Bullying victimization and brain development: a longitudinal structural magnetic resonance imaging study from adolescence to early adulthood.

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[1] § Results › Sex differences in the associations of bullying victimization on brain development ↔ LME_Model_Metrics.R, lines 2599–2658 · score 0.69 · Female Low, Female High, quartile, bullying victim, score, predicted
[2] § Results › Sex differences in the associations of bullying victimization on brain development ↔ LME_Model_Metrics.R, lines 157–209 · score 0.55 · left cerebellar cortex, right caudate, left putamen, left ventral, supramarginal, parahippocampal

Paper

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The authors' code

R · 2,880 lines · 195 KB · no license · 2 matches

# Load the gamm4 library
{
library(gamm4)
library(mediation)
library(tidyverse)
library(lmerTest)
library(knitr)
library(lavaan)
library(psych)
library(MBESS)
library(greybox)
library(lme4)
library(haven)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(ggseg)
library(effects)
library(rstatix)
library(broom)
library(ggpubr)
library(effectsize)
library(ggeffects)
library(plyr)
library(methods)
library(stargazer)
library(sjPlot)
library(nlme)
library(data.table)
library(MuMIn)
library(lmerTest)
library(lmtest)
library(officer)
library(knitr)
library(readxl)
library(RColorBrewer)
library(dvmisc)
library(qwraps2)
library(AICcmodavg)
library(simr)
library(emmeans)
library(stats)
library(mutoss)
library(powerlmm)
}
#Load Data
{
# read in excel file
IMAGEN_bully <- read_excel("/Users/michaelconnaughton/Desktop/IMAGEN/MASTER_FILE/IMAGEN_MASTER.xlsx", na = ".")
# read in csv file - read.csv() or read.csv2() functions. The former function is used if the separator is a ,, the latter if ; is used to separate the values in your data file.
IMAGEN_bully <- as.data.frame(IMAGEN_bully)
# convert to r dataframe
IMAGEN_bully <- as.data.frame(IMAGEN_bully)
# Find the index of the row with the lowest age
index_of_lowest_age <- which.min(IMAGEN_bully$age_years)
# Remove the row with the lowest age
IMAGEN_bully <- IMAGEN_bully[-index_of_lowest_age, ]
# Verify the row removal (optional)
print(data)
# List of failed QC IDs to be removed (include all variations of the IDs)
outliers <- c("10400245_1", "12645188_1", "12953830_1", "20015942_1",
"22888407_1", "26135259_1", "28852607_1", "29257136_1",
"30276332_1", "30383377_1", "32506627_1", "35587656_1",
"38976393_1", "41748802_1", "47975799_1", "54552397_1",
"64999269_1", "82654000_1", "83308215_1", "90579038_1",
"93239552_1", "95136410_1", "98720163_1", "99550415_1",
"3328367_2", "20418061_2", "6272781_3", "22053782_2",
"10646873_3", "61507009_2", "13361778_3", "68802565_2",
"20015942_3", "68849025_2", "25193415_3", "30276332_2",
"30276332_3", "87961112_2", "38916315_3", "35587656_2",
"80627914_3", "75438006_2", "95958758_3", "97329782_2",
"97555742_2", "3504454_2", "67766736_2", "73628015_2",
"18632052_2", "69895644_2")
# Removed failed QC IDs from data frame
IMAGEN_bully <- IMAGEN_bully %>%
filter(!(ID %in% outliers))
# mutate factors
IMAGEN_bully <- IMAGEN_bully%>%
mutate(sex = factor(sex,levels = c(1,2),labels = c("female", "male")))
IMAGEN_bully <- IMAGEN_bully%>%
mutate(scan_site = factor(scan_site,levels = c(1,2,3,4,5,6,7,8),labels = c("London", "Northamptom", "Dublin", "Berlin", "Hamberg", "Mannheim", "Paris", "Dresden")))
IMAGEN_bully <- IMAGEN_bully%>%
mutate(p_bully = factor(p_bully,levels = c(0,1),labels = c("No", "Yes")))
IMAGEN_bully <- IMAGEN_bully%>%
mutate(timepoint = factor(timepoint,levels = c(1,2,3),labels = c("1", "2", "3")))
summary(IMAGEN_bully$lh_cuneus_volume)
IMAGEN_bully <- IMAGEN_bully%>%
mutate(p_perpetrator = factor(p_perpetrator,levels = c(0,1),labels = c("No", "Yes")))
IMAGEN_bully <- IMAGEN_bully%>%
mutate(has_a_good_friend = factor(has_a_good_friend,levels = c(0,1,2),labels = c("Not True", "Partially True", "Certainly True")))
IMAGEN_bully <- IMAGEN_bully%>%
mutate(attached_to_parent = factor(attached_to_parent,levels = c(0,1,2),labels = c("Not True", "Partially True", "Certainly True")))
#Exclude entries with bully status of "2"
#IMAGEN_bully <- IMAGEN_bully %>%
#filter(bully_status != 2)
#code factor variables
contrasts(IMAGEN_bully$sex) <- c(-.5, .5)
contrasts(IMAGEN_bully$p_bully) <- c(-.5, .5)
#contrasts(IMAGEN_bully$p_perpetrator) <- c(-.5, .5)
#diff_LMM <- na.omit(diff_LMM, diff_local_eff_log_c)
summary(IMAGEN_bully)
# Define your dependent variable
IMAGEN_bully$EstimatedTotalIntraCranialVol<- as.numeric(IMAGEN_bully$EstimatedTotalIntraCranialVol)
IMAGEN_bully$age<- as.numeric(IMAGEN_bully$age)
IMAGEN_bully$bully_victim<- as.numeric(IMAGEN_bully$bully_victim)
IMAGEN_bully$SES<- as.numeric(IMAGEN_bully$SES)
IMAGEN_bully$age_baseline<- as.numeric(IMAGEN_bully$age_baseline)
IMAGEN_bully$mode_c_pds<- as.numeric(IMAGEN_bully$mode_c_pds)
IMAGEN_bully$age_years<- as.numeric(IMAGEN_bully$age_years)
IMAGEN_bully$WHO_Total<- as.numeric(IMAGEN_bully$WHO_Total)
IMAGEN_bully$LEQ<- as.numeric(IMAGEN_bully$LEQ)
IMAGEN_bully$MatrixReasoning_Baseline<- as.numeric(IMAGEN_bully$MatrixReasoning_Baseline)
IMAGEN_bully$bmi_Baseline<- as.numeric(IMAGEN_bully$bmi_Baseline)
# Scale Variables
IMAGEN_bully$age <- scale(IMAGEN_bully$age)
IMAGEN_bully$bully_victim <- scale(IMAGEN_bully$bully_victim)
IMAGEN_bully$SES <- scale(IMAGEN_bully$SES)
IMAGEN_bully$mode_c_pds <- scale(IMAGEN_bully$mode_c_pds)
IMAGEN_bully$age_baseline <- scale(IMAGEN_bully$age_baseline)
IMAGEN_bully$EstimatedTotalIntraCranialVol <- scale(IMAGEN_bully$EstimatedTotalIntraCranialVol)
IMAGEN_bully$WHO_Total <- scale(IMAGEN_bully$WHO_Total)
IMAGEN_bully$LEQ <- scale(IMAGEN_bully$LEQ)
IMAGEN_bully$MatrixReasoning_Baseline <- scale(IMAGEN_bully$MatrixReasoning_Baseline)
IMAGEN_bully$bmi_Baseline <- scale(IMAGEN_bully$bmi_Baseline)
# Scale Variables
# Create the interaction term and scale it
#IMAGEN_bully$bully_victim_age_interaction <- scale(IMAGEN_bully$age * IMAGEN_bully$bully_victim)
# Remove Missing Data
missing_bully_victim <- is.na(IMAGEN_bully$bully_victim)
missing_scan_site <- is.na(IMAGEN_bully$scan_site)
# Remove observations with missing bully_victim values
IMAGEN_bully <- IMAGEN_bully[complete.cases(IMAGEN_bully$bully_victim), ]
IMAGEN_bully <- IMAGEN_bully[complete.cases(IMAGEN_bully$scan_site), ]
sum(is.na(IMAGEN_bully$bully_victim))
sum(is.na(IMAGEN_bully$scan_site))
DVlist <- c(
"lh_bankssts_volume", "lh_caudalanteriorcingulate_volume", "lh_caudalmiddlefrontal_volume",
"lh_cuneus_volume", "lh_entorhinal_volume", "lh_fusiform_volume",
"lh_inferiorparietal_volume", "lh_inferiortemporal_volume", "lh_isthmuscingulate_volume",
"lh_lateraloccipital_volume", "lh_lateralorbitofrontal_volume", "lh_lingual_volume",
"lh_medialorbitofrontal_volume", "lh_middletemporal_volume", "lh_parahippocampal_volume",
"lh_paracentral_volume", "lh_parsopercularis_volume", "lh_parsorbitalis_volume",
"lh_parstriangularis_volume", "lh_pericalcarine_volume", "lh_postcentral_volume",
"lh_posteriorcingulate_volume", "lh_precentral_volume", "lh_precuneus_volume",
"lh_rostralanteriorcingulate_volume", "lh_rostralmiddlefrontal_volume", "lh_superiorfrontal_volume",
"lh_superiorparietal_volume", "lh_superiortemporal_volume", "lh_supramarginal_volume",
"lh_frontalpole_volume", "lh_temporalpole_volume", "lh_transversetemporal_volume",
"lh_insula_volume", "rh_bankssts_volume", "rh_caudalanteriorcingulate_volume",
"rh_caudalmiddlefrontal_volume", "rh_cuneus_volume", "rh_entorhinal_volume",
"rh_fusiform_volume", "rh_inferiorparietal_volume", "rh_inferiortemporal_volume",
"rh_isthmuscingulate_volume", "rh_lateraloccipital_volume", "rh_lateralorbitofrontal_volume",
"rh_lingual_volume", "rh_medialorbitofrontal_volume", "rh_middletemporal_volume",
"rh_parahippocampal_volume", "rh_paracentral_volume", "rh_parsopercularis_volume",
"rh_parsorbitalis_volume", "rh_parstriangularis_volume", "rh_pericalcarine_volume",
"rh_postcentral_volume", "rh_posteriorcingulate_volume", "rh_precentral_volume",
"rh_precuneus_volume", "rh_rostralanteriorcingulate_volume", "rh_rostralmiddlefrontal_volume",
"rh_superiorfrontal_volume", "rh_superiorparietal_volume", "rh_superiortemporal_volume",
"rh_supramarginal_volume", "rh_frontalpole_volume", "rh_temporalpole_volume",
"rh_transversetemporal_volume", "rh_insula_volume", "Left_Cerebellum_Cortex",
"Left_Thalamus_Proper", "Left_Caudate", "Left_Putamen",
"Left_Pallidum", "Brain_Stem", "Left_Hippocampus",
"Left_Amygdala","Left_Accumbens_area", "Left_VentralDC",
"Right_Cerebellum_Cortex", "Right_Thalamus_Proper",
"Right_Caudate", "Right_Putamen", "Right_Pallidum",
"Right_Hippocampus", "Right_Amygdala", "Right_Accumbens_area",
"Right_VentralDC", "TotalGrayVol")
for (dv in DVlist) {
IMAGEN_bully[[dv]] <- scale(IMAGEN_bully[[dv]])
}
# Initialize a list to store outlier information
outlier_info <- list()
# Loop through each DV
for (dv in DVlist) {
# Convert to numeric
IMAGEN_bully[[dv]] <- as.numeric(IMAGEN_bully[[dv]])
# Calculate mean and standard deviation
mean_DV <- mean(IMAGEN_bully[[dv]], na.rm = TRUE)
sd_DV <- sd(IMAGEN_bully[[dv]], na.rm = TRUE)
# Define cutoffs for outliers
lower_bound <- mean_DV - 3 * sd_DV
upper_bound <- mean_DV + 3 * sd_DV
# Identify outliers
outliers <- which(IMAGEN_bully[[dv]] < lower_bound | IMAGEN_bully[[dv]] > upper_bound)
# Store outlier information
outlier_info[[dv]] <- IMAGEN_bully[outliers, dv]
# Replace outliers with NA
IMAGEN_bully[[dv]][outliers] <- NA
}
}
# The outlier_info list now contains the outliers for each DV
# frames
{
AIC_fit_1 <- c();
AIC_fit_2 <- c();
AIC_Q_RFX_1 <- c();
AIC_Q_RFX_2 <- c();
AIC_L_RFX_1 <- c();
AIC_L_RFX_2 <- c();
AIC_FFX_null_Q <- c();
AIC_FFX_simple_Q <- c();
AIC_FFX_complex_Q <- c();
AIC_FFX_null_L <- c();
AIC_FFX_simple_L <- c();
AIC_FFX_complex_L <- c();
LL_fit_1 <- c();
LL_fit_2 <- c();
LL_Q_RFX_1 <- c();
LL_Q_RFX_2 <- c();
LL_L_RFX_1 <- c();
LL_L_RFX_2 <- c();
LL_FFX_null_Q <- c();
LL_FFX_simple_Q <- c();
LL_FFX_complex_Q <- c();
LL_FFX_null_L <- c();
LL_FFX_simple_L <- c();
LL_FFX_complex_L <- c();
BIC_fit_1 <- c();
BIC_fit_2 <- c();
BIC_Q_RFX_1 <- c();
BIC_Q_RFX_2 <- c();
BIC_L_RFX_1 <- c();
BIC_L_RFX_2 <- c();
BIC_FFX_null_Q <- c();
BIC_FFX_simple_Q <- c();
BIC_FFX_complex_Q <- c();
BIC_FFX_null_L <- c();
BIC_FFX_simple_L <- c();
BIC_FFX_complex_L <- c();
LL.Fit.pmat <- c();
dif.1.2_Fit <- c();
dif_Q_RFX <- c();
dif.0.2_FFX_Q <- c();
dif.2.2B_FFX_Q <- c();
dif.0.2B_FFX_Q <- c();
dif.0.2_FFX_L<- c();
dif.2.2B_FFX_L <- c();
dif.0.2B_FFX_L <- c();
LL.FFX_Q.pmat <- c();
LLcompare_Q_FFX <- c();
AICcompare_Q_FFX <- c();
BICcompare_Q_FFX <- c();
LL.FFX_L.pmat <- c();
LLcompare_L_FFX <- c();
AICcompare_L_FFX <- c();
BICcompare_L_FFX <- c();
LL_Q_RFX.pmat <- c();
LL_L_RFX.pmat <- c();
LL_simple_Q_FFX.pmat <- c();
LL_complex_Q_FFX.pmat <- c();
LL_simple_L_FFX.pmat <- c();
LL_complex_L_FFX.pmat <- c();
AICccompare_fit <- c();
BICccompare_fit <- c();
LLccompare_fit <- c();
AICccompare_Q_RFX <- c();
BICccompare_Q_RFX <- c();
LLccompare_Q_RFX <- c();
AICccompare_L_RFX <- c();
BICccompare_L_RFX <- c();
LLccompare_L_RFX <- c();
AICccompare_Q_FFX <- c();
BICccompare_Q_FFX <- c();
LLccompare_Q_FFX <- c();
AICccompare_L_FFX <- c();
BICccompare_L_FFX <- c();
LLccompare_L_FFX <- c();
}
#QUAD_LINEAR
{
# Linear MODEL 1
Linear.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim * age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_fit_1 <- append(LL_fit_1, logLik(Linear.outcomes[[outcome]], REML=T))
AIC_fit_1 <- append(AIC_fit_1, AICc(Linear.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_fit_1 <- append(BIC_fit_1, BICc(Linear.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# Quadratic MODEL 1
Quad.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim * age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_fit_2 <- append(LL_fit_2, logLik(Quad.outcomes[[outcome]], REML=T))
AIC_fit_2 <- append(AIC_fit_2, AICc(Quad.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_fit_2 <- append(BIC_fit_2, BICc(Quad.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# observe all intercept AICc in a table
AICcompare_fit <- data.frame(DVlist, AIC_fit_1, AIC_fit_2)
BICcompare_fit <- data.frame(DVlist, BIC_fit_1, BIC_fit_2)
LLcompare_fit <- data.frame(DVlist, LL_fit_1, LL_fit_2)
# LL.FFX.pmat <- c()
for (outcome in 1:88) {
# extract p-values only
# do the interaction effect and the PE variable add anything to the null prediction model?
dif.1.2_Fit <- anova(Linear.outcomes[[outcome]], Quad.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.Fit.pmat <- append(LL.Fit.pmat,dif.1.2_Fit[2,1])
}
# create a list of FFX model comparisons (M0_M2 = null compared to full, M0_M2B = null compared to reduced model, M2_M2B = full compared to reduced)
compared_Fit <- c("Linear_Quadratic")
# IS create a dataframe with outcome scales as rows, and 3 columns showing model comparisons
# Create a dataframe with the p-values for different variables
LL_Fit.pvals <- data.frame(
compared_Fit,
"lh_bankssts_volume" = LL.Fit.pmat[1],
"lh_caudalanteriorcingulate_volume" = LL.Fit.pmat[2],
"lh_caudalmiddlefrontal_volume" = LL.Fit.pmat[3],
"lh_cuneus_volume" = LL.Fit.pmat[4],
"lh_entorhinal_volume" = LL.Fit.pmat[5],
"lh_fusiform_volume" = LL.Fit.pmat[6],
"lh_inferiorparietal_volume" = LL.Fit.pmat[7],
"lh_inferiortemporal_volume" = LL.Fit.pmat[8],
"lh_isthmuscingulate_volume" = LL.Fit.pmat[9],
"lh_lateraloccipital_volume" = LL.Fit.pmat[10],
"lh_lateralorbitofrontal_volume" = LL.Fit.pmat[11],
"lh_lingual_volume" = LL.Fit.pmat[12],
"lh_medialorbitofrontal_volume" = LL.Fit.pmat[13],
"lh_middletemporal_volume" = LL.Fit.pmat[14],
"lh_parahippocampal_volume" = LL.Fit.pmat[15],
"lh_paracentral_volume" = LL.Fit.pmat[16],
"lh_parsopercularis_volume" = LL.Fit.pmat[17],
"lh_parsorbitalis_volume" = LL.Fit.pmat[18],
"lh_parstriangularis_volume" = LL.Fit.pmat[19],
"lh_pericalcarine_volume" = LL.Fit.pmat[20],
"lh_postcentral_volume" = LL.Fit.pmat[21],
"lh_posteriorcingulate_volume" = LL.Fit.pmat[22],
"lh_precentral_volume" = LL.Fit.pmat[23],
"lh_precuneus_volume" = LL.Fit.pmat[24],
"lh_rostralanteriorcingulate_volume" = LL.Fit.pmat[25],
"lh_rostralmiddlefrontal_volume" = LL.Fit.pmat[26],
"lh_superiorfrontal_volume" = LL.Fit.pmat[27],
"lh_superiorparietal_volume" = LL.Fit.pmat[28],
"lh_superiortemporal_volume" = LL.Fit.pmat[29],
"lh_supramarginal_volume" = LL.Fit.pmat[30],
"lh_frontalpole_volume" = LL.Fit.pmat[31],
"lh_temporalpole_volume" = LL.Fit.pmat[32],
"lh_transversetemporal_volume" = LL.Fit.pmat[33],
"lh_insula_volume" = LL.Fit.pmat[34],
"rh_bankssts_volume" = LL.Fit.pmat[35],
"rh_caudalanteriorcingulate_volume" = LL.Fit.pmat[36],
"rh_caudalmiddlefrontal_volume" = LL.Fit.pmat[37],
"rh_cuneus_volume" = LL.Fit.pmat[38],
"rh_entorhinal_volume" = LL.Fit.pmat[39],
"rh_fusiform_volume" = LL.Fit.pmat[40],
"rh_inferiorparietal_volume" = LL.Fit.pmat[41],
"rh_inferiortemporal_volume" = LL.Fit.pmat[42],
"rh_isthmuscingulate_volume" = LL.Fit.pmat[43],
"rh_lateraloccipital_volume" = LL.Fit.pmat[44],
"rh_lateralorbitofrontal_volume" = LL.Fit.pmat[45],
"rh_lingual_volume" = LL.Fit.pmat[46],
"rh_medialorbitofrontal_volume" = LL.Fit.pmat[47],
"rh_middletemporal_volume" = LL.Fit.pmat[48],
"rh_parahippocampal_volume" = LL.Fit.pmat[49],
"rh_paracentral_volume" = LL.Fit.pmat[50],
"rh_parsopercularis_volume" = LL.Fit.pmat[51],
"rh_parsorbitalis_volume" = LL.Fit.pmat[52],
"rh_parstriangularis_volume" = LL.Fit.pmat[53],
"rh_pericalcarine_volume" = LL.Fit.pmat[54],
"rh_postcentral_volume" = LL.Fit.pmat[55],
"rh_posteriorcingulate_volume" = LL.Fit.pmat[56],
"rh_precentral_volume" = LL.Fit.pmat[57],
"rh_precuneus_volume" = LL.Fit.pmat[58],
"rh_rostralanteriorcingulate_volume" = LL.Fit.pmat[59],
"rh_rostralmiddlefrontal_volume" = LL.Fit.pmat[60],
"rh_superiorfrontal_volume" = LL.Fit.pmat[61],
"rh_superiorparietal_volume" = LL.Fit.pmat[62],
"rh_superiortemporal_volume" = LL.Fit.pmat[63],
"rh_supramarginal_volume" = LL.Fit.pmat[64],
"rh_frontalpole_volume" = LL.Fit.pmat[65],
"rh_temporalpole_volume" = LL.Fit.pmat[66],
"rh_transversetemporal_volume" = LL.Fit.pmat[67],
"rh_insula_volume" = LL.Fit.pmat[68],
"Left_Cerebellum_Cortex" = LL.Fit.pmat[69],
"Left_Thalamus_Proper" = LL.Fit.pmat[70],
"Left_Caudate" = LL.Fit.pmat[71],
"Left_Putamen" = LL.Fit.pmat[72],
"Left_Pallidum" = LL.Fit.pmat[73],
"Brain_Stem" = LL.Fit.pmat[74],
"Left_Hippocampus" = LL.Fit.pmat[75],
"Left_Amygdala" = LL.Fit.pmat[76],
"Left_Accumbens_area" = LL.Fit.pmat[77],
"Left_VentralDC" = LL.Fit.pmat[78],
"Right_Cerebellum_Cortex" = LL.Fit.pmat[79],
"Right_Thalamus_Proper" = LL.Fit.pmat[80],
"Right_Caudate" = LL.Fit.pmat[81],
"Right_Putamen" = LL.Fit.pmat[82],
"Right_Pallidum" = LL.Fit.pmat[83],
"Right_Hippocampus" = LL.Fit.pmat[84],
"Right_Amygdala" = LL.Fit.pmat[85],
"Right_Accumbens_area" = LL.Fit.pmat[86],
"Right_VentralDC" = LL.Fit.pmat[87],
"TotalGrayVol" = LL.Fit.pmat[88]
)
# transpose
LL_Fit.pvals<-t(LL_Fit.pvals)
#save IS output
saveRDS(LL_Fit.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/LL.Fit.pvals.Rds")
saveRDS(LLcompare_fit, file = "/Users/michaelconnaughton/Desktop/IMAGEN/LLcompare_fit.Rds")
saveRDS(AICcompare_fit, file = "/Users/michaelconnaughton/Desktop/IMAGEN/AICccompare_fit.Rds")
saveRDS(BICcompare_fit, file = "/Users/michaelconnaughton/Desktop/IMAGEN/AICccompare_fit.Rds")
write.table(LL_Fit.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/LL.Fit.pval.txt", quote = F, sep=",", col.names = F)
write.table(LLcompare_fit, file = "/Users/michaelconnaughton/Desktop/IMAGEN/LLcompare_fit.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(AICcompare_fit, file = "/Users/michaelconnaughton/Desktop/IMAGEN/AICcompare_fit.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(BICcompare_fit, file = "/Users/michaelconnaughton/Desktop/IMAGEN/BICcompare_fit.txt", quote = F, sep=",", row.names = F, col.names = T)
}
#Quad_MODEL_RFX
{
# Quad_RFX_1
Quad_RFX_1.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim * age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_Q_RFX_1 <- append(LL_fit_1, logLik(Quad_RFX_1.outcomes[[outcome]], REML=T))
AIC_Q_RFX_1 <- append(AIC_fit_1, AICc(Quad_RFX_1.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_Q_RFX_1 <- append(BIC_fit_1, BICc(Quad_RFX_1.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# Quad_RFX_2
Quad_RFX_2.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim * age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_Q_RFX_2 <- append(LL_Q_RFX_2, logLik(Quad.outcomes[[outcome]], REML=T))
AIC_Q_RFX_2 <- append(AIC_Q_RFX_2, AICc(Quad.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_Q_RFX_2 <- append(BIC_Q_RFX_2, BICc(Quad.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# observe all intercept AICc in a table
AICcompare_Q_RFX <- data.frame(DVlist, AIC_Q_RFX_1, AIC_Q_RFX_2)
BICcompare_Q_RFX <- data.frame(DVlist, BIC_Q_RFX_1, BIC_Q_RFX_2)
LLcompare_Q_RFX <- data.frame(DVlist, LL_Q_RFX_1, LL_Q_RFX_2)
# LL.FFX.pmat <- c()
for (outcome in 1:88) {
# extract p-values only
# do the interaction effect and the PE variable add anything to the null prediction model?
dif_Q_RFX <- anova(Quad_RFX_1.outcomes[[outcome]], Quad_RFX_2.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL_Q_RFX.pmat <- append(LL_Q_RFX.pmat,dif_Q_RFX[2,1])
}
# create a list of FFX model comparisons (M0_M2 = null compared to full, M0_M2B = null compared to reduced model, M2_M2B = full compared to reduced)
compared_Q_RFX <- c("Quad_RFX1_Quad_RFX2")
# IS create a dataframe with outcome scales as rows, and 3 columns showing model comparisons
# Create a dataframe with the p-values for different variables
LL_Q_RFX.pvals <- data.frame(
compared_Q_RFX,
"lh_bankssts_volume" = LL_Q_RFX.pmat[1],
"lh_caudalanteriorcingulate_volume" = LL_Q_RFX.pmat[2],
"lh_caudalmiddlefrontal_volume" = LL_Q_RFX.pmat[3],
"lh_cuneus_volume" = LL_Q_RFX.pmat[4],
"lh_entorhinal_volume" = LL_Q_RFX.pmat[5],
"lh_fusiform_volume" = LL_Q_RFX.pmat[6],
"lh_inferiorparietal_volume" = LL_Q_RFX.pmat[7],
"lh_inferiortemporal_volume" = LL_Q_RFX.pmat[8],
"lh_isthmuscingulate_volume" = LL_Q_RFX.pmat[9],
"lh_lateraloccipital_volume" = LL_Q_RFX.pmat[10],
"lh_lateralorbitofrontal_volume" = LL_Q_RFX.pmat[11],
"lh_lingual_volume" = LL_Q_RFX.pmat[12],
"lh_medialorbitofrontal_volume" = LL_Q_RFX.pmat[13],
"lh_middletemporal_volume" = LL_Q_RFX.pmat[14],
"lh_parahippocampal_volume" = LL_Q_RFX.pmat[15],
"lh_paracentral_volume" = LL_Q_RFX.pmat[16],
"lh_parsopercularis_volume" = LL_Q_RFX.pmat[17],
"lh_parsorbitalis_volume" = LL_Q_RFX.pmat[18],
"lh_parstriangularis_volume" = LL_Q_RFX.pmat[19],
"lh_pericalcarine_volume" = LL_Q_RFX.pmat[20],
"lh_postcentral_volume" = LL_Q_RFX.pmat[21],
"lh_posteriorcingulate_volume" = LL_Q_RFX.pmat[22],
"lh_precentral_volume" = LL_Q_RFX.pmat[23],
"lh_precuneus_volume" = LL_Q_RFX.pmat[24],
