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analysis_tms_fmri_gonogo.R
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analysis_tms_fmri_gonogo.R
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setwd('/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project')
df=read.csv('data/tms-plip-out_061523_edit.csv')
img_vars=c('act_gonogo_nogovsgo_013136', 'act_gonogo_nogovsgo_533294', 'act_gonogo_nogovsgo_291082', 'ppi_gonogo_thrvneu_013136_291082', 'ppi_gonogo_thrvneu_291082_013136','ppi_gonogo_thrvneu_533294_291082', 'ppi_gonogo_thrvneu_291082_533294')
img_vars_rename=c('act_gonogo_LdlPFC', 'act_gonogo_RdlPFC', 'act_gonogo_MdACC', 'ppi_gonogo_LdlPFC_MdACC', 'ppi_gonogo_MdACC_LdlPFC', 'ppi_gonogo_RdlPFC_MdACC', 'ppi_gonogo_MdACC_RdlPFC')
library(data.table)
setnames(df, img_vars, img_vars_rename)
#### COMBAT correction ####
# Import controls
hc=read.csv('data/data_hc.csv')
# Make hc and clinical have similar variables
img_vars_hc=c('id', 'scanner', 'act.gng.nogo_vs_go.013136_Left_dlPFC', 'act.gng.nogo_vs_go.533294_Right_dlPFC', 'act.gng.nogo_vs_go.291082_Medial_dACC', 'ppi.gng.nogo_vs_go.013136_Left_dlPFC.291082_Medial_dACC', 'ppi.gng.nogo_vs_go.291082_Medial_dACC.013136_Left_dlPFC', 'ppi.gng.nogo_vs_go.533294_Right_dlPFC.291082_Medial_dACC', 'ppi.gng.nogo_vs_go.291082_Medial_dACC.533294_Right_dlPFC')
img_vars_rename_hc=c('Subjects', 'Site', 'act_gonogo_LdlPFC', 'act_gonogo_RdlPFC', 'act_gonogo_MdACC', 'ppi_gonogo_LdlPFC_MdACC', 'ppi_gonogo_MdACC_LdlPFC', 'ppi_gonogo_RdlPFC_MdACC', 'ppi_gonogo_MdACC_RdlPFC')
setnames(hc, img_vars_hc, img_vars_rename_hc)
hc$Site=factor(hc$Site, labels = c('Sidney', 'Palo Alto Discovery'))
hc$Visit=0
df$group=1
# Merge the hc and clinical
img_only=rbind(df[, c('Visit', 'group', img_vars_rename_hc)], hc[, c('Visit', 'group', img_vars_rename_hc)])
# COMBAT
library(ez.combat)
img_only_adjusted_combat = img_only[complete.cases(img_only[c('Visit', 'Subjects', 'Site', 'group', img_vars_rename)]), ]
cb=ez.combat(img_only_adjusted_combat,
'Site',
adjust.var = img_vars_rename,
output = c("overwrite"),
use.eb = TRUE,
verbose = TRUE)
img_only_adjusted_combat=cb$df
# Scale clinical imaging variables to controls
for (var in img_vars_rename){
mn=mean(img_only_adjusted_combat[img_only_adjusted_combat$group==0, var])
sdev=sd(img_only_adjusted_combat[img_only_adjusted_combat$group==0, var])
img_only_adjusted_combat[, var]=(img_only_adjusted_combat[, var]-mn)/sdev
}
# Keep only clinical
img_only_adjusted_combat=img_only_adjusted_combat[img_only_adjusted_combat$group==1, ]
#### MERGING OF DATA
# Add questionnaire data
ques=read.csv('data/TMS Data 2.9.23.csv')
data_adjusted_combat=merge(img_only_adjusted_combat, ques, by = c('Subjects', 'Visit', 'Site'), all = TRUE)
qids=read.csv('data/clinical_edit.csv')
data_adjusted_combat=merge(data_adjusted_combat, qids, by = c('Subjects', 'Visit'), all = TRUE)
# Add behavioral data
beh=read.csv('data/PsychoStats_mod.csv')
data_adjusted_combat=merge(data_adjusted_combat, beh, by = c('Subjects', 'Visit'), all = TRUE)
# Add motion
fd=read.csv('data/fd.csv')
data_adjusted_combat=merge(data_adjusted_combat, fd, by = c('Subjects', 'Visit'), all = TRUE)
# Merge Site variable
library(dplyr)
library(purrr)
data_adjusted_combat = data_adjusted_combat %>%
mutate(Site = coalesce(Site.x, Site.y))
# Import dates as assessments
dates=read.csv('data/all_dates.csv')
# Calculate time from baseline
s0=as.Date(dates[, 'Symptom_date_0'], format = '%m/%d/%y')
for (var in c('Symptom_date_0', 'Symptom_date_1', 'Symptom_date_2')){
dates[, var] = difftime(as.Date(dates[, var], format = '%m/%d/%y'), s0 , units = c("days"))
}
s0=as.Date(dates[, 'WebNeuro_date_0'], format = '%m/%d/%y')
for (var in c('WebNeuro_date_0', 'WebNeuro_date_1', 'WebNeuro_date_2')){
dates[, var] = difftime(as.Date(dates[, var], format = '%m/%d/%y'), s0 , units = c("days"))
}
s0=as.Date(dates[, 'MRI_date_0'], format = '%m/%d/%y')
for (var in c('MRI_date_0', 'MRI_date_1', 'MRI_date_2')){
dates[, var] = difftime(as.Date(dates[, var], format = '%m/%d/%y'), s0 , units = c("days"))
}
# Add the times for each session
for (r in 1:nrow(data_adjusted_combat)){
sub=data_adjusted_combat[r, 'Subjects']
vis=data_adjusted_combat[r, 'Visit']
