neuroscience (Netherlands); University of Amsterdam (Netherlands); Chiba University (Japan); TUM Klinikum (Germany); Technical University of Munich (Germany); Kyushu University (Japan); University of California, Los Angeles (United States); Columbia University Irving Medical Center (United States); New York State Psychiatric Institute; Fondazione Santa Lucia (Italy); Hartford Hospital (United States); Yale University (United States); Nathan Kline Institute for Psychiatric Research (United States); NYU Langone Health (United States); New York University (United States); Shanghai Jiao Tong University (China); Shanghai Mental Health Center (China); Shanghai Key Laboratory of Psychotic Disorders (China); Radboud University Nijmegen (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands); Clinical Academic Center of Braga (Portugal); University of Minho (Portugal); Humboldt-Universität zu Berlin (Germany); MSH Medical School Hamburg – University of Applied Sciences and Medical University (Germany); South African Medical Research Council (South Africa); Stellenbosch University (South Africa); University of Cape Town (South Africa)
Journal: Molecular psychiatry, volume 31, issue 7, pages 4074-4082
Dates: received 7 February 2024; accepted 18 February 2026; published online 15 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41380-026-03500-y · PMID 41833995 · PMCID PMC13268973 · OpenAlex W7136084691
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Preprocessing
Keywords: Neuroscience, Psychiatric disorders
MeSH: Frontal Lobe*, Obsessive-Compulsive Disorder*, Occipital Lobe*, Adult, Brain Mapping, Female, Gray Matter, Humans, Magnetic Resonance Imaging, Male, Middle Aged, White Matter, Young Adult (* major topic)
Topic: Obsessive-Compulsive Spectrum Disorders (Clinical Psychology, Psychology), according to OpenAlex
Funding: National Research Foundation of Korea (2012-0005150); Dutch Research Council (NWO) (907-00-012, 912-02-050); Dutch Organization for Scientific Research; Foundation for the Support of Research in the State of São Paulo; Ministry of Education, Culture, Sports, Science and Technology; Trond Mohn Foundation; Helse Bergen HF; Helse West Health Authority; Italian ministry of Health; Universitetet i Bergen; Wellcome Trust (064846)
Citations: not cited yet (Europe PMC); 62 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 7 matches between paragraphs and lines of code.

anderslthorsen/OBIC-GWC

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 49c7e67c3c2e6b6567c2c54d58f4916b0c1bfb09, 19 June 2025
Languages: MATLAB (61), Shell (23), R (6)
Size: 105 files, 90 scripts
Software Heritage: not archived
Found in: the text, “Statistical analyses”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FreeSurfer (17 files), easystats (4 files), ggplot2 (4 files), lme4 (4 files), Statistics and Machine Learning Toolbox (4 files), tidyverse (3 files), ggseg (2 files), lmerTest (2 files), FSL (1 file), MRIQC (1 file), psych (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
91 files
At the source: github.com/anderslthorsen/OBIC-GWC

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;
  • 90 scripts, each with its path and the digest of its content;
  • 7 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

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

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 32 authors, 2 keywords, 13 MeSH terms, 9 funders, 60 references.

Cite

This paper

Thorsen, A. L., Brecke, V., Alnæs, D., Mataix-Cols, D., Kwon, J. S., Menchon, J. M., Abe, Y., Sakai, Y., Phillips, M. L., Hansen, B., Hoexter, M., Reddy, J., Benedetti, F., Brennan, B. P., Cheng, Y., Denys, D., Hirano, Y., Koch, K., Nakao, T., . . . Ousdal, O. T. (2026). Altered frontal and occipital cortical microstructure in obsessive-compulsive disorder - a multisite mega-analysis. Molecular psychiatry, 31(7), 4074-4082. https://doi.org/10.1038/s41380-026-03500-y

BibTeX

@article{thorsen2026altered,
author = {Thorsen, Anders Lillevik and Brecke, Vilde and Alnæs, Dag and Mataix-Cols, David and Kwon, Jun Soo and Menchon, Jose M and Abe, Yoshinari and Sakai, Yuki and Phillips, Mary L and Hansen, Bjarne and Hoexter, Marcelo and Reddy, Janardhan and Benedetti, Francesco and Brennan, Brian P and Cheng, Yuqi and Denys, Damiaan and Hirano, Yoshiyuki and Koch, Kathrin and Nakao, Tomohiro and Nurmi, Erika L and Simpson, Helen Blair and Piras, Fabrizio and Tolin, David F and Stern, Emily R and Wang, Zhen and Buitelaar, Jan and Morgado, Pedro and Beucke, Jan C and Lochner, Christine and Stein, Dan J and van den Heuvel, Odile A and Ousdal, Olga Therese},
title = {{Altered frontal and occipital cortical microstructure in obsessive-compulsive disorder - a multisite mega-analysis}},
journal = {Molecular psychiatry},
year = {2026},
month = mar,
volume = {31},
number = {7},
pages = {4074--4082},
publisher = {Springer Nature},
issn = {1359-4184},
doi = {10.1038/s41380-026-03500-y},
url = {https://doi.org/10.1038/s41380-026-03500-y},
pmid = {41833995},
pmcid = {PMC13268973}
}