"lh_rostralanteriorcingulate_volume" = LL_Q_RFX.pmat[25],
"lh_rostralmiddlefrontal_volume" = LL_Q_RFX.pmat[26],
"lh_superiorfrontal_volume" = LL_Q_RFX.pmat[27],
"lh_superiorparietal_volume" = LL_Q_RFX.pmat[28],
"lh_superiortemporal_volume" = LL_Q_RFX.pmat[29],
"lh_supramarginal_volume" = LL_Q_RFX.pmat[30],
"lh_frontalpole_volume" = LL_Q_RFX.pmat[31],
"lh_temporalpole_volume" = LL_Q_RFX.pmat[32],
"lh_transversetemporal_volume" = LL_Q_RFX.pmat[33],
"lh_insula_volume" = LL_Q_RFX.pmat[34],
"rh_bankssts_volume" = LL_Q_RFX.pmat[35],
"rh_caudalanteriorcingulate_volume" = LL_Q_RFX.pmat[36],
"rh_caudalmiddlefrontal_volume" = LL_Q_RFX.pmat[37],
"rh_cuneus_volume" = LL_Q_RFX.pmat[38],
"rh_entorhinal_volume" = LL_Q_RFX.pmat[39],
"rh_fusiform_volume" = LL_Q_RFX.pmat[40],
"rh_inferiorparietal_volume" = LL_Q_RFX.pmat[41],
"rh_inferiortemporal_volume" = LL_Q_RFX.pmat[42],
"rh_isthmuscingulate_volume" = LL_Q_RFX.pmat[43],
"rh_lateraloccipital_volume" = LL_Q_RFX.pmat[44],
"rh_lateralorbitofrontal_volume" = LL_Q_RFX.pmat[45],
"rh_lingual_volume" = LL_Q_RFX.pmat[46],
"rh_medialorbitofrontal_volume" = LL_Q_RFX.pmat[47],
"rh_middletemporal_volume" = LL_Q_RFX.pmat[48],
"rh_parahippocampal_volume" = LL_Q_RFX.pmat[49],
"rh_paracentral_volume" = LL_Q_RFX.pmat[50],
"rh_parsopercularis_volume" = LL_Q_RFX.pmat[51],
"rh_parsorbitalis_volume" = LL_Q_RFX.pmat[52],
"rh_parstriangularis_volume" = LL_Q_RFX.pmat[53],
"rh_pericalcarine_volume" = LL_Q_RFX.pmat[54],
"rh_postcentral_volume" = LL_Q_RFX.pmat[55],
"rh_posteriorcingulate_volume" = LL_Q_RFX.pmat[56],
"rh_precentral_volume" = LL_Q_RFX.pmat[57],
"rh_precuneus_volume" = LL_Q_RFX.pmat[58],
"rh_rostralanteriorcingulate_volume" = LL_Q_RFX.pmat[59],
"rh_rostralmiddlefrontal_volume" = LL_Q_RFX.pmat[60],
"rh_superiorfrontal_volume" = LL_Q_RFX.pmat[61],
"rh_superiorparietal_volume" = LL_Q_RFX.pmat[62],
"rh_superiortemporal_volume" = LL_Q_RFX.pmat[63],
"rh_supramarginal_volume" = LL_Q_RFX.pmat[64],
"rh_frontalpole_volume" = LL_Q_RFX.pmat[65],
"rh_temporalpole_volume" = LL_Q_RFX.pmat[66],
"rh_transversetemporal_volume" = LL_Q_RFX.pmat[67],
"rh_insula_volume" = LL_Q_RFX.pmat[68],
"Left_Cerebellum_Cortex" = LL_Q_RFX.pmat[69],
"Left_Thalamus_Proper" = LL_Q_RFX.pmat[70],
"Left_Caudate" = LL_Q_RFX.pmat[71],
"Left_Putamen" = LL_Q_RFX.pmat[72],
"Left_Pallidum" = LL_Q_RFX.pmat[73],
"Brain_Stem" = LL_Q_RFX.pmat[74],
"Left_Hippocampus" = LL_Q_RFX.pmat[75],
"Left_Amygdala" = LL_Q_RFX.pmat[76],
"Left_Accumbens_area" = LL_Q_RFX.pmat[77],
"Left_VentralDC" = LL_Q_RFX.pmat[78],
"Right_Cerebellum_Cortex" = LL_Q_RFX.pmat[79],
"Right_Thalamus_Proper" = LL_Q_RFX.pmat[80],
"Right_Caudate" = LL_Q_RFX.pmat[81],
"Right_Putamen" = LL_Q_RFX.pmat[82],
"Right_Pallidum" = LL_Q_RFX.pmat[83],
"Right_Hippocampus" = LL_Q_RFX.pmat[84],
"Right_Amygdala" = LL_Q_RFX.pmat[85],
"Right_Accumbens_area" = LL_Q_RFX.pmat[86],
"Right_VentralDC" = LL_Q_RFX.pmat[87],
"TotalGrayVol" = LL_Q_RFX.pmat[88]
)
# transpose
LL_Q_RFX.pvals<-t(LL_Q_RFX.pvals)
#save IS output
saveRDS(LL_Q_RFX.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LL.Q_RFX.pvals.Rds")
saveRDS(LLcompare_Q_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LLcompare_Q_RFX.Rds")
saveRDS(AICcompare_Q_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICccompare_Q_RFX.Rds")
saveRDS(BICcompare_Q_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICccompare_Q_RFX.Rds")
write.table(LL_Q_RFX.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LL.Q_RFX.pval.txt", quote = F, sep=",", col.names = F)
write.table(LLcompare_Q_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LLcompare_Q_RFX.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(AICcompare_Q_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICcompare_Q_RFX.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(BICcompare_Q_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/BICcompare_Q_RFX.txt", quote = F, sep=",", row.names = F, col.names = T)
}
#Linear_MODEL_RFX
{
# Linear_RFX_1
Linear_RFX_1.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim * age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_L_RFX_1 <- append(LL_L_RFX_1, logLik(Linear_RFX_1.outcomes[[outcome]], REML=T))
AIC_L_RFX_1 <- append(AIC_L_RFX_1, AICc(Linear_RFX_1.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_L_RFX_1 <- append(BIC_L_RFX_1, BICc(Linear_RFX_1.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# Linear_RFX_2
Linear_RFX_2.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim * age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_L_RFX_2 <- append(LL_L_RFX_2, logLik(Linear_RFX_2.outcomes[[outcome]], REML=T))
AIC_L_RFX_2 <- append(AIC_L_RFX_2, AICc(Linear_RFX_2.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_L_RFX_2 <- append(BIC_L_RFX_2, BICc(Linear_RFX_2.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# observe all intercept AICc in a table
AICcompare_L_RFX <- data.frame(DVlist, AIC_L_RFX_1, AIC_L_RFX_2)
BICcompare_L_RFX <- data.frame(DVlist, BIC_L_RFX_1, BIC_L_RFX_2)
LLcompare_L_RFX <- data.frame(DVlist, LL_L_RFX_1, LL_L_RFX_2)
# LL.FFX.pmat <- c()
for (outcome in 1:88) {
# extract p-values only
# do the interaction effect and the PE variable add anything to the null prediction model?
dif_L_RFX <- anova(Linear_RFX_1.outcomes[[outcome]], Linear_RFX_2.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL_L_RFX.pmat <- append(LL_L_RFX.pmat,dif_L_RFX[2,1])
}
# create a list of FFX model comparisons (M0_M2 = null compared to full, M0_M2B = null compared to reduced model, M2_M2B = full compared to reduced)
compared_L_RFX <- c("Linear_RFX1_Linear_RFX2")
# IS create a dataframe with outcome scales as rows, and 3 columns showing model comparisons
# Create a dataframe with the p-values for different variables
LL_L_RFX.pvals <- data.frame(
compared_L_RFX,
"lh_bankssts_volume" = LL_L_RFX.pmat[1],
"lh_caudalanteriorcingulate_volume" = LL_L_RFX.pmat[2],
"lh_caudalmiddlefrontal_volume" = LL_L_RFX.pmat[3],
"lh_cuneus_volume" = LL_L_RFX.pmat[4],
"lh_entorhinal_volume" = LL_L_RFX.pmat[5],
"lh_fusiform_volume" = LL_L_RFX.pmat[6],
"lh_inferiorparietal_volume" = LL_L_RFX.pmat[7],
"lh_inferiortemporal_volume" = LL_L_RFX.pmat[8],
"lh_isthmuscingulate_volume" = LL_L_RFX.pmat[9],
"lh_lateraloccipital_volume" = LL_L_RFX.pmat[10],
"lh_lateralorbitofrontal_volume" = LL_L_RFX.pmat[11],
"lh_lingual_volume" = LL_L_RFX.pmat[12],
"lh_medialorbitofrontal_volume" = LL_L_RFX.pmat[13],
"lh_middletemporal_volume" = LL_L_RFX.pmat[14],
"lh_parahippocampal_volume" = LL_L_RFX.pmat[15],
"lh_paracentral_volume" = LL_L_RFX.pmat[16],
"lh_parsopercularis_volume" = LL_L_RFX.pmat[17],
"lh_parsorbitalis_volume" = LL_L_RFX.pmat[18],
"lh_parstriangularis_volume" = LL_L_RFX.pmat[19],
"lh_pericalcarine_volume" = LL_L_RFX.pmat[20],
"lh_postcentral_volume" = LL_L_RFX.pmat[21],
"lh_posteriorcingulate_volume" = LL_L_RFX.pmat[22],
"lh_precentral_volume" = LL_L_RFX.pmat[23],
"lh_precuneus_volume" = LL_L_RFX.pmat[24],
"lh_rostralanteriorcingulate_volume" = LL_L_RFX.pmat[25],
"lh_rostralmiddlefrontal_volume" = LL_L_RFX.pmat[26],
"lh_superiorfrontal_volume" = LL_L_RFX.pmat[27],
"lh_superiorparietal_volume" = LL_L_RFX.pmat[28],
"lh_superiortemporal_volume" = LL_L_RFX.pmat[29],
"lh_supramarginal_volume" = LL_L_RFX.pmat[30],
"lh_frontalpole_volume" = LL_L_RFX.pmat[31],
"lh_temporalpole_volume" = LL_L_RFX.pmat[32],
"lh_transversetemporal_volume" = LL_L_RFX.pmat[33],
"lh_insula_volume" = LL_L_RFX.pmat[34],
"rh_bankssts_volume" = LL_L_RFX.pmat[35],
"rh_caudalanteriorcingulate_volume" = LL_L_RFX.pmat[36],
"rh_caudalmiddlefrontal_volume" = LL_L_RFX.pmat[37],
"rh_cuneus_volume" = LL_L_RFX.pmat[38],
"rh_entorhinal_volume" = LL_L_RFX.pmat[39],
"rh_fusiform_volume" = LL_L_RFX.pmat[40],
"rh_inferiorparietal_volume" = LL_L_RFX.pmat[41],
"rh_inferiortemporal_volume" = LL_L_RFX.pmat[42],
"rh_isthmuscingulate_volume" = LL_L_RFX.pmat[43],
"rh_lateraloccipital_volume" = LL_L_RFX.pmat[44],
"rh_lateralorbitofrontal_volume" = LL_L_RFX.pmat[45],
"rh_lingual_volume" = LL_L_RFX.pmat[46],
"rh_medialorbitofrontal_volume" = LL_L_RFX.pmat[47],
"rh_middletemporal_volume" = LL_L_RFX.pmat[48],
"rh_parahippocampal_volume" = LL_L_RFX.pmat[49],
"rh_paracentral_volume" = LL_L_RFX.pmat[50],
"rh_parsopercularis_volume" = LL_L_RFX.pmat[51],
"rh_parsorbitalis_volume" = LL_L_RFX.pmat[52],
"rh_parstriangularis_volume" = LL_L_RFX.pmat[53],
"rh_pericalcarine_volume" = LL_L_RFX.pmat[54],
"rh_postcentral_volume" = LL_L_RFX.pmat[55],
"rh_posteriorcingulate_volume" = LL_L_RFX.pmat[56],
"rh_precentral_volume" = LL_L_RFX.pmat[57],
"rh_precuneus_volume" = LL_L_RFX.pmat[58],
"rh_rostralanteriorcingulate_volume" = LL_L_RFX.pmat[59],
"rh_rostralmiddlefrontal_volume" = LL_L_RFX.pmat[60],
"rh_superiorfrontal_volume" = LL_L_RFX.pmat[61],
"rh_superiorparietal_volume" = LL_L_RFX.pmat[62],
"rh_superiortemporal_volume" = LL_L_RFX.pmat[63],
"rh_supramarginal_volume" = LL_L_RFX.pmat[64],
"rh_frontalpole_volume" = LL_L_RFX.pmat[65],
"rh_temporalpole_volume" = LL_L_RFX.pmat[66],
"rh_transversetemporal_volume" = LL_L_RFX.pmat[67],
"rh_insula_volume" = LL_L_RFX.pmat[68],
"Left_Cerebellum_Cortex" = LL_L_RFX.pmat[69],
"Left_Thalamus_Proper" = LL_L_RFX.pmat[70],
"Left_Caudate" = LL_L_RFX.pmat[71],
"Left_Putamen" = LL_L_RFX.pmat[72],
"Left_Pallidum" = LL_L_RFX.pmat[73],
"Brain_Stem" = LL_L_RFX.pmat[74],
"Left_Hippocampus" = LL_L_RFX.pmat[75],
"Left_Amygdala" = LL_L_RFX.pmat[76],
"Left_Accumbens_area" = LL_L_RFX.pmat[77],
"Left_VentralDC" = LL_L_RFX.pmat[78],
"Right_Cerebellum_Cortex" = LL_L_RFX.pmat[79],
"Right_Thalamus_Proper" = LL_L_RFX.pmat[80],
"Right_Caudate" = LL_L_RFX.pmat[81],
"Right_Putamen" = LL_L_RFX.pmat[82],
"Right_Pallidum" = LL_L_RFX.pmat[83],
"Right_Hippocampus" = LL_L_RFX.pmat[84],
"Right_Amygdala" = LL_L_RFX.pmat[85],
"Right_Accumbens_area" = LL_L_RFX.pmat[86],
"Right_VentralDC" = LL_L_RFX.pmat[87],
"TotalGrayVol" = LL_L_RFX.pmat[88]
)
# transpose
LL_L_RFX.pvals<-t(LL_L_RFX.pvals)
#save IS output
saveRDS(LL_L_RFX.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LL.L_RFX.pvals.Rds")
saveRDS(LLcompare_L_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LLcompare_L_RFX.Rds")
saveRDS(AICcompare_L_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICccompare_L_RFX.Rds")
saveRDS(BICcompare_L_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICccompare_L_RFX.Rds")
write.table(LL_L_RFX.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LL.L_RFX.pval.txt", quote = F, sep=",", col.names = F)
write.table(LLcompare_L_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LLcompare_L_RFX.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(AICcompare_L_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICcompare_L_RFX.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(BICcompare_L_RFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/BICcompare_L_RFX.txt", quote = F, sep=",", row.names = F, col.names = T)
}
#Quad_MODEL_FFX
{
# Null Quad MODEL
Null_Quad.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_FFX_null_Q <- append(LL_FFX_null_Q, logLik(Null_Quad.outcomes[[outcome]], REML=T))
AIC_FFX_null_Q <- append(AIC_FFX_null_Q, AICc(Null_Quad.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_FFX_null_Q <- append(BIC_FFX_null_Q, BICc(Null_Quad.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# Quad_Simple MODEL 1
Quad_simple.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_FFX_simple_Q <- append(LL_FFX_simple_Q, logLik(Quad_simple.outcomes[[outcome]], REML=T))
AIC_FFX_simple_Q <- append(AIC_FFX_simple_Q, AICc(Quad_simple.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_FFX_simple_Q <- append(BIC_FFX_simple_Q, BICc(Null_Quad.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# Quad MODEL 1
Quad_complex.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_FFX_complex_Q <- append(LL_FFX_complex_Q, logLik(Quad_complex.outcomes[[outcome]], REML=T))
AIC_FFX_complex_Q <- append(AIC_FFX_complex_Q, AICc(Quad_complex.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_FFX_complex_Q <- append(BIC_FFX_complex_Q, BICc(Null_Quad.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# observe all log likelihoods in a table
LLcompare_Q_FFX <- data.frame(DVlist, LL_FFX_null_Q, LL_FFX_simple_Q, LL_FFX_complex_Q)
AICcompare_Q_FFX <- data.frame(DVlist, AIC_FFX_null_Q, AIC_FFX_simple_Q, AIC_FFX_complex_Q)
BICcompare_Q_FFX <- data.frame(DVlist, BIC_FFX_null_Q, BIC_FFX_simple_Q, BIC_FFX_complex_Q)
compared_FFX_IS_Q <- c("Null_Simple", "Null_Complex", "Simple_Complex")
# LL.FFX.pmat <- c()
for (outcome in 1:88) {
# extract p-values only
# do the interaction effect and the PE variable add anything to the null prediction model?
dif.0.2_FFX_Q <- anova(Null_Quad.outcomes[[outcome]], Quad_simple.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_Q.pmat <- append(LL.FFX_Q.pmat,dif.0.2_FFX_Q[2,1])
dif.0.2B_FFX_Q <- anova(Null_Quad.outcomes[[outcome]], Quad_complex.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_Q.pmat <- append(LL.FFX_Q.pmat,dif.0.2B_FFX_Q[2,1])
dif.2.2B_FFX_Q <- anova(Quad_simple.outcomes[[outcome]], Quad_complex.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_Q.pmat <- append(LL.FFX_Q.pmat,dif.2.2B_FFX_Q[2,1])
}
LL.FFX_Q.pvals <- data.frame(
compared_FFX_IS_Q,
"lh_bankssts_volume" = LL.FFX_Q.pmat[1:3],
"lh_caudalanteriorcingulate_volume" = LL.FFX_Q.pmat[4:6],
"lh_caudalmiddlefrontal_volume" = LL.FFX_Q.pmat[7:9],
"lh_cuneus_volume" = LL.FFX_Q.pmat[10:12],
"lh_entorhinal_volume" = LL.FFX_Q.pmat[13:15],
"lh_fusiform_volume" = LL.FFX_Q.pmat[16:18],
"lh_inferiorparietal_volume" = LL.FFX_Q.pmat[19:21],
"lh_inferiortemporal_volume" = LL.FFX_Q.pmat[22:24],
"lh_isthmuscingulate_volume" = LL.FFX_Q.pmat[25:27],
"lh_lateraloccipital_volume" = LL.FFX_Q.pmat[28:30],
"lh_lateralorbitofrontal_volume" = LL.FFX_Q.pmat[31:33],
"lh_lingual_volume" = LL.FFX_Q.pmat[34:36],
"lh_medialorbitofrontal_volume" = LL.FFX_Q.pmat[37:39],
"lh_middletemporal_volume" = LL.FFX_Q.pmat[40:42],
"lh_parahippocampal_volume" = LL.FFX_Q.pmat[43:45],
"lh_paracentral_volume" = LL.FFX_Q.pmat[46:48],
"lh_parsopercularis_volume" = LL.FFX_Q.pmat[49:51],
"lh_parsorbitalis_volume" = LL.FFX_Q.pmat[52:54],
"lh_parstriangularis_volume" = LL.FFX_Q.pmat[55:57],
"lh_pericalcarine_volume" = LL.FFX_Q.pmat[58:60],
"lh_postcentral_volume" = LL.FFX_Q.pmat[61:63],
"lh_posteriorcingulate_volume" = LL.FFX_Q.pmat[64:66],
"lh_precentral_volume" = LL.FFX_Q.pmat[67:69],
"lh_precuneus_volume" = LL.FFX_Q.pmat[70:72],
"lh_rostralanteriorcingulate_volume" = LL.FFX_Q.pmat[73:75],
"lh_rostralmiddlefrontal_volume" = LL.FFX_Q.pmat[76:78],
"lh_superiorfrontal_volume" = LL.FFX_Q.pmat[79:81],
"lh_superiorparietal_volume" = LL.FFX_Q.pmat[82:84],
"lh_superiortemporal_volume" = LL.FFX_Q.pmat[85:87],
"lh_supramarginal_volume" = LL.FFX_Q.pmat[88:90],
"lh_frontalpole_volume" = LL.FFX_Q.pmat[91:93],
"lh_temporalpole_volume" = LL.FFX_Q.pmat[94:96],
"lh_transversetemporal_volume" = LL.FFX_Q.pmat[97:99],
"lh_insula_volume" = LL.FFX_Q.pmat[100:102],
"rh_bankssts_volume" = LL.FFX_Q.pmat[103:105],
"rh_caudalanteriorcingulate_volume" = LL.FFX_Q.pmat[106:108],
"rh_caudalmiddlefrontal_volume" = LL.FFX_Q.pmat[109:111],
"rh_cuneus_volume" = LL.FFX_Q.pmat[112:114],
"rh_entorhinal_volume" = LL.FFX_Q.pmat[115:117],
"rh_fusiform_volume" = LL.FFX_Q.pmat[118:120],
"rh_inferiorparietal_volume" = LL.FFX_Q.pmat[121:123],
"rh_inferiortemporal_volume" = LL.FFX_Q.pmat[124:126],
"rh_isthmuscingulate_volume" = LL.FFX_Q.pmat[127:129],
"rh_lateraloccipital_volume" = LL.FFX_Q.pmat[130:132],
"rh_lateralorbitofrontal_volume" = LL.FFX_Q.pmat[133:135],
"rh_lingual_volume" = LL.FFX_Q.pmat[136:138],
"rh_medialorbitofrontal_volume" = LL.FFX_Q.pmat[139:141],
"rh_middletemporal_volume" = LL.FFX_Q.pmat[142:144],
"rh_parahippocampal_volume" = LL.FFX_Q.pmat[145:147],
"rh_paracentral_volume" = LL.FFX_Q.pmat[148:150],
"rh_parsopercularis_volume" = LL.FFX_Q.pmat[151:153],
"rh_parsorbitalis_volume" = LL.FFX_Q.pmat[154:156],
"rh_parstriangularis_volume" = LL.FFX_Q.pmat[157:159],
"rh_pericalcarine_volume" = LL.FFX_Q.pmat[160:162],
"rh_postcentral_volume" = LL.FFX_Q.pmat[163:165],
"rh_posteriorcingulate_volume" = LL.FFX_Q.pmat[166:168],
"rh_precentral_volume" = LL.FFX_Q.pmat[169:171],
"rh_precuneus_volume" = LL.FFX_Q.pmat[172:174],
"rh_rostralanteriorcingulate_volume" = LL.FFX_Q.pmat[175:177],
"rh_rostralmiddlefrontal_volume" = LL.FFX_Q.pmat[178:180],
"rh_superiorfrontal_volume" = LL.FFX_Q.pmat[181:183],
"rh_superiorparietal_volume" = LL.FFX_Q.pmat[184:186],
"rh_superiortemporal_volume" = LL.FFX_Q.pmat[187:189],
"rh_supramarginal_volume" = LL.FFX_Q.pmat[190:192],
"rh_frontalpole_volume" = LL.FFX_Q.pmat[193:195],
"rh_temporalpole_volume" = LL.FFX_Q.pmat[196:198],
"rh_transversetemporal_volume" = LL.FFX_Q.pmat[199:201],
"rh_insula_volume" = LL.FFX_Q.pmat[202:204],
"Left_Cerebellum_Cortex" = LL.FFX_Q.pmat[205:207],
"Left_Thalamus_Proper" = LL.FFX_Q.pmat[208:210],
"Left_Caudate" = LL.FFX_Q.pmat[211:213],
"Left_Putamen" = LL.FFX_Q.pmat[214:216],
"Left_Pallidum" = LL.FFX_Q.pmat[217:219],
"Brain_Stem" = LL.FFX_Q.pmat[220:222],
"Left_Hippocampus" = LL.FFX_Q.pmat[223:225],
"Left_Amygdala" = LL.FFX_Q.pmat[226:228],
"Left_Accumbens_area" = LL.FFX_Q.pmat[229:231],
"Left_VentralDC" = LL.FFX_Q.pmat[232:234],
"Right_Cerebellum_Cortex" = LL.FFX_Q.pmat[235:237],
"Right_Thalamus_Proper" = LL.FFX_Q.pmat[238:240],
"Right_Caudate" = LL.FFX_Q.pmat[241:243],
"Right_Putamen" = LL.FFX_Q.pmat[244:246],
"Right_Pallidum" = LL.FFX_Q.pmat[247:249],
"Right_Hippocampus" = LL.FFX_Q.pmat[250:252],
"Right_Amygdala" = LL.FFX_Q.pmat[253:255],
"Right_Accumbens_area" = LL.FFX_Q.pmat[256:258],
"Right_VentralDC" = LL.FFX_Q.pmat[259:261],
"TotalGrayVol" = LL.FFX_Q.pmat[262:264]
)
LL.FFX_Q.pvals<-t(LL.FFX_Q.pvals)
#save IS output
saveRDS(LL.FFX_Q.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LL.Q_FFX.pvals.Rds")
saveRDS(LLcompare_Q_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LLcompare_Q_FFX.Rds")
saveRDS(AICcompare_Q_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICcompare_Q_FFX.Rds")
saveRDS(BICcompare_Q_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/BICcompare_Q_FFX.Rds")
write.table(LL.FFX_Q.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LL.Q_FFX.pval.txt", quote = F, sep=",", col.names = F)
write.table(LLcompare_Q_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LLcompare_Q_FFX.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(AICcompare_Q_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICcompare_Q_FFX.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(BICcompare_Q_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/BICcompare_Q_FFX.txt", quote = F, sep=",", row.names = F, col.names = T)
}
#Linear_MODEL_FFX
{
# Null Linear MODEL
Null_Linear.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_FFX_null_L <- append(LL_FFX_null_L, logLik(Null_Linear.outcomes[[outcome]], REML=T))
AIC_FFX_null_L <- append(AIC_FFX_null_L, AICc(Null_Linear.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_FFX_null_L <- append(BIC_FFX_null_L, BICc(Null_Linear.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# Linear_Simple MODEL 1
Linear_simple.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim + age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_FFX_simple_L <- append(LL_FFX_simple_L, logLik(Linear_simple.outcomes[[outcome]], REML=T))
AIC_FFX_simple_L <- append(AIC_FFX_simple_L, AICc(Linear_simple.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_FFX_simple_L <- append(BIC_FFX_simple_L, BICc(Null_Linear.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# Linear MODEL 1
Linear_complex.outcomes <- lapply(DVlist, function(x) {
lme4::lmer(substitute(i ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), list(i = as.name(x))), data=IMAGEN_bully, control = lmerControl(optimizer ="bobyqa"))})
for (outcome in 1:88) {
LL_FFX_complex_L <- append(LL_FFX_complex_L, logLik(Linear_complex.outcomes[[outcome]], REML=T))
AIC_FFX_complex_L <- append(AIC_FFX_complex_L, AICc(Linear_complex.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
BIC_FFX_complex_L <- append(BIC_FFX_complex_L, BICc(Null_Linear.outcomes[[outcome]], second.ord = TRUE, refit = TRUE))
}
# observe all log likelihoods in a table
LLcompare_L_FFX <- data.frame(DVlist, LL_FFX_null_L, LL_FFX_simple_L, LL_FFX_complex_L)
AICcompare_L_FFX <- data.frame(DVlist, AIC_FFX_null_L, AIC_FFX_simple_L, AIC_FFX_complex_L)
BICcompare_L_FFX <- data.frame(DVlist, BIC_FFX_null_L, BIC_FFX_simple_L, BIC_FFX_complex_L)
compared_FFX_L <- c("Null_Simple", "Null_Complex", "Simple_Complex")
# LL.FFX.pmat <- c()
for (outcome in 1:88) {
# extract p-values only
# do the interaction effect and the PE variable add anything to the null prediction model?
dif.0.2_FFX_L <- anova(Null_Linear.outcomes[[outcome]], Linear_simple.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_L.pmat <- append(LL.FFX_L.pmat,dif.0.2_FFX_L[2,1])