data_adjusted_combat[r, 'Symptom_timediff']=as.numeric(dates[dates$Subjects==sub, paste('Symptom_date_', vis, sep='')])
data_adjusted_combat[r, 'WebNeuro_timediff']=as.numeric(dates[dates$Subjects==sub, paste('WebNeuro_date_', vis, sep='')])
data_adjusted_combat[r, 'MRI_timediff']=as.numeric(dates[dates$Subjects==sub, paste('MRI_date_', vis, sep='')])
}
# Keep only people in the first half of the sample
subs_orig=c('1976-320', '1976-322', '1976-318', '2000-012', '2000-018', '2000-030', '1976-312', '2000-023', '1976-311', '1991-64', '2000-027', '1984-5', '2000-016', '1976-323', '1976-315', '1976-319', '1976-313', '2000-029', '1976-325', '1991-72', '1991-92', '1991-43', '1976-317', '2000-022', '1991-107', '2000-010', '2000-031', '1991-70', '1984-11', '1984-10', '1991-103', '1991-114', '1984-7', '1991-90', '1984-26', '1976-309', '2000-011', '1991-58', '1976-308', '1991-93', '1991-74', '2000-013', '1976-314')
data_adjusted_combat=data_adjusted_combat[data_adjusted_combat$Subjects %in% subs_orig, ]
# Change Visit to factor
data_adjusted_combat$Visit=factor(data_adjusted_combat$Visit, labels = c('Baseline', 'Early Treatment', 'Post-Treatment'))
# Write data
write.csv(data_adjusted_combat, 'data/df_merged.csv', row.names = F)
#### GENERATE NEW VARIABLES ####
# Define change, responders, remitters and 0 split
for (sub in unique(data_adjusted_combat$Subjects)){
# Store ppi_gonogo_LdlPFC_MdACC change from baseline
ppi_bl=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'ppi_gonogo_LdlPFC_MdACC']
ppi_v1=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'ppi_gonogo_LdlPFC_MdACC']
ppi_v2=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'ppi_gonogo_LdlPFC_MdACC']
data_adjusted_combat[data_adjusted_combat$Subjects==sub && data_adjusted_combat$Visit=='Baseline', 'ppi_diff_bl']= 0
if (length(ppi_bl)>0 & length(ppi_v1)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'ppi_diff_bl']= ppi_v1-ppi_bl}
if (length(ppi_bl)>0 & length(ppi_v2)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'ppi_diff_bl']= ppi_v2-ppi_bl}
if (length(ppi_bl)>0 ){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'ppi_bl']= ppi_bl}
# Store QIDS change from baseline
q_total_bl=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'q_total']
q_total_v1=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'q_total']
q_total_v2=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'q_total']
data_adjusted_combat[data_adjusted_combat$Subjects==sub && data_adjusted_combat$Visit=='Baseline', 'q_total_diff_bl']= 0
if (length(q_total_bl)>0 & length(q_total_v1)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'q_total_diff_bl']= q_total_v1-q_total_bl}
if (length(q_total_bl)>0 & length(q_total_v2)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'q_total_diff_bl']= q_total_v2-q_total_bl}
if (length(q_total_bl)>0 ){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'q_total_bl']=q_total_bl}
# Store errors change from baseline
g2errk_norm_bl=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'g2errk_norm']
g2errk_norm_v1=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'g2errk_norm']
g2errk_norm_v2=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'g2errk_norm']
data_adjusted_combat[data_adjusted_combat$Subjects==sub && data_adjusted_combat$Visit=='Baseline', 'g2errk_norm_diff_bl']= 0
if (length(g2errk_norm_bl)>0 & length(g2errk_norm_v1)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'g2errk_norm_diff_bl']= g2errk_norm_v1-g2errk_norm_bl}
if (length(g2errk_norm_bl)>0 & length(g2errk_norm_v2)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'g2errk_norm_diff_bl']= g2errk_norm_v2-g2errk_norm_bl}
if (length(g2errk_norm_bl)>0 ){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'g2errk_norm_bl']=g2errk_norm_bl}
# Store QIDS change from baseline
qids_bl=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'q_total']
qids_fu=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'q_total']
if (length(qids_bl)>0 & length(qids_fu)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'qids_diff']=qids_fu-qids_bl}
if (length(qids_bl)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'qids_bl']=qids_bl}
if (length(qids_fu)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'qids_fu']=qids_fu}