RIS

TY - JOUR
AU - Thorsen, Anders Lillevik
AU - Brecke, Vilde
AU - Alnæs, Dag
AU - Mataix-Cols, David
AU - Kwon, Jun Soo
AU - Menchon, Jose M
AU - Abe, Yoshinari
AU - Sakai, Yuki
AU - Phillips, Mary L
AU - Hansen, Bjarne
AU - Hoexter, Marcelo
AU - Reddy, Janardhan
AU - Benedetti, Francesco
AU - Brennan, Brian P
AU - Cheng, Yuqi
AU - Denys, Damiaan
AU - Hirano, Yoshiyuki
AU - Koch, Kathrin
AU - Nakao, Tomohiro
AU - Nurmi, Erika L
AU - Simpson, Helen Blair
AU - Piras, Fabrizio
AU - Tolin, David F
AU - Stern, Emily R
AU - Wang, Zhen
AU - Buitelaar, Jan
AU - Morgado, Pedro
AU - Beucke, Jan C
AU - Lochner, Christine
AU - Stein, Dan J
AU - van den Heuvel, Odile A
AU - Ousdal, Olga Therese
TI - Altered frontal and occipital cortical microstructure in obsessive-compulsive disorder - a multisite mega-analysis
T2 - Molecular psychiatry
J2 - Mol Psychiatry
PY - 2026
DA - 2026/03/15
VL - 31
IS - 7
SP - 4074
EP - 4082
SN - 1359-4184
PB - Springer Nature
DO - 10.1038/s41380-026-03500-y
UR - https://doi.org/10.1038/s41380-026-03500-y
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41380-026-03500-y",
"type": "article-journal",
"title": "Altered frontal and occipital cortical microstructure in obsessive-compulsive disorder - a multisite mega-analysis",
"container-title": "Molecular psychiatry",
"author": [
{
"family": "Thorsen",
"given": "Anders Lillevik"
},
{
"family": "Brecke",
"given": "Vilde"
},
{
"family": "Alnæs",
"given": "Dag"
},
{
"family": "Mataix-Cols",
"given": "David"
},
{
"family": "Kwon",
"given": "Jun Soo"
},
{
"family": "Menchon",
"given": "Jose M"
},
{
"family": "Abe",
"given": "Yoshinari"
},
{
"family": "Sakai",
"given": "Yuki"
},
{
"family": "Phillips",
"given": "Mary L"
},
{
"family": "Hansen",
"given": "Bjarne"
},
{
"family": "Hoexter",
"given": "Marcelo"
},
{
"family": "Reddy",
"given": "Janardhan"
},
{
"family": "Benedetti",
"given": "Francesco"
},
{
"family": "Brennan",
"given": "Brian P"
},
{
"family": "Cheng",
"given": "Yuqi"
},
{
"family": "Denys",
"given": "Damiaan"
},
{
"family": "Hirano",
"given": "Yoshiyuki"
},
{
"family": "Koch",
"given": "Kathrin"
},
{
"family": "Nakao",
"given": "Tomohiro"
},
{
"family": "Nurmi",
"given": "Erika L"
},
{
"family": "Simpson",
"given": "Helen Blair"
},
{
"family": "Piras",
"given": "Fabrizio"
},
{
"family": "Tolin",
"given": "David F"
},
{
"family": "Stern",
"given": "Emily R"
},
{
"family": "Wang",
"given": "Zhen"
},
{
"family": "Buitelaar",
"given": "Jan"
},
{
"family": "Morgado",
"given": "Pedro"
},
{
"family": "Beucke",
"given": "Jan C"
},
{
"family": "Lochner",
"given": "Christine"
},
{
"family": "Stein",
"given": "Dan J"
},
{
"family": "van den Heuvel",
"given": "Odile A"
},
{
"family": "Ousdal",
"given": "Olga Therese"
}
],
"container-title-short": "Mol Psychiatry",
"volume": "31",
"issue": "7",
"page": "4074-4082",
"DOI": "10.1038/s41380-026-03500-y",
"PMID": "41833995",
"PMCID": "PMC13268973",
"ISSN": "1359-4184",
"publisher": "Springer Nature",
"URL": "https://doi.org/10.1038/s41380-026-03500-y",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
15
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment.
Journal: Human brain mapping
In common: ggseg, easystats, FreeSurfer, 4 other tools, 2 references
[10] doi:10.1038/s41398-026-04010-9 [code]
Bullying victimization and brain development: a longitudinal structural magnetic resonance imaging study from adolescence to early adulthood.
Journal: Translational psychiatry
In common: ggseg, psych, easystats, 4 other tools, structural MRI / diffusion, 1 reference

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