dif.0.2B_FFX_L <- anova(Null_Linear.outcomes[[outcome]], Linear_complex.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_L.pmat <- append(LL.FFX_L.pmat,dif.0.2B_FFX_L[2,1])
dif.2.2B_FFX_L <- anova(Linear_simple.outcomes[[outcome]], Linear_complex.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_L.pmat <- append(LL.FFX_L.pmat,dif.2.2B_FFX_L[2,1])
}
LL.FFX_L.pvals <- data.frame(
compared_FFX_L,
"lh_bankssts_volume" = LL.FFX_L.pmat[1:3],
"lh_caudalanteriorcingulate_volume" = LL.FFX_L.pmat[4:6],
"lh_caudalmiddlefrontal_volume" = LL.FFX_L.pmat[7:9],
"lh_cuneus_volume" = LL.FFX_L.pmat[10:12],
"lh_entorhinal_volume" = LL.FFX_L.pmat[13:15],
"lh_fusiform_volume" = LL.FFX_L.pmat[16:18],
"lh_inferiorparietal_volume" = LL.FFX_L.pmat[19:21],
"lh_inferiortemporal_volume" = LL.FFX_L.pmat[22:24],
"lh_isthmuscingulate_volume" = LL.FFX_L.pmat[25:27],
"lh_lateraloccipital_volume" = LL.FFX_L.pmat[28:30],
"lh_lateralorbitofrontal_volume" = LL.FFX_L.pmat[31:33],
"lh_lingual_volume" = LL.FFX_L.pmat[34:36],
"lh_medialorbitofrontal_volume" = LL.FFX_L.pmat[37:39],
"lh_middletemporal_volume" = LL.FFX_L.pmat[40:42],
"lh_parahippocampal_volume" = LL.FFX_L.pmat[43:45],
"lh_paracentral_volume" = LL.FFX_L.pmat[46:48],
"lh_parsopercularis_volume" = LL.FFX_L.pmat[49:51],
"lh_parsorbitalis_volume" = LL.FFX_L.pmat[52:54],
"lh_parstriangularis_volume" = LL.FFX_L.pmat[55:57],
"lh_pericalcarine_volume" = LL.FFX_L.pmat[58:60],
"lh_postcentral_volume" = LL.FFX_L.pmat[61:63],
"lh_posteriorcingulate_volume" = LL.FFX_L.pmat[64:66],
"lh_precentral_volume" = LL.FFX_L.pmat[67:69],
"lh_precuneus_volume" = LL.FFX_L.pmat[70:72],
"lh_rostralanteriorcingulate_volume" = LL.FFX_L.pmat[73:75],
"lh_rostralmiddlefrontal_volume" = LL.FFX_L.pmat[76:78],
"lh_superiorfrontal_volume" = LL.FFX_L.pmat[79:81],
"lh_superiorparietal_volume" = LL.FFX_L.pmat[82:84],
"lh_superiortemporal_volume" = LL.FFX_L.pmat[85:87],
"lh_supramarginal_volume" = LL.FFX_L.pmat[88:90],
"lh_frontalpole_volume" = LL.FFX_L.pmat[91:93],
"lh_temporalpole_volume" = LL.FFX_L.pmat[94:96],
"lh_transversetemporal_volume" = LL.FFX_L.pmat[97:99],
"lh_insula_volume" = LL.FFX_L.pmat[100:102],
"rh_bankssts_volume" = LL.FFX_L.pmat[103:105],
"rh_caudalanteriorcingulate_volume" = LL.FFX_L.pmat[106:108],
"rh_caudalmiddlefrontal_volume" = LL.FFX_L.pmat[109:111],
"rh_cuneus_volume" = LL.FFX_L.pmat[112:114],
"rh_entorhinal_volume" = LL.FFX_L.pmat[115:117],
"rh_fusiform_volume" = LL.FFX_L.pmat[118:120],
"rh_inferiorparietal_volume" = LL.FFX_L.pmat[121:123],
"rh_inferiortemporal_volume" = LL.FFX_L.pmat[124:126],
"rh_isthmuscingulate_volume" = LL.FFX_L.pmat[127:129],
"rh_lateraloccipital_volume" = LL.FFX_L.pmat[130:132],
"rh_lateralorbitofrontal_volume" = LL.FFX_L.pmat[133:135],
"rh_lingual_volume" = LL.FFX_L.pmat[136:138],
"rh_medialorbitofrontal_volume" = LL.FFX_L.pmat[139:141],
"rh_middletemporal_volume" = LL.FFX_L.pmat[142:144],
"rh_parahippocampal_volume" = LL.FFX_L.pmat[145:147],
"rh_paracentral_volume" = LL.FFX_L.pmat[148:150],
"rh_parsopercularis_volume" = LL.FFX_L.pmat[151:153],
"rh_parsorbitalis_volume" = LL.FFX_L.pmat[154:156],
"rh_parstriangularis_volume" = LL.FFX_L.pmat[157:159],
"rh_pericalcarine_volume" = LL.FFX_L.pmat[160:162],
"rh_postcentral_volume" = LL.FFX_L.pmat[163:165],
"rh_posteriorcingulate_volume" = LL.FFX_L.pmat[166:168],
"rh_precentral_volume" = LL.FFX_L.pmat[169:171],
"rh_precuneus_volume" = LL.FFX_L.pmat[172:174],
"rh_rostralanteriorcingulate_volume" = LL.FFX_L.pmat[175:177],
"rh_rostralmiddlefrontal_volume" = LL.FFX_L.pmat[178:180],
"rh_superiorfrontal_volume" = LL.FFX_L.pmat[181:183],
"rh_superiorparietal_volume" = LL.FFX_L.pmat[184:186],
"rh_superiortemporal_volume" = LL.FFX_L.pmat[187:189],
"rh_supramarginal_volume" = LL.FFX_L.pmat[190:192],
"rh_frontalpole_volume" = LL.FFX_L.pmat[193:195],
"rh_temporalpole_volume" = LL.FFX_L.pmat[196:198],
"rh_transversetemporal_volume" = LL.FFX_L.pmat[199:201],
"rh_insula_volume" = LL.FFX_L.pmat[202:204],
"Left_Cerebellum_Cortex" = LL.FFX_L.pmat[205:207],
"Left_Thalamus_Proper" = LL.FFX_L.pmat[208:210],
"Left_Caudate" = LL.FFX_L.pmat[211:213],
"Left_Putamen" = LL.FFX_L.pmat[214:216],
"Left_Pallidum" = LL.FFX_L.pmat[217:219],
"Brain_Stem" = LL.FFX_L.pmat[220:222],
"Left_Hippocampus" = LL.FFX_L.pmat[223:225],
"Left_Amygdala" = LL.FFX_L.pmat[226:228],
"Left_Accumbens_area" = LL.FFX_L.pmat[229:231],
"Left_VentralDC" = LL.FFX_L.pmat[232:234],
"Right_Cerebellum_Cortex" = LL.FFX_L.pmat[235:237],
"Right_Thalamus_Proper" = LL.FFX_L.pmat[238:240],
"Right_Caudate" = LL.FFX_L.pmat[241:243],
"Right_Putamen" = LL.FFX_L.pmat[244:246],
"Right_Pallidum" = LL.FFX_L.pmat[247:249],
"Right_Hippocampus" = LL.FFX_L.pmat[250:252],
"Right_Amygdala" = LL.FFX_L.pmat[253:255],
"Right_Accumbens_area" = LL.FFX_L.pmat[256:258],
"Right_VentralDC" = LL.FFX_L.pmat[259:261],
"TotalGrayVol" = LL.FFX_L.pmat[262:264]
)
LL.FFX_L.pvals<-t(LL.FFX_L.pvals)
#save IS output
saveRDS(LL.FFX_L.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LL.FFX_L.pvals.Rds")
saveRDS(LLcompare_L_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LLcompare_L_FFX.Rds")
saveRDS(AICcompare_L_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICcompare_L_FFX.Rds")
saveRDS(BICcompare_L_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/BICcompare_L_FFX.Rds")
write.table(LL.FFX_L.pvals, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LL.FFX_L.pvals.txt", quote = F, sep=",", col.names = F)
write.table(LLcompare_L_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/LLcompare_L_FFX.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(AICcompare_L_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/AICcompare_L_FFX.txt", quote = F, sep=",", row.names = F, col.names = T)
write.table(BICcompare_L_FFX, file = "/Users/michaelconnaughton/Desktop/IMAGEN/Model_Performance_results/BICcompare_L_FFX.txt", quote = F, sep=",", row.names = F, col.names = T)
# observe all log likelihoods in a table
LLcompare_Quad_model <- data.frame(DVlist, LL_FFX_simple_1, LL_FFX_complex_1)
# observe all AICc in a table
AICccompare_Quad_model <- data.frame(DVlist, AIC_FFX_simple_1, AIC_FFX_complex_1)
compared_FFX_IS_Q <- c("Null_Simple", "Null_Complex", "Simple_Complex")
# LL.FFX.pmat <- c()
for (outcome in 1:88) {
# extract p-values only
# do the interaction effect and the PE variable add anything to the null prediction model?
dif.0.2_IS_Q <- anova(Null_Quad.outcomes[[outcome]], Quad_simple.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_IS_Q.pmat <- append(LL.FFX_IS_Q.pmat,dif.0.2_IS_Q[2,1])
dif.0.2B_IS_Q <- anova(Null_Quad.outcomes[[outcome]], Quad_complex.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_IS_Q.pmat <- append(LL.FFX_IS_Q.pmat,dif.0.2B_IS_Q[2,1])
dif.2.2B_IS_Q <- anova(Quad_simple.outcomes[[outcome]], Quad_complex.outcomes[[outcome]], refit = TRUE)["Pr(>Chisq)"]
LL.FFX_IS_Q.pmat <- append(LL.FFX_IS_Q.pmat,dif.2.2B_IS_Q[2,1])
}
}
#LL.FFX_IS_Q.pvals_DATA_FRAME
{
LL.FFX_IS_Q.pvals <- data.frame(
compared_FFX_IS_Q,
"lh_bankssts_volume" = LL.FFX_IS_Q.pmat[1:3],
"lh_caudalanteriorcingulate_volume" = LL.FFX_IS_Q.pmat[4:6],
"lh_caudalmiddlefrontal_volume" = LL.FFX_IS_Q.pmat[7:9],
"lh_cuneus_volume" = LL.FFX_IS_Q.pmat[10:12],
"lh_entorhinal_volume" = LL.FFX_IS_Q.pmat[13:15],
"lh_fusiform_volume" = LL.FFX_IS_Q.pmat[16:18],
"lh_inferiorparietal_volume" = LL.FFX_IS_Q.pmat[19:21],
"lh_inferiortemporal_volume" = LL.FFX_IS_Q.pmat[22:24],
"lh_isthmuscingulate_volume" = LL.FFX_IS_Q.pmat[25:27],
"lh_lateraloccipital_volume" = LL.FFX_IS_Q.pmat[28:30],
"lh_lateralorbitofrontal_volume" = LL.FFX_IS_Q.pmat[31:33],
"lh_lingual_volume" = LL.FFX_IS_Q.pmat[34:36],
"lh_medialorbitofrontal_volume" = LL.FFX_IS_Q.pmat[37:39],
"lh_middletemporal_volume" = LL.FFX_IS_Q.pmat[40:42],
"lh_parahippocampal_volume" = LL.FFX_IS_Q.pmat[43:45],
"lh_paracentral_volume" = LL.FFX_IS_Q.pmat[46:48],
"lh_parsopercularis_volume" = LL.FFX_IS_Q.pmat[49:51],
"lh_parsorbitalis_volume" = LL.FFX_IS_Q.pmat[52:54],
"lh_parstriangularis_volume" = LL.FFX_IS_Q.pmat[55:57],
"lh_pericalcarine_volume" = LL.FFX_IS_Q.pmat[58:60],
"lh_postcentral_volume" = LL.FFX_IS_Q.pmat[61:63],
"lh_posteriorcingulate_volume" = LL.FFX_IS_Q.pmat[64:66],
"lh_precentral_volume" = LL.FFX_IS_Q.pmat[67:69],
"lh_precuneus_volume" = LL.FFX_IS_Q.pmat[70:72],
"lh_rostralanteriorcingulate_volume" = LL.FFX_IS_Q.pmat[73:75],
"lh_rostralmiddlefrontal_volume" = LL.FFX_IS_Q.pmat[76:78],
"lh_superiorfrontal_volume" = LL.FFX_IS_Q.pmat[79:81],
"lh_superiorparietal_volume" = LL.FFX_IS_Q.pmat[82:84],
"lh_superiortemporal_volume" = LL.FFX_IS_Q.pmat[85:87],
"lh_supramarginal_volume" = LL.FFX_IS_Q.pmat[88:90],
"lh_frontalpole_volume" = LL.FFX_IS_Q.pmat[91:93],
"lh_temporalpole_volume" = LL.FFX_IS_Q.pmat[94:96],
"lh_transversetemporal_volume" = LL.FFX_IS_Q.pmat[97:99],
"lh_insula_volume" = LL.FFX_IS_Q.pmat[100:102],
"rh_bankssts_volume" = LL.FFX_IS_Q.pmat[103:105],
"rh_caudalanteriorcingulate_volume" = LL.FFX_IS_Q.pmat[106:108],
"rh_caudalmiddlefrontal_volume" = LL.FFX_IS_Q.pmat[109:111],
"rh_cuneus_volume" = LL.FFX_IS_Q.pmat[112:114],
"rh_entorhinal_volume" = LL.FFX_IS_Q.pmat[115:117],
"rh_fusiform_volume" = LL.FFX_IS_Q.pmat[118:120],
"rh_inferiorparietal_volume" = LL.FFX_IS_Q.pmat[121:123],
"rh_inferiortemporal_volume" = LL.FFX_IS_Q.pmat[124:126],
"rh_isthmuscingulate_volume" = LL.FFX_IS_Q.pmat[127:129],
"rh_lateraloccipital_volume" = LL.FFX_IS_Q.pmat[130:132],
"rh_lateralorbitofrontal_volume" = LL.FFX_IS_Q.pmat[133:135],
"rh_lingual_volume" = LL.FFX_IS_Q.pmat[136:138],
"rh_medialorbitofrontal_volume" = LL.FFX_IS_Q.pmat[139:141],
"rh_middletemporal_volume" = LL.FFX_IS_Q.pmat[142:144],
"rh_parahippocampal_volume" = LL.FFX_IS_Q.pmat[145:147],
"rh_paracentral_volume" = LL.FFX_IS_Q.pmat[148:150],
"rh_parsopercularis_volume" = LL.FFX_IS_Q.pmat[151:153],
"rh_parsorbitalis_volume" = LL.FFX_IS_Q.pmat[154:156],
"rh_parstriangularis_volume" = LL.FFX_IS_Q.pmat[157:159],
"rh_pericalcarine_volume" = LL.FFX_IS_Q.pmat[160:162],
"rh_postcentral_volume" = LL.FFX_IS_Q.pmat[163:165],
"rh_posteriorcingulate_volume" = LL.FFX_IS_Q.pmat[166:168],
"rh_precentral_volume" = LL.FFX_IS_Q.pmat[169:171],
"rh_precuneus_volume" = LL.FFX_IS_Q.pmat[172:174],
"rh_rostralanteriorcingulate_volume" = LL.FFX_IS_Q.pmat[175:177],
"rh_rostralmiddlefrontal_volume" = LL.FFX_IS_Q.pmat[178:180],
"rh_superiorfrontal_volume" = LL.FFX_IS_Q.pmat[181:183],
"rh_superiorparietal_volume" = LL.FFX_IS_Q.pmat[184:186],
"rh_superiortemporal_volume" = LL.FFX_IS_Q.pmat[187:189],
"rh_supramarginal_volume" = LL.FFX_IS_Q.pmat[190:192],
"rh_frontalpole_volume" = LL.FFX_IS_Q.pmat[193:195],
"rh_temporalpole_volume" = LL.FFX_IS_Q.pmat[196:198],
"rh_transversetemporal_volume" = LL.FFX_IS_Q.pmat[199:201],
"rh_insula_volume" = LL.FFX_IS_Q.pmat[202:204],
"Left_Cerebellum_Cortex" = LL.FFX_IS_Q.pmat[205:207],
"Left_Thalamus_Proper" = LL.FFX_IS_Q.pmat[208:210],
"Left_Caudate" = LL.FFX_IS_Q.pmat[211:213],
"Left_Putamen" = LL.FFX_IS_Q.pmat[214:216],
"Left_Pallidum" = LL.FFX_IS_Q.pmat[217:219],
"Brain_Stem" = LL.FFX_IS_Q.pmat[220:222],
"Left_Hippocampus" = LL.FFX_IS_Q.pmat[223:225],
"Left_Amygdala" = LL.FFX_IS_Q.pmat[226:228],
"Left_Accumbens_area" = LL.FFX_IS_Q.pmat[229:231],
"Left_VentralDC" = LL.FFX_IS_Q.pmat[232:234],
"Right_Cerebellum_Cortex" = LL.FFX_IS_Q.pmat[235:237],
"Right_Thalamus_Proper" = LL.FFX_IS_Q.pmat[238:240],
"Right_Caudate" = LL.FFX_IS_Q.pmat[241:243],
"Right_Putamen" = LL.FFX_IS_Q.pmat[244:246],
"Right_Pallidum" = LL.FFX_IS_Q.pmat[247:249],
"Right_Hippocampus" = LL.FFX_IS_Q.pmat[250:252],
"Right_Amygdala" = LL.FFX_IS_Q.pmat[253:255],
"Right_Accumbens_area" = LL.FFX_IS_Q.pmat[256:258],
"Right_VentralDC" = LL.FFX_IS_Q.pmat[259:261],
"TotalGrayVol" = LL.FFX_IS_Q.pmat[262:264]
)
LL.FFX_IS_Q.pvals<-t(LL.FFX_IS_Q.pvals)
}
#Get Model Metrics
IMAGEN_bully$MatrixReasoning_Baseline <- scale(IMAGEN_bully$MatrixReasoning_Baseline)
IMAGEN_bully$bmi_Baseline <- scale(IMAGEN_bully$bmi_Baseline)
{
Linear_complex_results <- list()
# Loop through each DV
for (dv in DVlist) {
# Construct the formula
formula <- as.formula(paste(dv, "~ bully_victim + age + MatrixReasoning_Baseline + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site)"))
# Fit the model
model <- lmer(formula, data=IMAGEN_bully, REML=T, control=lmerControl(optimizer="bobyqa"))
# Get summary
model_summary <- summary(model)
# Extract estimates, standard errors, t-values, and p-values
coefficients <- data.frame(Effect = rownames(model_summary$coefficients),
Estimate = model_summary$coefficients[, "Estimate"],
Std_Error = model_summary$coefficients[, "Std. Error"],
T_Value = model_summary$coefficients[, "t value"],
P_Value = model_summary$coefficients[, "Pr(>|t|)"])
# Store results
Linear_complex_results[[dv]] <- coefficients
# Combine results into a single data frame
final_results <- bind_rows(Linear_complex_results, .id = "DV")
# Export results to a CSV file (can be opened in Word)
write.csv(final_results, "/Users/michaelconnaughton/Desktop/IMAGEN/sensitivity/Linear_simple_MatrixReasoning_Baseline.csv", row.names = FALSE)
}
}
#No RFX Models
{
Left_Thalamus_Proper_null <- lmer(Left_Thalamus_Proper ~ age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data=IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer ="bobyqa"))
Left_Thalamus_Proper_simple <- lmer(Left_Thalamus_Proper ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data=IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer ="bobyqa"))
Left_Thalamus_Proper_complex <- lmer(Left_Thalamus_Proper ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data=IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer ="bobyqa"))
lrtest(Left_Thalamus_Proper_simple, Left_Thalamus_Proper_null)
lrtest(Left_Thalamus_Proper_complex, Left_Thalamus_Proper_null)
lrtest(Left_Thalamus_Proper_simple, Left_Thalamus_Proper_complex)
BIC(Left_Thalamus_Proper_null)
BIC(Left_Thalamus_Proper_simple)
BIC(Left_Thalamus_Proper_complex)
AIC(Left_Thalamus_Proper_null)
AIC(Left_Thalamus_Proper_simple)
AIC(Left_Thalamus_Proper_complex)
#######
Right_Cerebellum_Cortex_null <- lmer(Right_Cerebellum_Cortex ~ age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data=IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer ="bobyqa"))
Right_Cerebellum_Cortex_simple <- lmer(Right_Cerebellum_Cortex ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data=IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer ="bobyqa"))
Right_Cerebellum_Cortex_complex <- lmer(Right_Cerebellum_Cortex ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data=IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer ="bobyqa"))
lrtest(Right_Cerebellum_Cortex_simple, Right_Cerebellum_Cortex_null)
lrtest(Right_Cerebellum_Cortex_complex, Right_Cerebellum_Cortex_null)
lrtest(Right_Cerebellum_Cortex_simple, Right_Cerebellum_Cortex_complex)
BIC(Right_Cerebellum_Cortex_null)
BIC(Right_Cerebellum_Cortex_simple)
BIC(Right_Cerebellum_Cortex_complex)
AIC(Right_Cerebellum_Cortex_null)
AIC(Right_Cerebellum_Cortex_simple)
AIC(Right_Cerebellum_Cortex_complex)
}
#Post Hoc Pval
{
# Numbers from the given list
Quad_complex_bully_age <- c(0.505623701, 2.45e-06, 0.198773759, 4.89e-05, 1.58e-05, 0.265626004, 0.197911997, 0.021335117, 0.537914697, 1.77e-06, 0.277460992, 0.199783815, 4.74e-05, 0.450645531, 0.006041167, 0.471654501, 0.212289872, 0.000591639, 0.715736216, 0.000452553, 0.636675949, 0.118869511, 0.815526581, 0.770498423, 0.03438106, 0.31982337, 0.783639694, 0.212788264, 0.200641608, 0.000805438, 1.41e-06, 0.850931688, 0.621632386, 1.09e-12, 3.39e-05, 5.79e-05, 0.850253806, 1.47e-07, 6.31e-12, 0.071025554, 0.057351558, 0.986682604, 0.035022542, 3.23e-09, 0.134260495, 2.42e-05, 7.85e-08, 0.10010147, 0.000469328, 0.131948909, 0.423869214, 0.099990689, 0.125202078, 0.025319366, 0.870933767, 0.281587122, 0.242311931, 0.043334594, 0.142890518, 0.585764865, 0.081953139, 0.656114872, 0.920863021, 0.814207777, 3.83e-07, 0.00371681, 0.490274083, 1.37e-09, 4.80e-20, 0.001678205, 0.003121806, 2.48e-26, 1.12e-12, 0.734695633, 0.000721844, 0.000951893, 1.16e-05, 1.28e-09, 1.12e-08, 0.324926341, 0.000109885, 2.10e-24, 1.30e-09, 2.18e-09, 0.000358824, 0.191677499, 0.021657286, 0.182665553)
Quad_complex_bully <- c(0.959560151, 0.026494022, 0.010017234, 0.018973174, 1.95e-05, 0.101492471, 0.785362659, 0.152013468, 0.967615577, 9.73e-08, 0.010649991, 0.029233599, 5.75e-05, 0.504689162, 0.017990783, 0.005779313, 0.190600519, 0.000833704, 0.025438035, 0.180307908, 0.100749946, 0.979511563, 0.001995369, 0.263055383, 0.118653915, 0.001051699, 0.003106154, 0.020796692, 0.000738825, 6.23e-07, 2.87e-07, 0.441909229, 0.200472262, 8.13e-18, 0.034441015, 0.031211289, 0.407183363, 9.41e-07, 2.08e-12, 0.624074873, 0.639362304, 0.517321052, 0.346223873, 0.000246323, 0.002190363, 2.26e-05, 1.79e-10, 0.47677441, 3.27e-05, 0.017678136, 0.54661916, 0.006207351, 0.102310016, 0.038855287, 0.017255026, 0.377809636, 0.120499776, 0.030343461, 0.506935849, 0.024958176, 0.000151797, 0.351720904, 0.509259273, 0.561800187, 5.50e-05, 0.001631626, 0.480598508, 1.42e-13, 1.33e-18, 0.080896176, 0.006194732, 6.49e-22, 7.83e-18, 0.590223909, 0.00067338, 0.003270591, 1.66e-07, 4.47e-11, 2.32e-09, 0.162015489, 6.31e-09, 5.46e-30, 2.06e-08, 5.92e-07, 0.000913152, 1.34e-05, 2.74e-05, 0.000254576)
Quad_simple_bully <- c(0.867073405, 0.21697344, 0.004664747, 0.223031524, 2.35e-03, 0.129540735, 6.09e-01, 0.319151761, 8.68e-01, 3.41e-05, 0.015832085, 5.31e-02, 1.51e-03, 5.89e-01, 7.37e-02, 3.23e-03, 1.18e-01, 0.010312099, 0.027184673, 0.773878473, 0.07795941, 0.728119955, 0.001135231, 0.231842375, 0.038609673, 4.23e-04, 0.001720944, 3.73e-02, 0.001521935, 1.87e-05, 8.77e-05, 0.397435286, 0.231282327, 1.69e-11, 0.126565964, 0.171809967, 0.378386648, 0.000854956, 4.06e-07, 0.829376447, 0.379859831, 0.514174312, 0.587786574, 0.040883818, 0.004588848, 0.001584751, 3.07e-07, 0.64327322, 0.000598356, 0.035609942, 0.435039966, 0.014930018, 0.171723705, 0.156678592, 0.01388322, 0.260335748, 0.056258761, 0.069995762, 0.317095263, 0.029367006, 4.87e-04, 0.281060647, 0.491783686, 0.587015143, 0.00503075, 0.014254203, 0.551636286, 3.47e-09, 6.34e-10, 0.470566356, 0.026465373, 3.31e-13, 9.83e-13, 0.494677248, 0.008021067, 0.036042515, 1.92e-05, 2.03e-06, 5.68e-06, 0.069088018, 2.70e-07, 5.43e-19, 0.00024012, 0.000293536, 0.016379827, 3.22e-05, 0.00027983, 0.000391517)
Linear_complex_bully <- c(0.049360101, 1.87e-07, 0.020665151, 5.03e-11, 1.02e-16, 9.78e-08, 0.709296517, 0.98884133, 0.035088618, 6.74e-27, 1.24e-06, 0.185611516, 4.26e-21, 0.499351137, 2.63e-13, 1.62e-08, 0.550308441, 1.19e-18, 2.80e-10, 0.000215386, 0.000230872, 0.734122159, 5.03e-08, 0.000228054, 0.554726643, 2.41e-06, 2.76e-07, 3.55e-07, 8.96e-14, 6.54e-19, 3.31e-34, 0.046882697, 0.024113462, 2.12e-65, 1.84e-05, 5.25e-11, 0.219135369, 1.10e-26, 9.84e-38, 0.00028152, 0.376209909, 0.980186117, 0.000104277, 1.61e-23, 1.67e-08, 7.51e-12, 8.27e-33, 0.581473011, 2.18e-16, 6.86e-05, 0.406846789, 3.22e-13, 0.000217102, 4.55e-05, 1.11e-08, 0.019991468, 0.000219087, 0.000535843, 0.003016076, 0.004438129, 4.60e-06, 0.028505214, 0.012180172, 0.728980997, 4.32e-12, 1.44e-10, 0.011863099, 4.91e-57, 2.89e-59, 3.19e-08, 3.41e-19, 1.15e-75, 9.11e-71, 0.064209034, 1.69e-15, 3.52e-05, 1.03e-25, 1.53e-53, 8.29e-37, 0.004048904, 3.66e-40, 7.25e-115, 5.38e-68, 7.96e-23, 2.27e-06, 5.35e-30, 1.41e-23, 1.59e-11)
Linear_complex_bully_age <- c(2.77e-07, 9.60e-33, 0.002444818, 1.38e-37, 2.73e-35, 1.85e-21, 0.087100824, 0.948518376, 0.001053434, 2.15e-54, 4.29e-09, 0.713573018, 4.66e-52, 0.355543305, 1.29e-37, 1.49e-05, 0.000332527, 3.22e-48, 7.51e-16, 8.87e-20, 0.001787736, 0.101646326, 0.000511364, 3.24e-06, 1.11e-07, 0.379307782, 0.001707982, 1.18e-11, 4.86e-20, 1.34e-28, 2.59e-74, 0.050539229, 0.02345656, 3.30e-117, 2.06e-23, 6.47e-43, 0.717644428, 7.60e-60, 3.10e-73, 6.23e-19, 0.03880478, 0.11704502, 7.08e-18, 7.58e-79, 7.53e-12, 7.50e-23, 1.31e-60, 0.072763053, 1.73e-31, 1.56e-06, 0.396356701, 5.43e-27, 1.40e-09, 4.58e-10, 2.78e-09, 0.2583291, 0.037690655, 3.27e-07, 4.37e-20, 0.121504925, 0.001369375, 0.115894519, 0.000352616, 0.088204751, 1.82e-30, 5.75e-21, 7.59e-06, 1.25e-110, 3.27e-119, 2.20e-25, 4.85e-56, 1.52e-170, 1.07e-135, 9.03e-07, 4.50e-36, 8.15e-10, 1.34e-48, 2.72e-103, 2.68e-72, 0.436896743, 1.40e-78, 2.84e-216, 3.30e-151, 2.27e-58, 1.26e-11, 7.06e-44, 1.57e-34, 1.83e-17)