# Zero split of Biotype values
bl_conn=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'ppi_gonogo_LdlPFC_MdACC']
if (length(bl_conn)>0 && bl_conn>=0 && !is.na(bl_conn)){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'split_0']=1}
if (length(bl_conn)>0 && bl_conn<0 && !is.na(bl_conn)){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'split_0']=0}
# Store QIDS10 change from baseline
q_10_bl=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'q10']
q_10_v1=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'q10']
q_10_v2=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'q10']
data_adjusted_combat[data_adjusted_combat$Subjects==sub && data_adjusted_combat$Visit=='Baseline', 'q_10_diff_bl']= 0
if (length(q_total_bl)>0 & length(q_total_v1)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'q_10_diff_bl']= q_10_v1-q_10_bl}
if (length(q_total_bl)>0 & length(q_total_v2)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'q_10_diff_bl']= q_10_v2-q_10_bl}
if (length(q_total_bl)>0 ){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'q_10_bl']=q_10_bl}
}
data_adjusted_combat$remit=factor((data_adjusted_combat$qids_fu<=5)*1)
data_adjusted_combat$qids_perc=(data_adjusted_combat$qids_bl-data_adjusted_combat$qids_fu)/data_adjusted_combat$qids_bl
data_adjusted_combat$respond=factor((data_adjusted_combat$qids_perc>0.5)*1)
data_adjusted_combat$split_0 = factor(data_adjusted_combat$split_0, levels = c(0, 1), labels = c("Cognitive biotype +", "Cognitive biotype -"))
#### Connectivity and time ####
# Mixed model
library(lme4)
library(lmerTest)
library(emmeans)
# Compare at baseline
bl=data_adjusted_combat[data_adjusted_combat$Visit=='Baseline', ]
t.test(data=bl, ppi_gonogo_LdlPFC_MdACC~split_0)
# Comparison between groups for demographics
t.test(data=bl, sessage ~split_0)
chisq.test(bl$Gender, bl$split_0)
# Fit the linear mixed effects model
model = lmer(data=data_adjusted_combat, ppi_diff_bl ~ split_0*Visit + MRI_timediff + (1 | Subjects))
# Display the model summary
summary(model)
anova(model)
# Extract the estimated marginal means and their standard errors
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_summary = summary(emmeans_results)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
# Create a dataframe from the summary object
emmeans_df = data.frame(emmeans_summary)
#### Behavior and time ####
# Compare at baseline
bl=data_adjusted_combat[data_adjusted_combat$Visit=='Baseline', ]
t.test(data=bl, g2errk_norm_bl~split_0)
# Fit the linear mixed effects model
model = lmer(data=data_adjusted_combat, g2errk_norm_diff_bl ~ split_0*Visit + WebNeuro_timediff + (1 | Subjects))
# Display the model summary
summary(model)
anova(model)
# Extract the estimated marginal means and their standard errors
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_summary = summary(emmeans_results)
# Create a dataframe from the summary object
emmeans_df = data.frame(emmeans_summary)
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
#### QIDS and time ####
# Compare at baseline
bl=data_adjusted_combat[data_adjusted_combat$Visit=='Baseline', ]
wilcox.test(data=bl, q_total~split_0)
# Fit the linear mixed effects model
model = lmer(data=data_adjusted_combat, q_total_diff_bl ~ split_0*Visit + Symptom_timediff + (1 | Subjects))
# Display the model summary
summary(model)
anova(model)
# Extract the estimated marginal means and their standard errors
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_summary = summary(emmeans_results)
# Create a dataframe from the summary object
emmeans_df = data.frame(emmeans_summary)
emmeans_results = emmeans(model, ~Visit)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
#### Correlations
# Correlation between PPI change and GNG error change
cor.test(data_adjusted_combat$ppi_diff_bl, data_adjusted_combat$g2errk_norm_diff_bl, use = 'pairwise.complete.obs')
# Correlation between in scanner GNG errors change and GNG errors
data_adjusted_combat$scanner_errors=data_adjusted_combat$Commission.Errors+data_adjusted_combat$Omission.Errors
temp=data_adjusted_combat[data_adjusted_combat$scanner_errors<50 & data_adjusted_combat$g2errk<50, ]
cor.test(temp$scanner_errors, temp$g2errk, use = 'pairwise.complete.obs', method = 'spearman')