Linear_simple_bully <- c(0.001841273, 1.51e-12, 0.103376595, 8.18e-14, 3.15e-16, 9.63e-14, 0.987529115, 0.979258137, 0.004840689, 3.32e-30, 3.87e-09, 0.201237142, 5.75e-22, 0.39184371, 2.18e-17, 6.35e-11, 0.807538237, 6.65e-22, 1.77e-14, 5.02e-07, 9.87e-06, 0.986193407, 2.05e-09, 2.75e-06, 0.768699183, 6.33e-07, 8.49e-09, 2.79e-10, 2.50e-18, 8.88e-25, 4.36e-29, 0.029958095, 0.006281061, 8.74e-51, 2.15e-10, 2.86e-18, 0.180980093, 5.29e-25, 3.33e-30, 4.13e-07, 0.669040515, 0.709467917, 3.15e-08, 6.42e-22, 6.12e-11, 3.20e-16, 4.11e-32, 0.349365443, 4.00e-20, 1.53e-06, 0.504844612, 2.06e-18, 3.68e-07, 3.47e-06, 1.22e-11, 0.008709649, 4.77e-05, 3.16e-06, 4.77e-05, 0.0011133, 1.57e-07, 0.013720909, 0.000805557, 0.487848523, 3.87e-13, 3.58e-11, 0.000597321, 1.62e-45, 1.51e-50, 1.87e-07, 5.28e-29, 5.95e-67, 1.66e-57, 0.015606933, 1.04e-18, 6.82e-06, 3.97e-26, 3.60e-40, 8.02e-39, 0.003067314, 2.33e-46, 1.77e-88, 3.99e-42, 4.24e-26, 5.52e-07, 1.52e-28, 5.83e-23, 2.82e-17)
Quad_sex_bully_age_simple <- c(0.887747894, 0.925620026, 0.00052688, 0.452481702, 0.36014146, 0.837441107, 3.30E-07, 0.12137398, 6.90E-06, 0.02963273, 0.001364031, 0.00238828, 0.170322143, 0.137207516, 0.006908485, 0.026769835, 0.165897457, 0.319414536, 0.081070065, 0.002575782, 0.00390065, 0.450248793, 0.028493164, 0.000712734, 0.182106415, 0.686795052, 0.223902528, 0.000187869, 0.848724456, 0.291580468, 0.867475796, 0.85124788, 0.009915836, 0.792346297, 6.96E-05, 0.983394315, 0.001930183, 0.455173905, 0.309129159, 0.65866872, 0.046516604, 0.149704009, 0.006278389, 0.318488579, 0.894342884, 0.648130463, 0.743758842, 0.872941643, 2.31E-07, 0.000303726, 0.422645413, 0.886807444, 0.242197405, 0.010000973, 0.000868774, 0.470107819, 8.35E-05, 0.002416943, 0.066596711, 0.677948381, 0.069271614, 0.000585776, 0.232057728, 0.078735784, 0.065453644, 0.234377592, 0.001208223, 0.022529527, 0.098151476, 0.922545377, 0.084195068, 0.585501813, 0.332793036, 0.000448911, 0.864609316, 0.015554019, 0.000141508, 4.23E-07, 0.178543521, 0.40286421, 0.268058175, 0.006692774, 0.131972466, 0.247020465, 0.006079444, 0.115246438, 7.46E-08, 0.181430045)
Quad_sex_bully_age_complex <- c(0.894782793, 0.945236759, 0.000499554, 0.455049639, 0.411113041, 0.818745925, 2.89E-07, 0.109236944, 6.80E-06, 0.032452336, 0.001502326, 0.002450623, 0.137705682, 0.133755183, 0.00635687, 0.02592859, 0.162331972, 0.293021114, 0.081653209, 0.002507509, 0.003855694, 0.441665747, 0.02822474, 0.000698318, 0.191771023, 0.669328294, 0.222051496, 0.000204815, 0.864653728, 0.273791683, 0.78750864, 0.847416845, 0.010035637, 0.884560343, 4.80E-05, 0.959546771, 0.001905383, 0.486746764, 0.376596535, 0.683546041, 0.044231753, 0.149492664, 0.006528989, 0.354213524, 0.928721397, 0.625385942, 0.667069114, 0.840460249, 1.66E-07, 0.000327996, 0.415933184, 0.906478217, 0.251554388, 0.010318252, 0.000863976, 0.463669867, 7.76E-05, 0.002673183, 0.068962614, 0.686025804, 0.075102018, 0.000568692, 0.231150203, 0.077957407, 0.086740746, 0.201808429, 0.001242554, 0.01678691, 0.100498936, 0.900891465, 0.081188043, 0.465962989, 0.237803674, 0.000449867, 0.907735853, 0.018024499, 0.000192989, 2.14E-07, 0.183859119, 0.407667341, 0.251504165, 0.004722837, 0.158682291, 0.263326124, 0.007377224, 0.120564553, 5.75E-08, 0.197077917)
Linear_sex_bully_age_complex <- c(0.955771706, 0.983863597, 0.00046293, 0.470226126, 0.472783385, 0.648873298, 2.97E-07, 0.129569602, 8.42E-06, 0.041844841, 0.00242477, 0.00226086, 0.089937118, 0.134322068, 0.00458465, 0.035328738, 0.155212534, 0.232302403, 0.099543008, 0.002572197, 0.004549664, 0.447384688, 0.033220544, 0.001066577, 0.205087008, 0.705247555, 0.244426016, 0.000310484, 0.956684065, 0.243804897, 0.611346294, 0.892948413, 0.010553781, 0.954195559, 3.40E-05, 0.871840647, 0.00199834, 0.534878757, 0.461419789, 0.811059937, 0.045415033, 0.161220833, 0.008106005, 0.397730116, 0.966675367, 0.601303627, 0.577491886, 0.850893244, 1.04E-07, 0.00040435, 0.419164761, 0.998506085, 0.278856105, 0.010934704, 0.001299438, 0.489885266, 0.00010046, 0.003071845, 0.091079644, 0.697395963, 0.077315151, 0.000670019, 0.260625476, 0.08442438, 0.10875662, 0.160201275, 0.001566581, 0.014542468, 0.111098129, 0.820930414, 0.060221339, 0.304022128, 0.095383473, 0.000469989, 0.975394604, 0.019628424, 0.000531253, 4.03E-07, 0.211449556, 0.395892475, 0.169205238, 0.003547114, 0.298722783, 0.314018443, 0.008373548, 0.218004788, 4.73E-08, 0.286127938)
Linear_sex_bully_age_simple <- list(0.965904933, 0.975700113, 0.00044561, 0.438635216, 0.327238911, 0.650230588, 3.01E-07, 0.129407263, 8.36E-06, 0.024326848, 0.00183257, 0.002248677, 0.164295535, 0.135439979, 0.003039059, 0.034128334, 0.153433838, 0.291992808, 0.102131949, 0.002667102, 0.004426717, 0.444036827, 0.031200596, 0.000950542, 0.203083253, 0.696332099, 0.236085534, 0.000251442, 0.884750789, 0.284359506, 0.699978181, 0.875087641, 0.010610153, 0.512088273, 4.12E-05, 0.788793794, 0.001983885, 0.418124077, 0.24439556, 0.793287422, 0.04508123, 0.153125246, 0.008816723, 0.293363386, 0.927216729, 0.590873464, 0.786212669, 0.865349339, 1.54E-07, 0.000408769, 0.419992272, 0.915130147, 0.263861774, 0.010994653, 0.001167369, 0.491351725, 9.52E-05, 0.002889564, 0.096962773, 0.688495613, 0.072383467, 0.000646593, 0.249508576, 0.081162371, 0.077826983, 0.229066652, 0.001536325, 0.047841228, 0.125313541, 0.807658782, 0.082195819, 0.407819399, 0.247616821, 0.000427879, 0.931338284, 0.018511352, 0.00049907, 1.42E-06, 0.267235281, 0.395938494, 0.248359841, 0.009018565, 0.266711084, 0.280458372, 0.00769199, 0.173287729, 5.07E-08, 0.211148145)
Quad_complex_bully_LEQ <- c(0.95847686, 0.03271316, 0.022891991, 0.037464224, 2.83e-05, 0.077958182, 0.975052641, 0.209, 0.558, 4.29e-07, 0.0271, 0.070867793, 0.00065683, 0.658848297, 0.00319, 0.022187065, 0.265495201, 0.00221, 0.061999122, 0.522564143, 0.132261827, 0.590734355, 4.35e-03, 3.99e-01, 3.18e-01, 4.80e-03, 8.19e-03, 3.67e-02, 0.0020904, 4.34e-06, 5.84e-06, 0.610540803, 3.79e-01, 2.45e-16, 0.029900174, 0.012528577, 0.589396822, 6.52e-06, 3.74e-12, 5.23e-01, 0.734695707, 0.709875961, 6.16e-01, 0.00100788, 7.74e-03, 3.05e-04, 3.63e-08, 0.383498803, 4.68e-06, 5.99e-02, 8.76e-01, 6.97e-03, 0.259865434, 1.32e-01, 0.040279749, 0.559264225, 0.154414918, 0.063242606, 0.643813908, 0.048142266, 0.000832993, 0.424972381, 0.742083484, 0.396499836, 0.000183571, 0.002212774, 0.572749869, 7.08e-12, 6.07e-17, 0.044093648, 0.010965147, 1.24e-19, 5.16e-15, 0.650523256, 0.000653437, 0.000991564, 2.58e-08, 6.41e-10, 3.53e-08, 0.203061721, 1.48e-08, 1.34e-29, 3.99e-07, 9.93e-08, 0.000499763, 3.83e-06, 0.00011749, 0.001106536)
Quad_complex_bully_INT_LEQ <- c(0.457844812, 1.82e-06, 0.116342564, 0.000100224, 1.30e-05, 1.82e-01, 9.17e-02, 4.76e-02, 2.65e-01, 2.15e-05, 0.583127731, 0.424263594, 0.000102164, 0.617367023, 0.011079255, 0.36203837, 0.095482216, 1.14e-03, 0.975732302, 1.79e-03, 0.360453563, 4.77e-02, 5.88e-01, 5.82e-01, 7.86e-03, 1.99e-01, 6.18e-01, 3.86e-01, 5.26e-01, 2.60e-03, 2.48e-05, 0.668139762, 8.46e-01, 1.23e-11, 4.94e-06, 0.000174804, 6.77e-01, 9.61e-07, 3.12e-11, 4.22e-02, 0.025066888, 0.945157772, 0.054537312, 7.16e-08, 2.25e-01, 0.000168379, 2.42e-06, 0.059601008, 5.55e-04, 1.96e-01, 3.44e-01, 1.97e-01, 2.03e-01, 0.044600403, 0.591816453, 0.170492652, 0.12305281, 0.080093341, 0.128354011, 0.914111831, 0.157336191, 0.515710127, 0.58894848, 0.656986846, 1.69e-06, 0.009302644, 0.938523772, 1.25e-08, 2.56e-19, 0.000856246, 0.001614797, 7.64e-25, 9.79e-12, 0.789188815, 0.001809917, 0.000403335, 2.88e-05, 6.08e-09, 1.16e-08, 0.199916497, 1.27e-05, 6.59e-24, 3.18e-08, 1.10e-08, 0.000323555, 0.132346633, 0.032430291, 0.348539152)
Quad_simple_bully_LEQ <- c(0.862949833, 0.220768959, 0.010651499, 0.29681636, 0.003315968, 0.104851637, 0.796768066, 0.371919385, 0.698687769, 5.18e-05, 0.031410277, 0.094388329, 0.009564045, 0.718715164, 0.013715234, 0.013122481, 0.156695222, 0.019070996, 0.057334285, 0.791949242, 0.090381001, 0.86886211, 0.002082252, 0.336760578, 0.11483787, 0.001906973, 0.004086081, 0.051618978, 0.002604271, 7.00e-05, 0.000475456, 0.522388362, 0.391111371, 1.29e-10, 0.119654944, 0.065126216, 0.531300306, 0.002044107, 3.56e-07, 0.7302149, 0.431269272, 0.714551366, 0.87217275, 0.06082252, 0.013145689, 0.006941571, 9.63e-06, 0.550388692, 9.48e-05, 0.098235374, 0.72950735, 0.013134958, 0.359475564, 0.355199844, 0.028935136, 0.390381593, 0.063655646, 0.117233108, 0.430591037, 0.046706683, 0.001916706, 0.323839748, 0.672682527, 0.438783987, 0.008876492, 0.014232471, 0.576421014, 4.30e-08, 3.79e-09, 0.335710292, 0.040304074, 6.93e-12, 9.52e-11, 0.575569808, 0.005631387, 0.015777362, 2.53e-06, 8.52e-06, 3.42e-05, 0.077327078, 7.27e-07, 3.53e-19, 0.000846082, 3.80e-05, 0.009842282, 1.19e-05, 0.000838146, 0.001437121)
Linear_simple_bully_LEQ <- c(0.021337028, 3.39e-08, 0.223363007, 1.32e-07, 9.40e-11, 2.42e-09, 0.514992847, 0.84070655, 0.405632816, 1.26e-19, 1.78e-05, 0.623936612, 5.88e-13, 0.860332856, 1.26e-13, 4.11e-06, 0.831416274, 1.55e-13, 7.70e-08, 0.021471935, 0.001981895, 0.292693125, 5.52e-06, 0.002428352, 0.592371348, 0.00022381, 1.74e-05, 9.50e-06, 8.44e-11, 2.14e-14, 4.10e-18, 0.126437532, 0.153236321, 3.97e-35, 1.79e-07, 8.09e-14, 0.593241027, 6.24e-16, 2.54e-22, 8.44e-05, 0.977102418, 0.567039629, 0.00070545, 1.07e-13, 1.22e-06, 6.52e-08, 3.24e-20, 0.200055202, 2.09e-16, 0.001542507, 0.950421751, 1.14e-11, 0.003306759, 0.014420674, 1.62e-06, 0.139011106, 0.002327167, 0.002275796, 0.001780892, 0.01862203, 9.16e-05, 0.105970928, 0.060743173, 0.683442524, 8.78e-09, 4.57e-08, 0.033313383, 1.67e-32, 6.34e-34, 1.60e-05, 3.31e-19, 2.64e-46, 1.83e-38, 0.085290112, 2.15e-13, 1.08e-05, 2.25e-21, 4.39e-26, 4.93e-24, 0.01686153, 8.49e-33, 5.27e-66, 2.02e-26, 4.87e-21, 6.73e-06, 9.74e-22, 6.33e-15, 2.60e-10)
Linear_complex_bully_LEQ <- c(0.099195168, 5.58e-06, 0.077017816, 3.54e-07, 4.70e-13, 2.11e-06, 0.801137009, 0.88850492, 0.505282942, 2.05e-19, 0.000122285, 0.510199591, 9.83e-14, 0.861368671, 3.76e-12, 2.02e-05, 0.69823406, 4.59e-13, 1.88e-06, 0.062986602, 0.003990614, 0.501707048, 9.93e-06, 0.007661242, 0.989746674, 0.000218438, 3.29e-05, 6.44e-05, 1.94e-09, 5.81e-13, 4.84e-24, 0.132012392, 0.188215677, 4.83e-50, 6.28e-05, 4.84e-10, 0.549547773, 2.02e-18, 8.09e-31, 0.001683747, 0.702353789, 0.723171659, 0.0114698, 3.38e-15, 5.85e-06, 7.69e-07, 3.15e-22, 0.338303513, 2.66e-15, 0.004049959, 0.836401275, 6.72e-10, 0.018925655, 0.015996392, 1.23e-05, 0.139584883, 0.002873762, 0.01034989, 0.009300334, 0.022813971, 0.000196783, 0.114873496, 0.111138461, 0.652848741, 2.77e-09, 8.57e-09, 0.074753882, 9.93e-42, 1.14e-44, 4.23e-07, 1.10e-13, 6.97e-58, 4.37e-50, 0.115127726, 1.73e-12, 7.28e-06, 9.41e-24, 3.89e-39, 8.75e-26, 0.01689583, 1.25e-30, 1.74e-91, 1.78e-47, 1.90e-20, 3.12e-06, 6.43e-25, 9.87e-17, 5.48e-08)
Linear_complex_bully_INT_LEQ <- c(1.85e-06, 4.53e-28, 0.000212804, 1.85e-26, 2.70e-28, 3.86e-18, 0.005270096, 0.516078847, 0.20623306, 2.32e-36, 3.28e-05, 0.106952893, 1.06e-38, 0.997915608, 5.52e-29, 0.005734287, 5.02e-05, 2.54e-35, 7.57e-09, 4.63e-11, 0.139706281, 0.003754819, 0.053618973, 0.001862784, 3.22e-09, 0.790673493, 0.059214633, 1.47e-06, 8.19e-11, 1.41e-17, 5.05e-56, 0.150603379, 0.328688852, 7.61e-92, 4.91e-23, 9.37e-35, 0.586819233, 1.86e-42, 9.10e-60, 3.62e-16, 0.002057818, 0.099644056, 1.10e-11, 3.59e-54, 7.21e-08, 1.89e-12, 4.14e-43, 0.01054666, 2.12e-26, 0.000423269, 0.222952451, 6.08e-18, 2.82e-05, 3.91e-05, 9.32e-05, 0.937967546, 0.421764443, 0.000302282, 4.12e-17, 0.640660352, 0.034923889, 0.577315854, 0.043427493, 0.674804909, 3.88e-24, 6.26e-17, 0.008148056, 1.46e-84, 2.56e-95, 2.78e-22, 1.92e-44, 1.48e-142, 8.16e-109, 1.85e-05, 3.15e-28, 7.45e-11, 2.53e-43, 1.97e-80, 3.77e-56, 0.987410248, 4.57e-65, 1.31e-179, 5.36e-118, 2.23e-47, 7.57e-11, 2.38e-36, 2.66e-25, 1.41e-10)
Linear_complex_bully_IQ <- c(0.048446345, 4.11e-06, 0.105643534, 5.54e-11, 8.85e-17, 3.19e-07, 0.915842414, 0.584226379, 0.035774721, 1.66e-26, 9.21e-06, 0.102590825, 3.01e-18, 0.481378156, 2.29e-11, 3.24e-09, 0.919028565, 3.75e-18, 1.14e-11, 0.000131137, 2.27e-05, 0.74620228, 2.36e-07, 0.000161599, 0.505853511, 2.17e-06, 5.46e-06, 2.13e-07, 3.59e-14, 4.79e-19, 1.64e-35, 0.164447203, 0.004090238, 2.28e-62, 9.48e-07, 2.24e-10, 0.267328199, 1.08e-27, 5.95e-40, 7.37e-05, 0.458586918, 0.826589945, 0.00039262, 2.56e-23, 3.56e-08, 2.11e-11, 3.65e-30, 0.828007014, 5.87e-14, 0.000375044, 0.629617454, 6.92e-13, 0.000745747, 3.63e-06, 5.90e-08, 0.013562997, 0.000319161, 0.003404487, 0.001311809, 0.003170769, 2.33e-05, 0.020069009, 0.020046649, 0.365779232, 1.76e-12, 6.75e-12, 0.022637745, 7.59e-53, 4.86e-60, 5.43e-07, 1.18e-18, 2.82e-77, 4.55e-67, 0.087442284, 9.16e-16, 3.29e-05, 4.16e-25, 2.82e-51, 1.84e-38, 0.016996635, 1.51e-37, 2.15e-117, 1.34e-67, 7.17e-23, 2.05e-06, 2.53e-29, 1.07e-23, 1.60e-11)
Linear_complex_bully_INT_IQ <- c(9.76e-08, 2.37e-31, 0.009206728, 1.64e-34, 3.77e-36, 2.91e-19, 0.051277191, 0.735691631, 0.001309778, 1.50e-52, 5.85e-10, 0.729587954, 1.07e-51, 0.539112745, 3.27e-36, 7.32e-06, 0.002118735, 2.68e-46, 1.77e-15, 9.85e-18, 0.004770896, 0.058827242, 0.000880123, 1.20e-06, 1.50e-07, 0.322470539, 0.000621372, 2.11e-11, 1.68e-19, 1.01e-26, 1.85e-69, 0.035969115, 0.116534246, 2.44e-113, 8.92e-19, 1.21e-38, 0.380497402, 1.80e-52, 7.18e-74, 1.06e-16, 0.055050958, 0.19543903, 8.74e-17, 6.43e-77, 6.24e-11, 1.29e-19, 3.33e-58, 0.099503633, 1.26e-27, 7.83e-07, 0.625969035, 5.12e-27, 4.35e-10, 6.23e-07, 8.64e-09, 0.480685177, 0.01693886, 2.78e-07, 3.69e-18, 0.040979523, 0.000820557, 0.120083901, 0.000274682, 0.087720981, 7.44e-30, 9.80e-21, 1.57e-05, 5.52e-112, 8.63e-113, 5.99e-26, 4.44e-53, 8.18e-163, 5.48e-134, 4.62e-08, 4.73e-35, 4.29e-11, 1.45e-49, 2.16e-95, 2.72e-69, 0.474260872, 5.31e-79, 1.16e-217, 1.92e-153, 1.36e-62, 5.57e-12, 2.35e-46, 2.26e-30, 2.30e-17)
Linear_complex_bully_bmi <- c(0.048446345, 4.11e-06, 0.105643534, 5.54e-11, 8.85e-17, 3.19e-07, 0.915842414, 0.584226379, 0.035774721, 1.66e-26, 9.21e-06, 0.102590825, 3.01e-18, 0.481378156, 2.29e-11, 3.24e-09, 0.919028565, 3.75e-18, 1.14e-11, 0.000131137, 2.27e-05, 0.74620228, 2.36e-07, 0.000161599, 0.505853511, 2.17e-06, 5.46e-06, 2.13e-07, 3.59e-14, 4.79e-19, 1.64e-35, 0.164447203, 0.004090238, 2.28e-62, 9.48e-07, 2.24e-10, 0.267328199, 1.08e-27, 5.95e-40, 7.37e-05, 0.458586918, 0.826589945, 0.00039262, 2.56e-23, 3.56e-08, 2.11e-11, 3.65e-30, 0.828007014, 5.87e-14, 0.000375044, 0.629617454, 6.92e-13, 0.000745747, 3.63e-06, 5.90e-08, 0.013562997, 0.000319161, 0.003404487, 0.001311809, 0.003170769, 2.33e-05, 0.020069009, 0.020046649, 0.365779232, 1.76e-12, 6.75e-12, 0.022637745, 7.59e-53, 4.86e-60, 5.43e-07, 1.18e-18, 2.82e-77, 4.55e-67, 0.087442284, 9.16e-16, 3.29e-05, 4.16e-25, 2.82e-51, 1.84e-38, 0.016996635, 1.51e-37, 2.15e-117, 1.34e-67, 7.17e-23, 2.05e-06, 2.53e-29, 1.07e-23, 1.60e-11)
Linear_complex_bully_bmi_INT <- c(9.76e-08, 2.37e-31, 0.009206728, 1.64e-34, 3.77e-36, 2.91e-19, 0.051277191, 0.735691631, 0.001309778, 1.50e-52, 5.85e-10, 0.729587954, 1.07e-51, 0.539112745, 3.27e-36, 7.32e-06, 0.002118735, 2.68e-46, 1.77e-15, 9.85e-18, 0.004770896, 0.058827242, 0.000880123, 1.20e-06, 1.50e-07, 0.322470539, 0.000621372, 2.11e-11, 1.68e-19, 1.01e-26, 1.85e-69, 0.035969115, 0.116534246, 2.44e-113, 8.92e-19, 1.21e-38, 0.380497402, 1.80e-52, 7.18e-74, 1.06e-16, 0.055050958, 0.19543903, 8.74e-17, 6.43e-77, 6.24e-11, 1.29e-19, 3.33e-58, 0.099503633, 1.26e-27, 7.83e-07, 0.625969035, 5.12e-27, 4.35e-10, 6.23e-07, 8.64e-09, 0.480685177, 0.01693886, 2.78e-07, 3.69e-18, 0.040979523, 0.000820557, 0.120083901, 0.000274682, 0.087720981, 7.44e-30, 9.80e-21, 1.57e-05, 5.52e-112, 8.63e-113, 5.99e-26, 4.44e-53, 8.18e-163, 5.48e-134, 4.62e-08, 4.73e-35, 4.29e-11, 1.45e-49, 2.16e-95, 2.72e-69, 0.474260872, 5.31e-79, 1.16e-217, 1.92e-153, 1.36e-62, 5.57e-12, 2.35e-46, 2.26e-30, 2.30e-17)
# Your existing code
# Your existing code
# Assuming 'Quad_complex_bully' is already defined
# Adjust p-values using False Discovery Rate (FDR) method
p_adjusted_Linear_complex_bully_bmi_INT <- p.adjust(Linear_complex_bully_bmi_INT, method = "fdr")
# Print adjusted p-values
# print(p_adjusted_Quad_complex_bully_age)
# Significance threshold after FDR correction
alpha <- 0.05
# Identify significant p-values
significant_indices <- p_adjusted_Linear_complex_bully_bmi_INT <= alpha
# Create a DataFrame with DVlist, adjusted p-values, and significance information
data <- data.frame(
DV = DVlist,
Adjusted_p_value = p_adjusted_Linear_complex_bully_bmi_INT,
Significant = significant_indices
)
# Write data to CSV file
write.csv(data, file = "/Users/michaelconnaughton/Desktop/IMAGEN/sensitivity/p_adjusted_Linear_complex_bully_bmi_INT", row.names = FALSE)
}
# PLOTS_REMEBER_FIX_AGE
#define_predictions
{
mean_bully_victim <- mean(IMAGEN_bully$bully_victim, na.rm = TRUE)
p25_bully_victim <- quantile(IMAGEN_bully$bully_victim, 0.25, na.rm = TRUE)
p75_bully_victim <- quantile(IMAGEN_bully$bully_victim, 0.75, na.rm = TRUE)
newdata <- expand.grid(
EstimatedTotalIntraCranialVol = mean(IMAGEN_bully$EstimatedTotalIntraCranialVol, na.rm = TRUE),
sex = factor(levels(IMAGEN_bully$sex)[1]),
age = seq(min(IMAGEN_bully$age, na.rm = TRUE), max(IMAGEN_bully$age, na.rm = TRUE), length.out = 100),
SES = mean(IMAGEN_bully$SES, na.rm = TRUE),
mode_c_pds = mean(IMAGEN_bully$mode_c_pds, na.rm = TRUE),
bully_victim = c(mean_bully_victim, p25_bully_victim, p75_bully_victim),
subID = unique(IMAGEN_bully$subID)[1]
)
}
#Optimal Models Effect Sizes
#Quad_Complex
{
lh_caudalanteriorcingulate_volume <- lmer(lh_caudalanteriorcingulate_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_caudalanteriorcingulate_volume)
lh_cuneus_volume <- lmer(lh_cuneus_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_cuneus_volume)
lh_entorhinal_volume <- lmer(lh_entorhinal_volume ~ bully_victim * age + I(age^2) + bully_victim*sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
summary(lh_entorhinal_volume)
effectsize(lh_entorhinal_volume)
lh_frontalpole_volume <- lmer(lh_frontalpole_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_lateraloccipital_volume <- lmer(lh_lateraloccipital_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_lateraloccipital_volume)
lh_medialorbitofrontal_volume <- lmer(lh_medialorbitofrontal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_medialorbitofrontal_volume)
lh_parahippocampal_volume <- lmer(lh_parahippocampal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_parahippocampal_volume)
lh_parsorbitalis_volume <- lmer(lh_parsorbitalis_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_parsorbitalis_volume)
lh_pericalcarine_volume <- lmer(lh_pericalcarine_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_pericalcarine_volume)
lh_precentral_volume <- lmer(lh_precentral_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_precentral_volume)
lh_rostralanteriorcingulate_volume <- lmer(lh_rostralanteriorcingulate_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_rostralanteriorcingulate_volume)
lh_rostralmiddlefrontal_volume <- lmer(lh_rostralmiddlefrontal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_rostralmiddlefrontal_volume)
lh_superiorfrontal_volume <- lmer(lh_superiorfrontal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_superiorfrontal_volume)
lh_superiortemporal_volume <- lmer(lh_superiortemporal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_superiortemporal_volume)
lh_supramarginal_volume <- lmer(lh_supramarginal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_supramarginal_volume)
lh_transversetemporal_volume <- lmer(lh_transversetemporal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_transversetemporal_volume)
lh_insula_volume <- lmer(lh_insula_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_insula_volume)
rh_bankssts_volume <- lmer(rh_bankssts_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_bankssts_volume)
rh_caudalanteriorcingulate_volume <- lmer(rh_caudalanteriorcingulate_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_caudalanteriorcingulate_volume)
rh_cuneus_volume <- lmer(rh_cuneus_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_cuneus_volume)
rh_entorhinal_volume <- lmer(rh_entorhinal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_entorhinal_volume)
rh_lateraloccipital_volume <- lmer(rh_lateraloccipital_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_lateraloccipital_volume)
rh_lateralorbitofrontal_volume <- lmer(rh_lateralorbitofrontal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_lateralorbitofrontal_volume)
rh_lingual_volume <- lmer(rh_lingual_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_lingual_volume)
rh_medialorbitofrontal_volume <- lmer(rh_medialorbitofrontal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_medialorbitofrontal_volume)
rh_parahippocampal_volume <- lmer(rh_parahippocampal_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_parahippocampal_volume)
rh_pericalcarine_volume <- lmer(rh_pericalcarine_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_pericalcarine_volume)
rh_precuneus_volume <- lmer(rh_precuneus_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_precuneus_volume)