#### Responders and remitters
data_adjusted_combat_bl=data_adjusted_combat[data_adjusted_combat$Visit=='Baseline', ]
# Responders
table(data_adjusted_combat_bl$respond, data_adjusted_combat_bl$split_0)
chisq.test(data_adjusted_combat_bl$respond, data_adjusted_combat_bl$split_0)
# Remitters
table(data_adjusted_combat_bl$remit, data_adjusted_combat_bl$split_0)
chisq.test(data_adjusted_combat_bl$remit, data_adjusted_combat_bl$split_0)
# Save the data frame
write.csv(data_adjusted_combat, 'data/data_adjusted_combat.csv', row.names = F)
#### Models with covariates ####
# Activation
model = lmer(data=data_adjusted_combat, ppi_diff_bl ~ split_0*Visit + act_gonogo_LdlPFC + MRI_timediff + (1 | Subjects))
anova(model)
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
model = lmer(data=data_adjusted_combat, g2errk_norm_diff_bl ~ split_0*Visit + act_gonogo_LdlPFC + WebNeuro_timediff + (1 | Subjects))
anova(model)
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
model = lmer(data=data_adjusted_combat, q_total_diff_bl ~ split_0*Visit + act_gonogo_LdlPFC + Symptom_timediff + (1 | Subjects))
anova(model)
emmeans_results = emmeans(model, ~Visit)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
# Motion
model = lmer(data=data_adjusted_combat, ppi_diff_bl ~ split_0*Visit + FD_03 + MRI_timediff + (1 | Subjects))
anova(model)
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
model = lmer(data=data_adjusted_combat, g2errk_norm_diff_bl ~ split_0*Visit + FD_03 + WebNeuro_timediff + (1 | Subjects))
anova(model)
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
model = lmer(data=data_adjusted_combat, q_total_diff_bl ~ split_0*Visit + FD_03 + Symptom_timediff + (1 | Subjects))
anova(model)
emmeans_results = emmeans(model, ~Visit)
emmeans_contrast = pairs(emmeans_results, adjust = "none", reverse = TRUE)
confint(emmeans_contrast)
#### Behavior
# Fit the linear mixed effects model
model = lmer(data=data_adjusted_combat, g2errk_norm_diff_bl ~ split_0*Visit + WebNeuro_timediff + (1 | Subjects))
# Extract the estimated marginal means and their standard errors
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_summary = summary(emmeans_results)
# Create a dataframe from the summary object
emmeans_df = data.frame(emmeans_summary)
emmeans_results = emmeans(model, ~Visit|split_0)
#### QIDS and time ####
# Fit the linear mixed effects model
model = lmer(data=data_adjusted_combat, q_total_diff_bl ~ split_0*Visit + Symptom_timediff + (1 | Subjects))
# Extract the estimated marginal means and their standard errors
emmeans_results = emmeans(model, ~Visit|split_0)
emmeans_summary = summary(emmeans_results)
# Create a dataframe from the summary object
emmeans_df = data.frame(emmeans_summary)
emmeans_results = emmeans(model, ~Visit)
#### MEDIATION MODEL
library("mediation")
set.seed(123124)
temp=temp = data_adjusted_combat %>%
filter(!is.na(split_0) & !is.na(ppi_diff_bl) & !is.na(g2errk_norm_diff_bl))
fit.mediator=lm(ppi_diff_bl~split_0,temp)
summary(fit.mediator)
fit.dv=lm(g2errk_norm_diff_bl~ppi_diff_bl+split_0,temp)
summary(fit.dv)
results = mediate(fit.mediator, fit.dv, treat='split_0', mediator='ppi_diff_bl', boot=F, control.value = '>=0', treat.value = '<0')
summary(results)
#### COMPARE GONOGO PERFORMANCE TO OTHER BEHAVIORS
# Create new webneuro variables
bl$cog_con=rowMeans(bl[, c('g2fpk_norm', 'g2avrtk_norm', 'g2fnk_norm')], na.rm = T)
bl$verbal_mem=rowMeans(bl[, c('ctmlearn_norm', 'ctmrec4_norm', 'ctmsco13_norm')], na.rm = T)
bl$working_mem=rowMeans(bl[, c('digitot_norm', 'digitsp_norm')], na.rm = T)
bl$sustained_att=rowMeans(bl[, c('wmfpk_norm', 'wmfnk_norm', 'wmrtk_norm')], na.rm = T)
# Correlation between gonogo and other variables
cor.test(bl$g2errk_norm_bl, bl$verbal_mem, method='spearman')
cor.test(bl$g2errk_norm_bl, bl$working_mem, method='spearman')
cor.test(bl$g2errk_norm_bl, bl$sustained_att, method='spearman')
# Comparison between groups
t.test(data=bl, verbal_mem ~split_0)
t.test(data=bl, working_mem ~split_0)
t.test(data=bl, sustained_att ~split_0)
# Correlation between measures that are not gonogo
data_adjusted_combat$cog_con=rowMeans(data_adjusted_combat[, c('g2fpk_norm', 'g2avrtk_norm', 'g2fnk_norm')], na.rm = T)
data_adjusted_combat$verbal_mem=rowMeans(data_adjusted_combat[, c('ctmlearn_norm', 'ctmrec4_norm', 'ctmsco13_norm')], na.rm = T)
data_adjusted_combat$working_mem=rowMeans(data_adjusted_combat[, c('digitot_norm', 'digitsp_norm')], na.rm = T)
data_adjusted_combat$sustained_att=rowMeans(data_adjusted_combat[, c('wmfpk_norm', 'wmfnk_norm', 'wmrtk_norm')], na.rm = T)
for (sub in unique(data_adjusted_combat$Subjects)){
# Store change from baseline
verbal_mem_bl=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'verbal_mem']
verbal_mem_v1=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'verbal_mem']
verbal_mem_v2=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'verbal_mem']
data_adjusted_combat[data_adjusted_combat$Subjects==sub && data_adjusted_combat$Visit=='Baseline', 'verbal_mem_diff_bl']= 0
if (length(verbal_mem_bl)>0 & length(verbal_mem_v1)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'verbal_mem_diff_bl']= verbal_mem_v1-verbal_mem_bl}
if (length(verbal_mem_bl)>0 & length(verbal_mem_v2)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'verbal_mem_diff_bl']= verbal_mem_v2-verbal_mem_bl}
if (length(verbal_mem_bl)>0 ){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'verbal_mem_bl']=verbal_mem_bl}
# Store change from baseline
working_mem_bl=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'working_mem']
working_mem_v1=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'working_mem']
working_mem_v2=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'working_mem']
data_adjusted_combat[data_adjusted_combat$Subjects==sub && data_adjusted_combat$Visit=='Baseline', 'working_mem_diff_bl']= 0
if (length(working_mem_bl)>0 & length(working_mem_v1)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'working_mem_diff_bl']= working_mem_v1-working_mem_bl}
if (length(working_mem_bl)>0 & length(working_mem_v2)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'working_mem_diff_bl']= working_mem_v2-working_mem_bl}
if (length(working_mem_bl)>0 ){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'working_mem_bl']=working_mem_bl}
# Store change from baseline
sustained_att_bl=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Baseline', 'sustained_att']
sustained_att_v1=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'sustained_att']
sustained_att_v2=data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'sustained_att']
data_adjusted_combat[data_adjusted_combat$Subjects==sub && data_adjusted_combat$Visit=='Baseline', 'sustained_att_diff_bl']= 0
if (length(sustained_att_bl)>0 & length(sustained_att_v1)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Early Treatment', 'sustained_att_diff_bl']= sustained_att_v1-sustained_att_bl}
if (length(sustained_att_bl)>0 & length(sustained_att_v2)>0){data_adjusted_combat[data_adjusted_combat$Subjects==sub & data_adjusted_combat$Visit=='Post-Treatment', 'sustained_att_diff_bl']= sustained_att_v2-sustained_att_bl}
if (length(sustained_att_bl)>0 ){data_adjusted_combat[data_adjusted_combat$Subjects==sub, 'sustained_att_bl']=sustained_att_bl}
}
cor.test(data_adjusted_combat$ppi_diff_bl, data_adjusted_combat$verbal_mem_diff_bl, use = 'pairwise.complete.obs')
cor.test(data_adjusted_combat$ppi_diff_bl, data_adjusted_combat$working_mem_diff_bl, use = 'pairwise.complete.obs')
cor.test(data_adjusted_combat$ppi_diff_bl, data_adjusted_combat$sustained_att_diff_bl, use = 'pairwise.complete.obs')
#### PLOTS ####
library(ggplot2)
#### Behavior at baseline
# Compute means and standard errors
bl_summary = bl %>%
group_by(split_0) %>%
summarise(
mean_value = mean(g2errk_norm_bl, na.rm = TRUE),
se_value = sd(g2errk_norm_bl, na.rm = TRUE) / sqrt(n())
)
# Create the bar plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/gng_bl_bar.svg", width = 7, height = 8.5)
ggplot(bl_summary, aes(x = split_0, y = mean_value, fill = split_0)) +
geom_bar(stat = "identity") +
geom_errorbar(aes(ymin = mean_value - se_value, ymax = mean_value + se_value), width = 0.2, size=8) +
geom_jitter(data = bl, aes(x = split_0, y = ppi_gonogo_LdlPFC_MdACC, fill = split_0, color='black'),
shape = 21, size = 2, alpha = 0.5, position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0)) +
theme_minimal() +
xlab('') +
ylab('Behavioral Performance at Baseline') +
theme(legend.position = "none") +