rh_temporalpole_volume <- lmer(rh_temporalpole_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_temporalpole_volume)
rh_insula_volume <- lmer(rh_insula_volume ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_insula_volume)
Left_Cerebellum_Cortex <- lmer(Left_Cerebellum_Cortex ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Left_Cerebellum_Cortex)
Left_Caudate <- lmer(Left_Caudate ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Left_Caudate)
Left_Putamen <- lmer(Left_Putamen ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Left_Putamen)
Left_Pallidum <- lmer(Left_Pallidum ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Left_Pallidum)
Left_Hippocampus <- lmer(Left_Hippocampus ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Left_Hippocampus)
Left_Accumbens_area <- lmer(Left_Accumbens_area ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Left_Accumbens_area)
Left_VentralDC <- lmer(Left_VentralDC ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Left_VentralDC)
Right_Cerebellum_Cortex <- lmer(Right_Cerebellum_Cortex ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Right_Cerebellum_Cortex)
Right_Caudate <- lmer(Right_Caudate ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Right_Caudate)
Right_Putamen <- lmer(Right_Putamen ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Right_Putamen)
Right_Pallidum <- lmer(Right_Pallidum ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Right_Pallidum)
Right_Hippocampus <- lmer(Right_Hippocampus ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Right_Hippocampus)
Right_Accumbens_area <- lmer(Right_Accumbens_area ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Right_Accumbens_area)
Right_VentralDC <- lmer(Right_VentralDC ~ bully_victim*age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Right_VentralDC)
}
#Quad_Simple
{
lh_bankssts_volume <- lmer(lh_bankssts_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_bankssts_volume)
lh_fusiform_volume <- lmer(lh_fusiform_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_fusiform_volume)
lh_isthmuscingulate_volume <- lmer(lh_isthmuscingulate_volume ~ bully_victim * age + I(age^2) + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_isthmuscingulate_volume)
lh_lateralorbitofrontal_volume <- lmer(lh_lateralorbitofrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_lateralorbitofrontal_volume)
lh_lingual_volume <- lmer(lh_lingual_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_lingual_volume)
lh_paracentral_volume <- lmer(lh_paracentral_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_paracentral_volume)
lh_parstriangularis_volume <- lmer(lh_parstriangularis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_parstriangularis_volume)
lh_postcentral_volume <- lmer(lh_postcentral_volume ~ bully_victim * age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_postcentral_volume)
lh_precuneus_volume <- lmer(lh_precuneus_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_precuneus_volume)
lh_superiorparietal_volume <- lmer(lh_superiorparietal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_superiorparietal_volume)
lh_temporalpole_volume <- lmer(lh_temporalpole_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(lh_temporalpole_volume)
rh_fusiform_volume <- lmer(rh_fusiform_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_fusiform_volume)
rh_isthmuscingulate_volume <- lmer(rh_isthmuscingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_isthmuscingulate_volume)
rh_paracentral_volume <- lmer(rh_paracentral_volume ~ bully_victim * age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_paracentral_volume)
rh_parsorbitalis_volume <- lmer(rh_parsorbitalis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_parsorbitalis_volume)
rh_parstriangularis_volume <- lmer(rh_parstriangularis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_parstriangularis_volume)
rh_postcentral_volume <- lmer(rh_postcentral_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_postcentral_volume)
rh_precentral_volume <- lmer(rh_precentral_volume ~ bully_victim * age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_precentral_volume)
rh_rostralanteriorcingulate_volume <- lmer(rh_rostralanteriorcingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_rostralanteriorcingulate_volume)
rh_superiortemporal_volume <- lmer(rh_superiortemporal_volume ~ bully_victim * age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_superiortemporal_volume)
rh_frontalpole_volume <- lmer(rh_frontalpole_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_frontalpole_volume)
rh_transversetemporal_volume <- lmer(rh_transversetemporal_volume ~ bully_victim * age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(rh_transversetemporal_volume)
Left_Thalamus_Proper <- lmer(Left_Thalamus_Proper ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Left_Thalamus_Proper)
Brain_Stem <- lmer(Brain_Stem ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Brain_Stem)
Right_Thalamus_Proper <- lmer(Right_Thalamus_Proper ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(Right_Thalamus_Proper)
TotalGrayVol <- lmer(TotalGrayVol ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
effectsize(TotalGrayVol)
}
#Linear_Complex
{
lh_caudalmiddlefrontal_volume <- lmer(lh_caudalmiddlefrontal_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_caudalmiddlefrontal_volume)
lh_parsopercularis_volume <- lmer(lh_parsopercularis_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_parsopercularis_volume)
lh_isthmuscingulate_volume <- lmer(lh_isthmuscingulate_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_isthmuscingulate_volume)
lh_pericalcarine_volume <- lmer(lh_pericalcarine_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_pericalcarine_volume)
lh_isthmuscingulate_volume <- lmer(lh_isthmuscingulate_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_isthmuscingulate_volume)
lh_postcentral_volume <- lmer(lh_postcentral_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_postcentral_volume)
lh_precentral_volume <- lmer(lh_precentral_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_precentral_volume)
lh_rostralanteriorcingulate_volume <- lmer(lh_rostralanteriorcingulate_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_rostralanteriorcingulate_volume)
rh_rostralmiddlefrontal_volume <- lmer(rh_rostralmiddlefrontal_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_rostralmiddlefrontal_volume)
rh_superiorfrontal_volume <- lmer(rh_superiorfrontal_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_superiorfrontal_volume)
rh_lingual_volume <- lmer(rh_lingual_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_lingual_volume)
rh_paracentral_volume <- lmer(rh_paracentral_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_paracentral_volume)
rh_pericalcarine_volume <- lmer(rh_pericalcarine_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_pericalcarine_volume)
rh_precentral_volume <- lmer(rh_precentral_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_precentral_volume)
rh_precuneus_volume <- lmer(rh_precuneus_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_precuneus_volume)
rh_superiorfrontal_volume <- lmer(rh_superiorfrontal_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_superiorfrontal_volume)
lh_superiorfrontal_volume <- lmer(lh_superiorfrontal_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_superiorfrontal_volume)
rh_superiorfrontal_volume <- lmer(rh_superiorfrontal_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_superiorfrontal_volume)
rh_superiortemporal_volume <- lmer(rh_superiortemporal_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_superiortemporal_volume)
rh_transversetemporal_volume <- lmer(rh_transversetemporal_volume ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_transversetemporal_volume)
Brain_Stem <- lmer(Brain_Stem ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(Brain_Stem)
Left_Amygdala <- lmer(Left_Amygdala ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(Left_Amygdala)
Right_Amygdala <- lmer(Right_Amygdala ~ bully_victim*age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(Right_Amygdala)
}
#Linear_Simple
{
lh_temporalpole_volume <- lmer(lh_temporalpole_volume ~ bully_victim + age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(lh_temporalpole_volume)
rh_posteriorcingulate_volume <- lmer(rh_posteriorcingulate_volume ~ bully_victim + age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_posteriorcingulate_volume)
rh_rostralmiddlefrontal_volume <- lmer(rh_rostralmiddlefrontal_volume ~ bully_victim + age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_rostralmiddlefrontal_volume)
rh_superiorparietal_volume <- lmer(rh_superiorparietal_volume ~ bully_victim + age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_superiorparietal_volume)
Right_Thalamus_Proper <- lmer(Right_Thalamus_Proper ~ bully_victim + age + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(Right_Thalamus_Proper)
}
#Cohen's D
# Extract the coefficient and standard error for 'bully_victim'
coef_bully_victim <- fixef(TotalGrayVol)["bully_victim"]
se_bully_victim <- sqrt(diag(vcov(TotalGrayVol))["bully_victim"])
# Approximate the standard deviation of the dependent variable
# We use the residual standard Left_VentralDC as a proxy for the predictor's standard deviation
sigma_residual <- sigma(TotalGrayVol)
# Calculate Cohen's d
cohens_d <- coef_bully_victim / sigma_residual
print(cohens_d)
# Extract the coefficient and standard error for 'bully_victim:age' interaction
coef_interaction <- fixef(Right_Pallidum)["bully_victim:age"]
se_interaction <- sqrt(diag(vcov(Right_Pallidum))["bully_victim:age"])
# Approximate the standard deviation of the dependent variable
sigma_residual <- sigma(Right_Pallidum)
# Calculate Cohen's d for the interaction term
cohens_d_interaction <- coef_interaction / sigma_residual
print(cohens_d_interaction)
effectsize(Right_VentralDC)
#MODELS_TO_VISUALISE_
#Quad_Complex
{
lh_bankssts_volume <- lmer(lh_bankssts_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
summary(lh_bankssts_volume)
lh_bankssts_volume <- lmer(lh_bankssts_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_caudalanteriorcingulate_volume <- lmer(lh_caudalanteriorcingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_caudalmiddlefrontal_volume <- lmer(lh_caudalmiddlefrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_cuneus_volume <- lmer(lh_cuneus_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_entorhinal_volume <- lmer(lh_entorhinal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_fusiform_volume <- lmer(lh_fusiform_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_inferiorparietal_volume <- lmer(lh_inferiorparietal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_inferiortemporal_volume <- lmer(lh_inferiortemporal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_isthmuscingulate_volume <- lmer(lh_isthmuscingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_lateraloccipital_volume <- lmer(lh_lateraloccipital_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_lateralorbitofrontal_volume <- lmer(lh_lateralorbitofrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_lingual_volume <- lmer(lh_lingual_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_medialorbitofrontal_volume <- lmer(lh_medialorbitofrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_middletemporal_volume <- lmer(lh_middletemporal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_parahippocampal_volume <- lmer(lh_parahippocampal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_paracentral_volume <- lmer(lh_paracentral_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_parsopercularis_volume <- lmer(lh_parsopercularis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_parsorbitalis_volume <- lmer(lh_parsorbitalis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_parstriangularis_volume <- lmer(lh_parstriangularis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_pericalcarine_volume <- lmer(lh_pericalcarine_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_postcentral_volume <- lmer(lh_postcentral_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_posteriorcingulate_volume <- lmer(lh_posteriorcingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_precentral_volume <- lmer(lh_precentral_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_precuneus_volume <- lmer(lh_precuneus_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_rostralanteriorcingulate_volume <- lmer(lh_rostralanteriorcingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_rostralmiddlefrontal_volume <- lmer(lh_rostralmiddlefrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_superiorfrontal_volume <- lmer(lh_superiorfrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_superiorparietal_volume <- lmer(lh_superiorparietal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_superiortemporal_volume <- lmer(lh_superiortemporal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_supramarginal_volume <- lmer(lh_supramarginal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_frontalpole_volume <- lmer(lh_frontalpole_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_temporalpole_volume <- lmer(lh_temporalpole_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_transversetemporal_volume <- lmer(lh_transversetemporal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
lh_insula_volume <- lmer(lh_insula_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_bankssts_volume <- lmer(rh_bankssts_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_caudalanteriorcingulate_volume <- lmer(rh_caudalanteriorcingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_caudalmiddlefrontal_volume <- lmer(rh_caudalmiddlefrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_cuneus_volume <- lmer(rh_cuneus_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_entorhinal_volume <- lmer(rh_entorhinal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_fusiform_volume <- lmer(rh_fusiform_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_inferiorparietal_volume <- lmer(rh_inferiorparietal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_inferiortemporal_volume <- lmer(rh_inferiortemporal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_isthmuscingulate_volume <- lmer(rh_isthmuscingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_lateraloccipital_volume <- lmer(rh_lateraloccipital_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_lateralorbitofrontal_volume <- lmer(rh_lateralorbitofrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_lingual_volume <- lmer(rh_lingual_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_medialorbitofrontal_volume <- lmer(rh_medialorbitofrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_middletemporal_volume <- lmer(rh_middletemporal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_parahippocampal_volume <- lmer(rh_parahippocampal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_paracentral_volume <- lmer(rh_paracentral_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_parsopercularis_volume <- lmer(rh_parsopercularis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_parsorbitalis_volume <- lmer(rh_parsorbitalis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_parstriangularis_volume <- lmer(rh_parstriangularis_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_pericalcarine_volume <- lmer(rh_pericalcarine_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_postcentral_volume <- lmer(rh_postcentral_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_posteriorcingulate_volume <- lmer(rh_posteriorcingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_precentral_volume <- lmer(rh_precentral_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_precuneus_volume <- lmer(rh_precuneus_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_rostralanteriorcingulate_volume <- lmer(rh_rostralanteriorcingulate_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_rostralmiddlefrontal_volume <- lmer(rh_rostralmiddlefrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_superiorfrontal_volume <- lmer(rh_superiorfrontal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_superiorparietal_volume <- lmer(rh_superiorparietal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_superiortemporal_volume <- lmer(rh_superiortemporal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_supramarginal_volume <- lmer(rh_supramarginal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_frontalpole_volume <- lmer(rh_frontalpole_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_temporalpole_volume <- lmer(rh_temporalpole_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_transversetemporal_volume <- lmer(rh_transversetemporal_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
rh_insula_volume <- lmer(rh_insula_volume ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_Cerebellum_Cortex <- lmer(Left_Cerebellum_Cortex ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_Thalamus_Proper <- lmer(Left_Thalamus_Proper ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_Caudate <- lmer(Left_Caudate ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_Putamen <- lmer(Left_Putamen ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_Pallidum <- lmer(Left_Pallidum ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Brain_Stem <- lmer(Brain_Stem ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_Hippocampus <- lmer(Left_Hippocampus ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_Amygdala <- lmer(Left_Amygdala ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_Accumbens_area <- lmer(Left_Accumbens_area ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Left_VentralDC <- lmer(Left_VentralDC ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_Cerebellum_Cortex <- lmer(Right_Cerebellum_Cortex ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_Thalamus_Proper <- lmer(Right_Thalamus_Proper ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_Caudate <- lmer(Right_Caudate ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_Putamen <- lmer(Right_Putamen ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_Pallidum <- lmer(Right_Pallidum ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_Hippocampus <- lmer(Right_Hippocampus ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_Amygdala <- lmer(Right_Amygdala ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_Accumbens_area <- lmer(Right_Accumbens_area ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
Right_VentralDC <- lmer(Right_VentralDC ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
TotalGrayVol <- lmer(TotalGrayVol ~ bully_victim + age + I(age^2) + sex + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "bobyqa"))
#Quad_simple
#Linear_complex
#Linear_simple
# Predicting all dependent variables (DVs) and storing predictions in newdata
newdata$predicted_lh_bankssts_volume <- predict(lh_bankssts_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_caudalanteriorcingulate_volume <- predict(lh_caudalanteriorcingulate_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_caudalmiddlefrontal_volume <- predict(lh_caudalmiddlefrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_cuneus_volume <- predict(lh_cuneus_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_entorhinal_volume <- predict(lh_entorhinal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_fusiform_volume <- predict(lh_fusiform_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_inferiorparietal_volume <- predict(lh_inferiorparietal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_inferiortemporal_volume <- predict(lh_inferiortemporal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_isthmuscingulate_volume <- predict(lh_isthmuscingulate_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_lateraloccipital_volume <- predict(lh_lateraloccipital_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_lateralorbitofrontal_volume <- predict(lh_lateralorbitofrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_lingual_volume <- predict(lh_lingual_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_medialorbitofrontal_volume <- predict(lh_medialorbitofrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_middletemporal_volume <- predict(lh_middletemporal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_parahippocampal_volume <- predict(lh_parahippocampal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_paracentral_volume <- predict(lh_paracentral_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_parsopercularis_volume <- predict(lh_parsopercularis_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_parsorbitalis_volume <- predict(lh_parsorbitalis_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_parstriangularis_volume <- predict(lh_parstriangularis_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_pericalcarine_volume <- predict(lh_pericalcarine_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_postcentral_volume <- predict(lh_postcentral_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_posteriorcingulate_volume <- predict(lh_posteriorcingulate_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_precentral_volume <- predict(lh_precentral_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_precuneus_volume <- predict(lh_precuneus_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_rostralanteriorcingulate_volume <- predict(lh_rostralanteriorcingulate_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_rostralmiddlefrontal_volume <- predict(lh_rostralmiddlefrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_superiorfrontal_volume <- predict(lh_superiorfrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_superiorparietal_volume <- predict(lh_superiorparietal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_superiortemporal_volume <- predict(lh_superiortemporal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_supramarginal_volume <- predict(lh_supramarginal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_frontalpole_volume <- predict(lh_frontalpole_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_temporalpole_volume <- predict(lh_temporalpole_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_transversetemporal_volume <- predict(lh_transversetemporal_volume, newdata = newdata, re.form = NA)