theme(text = element_text(size = 24)) +
scale_fill_manual(values = c("#B75A65", "gray40", "#B75A65", "gray40")) +
scale_color_manual(values = c("black")) +
ylim(c(-3, 3))
dev.off()
### Connectivity raw
# Calculate means and standard errors
data_means = data_adjusted_combat %>%
group_by(Visit, split_0) %>%
summarise(mean = mean(ppi_gonogo_LdlPFC_MdACC, na.rm = TRUE),
se = sd(ppi_gonogo_LdlPFC_MdACC, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
# Line plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/conn_raw_mean.svg", width = 8.5, height = 8.5)
ggplot(data_means, aes(x = Visit, y = mean, color = split_0)) +
geom_line(aes(group = split_0), size = 8) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2, size=8) +
labs(y = "Connectivity", x = "") +
theme_minimal() +
theme(text = element_text(size = 24))+
theme(legend.position = "None") +
scale_color_manual(name = "Biotype", values = c("#B75A65", "gray40"))+
ylim(c(-1, 1))
dev.off()
# Dots and lines plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/conn_raw_mean_dots.svg", width = 15, height = 8.5)
ggplot() +
geom_jitter(data = data_adjusted_combat, aes(x = Visit, y = ppi_gonogo_LdlPFC_MdACC, color = split_0),
size = 2, alpha = 2, position = position_dodge(0.8)) +
geom_line(data = data_adjusted_combat, aes(x = Visit, y = ppi_gonogo_LdlPFC_MdACC, group = interaction(Subjects, split_0), color = split_0),
alpha = 0.5, size = 2) +
geom_line(data = data_means, aes(x = Visit, y = mean, group = split_0, color = split_0), size = 8) +
geom_errorbar(data = data_means, aes(x = Visit, y = mean, ymin = mean - se, ymax = mean + se, color = split_0),
width = 0.2, size = 8) +
facet_wrap(~ split_0) +
labs(y = 'Connectivity', color = 'Biotype', x = '') +
theme_minimal() +
theme(legend.position = "None", text = element_text(size = 24), strip.text = element_blank()) +
scale_color_manual(values = c("#B75A65", "gray40")) +
scale_fill_manual(values = c("#B75A65", "gray40"))
dev.off()
#### Behavior raw
# Calculate means and standard errors
data_means = data_adjusted_combat %>%
group_by(Visit, split_0) %>%
summarise(mean = mean(g2errk_norm, na.rm = TRUE),
se = sd(g2errk_norm, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
# Line plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/beh_raw_mean.svg", width = 8.5, height = 8.5)
ggplot(data_means, aes(x = Visit, y = mean, color = split_0)) +
geom_line(aes(group = split_0), size = 8) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2, size=8) +
labs(y = "Go-NoGo Performance", x = "") +
theme_minimal() +
theme(text = element_text(size = 24))+
theme(legend.position = "None") +
scale_color_manual(name = "Biotype", values = c("#B75A65", "gray40"))+
ylim(c(-1, 1))
dev.off()
# Dots and lines plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/beh_raw_mean_dots.svg", width = 15, height = 8.5)
ggplot() +
geom_jitter(data = data_adjusted_combat, aes(x = Visit, y = g2errk_norm, color = split_0),
size = 2, alpha = 0.5, position = position_dodge(0.8)) +
geom_line(data = data_adjusted_combat, aes(x = Visit, y = g2errk_norm, group = interaction(Subjects, split_0), color = split_0),
alpha = 0.5, size = 2) +
geom_line(data = data_means, aes(x = Visit, y = mean, group = split_0, color = split_0), size = 8) +
geom_errorbar(data = data_means, aes(x = Visit, y = mean, ymin = mean - se, ymax = mean + se, color = split_0),
width = 0.2, size = 8) +
facet_wrap(~ split_0) +
labs(y = 'Go-NoGo Performance', color = 'Biotype', x = '') +
theme_minimal() +
theme(legend.position = "None", text = element_text(size = 24), strip.text = element_blank()) +
scale_color_manual(values = c("#B75A65", "gray40")) +
scale_fill_manual(values = c("#B75A65", "gray40"))
dev.off()
#### QIDS raw
# Calculate means and standard errors
data_means = data_adjusted_combat %>%
group_by(Visit, split_0) %>%
summarise(mean = mean(q_total, na.rm = TRUE),
se = sd(q_total, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
# Line plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/qids_raw_mean.svg", width = 8.5, height = 8.5)
ggplot(data_means, aes(x = Visit, y = mean, color = split_0)) +
geom_line(aes(group = split_0), size = 8) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2, size=8) +
labs(y = "QIDS total", x = "") +