newdata$predicted_lh_insula_volume <- predict(lh_insula_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_bankssts_volume <- predict(rh_bankssts_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_caudalanteriorcingulate_volume <- predict(rh_caudalanteriorcingulate_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_caudalmiddlefrontal_volume <- predict(rh_caudalmiddlefrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_cuneus_volume <- predict(rh_cuneus_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_entorhinal_volume <- predict(rh_entorhinal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_fusiform_volume <- predict(rh_fusiform_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_inferiorparietal_volume <- predict(rh_inferiorparietal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_inferiortemporal_volume <- predict(rh_inferiortemporal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_isthmuscingulate_volume <- predict(rh_isthmuscingulate_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_lateraloccipital_volume <- predict(rh_lateraloccipital_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_lateralorbitofrontal_volume <- predict(rh_lateralorbitofrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_lingual_volume <- predict(rh_lingual_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_medialorbitofrontal_volume <- predict(rh_medialorbitofrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_middletemporal_volume <- predict(rh_middletemporal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_parahippocampal_volume <- predict(rh_parahippocampal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_paracentral_volume <- predict(rh_paracentral_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_parsopercularis_volume <- predict(rh_parsopercularis_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_parsorbitalis_volume <- predict(rh_parsorbitalis_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_parstriangularis_volume <- predict(rh_parstriangularis_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_pericalcarine_volume <- predict(rh_pericalcarine_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_postcentral_volume <- predict(rh_postcentral_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_posteriorcingulate_volume <- predict(rh_posteriorcingulate_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_precentral_volume <- predict(rh_precentral_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_precuneus_volume <- predict(rh_precuneus_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_rostralanteriorcingulate_volume <- predict(rh_rostralanteriorcingulate_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_rostralmiddlefrontal_volume <- predict(rh_rostralmiddlefrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_superiorfrontal_volume <- predict(rh_superiorfrontal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_superiorparietal_volume <- predict(rh_superiorparietal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_superiortemporal_volume <- predict(rh_superiortemporal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_supramarginal_volume <- predict(rh_supramarginal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_frontalpole_volume <- predict(rh_frontalpole_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_temporalpole_volume <- predict(rh_temporalpole_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_transversetemporal_volume <- predict(rh_transversetemporal_volume, newdata = newdata, re.form = NA)
newdata$predicted_rh_insula_volume <- predict(rh_insula_volume, newdata = newdata, re.form = NA)
newdata$predicted_Left_Cerebellum_Cortex <- predict(Left_Cerebellum_Cortex, newdata = newdata, re.form = NA)
newdata$predicted_Left_Thalamus_Proper <- predict(Left_Thalamus_Proper, newdata = newdata, re.form = NA)
newdata$predicted_Left_Caudate <- predict(Left_Caudate, newdata = newdata, re.form = NA)
newdata$predicted_Left_Putamen <- predict(Left_Putamen, newdata = newdata, re.form = NA)
newdata$predicted_Left_Pallidum <- predict(Left_Pallidum, newdata = newdata, re.form = NA)
newdata$predicted_Brain_Stem <- predict(Brain_Stem, newdata = newdata, re.form = NA)
newdata$predicted_Left_Hippocampus <- predict(Left_Hippocampus, newdata = newdata, re.form = NA)
newdata$predicted_Left_Amygdala <- predict(Left_Amygdala, newdata = newdata, re.form = NA)
newdata$predicted_Left_Accumbens_area <- predict(Left_Accumbens_area, newdata = newdata, re.form = NA)
newdata$predicted_Left_VentralDC <- predict(Left_VentralDC, newdata = newdata, re.form = NA)
newdata$predicted_Right_Cerebellum_Cortex <- predict(Right_Cerebellum_Cortex, newdata = newdata, re.form = NA)
newdata$predicted_Right_Thalamus_Proper <- predict(Right_Thalamus_Proper, newdata = newdata, re.form = NA)
newdata$predicted_Right_Caudate <- predict(Right_Caudate, newdata = newdata, re.form = NA)
newdata$predicted_Right_Putamen <- predict(Right_Putamen, newdata = newdata, re.form = NA)
newdata$predicted_Right_Pallidum <- predict(Right_Pallidum, newdata = newdata, re.form = NA)
newdata$predicted_Right_Hippocampus <- predict(Right_Hippocampus, newdata = newdata, re.form = NA)
newdata$predicted_Right_Amygdala <- predict(Right_Amygdala, newdata = newdata, re.form = NA)
newdata$predicted_Right_Accumbens_area <- predict(Right_Accumbens_area, newdata = newdata, re.form = NA)
newdata$predicted_Right_VentralDC <- predict(Right_VentralDC, newdata = newdata, re.form = NA)
newdata$predicted_TotalGrayVol <- predict(TotalGrayVol, newdata = newdata, re.form = NA)
# Create a vector containing the names of all predicted volumes
predicted_volumes <- c(
"predicted_lh_bankssts_volume",
"predicted_lh_caudalanteriorcingulate_volume",
"predicted_lh_caudalmiddlefrontal_volume",
"predicted_lh_cuneus_volume",
"predicted_lh_entorhinal_volume",
"predicted_lh_fusiform_volume",
"predicted_lh_inferiorparietal_volume",
"predicted_lh_inferiortemporal_volume",
"predicted_lh_isthmuscingulate_volume",
"predicted_lh_lateraloccipital_volume",
"predicted_lh_lateralorbitofrontal_volume",
"predicted_lh_lingual_volume",
"predicted_lh_medialorbitofrontal_volume",
"predicted_lh_middletemporal_volume",
"predicted_lh_parahippocampal_volume",
"predicted_lh_paracentral_volume",
"predicted_lh_parsopercularis_volume",
"predicted_lh_parsorbitalis_volume",
"predicted_lh_parstriangularis_volume",
"predicted_lh_pericalcarine_volume",
"predicted_lh_postcentral_volume",
"predicted_lh_posteriorcingulate_volume",
"predicted_lh_precentral_volume",
"predicted_lh_precuneus_volume",
"predicted_lh_rostralanteriorcingulate_volume",
"predicted_lh_rostralmiddlefrontal_volume",
"predicted_lh_superiorfrontal_volume",
"predicted_lh_superiorparietal_volume",
"predicted_lh_superiortemporal_volume",
"predicted_lh_supramarginal_volume",
"predicted_lh_frontalpole_volume",
"predicted_lh_temporalpole_volume",
"predicted_lh_transversetemporal_volume",
"predicted_lh_insula_volume",
"predicted_rh_bankssts_volume",
"predicted_rh_caudalanteriorcingulate_volume",
"predicted_rh_caudalmiddlefrontal_volume",
"predicted_rh_cuneus_volume",
"predicted_rh_entorhinal_volume",
"predicted_rh_fusiform_volume",
"predicted_rh_inferiorparietal_volume",
"predicted_rh_inferiortemporal_volume",
"predicted_rh_isthmuscingulate_volume",
"predicted_rh_lateraloccipital_volume",
"predicted_rh_lateralorbitofrontal_volume",
"predicted_rh_lingual_volume",
"predicted_rh_medialorbitofrontal_volume",
"predicted_rh_middletemporal_volume",
"predicted_rh_parahippocampal_volume",
"predicted_rh_paracentral_volume",
"predicted_rh_parsopercularis_volume",
"predicted_rh_parsorbitalis_volume",
"predicted_rh_parstriangularis_volume",
"predicted_rh_pericalcarine_volume",
"predicted_rh_postcentral_volume",
"predicted_rh_posteriorcingulate_volume",
"predicted_rh_precentral_volume",
"predicted_rh_precuneus_volume",
"predicted_rh_rostralanteriorcingulate_volume",
"predicted_rh_rostralmiddlefrontal_volume",
"predicted_rh_superiorfrontal_volume",
"predicted_rh_superiorparietal_volume",
"predicted_rh_superiortemporal_volume",
"predicted_rh_supramarginal_volume",
"predicted_rh_frontalpole_volume",
"predicted_rh_temporalpole_volume",
"predicted_rh_transversetemporal_volume",
"predicted_rh_insula_volume",
"predicted_Left_Cerebellum_Cortex",
"predicted_Left_Thalamus_Proper",
"predicted_Left_Caudate",
"predicted_Left_Putamen",
"predicted_Left_Pallidum",
"predicted_Brain_Stem",
"predicted_Left_Hippocampus",
"predicted_Left_Amygdala",
"predicted_Left_Accumbens_area",
"predicted_Left_VentralDC",
"predicted_Right_Cerebellum_Cortex",
"predicted_Right_Thalamus_Proper",
"predicted_Right_Caudate",
"predicted_Right_Putamen",
"predicted_Right_Pallidum",
"predicted_Right_Hippocampus",
"predicted_Right_Amygdala",
"predicted_Right_Accumbens_area",
"predicted_Right_VentralDC",
"predicted_TotalGrayVol"
)
}
#Figure Loop
{
# Create plots for each predicted volume and save them to the desktop
for (volume in predicted_volumes) {
# Create plot
p <- ggplot(newdata, aes(x = age, y = !!sym(volume), color = as.factor(bully_victim))) +
geom_line(size = 1.5, alpha = 0.7) +
scale_color_manual(values = c("red", "green", "blue"),
labels = c("25th percentile", "Mean", "75th percentile"),
name = "Bully Victim Percentiles") +
labs(title = paste("Trajectory of", volume, "by Bully Victim Percentiles"),
x = "Age",
y = "Volume mm^3") +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5),
legend.position = "bottom"
) +
guides(color = guide_legend(reverse = TRUE))
# Save plot to desktop with appropriate filename
ggsave(filename = paste("~/Desktop/IMAGEN/results_graphs/Quad_simple/Graph_", volume, ".png", sep = ""), plot = p, dpi = 400, width = 8, height = 6, units = "in")
}
}
#Figure
# Find the minimum age
min_age <- min(IMAGEN_bully$age_years, na.rm = TRUE)
# Remove the youngest participant
IMAGEN_bully <- IMAGEN_bully[IMAGEN_bully$age_years != min_age, ]
# Check the dataset after removal
head(IMAGEN_bully)
#Quad
# Loop through brain volumes
for (volume in DVlist) {
# Plot the data with quadratic lines for each group
p <- ggplot(data = IMAGEN_bully, aes_string(x = "age_years", y = volume, color = "bully_group")) +
geom_point(alpha = 0.1) + # Scatter plot
geom_smooth(aes(group = bully_group), method = "lm", formula = y ~ poly(x, 2), se = FALSE) + # Add quadratic regression lines
scale_color_manual(values = colors) + # Assign colors to groups
labs(title = paste("Trajectory of", volume, "by Bully Victim Percentiles"),
x = "Age",
y = "Volume mm^3") +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5),
legend.position = "bottom"
) +
guides(color = guide_legend(reverse = TRUE))
# Save the plot
ggsave(filename = paste("~/Desktop/IMAGEN/results_graphs/Quad/Graph_lm_", volume, ".png", sep = ""),
plot = p, dpi = 400, width = 6, height = 6, units = "in")
}
#Linear
# Loop through brain volumes
for (volume in DVlist) {
# Plot the data with linear regression lines for each group
p <- ggplot(data = IMAGEN_bully, aes_string(x = "age_years", y = volume, color = "bully_group")) +
geom_point(alpha = 0.1) + # Scatter plot
geom_smooth(aes(group = bully_group), method = "lm", se = FALSE) + # Add linear regression lines
scale_color_manual(values = colors) + # Assign colors to groups
labs(title = paste("Trajectory of", volume, "by Bully Victim Percentiles"),
x = "Age",
y = "Volume mm^3") +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5),
legend.position = "bottom"
) +
guides(color = guide_legend(reverse = TRUE))
# Save the plot
ggsave(filename = paste("~/Desktop/IMAGEN/results_graphs/Linear/Graph_lm_", volume, ".png", sep = ""),
plot = p, dpi = 400, width = 6, height = 6, units = "in")
}
# Plot the data with quadratic lines for each group
ggplot(data = IMAGEN_bully, aes(x = age_years, y = Left_Amygdala, color = bully_group)) +
geom_point(alpha = 0.1) + # Scatter plot
geom_smooth(aes(group = bully_group), method = "lm", se = FALSE) + # Add quadratic regression lines
scale_color_manual(values = colors) + # Assign colors to groups
labs(x = "Age", y = "Brain Volume (lh_cuneus)", color = "Bullying Score") + # Set axis labels
theme_minimal() # Use a minimal theme
#Sex_Figures
{
# Calculate percentiles
p25_bully_victim <- quantile(IMAGEN_bully$bully_victim, 0.25, na.rm = TRUE)
p75_bully_victim <- quantile(IMAGEN_bully$bully_victim, 0.75, na.rm = TRUE)
# Categorize bully_victim variable into two groups based on percentiles
IMAGEN_bully$bully_victim_group <- ifelse(IMAGEN_bully$bully_victim < p25_bully_victim, "Low", "High")
}
#Quad_simple
lh_lateralorbitofrontal_volume <- lmer(lh_lateralorbitofrontal_volume ~ bully_victim + age + I(age^2) + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
summary(lh_lateralorbitofrontal_volume)
effectsize(lh_lateralorbitofrontal_volume)
lh_precuneus_volume <- lmer(lh_precuneus_volume ~ bully_victim + age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
lh_superiorparietal_volume <- lmer(lh_superiorparietal_volume ~ bully_victim + age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_isthmuscingulate_volume <- lmer(rh_isthmuscingulate_volume ~ bully_victim + age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_postcentral_volume <- lmer(rh_postcentral_volume ~ bully_victim + age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
#Quad_complex
lh_parahippocampal_volume <- lmer(lh_parahippocampal_volume ~ bully_victim * age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_parahippocampal_volume <- lmer(rh_parahippocampal_volume ~ bully_victim * age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
effectsize(rh_parahippocampal_volume)
lh_transversetemporal_volume <- lmer(lh_transversetemporal_volume ~ bully_victim * age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_bankssts_volume <- lmer(rh_bankssts_volume ~ bully_victim * age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
Left_Accumbens_area <- lmer(Left_Accumbens_area ~ bully_victim * age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
Left_VentralDC <- lmer(Left_VentralDC ~ bully_victim * age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
Right_VentralDC <- lmer(Right_VentralDC ~ bully_victim * age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
Right_Putamen <- lmer(Right_Putamen ~ bully_victim * age + I(age^2) + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
#Linear_complex
lh_caudalmiddlefrontal_volume <- lmer(lh_caudalmiddlefrontal_volume ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
lh_isthmuscingulate_volume <- lmer(lh_isthmuscingulate_volume ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
lh_pericalcarine_volume <- lmer(lh_pericalcarine_volume ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
lh_postcentral_volume <- lmer(lh_postcentral_volume ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_paracentral_volume <- lmer(rh_paracentral_volume ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_precentral_volume <- lmer(rh_precentral_volume ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_precuneus_volume <- lmer(rh_precuneus_volume ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_transversetemporal_volume <- lmer(rh_transversetemporal_volume ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
Brain_Stem <- lmer(Brain_Stem ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
Right_Amygdala <- lmer(Right_Amygdala ~ bully_victim * age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
#Linear_linear
lh_inferiorparietal_volume <- lmer(lh_inferiorparietal_volume ~ bully_victim + age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
lh_lingual_volume <- lmer(lh_lingual_volume ~ bully_victim + age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_caudalmiddlefrontal_volume <- lmer(rh_caudalmiddlefrontal_volume ~ bully_victim + age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
rh_superiorparietal_volume <- lmer(rh_superiorparietal_volume ~ bully_victim + age + sex*bully_victim + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1 + age|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
{
ggpredict(lh_precuneus_volume, terms = c("bully_victim[all]", "sex")) %>%
plot() +
scale_x_continuous(name="Months Post Baseline", breaks = seq(0, 20, 2)) +
geom_point(data=IMAGEN_bully, aes(x = bully_victim, y = lh_precuneus_volume, group = sex, color = bully_victim_group), alpha = 0.1, inherit.aes = F) +
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Left Lateral Orbitofrontal Volume") +
theme_minimal()
}
{
ggpredict(lh_lateralorbitofrontal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name="Months Post Baseline", breaks = seq(0, 20, 2)) +
geom_point(data=IMAGEN_bully, aes(x = bully_victim, y = lh_lateralorbitofrontal_volume, group = subID, color = sex), inherit.aes = F, alpha = 0.1) +
labs(x = "Months post baseline", y = "Volume mm³", title = "Left Lateral Orbitofrontal Cortex") +
theme_minimal()
ggpredict(lh_precuneus_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name="Months Post Baseline", breaks = seq(0, 20, 2)) +
geom_point(data=IMAGEN_bully, aes(x = bully_victim, y = lh_precuneus_volume, group = subID, color = sex), inherit.aes = F, alpha = 0.1) +
labs(x = "Months post baseline", y = "Volume mm³", title = "Left Precuneus") +
theme_minimal()
ggpredict(lh_superiorparietal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name="Months Post Baseline", breaks = seq(0, 20, 2)) +
geom_point(data=IMAGEN_bully, aes(x = bully_victim, y = lh_superiorparietal_volume, group = subID, color = sex), inherit.aes = F, alpha = 0.1) +
labs(x = "Months post baseline", y = "Volume mm³", title = "Left Superior Parietal") +
theme_minimal()
ggpredict(rh_isthmuscingulate_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name="Months Post Baseline", breaks = seq(0, 20, 2)) +
geom_point(data=IMAGEN_bully, aes(x = bully_victim, y = rh_isthmuscingulate_volume, group = subID, color = sex), inherit.aes = F, alpha = 0.1) +
labs(x = "Months post baseline", y = "Volume mm³", title = "Right Isthmus Cingulate") +
theme_minimal()
ggpredict(rh_postcentral_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name="Months Post Baseline", breaks = seq(0, 20, 2)) +
geom_point(data=IMAGEN_bully, aes(x = bully_victim, y = rh_postcentral_volume, group = subID, color = sex), inherit.aes = F, alpha = 0.1) +
labs(x = "Months post baseline", y = "Volume mm³", title = "Right Post Cental") +
theme_minimal()
}
# Load necessary libraries
library(ggplot2)
library(patchwork)
#Quad_simple
{
# Generate predictions and plots for each brain region
plot1 <- ggpredict(lh_lateralorbitofrontal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_lateralorbitofrontal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Lateral Orbitofrontal Cortex") +
theme_minimal()
plot2 <- ggpredict(lh_precuneus_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_precuneus_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Precuneus") +
theme_minimal()
plot3 <- ggpredict(lh_superiorparietal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_superiorparietal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Superior Parietal") +
theme_minimal()
plot4 <- ggpredict(rh_isthmuscingulate_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_isthmuscingulate_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Isthmus Cingulate") +
theme_minimal()
plot5 <- ggpredict(rh_postcentral_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_postcentral_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Post Central") +
theme_minimal()
# Combine all plots into one figure
combined_plots_Quad_simple <- plot1 + plot2 + plot3 + plot4 + plot5
# Print the combined figure
print(plot1)
# Save the combined figure with 400 DPI
ggsave("combined_plots_Quad_simple.png", combined_plots_Quad_simple, dpi = 400)
}
#Quad_complex
{
# Generate predictions and plots for each brain region
plot6 <- ggpredict(lh_parahippocampal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_parahippocampal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Parahippocampal") +
theme_minimal()
plot7 <- ggpredict(rh_parahippocampal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_parahippocampal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Parahippocampal") +
theme_minimal()
plot8 <- ggpredict(lh_transversetemporal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_transversetemporal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Transverse Temporal") +
theme_minimal()
plot9 <- ggpredict(rh_bankssts_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_bankssts_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Bankssts") +
theme_minimal()
plot10 <- ggpredict(Left_Accumbens_area, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = Left_Accumbens_area, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Accumbens") +
theme_minimal()
plot11 <- ggpredict(Left_VentralDC, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = Left_VentralDC, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Ventral DC") +
theme_minimal()
plot12 <- ggpredict(Right_VentralDC, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = Right_VentralDC, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Ventral DC") +
theme_minimal()
plot13 <- ggpredict(Right_Putamen, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = Right_Putamen, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Putamen") +
theme_minimal()
# Combine all plots into one figure
combined_plots_Quad_complex <- plot6 + plot7 + plot8 + plot9 + plot10 + plot11 + plot12 + plot13
# Print the combined figure
print(plot1)
# Save the combined figure with 400 DPI
ggsave("combined_plots_Quad_complex", combined_plots_Quad_simple, dpi = 400)
}
ggpredict(rh_parahippocampal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_parahippocampal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Parahippocampal") +
theme_minimal() +
scale_color_manual(values = c("female" = "blue", "male" = "red")) # Specify the correct colors for females and males
#Linear_simple
{
# Generate predictions and plots for each brain region
plot14 <- ggpredict(lh_inferiorparietal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_inferiorparietal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Inferior Parietal") +
theme_minimal()
plot15 <- ggpredict(lh_lingual_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_lingual_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Lingual") +
theme_minimal()
plot16 <- ggpredict(rh_caudalmiddlefrontal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_caudalmiddlefrontal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Caudal Middle Frontal") +
theme_minimal()
plot17 <- ggpredict(rh_superiorparietal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_superiorparietal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Superior Parietal") +
theme_minimal()
# Combine all plots into one figure
combined_plots_L_simple <- plot14 + plot15 + plot16 + plot17
# Print the combined figure
print(plot1)
# Save the combined figure with 400 DPI
ggsave("combined_plots_L_simple.png", combined_plots_L_simple, dpi = 400)
}
#Linear_complex
{
# Generate predictions and plots for each brain region
plot18 <- ggpredict(lh_caudalmiddlefrontal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_caudalmiddlefrontal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Caudal Middle Frontal") +
theme_minimal()
plot19 <- ggpredict(lh_isthmuscingulate_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_isthmuscingulate_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Isthmus Cingulate") +
theme_minimal()
plot20 <- ggpredict(lh_pericalcarine_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_pericalcarine_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Pericalarine") +
theme_minimal()
plot21 <- ggpredict(lh_postcentral_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = lh_postcentral_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Left Postcentral") +
theme_minimal()
plot22 <- ggpredict(rh_paracentral_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_paracentral_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Paracentral") +
theme_minimal()
plot23 <- ggpredict(rh_precentral_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_precentral_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Precentral") +
theme_minimal()
plot24 <- ggpredict(rh_precuneus_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_precuneus_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Precuneus") +
theme_minimal()
plot25 <- ggpredict(rh_transversetemporal_volume, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = rh_transversetemporal_volume, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Transverse Temporal") +
theme_minimal()
plot26 <- ggpredict(Brain_Stem, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = Brain_Stem, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Brain Stem") +
theme_minimal()
plot27 <- ggpredict(Right_Amygdala, terms = c("bully_victim[all]", "sex[all]")) %>%
plot() +
scale_x_continuous(name = "OB/VQ Score", breaks = seq(0, 20, 4)) +
geom_point(data = IMAGEN_bully, aes(x = bully_victim, y = Right_Amygdala, group = subID, color = sex), inherit.aes = FALSE, alpha = 0.1) +
labs(x = "OB/VQ Score", y = "Volume mm³", title = "Right Amygdala") +
theme_minimal()
# Combine all plots into one figure
combined_plots_L_complex <- plot18 + plot19 + plot20 + plot21 + plot22 + plot23 + plot24 + plot25 + plot26 + plot27
# Print the combined figure
print(plot7)
# Save the combined figure with 400 DPI
ggsave("combined_plots_L_complex.png", combined_plots_L_complex, dpi = 400)
}
{
# Calculate quartiles separately for males and females
male_bully <- IMAGEN_bully$bully_victim[IMAGEN_bully$sex == "male"]
female_bully <- IMAGEN_bully$bully_victim[IMAGEN_bully$sex == "female"]
p25_male <- quantile(male_bully, 0.25, na.rm = TRUE) # 25th percentile for males
p75_male <- quantile(male_bully, 0.75, na.rm = TRUE) # 75th percentile for males
p25_female <- quantile(female_bully, 0.25, na.rm = TRUE) # 25th percentile for females
p75_female <- quantile(female_bully, 0.75, na.rm = TRUE) # 75th percentile for females
# Create a factor variable for grouping the bullying scores into four groups based on gender and quartiles
IMAGEN_bully$bully_victim_group <- ifelse(IMAGEN_bully$sex == "male",
ifelse(IMAGEN_bully$bully_victim <= p25_male, "male_low",
ifelse(IMAGEN_bully$bully_victim <= p75_male, "male_high", NA)),
ifelse(IMAGEN_bully$bully_victim <= p25_female, "female_low",
ifelse(IMAGEN_bully$bully_victim <= p75_female, "female_high", NA)))
IMAGEN_bully$bully_victim_group <- factor(IMAGEN_bully$bully_victim_group,
levels = c("male_low", "male_high", "female_low", "female_high"),
labels = c("Male Low", "Male High", "Female Low", "Female High"))
IMAGEN_bully <- IMAGEN_bully[!is.na(IMAGEN_bully$bully_victim_group), ]
# Create a color palette for differentiating the groups
bully_colors <- c("male_low" = "red", "male_high" = "blue", "female_low" = "darkgreen", "female_high" = "purple") # Only two colors for two groups
Left_Accumbens_area <- lmer(Left_Accumbens_area ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_Left_Accumbens_area <- ggpredict(Left_Accumbens_area,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_parahippocampal_volume <- lmer(rh_parahippocampal_volume ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_parahippocampal_volume <- ggpredict(rh_parahippocampal_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
lh_parahippocampal_volume <- lmer(lh_parahippocampal_volume ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_lh_parahippocampal_volume <- ggpredict(lh_parahippocampal_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
Right_Putamen <- lmer(Right_Putamen ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_Right_Putamen <- ggpredict(Right_Putamen,
terms = c("age_years[all]", "bully_victim_group[all]"))
Right_VentralDC <- lmer(Right_VentralDC ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_Right_VentralDC <- ggpredict(Right_VentralDC,
terms = c("age_years[all]", "bully_victim_group[all]"))
Left_VentralDC <- lmer(Left_VentralDC ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_Left_VentralDC <- ggpredict(Left_VentralDC,
terms = c("age_years[all]", "bully_victim_group[all]"))
Right_Amygdala <- lmer(Right_Amygdala ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_Right_Amygdala <- ggpredict(Right_Amygdala,
terms = c("age_years[all]", "bully_victim_group[all]"))
Brain_Stem <- lmer(Brain_Stem ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_Brain_Stem <- ggpredict(Brain_Stem,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_bankssts_volume <- lmer(rh_bankssts_volume ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_bankssts_volume <- ggpredict(rh_bankssts_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
lh_isthmuscingulate_volume <- lmer(lh_isthmuscingulate_volume ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_lh_isthmuscingulate_volume <- ggpredict(lh_isthmuscingulate_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
lh_caudalmiddlefrontal_volume <- lmer(lh_caudalmiddlefrontal_volume ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_lh_caudalmiddlefrontal_volume <- ggpredict(lh_caudalmiddlefrontal_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_precuneus_volume <- lmer(rh_precuneus_volume ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_precuneus_volume<- ggpredict(rh_precuneus_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_transversetemporal_volume <- lmer(rh_transversetemporal_volume ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_transversetemporal_volume<- ggpredict(rh_transversetemporal_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
lh_pericalcarine_volume <- lmer(lh_pericalcarine_volume ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_lh_pericalcarine_volume<- ggpredict(lh_pericalcarine_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
lh_postcentral_volume <- lmer(lh_postcentral_volume ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_lh_postcentral_volume<- ggpredict(lh_postcentral_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
Left_Pallidum <- lmer(Left_Pallidum ~ bully_victim_group * age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_Left_Pallidum<- ggpredict(Left_Pallidum,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_paracentral_volume <- lmer(rh_paracentral_volume ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_paracentral_volume<- ggpredict(rh_paracentral_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
lh_superiorparietal_volume <- lmer(lh_superiorparietal_volume ~ bully_victim_group + age_years + sex*bully_victim_group + I(age_years^2) + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_lh_superiorparietal_volume<- ggpredict(lh_superiorparietal_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_superiorparietal_volume <- lmer(rh_superiorparietal_volume ~ bully_victim_group + age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_superiorparietal_volume<- ggpredict(rh_superiorparietal_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
lh_inferiorparietal_volume <- lmer(lh_inferiorparietal_volume ~ bully_victim_group + age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_lh_inferiorparietal_volume<- ggpredict(lh_inferiorparietal_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_isthmuscingulate_volume <- lmer(rh_isthmuscingulate_volume ~ bully_victim_group + age_years + I(age_years^2) + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_isthmuscingulate_volume<- ggpredict(rh_isthmuscingulate_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_caudalmiddlefrontal_volume <- lmer(rh_caudalmiddlefrontal_volume ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_caudalmiddlefrontal_volume<- ggpredict(rh_caudalmiddlefrontal_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
rh_precentral_volume <- lmer(rh_precentral_volume ~ bully_victim_group * age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_rh_precentral_volume<- ggpredict(rh_precentral_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
lh_lingual_volume <- lmer(lh_lingual_volume ~ bully_victim_group + age_years + sex*bully_victim_group + SES + mode_c_pds + EstimatedTotalIntraCranialVol + (1|subID) + (1|scan_site), data = IMAGEN_bully, REML = TRUE, control = lmerControl(optimizer = "nloptwrap"))
predicted_values_lh_lingual_volume<- ggpredict(lh_lingual_volume,
terms = c("age_years[all]", "bully_victim_group[all]"))
#Bully sex graphs
plot18 <- plot(predicted_values_Left_Accumbens_area) +
scale_x_continuous(name = "Age (Years)", breaks = seq(0, 24, 4)) +
geom_point(data = IMAGEN_bully, aes(x = age_years, y = Left_Accumbens_area,
group = subID, color = bully_victim_group), alpha = 0.1, inherit.aes = FALSE) +
geom_smooth(method = "gam", se = TRUE, size = 1, alpha = 0.3) + # Increase line size for better visibility
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Left Accumbens") +
coord_cartesian(ylim = c(400, 1100)) + # Adjust the y-axis limits to zoom in on the range of interest
theme_minimal() + # Use minimal theme
theme(text = element_text(family = "Times New Roman"), # Set font family
axis.title = element_text(size = 10), # Customize axis title
axis.text = element_text(size = 8), # Customize axis text
legend.position = "none", # Hide the legend
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))# Cente
# Plot predicted values with smoother prediction lines
plot19 <- plot(predicted_values_rh_parahippocampal_volume) +
scale_x_continuous(name = "Age (Years)", breaks = seq(0, 24, 4)) +
geom_point(data = IMAGEN_bully, aes(x = age_years, y = rh_parahippocampal_volume,
group = subID, color = bully_victim_group), alpha = 0.1, inherit.aes = FALSE) +
geom_smooth(method = "gam", se = TRUE, size = 1, alpha = 0.3) + # Increase line size for better visibility
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Right Parahippocampal Gyrus") +
coord_cartesian(ylim = c(1800, 2800)) + # Adjust the y-axis limits to zoom in on the range of interest
theme_minimal() + # Use minimal theme
theme(text = element_text(family = "Times New Roman"), # Set font family
axis.title = element_text(size = 10), # Customize axis title
axis.text = element_text(size = 8), # Customize axis text
legend.position = "none", # Hide the legend
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))# Cente
# Plot predicted values with smoother prediction lines
plot20 <- plot(predicted_values_lh_parahippocampal_volume) +
scale_x_continuous(name = "Age (Years)", breaks = seq(0, 24, 4)) +
geom_point(data = IMAGEN_bully, aes(x = age_years, y = lh_parahippocampal_volume,
group = subID, color = bully_victim_group), alpha = 0.1, inherit.aes = FALSE) +
geom_smooth(method = "gam", se = TRUE, size = 1.5, alpha = 0.3) + # Increase line size for better visibility
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Left Parahippocampal Gyrus") +
coord_cartesian(ylim = c(1800, 3000)) + # Adjust the y-axis limits to zoom in on the range of interest
theme_minimal() + # Use minimal theme
theme(text = element_text(family = "Times New Roman"), # Set font family
axis.title = element_text(size = 10), # Customize axis title
axis.text = element_text(size = 8), # Customize axis text
legend.position = "none", # Hide the legend
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))# Cente
# Plot predicted values with smoother prediction lines
plot21 <- plot(predicted_values_Right_Putamen) +
scale_x_continuous(name = "Age (Years)", breaks = seq(0, 24, 4)) +
geom_point(data = IMAGEN_bully, aes(x = age_years, y = Right_Putamen,
group = subID, color = bully_victim_group), alpha = 0.1, inherit.aes = FALSE) +
geom_smooth(method = "gam", se = TRUE, size = 1.5, alpha = 0.3) + # Increase line size for better visibility
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Right Putamen") +
coord_cartesian(ylim = c(4000, 8000)) + # Adjust the y-axis limits to zoom in on the range of interest
theme_minimal() + # Use minimal theme
theme(text = element_text(family = "Times New Roman"), # Set font family
axis.title = element_text(size = 10), # Customize axis title
axis.text = element_text(size = 8), # Customize axis text
legend.position = "none", # Hide the legend
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))# Cente
# Plot predicted values with smoother prediction lines
plot22 <- plot(predicted_values_Right_VentralDC) +
scale_x_continuous(name = "Age (Years)", breaks = seq(0, 24, 4)) +
geom_point(data = IMAGEN_bully, aes(x = age_years, y = Right_VentralDC,
group = subID, color = bully_victim_group), alpha = 0.1, inherit.aes = FALSE) +
geom_smooth(method = "gam", se = TRUE, size = 1.5, alpha = 0.3) + # Increase line size for better visibility
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Right Ventral Dienchephalon") +
theme_minimal() + # Use minimal theme
coord_cartesian(ylim = c(3000, 5000)) + # Adjust the y-axis limits to zoom in on the range of interest
theme(text = element_text(family = "Times New Roman"), # Set font family
axis.title = element_text(size = 10), # Customize axis title
axis.text = element_text(size = 8), # Customize axis text
legend.position = "none", # Hide the legend
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))# Cente
plot23 <- plot(predicted_values_Left_VentralDC) +
scale_x_continuous(name = "Age (Years)", breaks = seq(0, 24, 4)) +
geom_point(data = IMAGEN_bully, aes(x = age_years, y = Left_VentralDC,
group = subID, color = bully_victim_group), alpha = 0.1, inherit.aes = FALSE) +
geom_smooth(method = "gam", se = TRUE, size = 1.5, alpha = 0.3) + # Increase line size for better visibility
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Left Ventral Dienchephalon") +
coord_cartesian(ylim = c(2750, 5250)) + # Adjust the y-axis limits to zoom in on the range of interest
theme_minimal() + # Use minimal theme
theme(text = element_text(family = "Times New Roman"), # Set font family
axis.title = element_text(size = 10), # Customize axis title
axis.text = element_text(size = 8), # Customize axis text
legend.position = "none", # Hide the legend
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))# Cente
plot24 <- plot(predicted_values_Right_Amygdala) +
scale_x_continuous(name = "Age (Years)", breaks = seq(0, 24, 4)) +
geom_point(data = IMAGEN_bully, aes(x = age_years, y = Right_Amygdala,
group = subID, color = bully_victim_group), alpha = 0.1, inherit.aes = FALSE) +
geom_smooth(method = "gam", se = TRUE, size = 1.5, alpha = 0.3) + # Increase line size for better visibility
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Right Amygdala") +
theme_minimal() + # Use minimal theme
theme(text = element_text(family = "Times New Roman"), # Set font family
axis.title = element_text(size = 10), # Customize axis title
axis.text = element_text(size = 8), # Customize axis text
legend.position = "none", # Hide the legend
plot.title = element_text(size = 14, face = "bold", hjust = 0.5))# Cente
plot27 <- plot(predicted_values_rh_bankssts_volume) +
scale_x_continuous(name = "Age (Years)", breaks = seq(0, 24, 4)) +
geom_point(data = IMAGEN_bully, aes(x = age_years, y = rh_bankssts_volume,
group = subID, color = bully_victim_group), alpha = 0.1, inherit.aes = FALSE) +
geom_smooth(method = "gam", se = TRUE, size = 1.5, alpha = 0.3) + # Increase line size for better visibility
labs(x = "Bully Victim Score", y = "Volume mm³", title = "Right Bankssts") +
coord_cartesian(ylim = c(1500, 4000)) + # Adjust the y-axis limits to zoom in on the range of interest
theme_minimal() + # Use minimal theme
theme(text = element_text(family = "Times New Roman"), # Set font family
axis.title = element_text(size = 10), # Customize axis title
axis.text = element_text(size = 8), # Customize axis text
legend.posi