theme_minimal() +
theme(text = element_text(size = 24))+
theme(legend.position = "None") +
scale_color_manual(name = "Biotype", values = c("#B75A65", "gray40"))+
ylim(c(0, 20))
dev.off()
# Dots and lines plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/qids_raw_mean_dots.svg", width = 15, height = 8.5)
ggplot() +
geom_jitter(data = data_adjusted_combat, aes(x = Visit, y = q_total, color = split_0),
size = 2, alpha = 2, position = position_dodge(0.8)) +
geom_line(data = data_adjusted_combat, aes(x = Visit, y = q_total, group = interaction(Subjects, split_0), color = split_0),
alpha = 0.5, size = 2) +
geom_line(data = data_means, aes(x = Visit, y = mean, group = split_0, color = split_0), size = 8) +
geom_errorbar(data = data_means, aes(x = Visit, y = mean, ymin = mean - se, ymax = mean + se, color = split_0),
width = 0.2, size = 8) +
facet_wrap(~ split_0) +
labs(y = 'QIDS total', color = 'Biotype', x = '') +
theme_minimal() +
theme(legend.position = "None", text = element_text(size = 24), strip.text = element_blank()) +
scale_color_manual(values = c("#B75A65", "gray40")) +
scale_fill_manual(values = c("#B75A65", "gray40"))
dev.off()
#### Change in connectivity
# Line plot
data_means = temp %>%
group_by(Visit, split_0) %>%
summarise(mean = mean(ppi_diff_bl, na.rm = TRUE),
se = sd(ppi_diff_bl, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
# Create the raw line plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/conn_raw_diff_mean.svg", width = 12, height = 8.5)
ggplot(data_means, aes(x = Visit, y = mean, color = split_0)) +
geom_line(aes(group = split_0), size = 2) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2, size=1) +
labs(y = "Connectivity Change", x = "") +
theme_minimal() +
theme(text = element_text(size = 24))+
theme(legend.position = "top") +
scale_color_manual(name = "Biotype", values = c("#B75A65", "gray40"))
dev.off()
# Dots and lines plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/conn_raw_diff_mean_dots.svg", width = 15, height = 8.5)
ggplot() +
geom_jitter(data = data_adjusted_combat, aes(x = Visit, y = ppi_diff_bl, color = split_0),
size = 2, alpha = 0.5, position = position_dodge(0.8)) +
geom_line(data = data_adjusted_combat, aes(x = Visit, y = ppi_diff_bl, group = interaction(Subjects, split_0), color = split_0),
alpha = 0.5, size = 0.5) +
geom_line(data = data_means, aes(x = Visit, y = mean, group = split_0, color = split_0), size = 2) +
geom_errorbar(data = data_means, aes(x = Visit, y = mean, ymin = mean - se, ymax = mean + se, color = split_0),
width = 0.2, size = 1) +
facet_wrap(~ split_0) +
labs(y = 'Connectivity Change', color = 'Biotype', x = '') +
theme_minimal() +
theme(legend.position = "top", text = element_text(size = 24)) +
scale_color_manual(values = c("#B75A65", "gray40")) +
scale_fill_manual(values = c("#B75A65", "gray40"))
dev.off()
#### Change in Behavior
# Calculate means and standard errors
data_means = temp %>%
group_by(Visit, split_0) %>%
summarise(mean = mean(g2errk_norm_diff_bl, na.rm = TRUE),
se = sd(g2errk_norm_diff_bl, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
# Line plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/beh_raw_diff.svg", width = 12, height = 8.5)
ggplot(data_means, aes(x = Visit, y = mean, color = split_0)) +
geom_line(aes(group = split_0), size = 2) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2, size=1) +
labs(y = "Go-NoGo Performance Change", x = "") +
theme_minimal() +
theme(text = element_text(size = 24))+
theme(legend.position = "top") +
scale_color_manual(name = "Biotype", values = c("#B75A65", "gray40"))
dev.off()
# Dots and lines plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/beh_raw_diff_mean_dots.svg", width = 12, height = 8.5)
ggplot() +
geom_jitter(data = data_adjusted_combat, aes(x = Visit, y = g2errk_norm_diff_bl, color = split_0),
size = 2, alpha = 0.5, position = position_dodge(0.8)) +
geom_line(data = data_adjusted_combat, aes(x = Visit, y = g2errk_norm_diff_bl, group = interaction(Subjects, split_0), color = split_0),
alpha = 0.5, size = 0.5) +
geom_line(data = data_means, aes(x = Visit, y = mean, group = split_0, color = split_0), size = 2) +
geom_errorbar(data = data_means, aes(x = Visit, y = mean, ymin = mean - se, ymax = mean + se, color = split_0),
width = 0.2, size = 1) +
facet_wrap(~ split_0) +
labs(y = 'Go-NoGo Performance Change', color = 'Biotype', x = '') +
theme_minimal() +
theme(legend.position = "top", text = element_text(size = 24)) +
scale_color_manual(values = c("#B75A65", "gray40")) +
scale_fill_manual(values = c("#B75A65", "gray40"))
dev.off()
#### Change in behavior vs. change in connectivity
fit <- lm(g2errk_norm_diff_bl ~ ppi_diff_bl, data = data_adjusted_combat)
# Plot the data with the linear model's fit
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/gng_conn_change.svg", width = 12, height = 8.5)
ggplot(data_adjusted_combat, aes(x=ppi_diff_bl, y=g2errk_norm_diff_bl)) +
geom_point(shape=16, size=2) +
geom_smooth(method = "lm", se = FALSE, color = "black", linetype="dashed") +
theme_minimal() +
xlab('Connectivity Change') +
ylab('Go-NoGo Performance Change') +
theme(text = element_text(size = 24))
dev.off()
#### Change in QIDS
# Calculate means and standard errors
data_means = temp %>%
group_by(Visit, split_0) %>%
summarise(mean = mean(q_total_diff_bl, na.rm = TRUE),
se = sd(q_total_diff_bl, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
# Line plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/qids_raw_diff.svg", width = 12, height = 8.5)
ggplot(data_means, aes(x = Visit, y = mean, color = split_0)) +
geom_line(aes(group = split_0), size = 2) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2, size=1) +
labs(y = "QIDS Total Change", x = "") +
theme_minimal() +
theme(text = element_text(size = 24))+
theme(legend.position = "top") +
scale_color_manual(name = "Biotype", values = c("#B75A65", "gray40"))
dev.off()
# Dots and lines plot
svg(file="/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/qids_raw_diff_mean_dots.svg", width = 12, height = 8.5)
ggplot() +
geom_jitter(data = data_adjusted_combat, aes(x = Visit, y = q_total_diff_bl, color = split_0),
size = 2, alpha = 0.5, position = position_dodge(0.8)) +
geom_line(data = data_adjusted_combat, aes(x = Visit, y = q_total_diff_bl, group = interaction(Subjects, split_0), color = split_0),
alpha = 0.5, size = 0.5) +
geom_line(data = data_means, aes(x = Visit, y = mean, group = split_0, color = split_0), size = 2) +
geom_errorbar(data = data_means, aes(x = Visit, y = mean, ymin = mean - se, ymax = mean + se, color = split_0),
width = 0.2, size = 1) +
facet_wrap(~ split_0) +
labs(y = 'QIDS Total Change', color = 'Biotype', x = '') +
theme_minimal() +
theme(legend.position = "top", text = element_text(size = 24)) +
scale_color_manual(values = c("#B75A65", "gray40")) +
scale_fill_manual(values = c("#B75A65", "gray40"))
dev.off()
#### Plots for each QIDS symptom ####
library(tidyverse)
library(scales)
# Rename the variables
data_adjusted_combat = rename(data_adjusted_combat, QIDS01 = q1, QIDS02 = q2, QIDS03 = q3, QIDS04 = q4,
QIDS05 = q5, QIDS06 = q6, QIDS07 = q7, QIDS08 = q8, QIDS09 = q9, QIDS10 = q10,
QIDS11 = q11, QIDS12 = q12, QIDS13 = q13, QIDS14 = q14, QIDS15 = q15, QIDS16 = q16)
# Reshape the data into a long format
long_data = data_adjusted_combat %>%
pivot_longer(
cols = c("QIDS01", "QIDS02", "QIDS03", "QIDS04", "QIDS05", "QIDS06", "QIDS07", "QIDS08",
"QIDS09", "QIDS10", "QIDS11", "QIDS12", "QIDS13", "QIDS14", "QIDS15", "QIDS16"),
names_to = "q_var",
values_to = "q_value"
) %>%
group_by(q_var, Visit, split_0)
# Calculate means and standard errors
data_means = long_data %>%
group_by(q_var, Visit, split_0) %>%
summarise(mean = mean(q_value, na.rm = TRUE),
se = sd(q_value, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
# Create the plot
svg(file=paste("/Users/ltozzi/My Drive (ltozzi@stanford.edu)/Projects/tms_gonogo_project/plots/qids_indiv.svg", sep=''),width = 8.5, height = 8.5)
ggplot(data_means, aes(x = Visit, y = mean, color = split_0, group = split_0)) +
geom_line(size = 1) +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2, size = 1) +
facet_wrap(~ q_var, scales = 'free_y') +
ylim(c(0, 3)) +
labs(y = 'QIDS Item Response', color = 'Biotype', x = '') +
theme_minimal() +
theme(legend.position = "top",
text = element_text(size = 24),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + # Adjust x-axis labels
scale_color_manual(values = c("#B75A65", "gray40"))
dev.off()