LME_Model_Metrics.R at commit 376ccea, no license · at the source

OverviewAuthors: Michael Connaughton1, Orla Mitchell1, Emer Cullen1, Michael O’Connor1, Tobias Banaschewski2,3, Gareth J Barker4, Arun L W Bokde5, Rüdiger Brühl6, Sylvane Desrivières7, Herta Flor8,9, Hugh Garavan10, Penny Gowland11, Antoine Grigis12, Andreas Heinz13,14, Herve Lemaitre12,15, Jean-Luc Martinot16, Marie-Laure Paillère Martinot16,17, Eric Artiges16,18, Frauke Nees2,3,19, Dimitri Papadopoulos Orfanos9and 9 other authorsLuise Poustka20, Michael N Smolka21, Sarah Hohmann2,3, Nathalie Holz2,3, Nilakshi Vaidya22, Henrik Walter13, Gunter Schumann21,23, Robert Whelan24, Darren Roddy1
ORCID iDs: Tobias Banaschewski, Gareth J Barker, Arun L W Bokde, Rüdiger Brühl, Sylvane Desrivières, Herta Flor, Hugh Garavan, Penny Gowland, Andreas Heinz, Herve Lemaitre, Jean-Luc Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Michael N Smolka, Sarah Hohmann, Nathalie Holz, Nilakshi Vaidya, Henrik Walter, Gunter Schumann, Robert Whelan, Darren Roddy
24 affiliationsDepartment of Psychiatry, Royal College of Surgeons in Ireland, Dublin, 2 Ireland
Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Mannheim, Germany
Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Braunschweig, Germany
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT USA
Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
German Center for Mental Health (DZPG), Berlin-Potsdam, Mannheim, Germany
Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076 Bordeaux, France
Institut National de la Santé et de la Recherce Médicale, INSERM U A10 “Trajectoires développementales & psychiatrie”, University Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS; Centre Borelli, Gif-sur-Yvette, France
AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
Psychiatry Department, EPS Barthélémy Durand, Etampes, France
Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
Institutions: Royal College of Surgeons in Ireland (Ireland); Heidelberg University (Germany); Central Institute of Mental Health (Germany); Medizinische Fakultät Mannheim; Deutsches Zentrum für Psychische Gesundheit (Germany); King's College London (United Kingdom); Trinity College Dublin (Ireland); Physikalisch-Technische Bundesanstalt (Germany); University of Mannheim (Germany); University of Vermont (United States); University of Nottingham (United Kingdom); Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); Université Paris-Saclay (France); CEA Paris-Saclay - Etablissement de Saclay (France); CEA Paris-Saclay (France); Humboldt-Universität zu Berlin (Germany); Charité - Universitätsmedizin Berlin (Germany); Centre National de la Recherche Scientifique (France); Université de Bordeaux (France); Institut des Maladies Neurodégénératives (France); École Normale Supérieure Paris-Saclay (France); Inserm (France); Trajectoires développementales & psychiatrie; Centre Borelli (France); Sorbonne Université (France); Assistance Publique – Hôpitaux de Paris (France); Pitié-Salpêtrière Hospital (France); Christian-Albrechts-Universität zu Kiel (Germany); University Hospital Schleswig-Holstein (Germany); University Hospital Heidelberg (Germany); Technische Universität Dresden (Germany); Fudan University (China)
Journal: Translational psychiatry, volume 16, issue 1, article 256
Dates: received 16 May 2025; accepted 24 March 2026; published online 10 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-04010-9 · PMID 41963295 · PMCID PMC13184360 · OpenAlex W4402519664
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.09.11.611600
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Statistics, Preprocessing, fMRI & imaging
Keywords: Human behaviour, Neuroscience
MeSH: Brain*, Bullying*, Crime Victims*, Adolescent, Adult, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Child and Adolescent Psychosocial and Emotional Development (Clinical Psychology, Psychology), according to OpenAlex
Funding: Fondation de l'Avenir pour la Recherche Médicale Appliquée (AP-RM-17-013); UK Research and Innovation (101057429, 10038599, 10041392); University of Oxford; European Regional Development Fund (NE 1383/14, NE 1383/14-1, 695313, ANR-18-NEUR00002-01-ADORe, TRR 265, 5U54EB020403-05, U54 EB020403, SFB 940, 00081242, 101057429, MR/S020306/1, 1R56AG058854-01, 16/ERCD/3797, 01GL1745B, AP-RM-17-013, 785907, R01DA049238, MR/R00465X/1, 82150710554, 01EE1406B, 01EE1406A, ANR-12-SAMA-0004, LSHMCT-2007-037286, 01EV0711, 945539, DPA20140629802); Science Foundation Ireland (01GL1745B, MR/R00465X/1, TRR 265, 101057429, 16/ERCD/3797, 945539, NE 1383/14-1, 82150710554, MR/S020306/1, ANR-12-SAMA-0004, 5U54EB020403-05, 01EV0711, DPA20140629802, 785907, 695313, SFB 940, 01EE1406A, ANR-18-NEUR00002-01-ADORe, LSHM-CT-2007-037286, AP-RM-17-013, R01DA049238, 01EE1406B, 00081242, 01GS08152, U54 EB020403, 1R56AG058854-01); Deutsche Forschungsgemeinschaft (16/ERCD/3797, ANR-12-SAMA-0004, 695313, FOR1617, AAPG2019, DPA20140629802, 178833530, LSHM-CT-2007-037286, U54 EB020403, 785907, MR/R00465X/1, 1R56AG058854-01, 01EE1406A, 01GS08152, TRR 265, 186318919, IRTG 2773, MR/S020306/1, NE1383/14-1, 00081242, 101057429, 458317126, 01GL1745B, 5U54EB020403-05, 945539, 82150710554, AP-RM-17-013, ANR-18-NEUR00002-01—ADORe, 386691645, 01EE1406B, /14-1, 402170461, 01EV0711, 454245598, R01DA049238, SFB940); Agence Nationale de la Recherche (82150710554, R01DA049238, ANR-18-NEUR00002-01-ADORe, DPA20140629802, 695313, SFB 940, 101057429, MR/S020306/1, 01GL1745B, 01EE1406A, AF12-NEUR0008-01, ANR-12-SAMA-0004, 785907, LSHM-CT-2007-037286, 01EV0711, TRR 265, ANR18, 5U54EB020403-05, AAPG2019, U54 EB020403, NE 1383/14-1, WM2NA, MR/R00465X/1, 945539, 01EE1406B, ANR-12, 16/ERCD/3797, 00081242, AP-RM-17-013); Institut National de la Santé et de la Recherche Médicale (IDEX-2012); National Natural Science Foundation of China (SFB 940, ANR-12-SAMA-0004, 82150710554, 01EE1406B, 785907, LSHM-CT-2007-037286, 00081242, 1R56AG058854-01, AP-RM-17-013, TRR 265, 01EE1406A, 16/ERCD/3797, 01GL1745B, ANR-18-NEUR00002-01-ADORe, U54 EB020403, 01EV0711, 695313, R01DA049238, DPA20140629802, MR/R00465X/1, 945539, 5U54EB020403-05, MR/S020306/1, NE 1383/14-1, 101057429); Bundesministerium für Bildung und Forschung (MR/S020306/1, 785907, 695313, 16/ERCD/3797, R01DA049238, U54 EB020403, MR/R00465X/1, 178833530, ANR-18-NEUR00002-01-ADORe, LSHM-CT-2007-037286, 402170461, SFB 940, ANR-12-SAMA-0004, 01GS08152, 01EE1406B, NE 1383/14-1, 945539, 00081242, DPA20140629802, TRR 265, 01EV0711, AERIAL 01EE1406A, 5U54EB020403-05, 01GL1745B, AP-RM-17-013, 101057429, 82150710554, 01EE1406A, 1R56AG058854-01); Fondation pour la Recherche Médicale (945539, LSHM-CT-2007-037286, 01EE1406B, 01GL1745B, 16/ERCD/3797, 101057429, DPP 20151033945, MR/R00465X/1, TRR 265, ANR-18-NEUR00002-01—ADORe, U54 EB020403, 82150710554, 1R56AG058854-01, NE 1383/14-1, 01EV0711, 695313, 00081242, SFB 940, MR/S020306/1, 01EE1406A, AP-RM-17-013, 01GS08152, ANR-12-SAMA-0004, R01DA049238, DPA20140629802, 785907, 5U54EB020403-05); Fondation de France (NE 1383/14-1, 01EE1406B, 01GS08152, SFB 940, 101057429, 695313, MR/R00465X/1, 01EE1406A, AP-RM-17-013, ANR-18-NEUR00002-01-ADORe, 82150710554, 1R56AG058854-01, TRR 265, R01DA049238, 01GL1745B, 5U54EB020403-05, LSHM-CT-2007-037286, 785907, 00081242, MR/S020306/1, 945539, ANR-12-SAMA-0004, DPA20140629802, 01EV0711, U54 EB020403, 16/ERCD/3797); Université Paris Descartes; Fédération pour la Recherche sur le Cerveau; National Institutes of Health (ANR-18-NEUR00002-01—ADORe, 00081242, MR/S020306/1, 1r56ag058854-01, 82150710554, 695313, SFB 940, AP-RM-17-013, NE 1383/14-1, 785907, 945539, TRR 265, DPA20140629802, 101057429, 01GL1745B, LSHM-CT-2007-037286, 5u54eb020403-03, 01GS08152, ANR-12-SAMA-0004, 5u54eb020403-05, 01EE1406A, 5r01da049238-05, 01EE1406B, MR/R00465X/1, 01EV0711, 16/ERCD/3797, R01DA049238, 1R56AG058854, U54‐EB020403, EB020403); HORIZON EUROPE Framework Programme (#101057429, 945539, 10041392, 785907, 10038599, 695313); Medical Research Council (01GL1745B, R01DA049238, LSHM-CT-2007-037286, 10041392, 00081242, MR/S020306/1, AP-RM-17-013, 10038599, TRR 265, 01EV0711, 01GS08152, 785907, 82150710554, DPA20140629802, 16/ERCD/3797, 101057429, 5U54EB020403-05, ANR-12-SAMA-0004, 1R56AG058854-01, 945539, SFB 940, ANR-18-NEUR00002-01-ADORe, U54 EB020403, NE 1383/14-1, 695313, 01EE1406A, 01EE1406B, MR/R00465X/1); Cilag
Citations: cited by 1 paper (Europe PMC); 89 references in the paper
AbstractThe 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 2 matches between paragraphs and lines of code.

mconnaug/Bullying_Brain_Development

License: none: the authors keep all their rights

State: the link answers, verified on 29 September 2026

Evidence: files inventoried

Commit: 376ccea89794f43793e6d195e949a31655fb5a5f, 19 August 2024

Languages: R (1)

Size: 2 files, 1 script

Software Heritage: not archived

Found in: “Code availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: broom (1 file), data.table (1 file), easystats (1 file), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), ggseg (1 file), lavaan (1 file), lme4 (1 file), lmerTest (1 file), nlme (1 file), patchwork (1 file), psych (1 file), rstatix (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 29 September 2026: the link answers

29 September 2026: the link answers

2 files

LME_Model_Metrics.R, R, 2,880 lines, 2 matches
README.md, Text, 50 lines

At the source: github.com/mconnaug/Bullying_Brain_Development

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: mconnaug/Bullying_Brain_Development

Read it in the paper: doi.org/10.1038/s41398-026-04010-9.

Tracing map

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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;
1 script, each with its path and the digest of its content;
2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

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Data

Datasets cited

github.com/imagen2/imagen_mri, at github.com; found in “Data availability”

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to a dataset: github.com/imagen2/imagen_mri
it points to the authors' code: mconnaug/Bullying_Brain_Development
it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41398-026-04010-9.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 29 authors, 2 keywords, 11 MeSH terms, 18 funders, 74 references.

Cite

This paper

Connaughton, M., Mitchell, O., Cullen, E., O’Connor, M., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Brühl, R., Desrivières, S., Flor, H., Garavan, H., Gowland, P., Grigis, A., Heinz, A., Lemaitre, H., Martinot, J.-L., Martinot, M.-L. P., Artiges, E., Nees, F., . . . Roddy, D. (2026). Bullying victimization and brain development: a longitudinal structural magnetic resonance imaging study from adolescence to early adulthood. Translational psychiatry, 16(1), 256. https://doi.org/10.1038/s41398-026-04010-9

BibTeX

@article{connaughton2026bullying, author = {Connaughton, Michael and Mitchell, Orla and Cullen, Emer and O’Connor, Michael and Banaschewski, Tobias and Barker, Gareth J and Bokde, Arun L W and Brühl, Rüdiger and Desrivières, Sylvane and Flor, Herta and Garavan, Hugh and Gowland, Penny and Grigis, Antoine and Heinz, Andreas and Lemaitre, Herve and Martinot, Jean-Luc and Martinot, Marie-Laure Paillère and Artiges, Eric and Nees, Frauke and Orfanos, Dimitri Papadopoulos and Poustka, Luise and Smolka, Michael N and Hohmann, Sarah and Holz, Nathalie and Vaidya, Nilakshi and Walter, Henrik and Schumann, Gunter and Whelan, Robert and Roddy, Darren}, title = {{Bullying victimization and brain development: a longitudinal structural magnetic resonance imaging study from adolescence to early adulthood}}, journal = {Translational psychiatry}, year = {2026}, month = apr, volume = {16}, number = {1}, pages = {256}, publisher = {Nature Publishing Group}, issn = {2158-3188}, doi = {10.1038/s41398-026-04010-9}, url = {https://doi.org/10.1038/s41398-026-04010-9}, pmid = {41963295}, pmcid = {PMC13184360} }

RIS

TY - JOUR AU - Connaughton, Michael AU - Mitchell, Orla AU - Cullen, Emer AU - O’Connor, Michael AU - Banaschewski, Tobias AU - Barker, Gareth J AU - Bokde, Arun L W AU - Brühl, Rüdiger AU - Desrivières, Sylvane AU - Flor, Herta AU - Garavan, Hugh AU - Gowland, Penny AU - Grigis, Antoine AU - Heinz, Andreas AU - Lemaitre, Herve AU - Martinot, Jean-Luc AU - Martinot, Marie-Laure Paillère AU - Artiges, Eric AU - Nees, Frauke AU - Orfanos, Dimitri Papadopoulos AU - Poustka, Luise AU - Smolka, Michael N AU - Hohmann, Sarah AU - Holz, Nathalie AU - Vaidya, Nilakshi AU - Walter, Henrik AU - Schumann, Gunter AU - Whelan, Robert AU - Roddy, Darren TI - Bullying victimization and brain development: a longitudinal structural magnetic resonance imaging study from adolescence to early adulthood T2 - Translational psychiatry J2 - Transl Psychiatry PY - 2026 DA - 2026/04/10 VL - 16 IS - 1 SP - 256 SN - 2158-3188 PB - Nature Publishing Group DO - 10.1038/s41398-026-04010-9 UR - https://doi.org/10.1038/s41398-026-04010-9 LA - en ER -

CSL-JSON

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