Neuromodulation Meta-Analysis in Schizophrenia: Subgroup Analysis
Danilo Assis Pereira
2025-03-10
Introduction
This document presents a comprehensive meta-analysis of neuromodulation techniques in schizophrenia, examining their effects on symptoms and cognitive functions across different intervention parameters.
Setting Up the Environment
First, we load the required packages for our analysis:
Data Import
We’ll import the data from the Excel file containing our meta-analysis data:
Data Preprocessing
Next, we preprocess the data for consistency and create domain categorizations:
# Convert to lowercase for consistency
baseline$Study <- tolower(baseline$Study)
baseline$Group <- tolower(baseline$Group)
outcomes$Study <- tolower(outcomes$Study)
outcomes$Group <- tolower(outcomes$Group)
# Create unique identifiers for each study-group combination
baseline$study_group_id <- paste(baseline$Study, baseline$Group, sep = "_")
outcomes$study_group_id <- paste(outcomes$Study, outcomes$Group, sep = "_")
# Categorize outcome measures
outcomes <- outcomes %>%
mutate(domain = case_when(
grepl("^P_", Outcome_Measure) ~ "positive_symptoms",
grepl("^N_", Outcome_Measure) ~ "negative_symptoms",
grepl("^T_", Outcome_Measure) ~ "total_psychopathology",
grepl("^G_", Outcome_Measure) ~ "general_psychopathology",
grepl("^PS_", Outcome_Measure) ~ "processing_speed",
grepl("^AV_", Outcome_Measure) ~ "attention_vigilance",
grepl("^WM_", Outcome_Measure) ~ "working_memory",
grepl("^VerL_", Outcome_Measure) ~ "verbal_learning",
grepl("^VisL_", Outcome_Measure) ~ "visual_learning",
grepl("^RPS_", Outcome_Measure) ~ "reasoning_problem_solving",
grepl("^SC_", Outcome_Measure) ~ "social_cognition",
grepl("^GC_", Outcome_Measure) ~ "global_cognition",
TRUE ~ "other"
))Calculate Effect Sizes
We calculate standardized mean differences (Hedges’ g) between active and sham groups:
effect_sizes <- data.frame()
# Identify active and sham groups
for(study in unique(outcomes$Study)) {
# Get all groups for this study
groups <- unique(outcomes$Group[outcomes$Study == study])
# Identify which is active and which is sham
active_groups <- groups[grepl("active", groups, ignore.case = TRUE)]
sham_groups <- groups[grepl("sham", groups, ignore.case = TRUE)]
for(active in active_groups) {
for(sham in sham_groups) {
# For each outcome measure
for(outcome in unique(outcomes$Outcome_Measure)) {
# Check if both groups have this outcome
active_data <- outcomes %>%
filter(Study == study, Group == active, Outcome_Measure == outcome)
sham_data <- outcomes %>%
filter(Study == study, Group == sham, Outcome_Measure == outcome)
if(nrow(active_data) >= 2 && nrow(sham_data) >= 2) {
# Get baseline and endpoint data
active_t0 <- active_data %>% filter(Time_Point == "T0")
active_t1 <- active_data %>% filter(Time_Point == "T1" | grepl("T1_Change", Time_Point))
sham_t0 <- sham_data %>% filter(Time_Point == "T0")
sham_t1 <- sham_data %>% filter(Time_Point == "T1" | grepl("T1_Change", Time_Point))
# Only proceed if we have complete data
if(nrow(active_t0) == 1 && nrow(active_t1) == 1 &&
nrow(sham_t0) == 1 && nrow(sham_t1) == 1) {
# Check if this is a change score
is_change_score <- grepl("T1_Change", active_t1$Time_Point)
# Calculate effect sizes based on whether it's a change score
if(is_change_score) {
# For change scores
n1 <- as.numeric(active_t1$N)
n2 <- as.numeric(sham_t1$N)
m1 <- as.numeric(active_t1$Mean)
m2 <- as.numeric(sham_t1$Mean)
sd1 <- as.numeric(active_t1$SD)
sd2 <- as.numeric(sham_t1$SD)
# Skip if any data is missing
if(any(is.na(c(n1, n2, m1, m2, sd1, sd2)))) next
# Calculate Hedges' g
es <- escalc(measure = "SMD",
m1i = m1, m2i = m2,
sd1i = sd1, sd2i = sd2,
n1i = n1, n2i = n2)
} else {
# For pre-post data
n1 <- as.numeric(active_t1$N)
n2 <- as.numeric(sham_t1$N)
m1_pre <- as.numeric(active_t0$Mean)
m1_post <- as.numeric(active_t1$Mean)
m2_pre <- as.numeric(sham_t0$Mean)
m2_post <- as.numeric(sham_t1$Mean)
sd1_pre <- as.numeric(active_t0$SD)
sd1_post <- as.numeric(active_t1$SD)
sd2_pre <- as.numeric(sham_t0$SD)
sd2_post <- as.numeric(sham_t1$SD)
# Skip if any data is missing
if(any(is.na(c(n1, n2, m1_pre, m1_post, m2_pre, m2_post,
sd1_pre, sd1_post, sd2_pre, sd2_post)))) next
# Pre-post correlation (estimate 0.7 if not known)
cor_pre_post <- 0.7
# Calculate change scores and their SDs
m1 <- m1_post - m1_pre
m2 <- m2_post - m2_pre
# Calculate SD of change scores
sd1 <- sqrt(sd1_pre^2 + sd1_post^2 - 2*cor_pre_post*sd1_pre*sd1_post)
sd2 <- sqrt(sd2_pre^2 + sd2_post^2 - 2*cor_pre_post*sd2_pre*sd2_post)
# Calculate Hedges' g
es <- escalc(measure = "SMD",
m1i = m1, m2i = m2,
sd1i = sd1, sd2i = sd2,
n1i = n1, n2i = n2)
}
# Determine if lower scores are better (for direction adjustment)
lower_is_better <- grepl("PANSS|SANS|SAPS|AHRS|BPRS|PSYRATS", outcome)
# Adjust direction so positive effect size always means improvement
if(lower_is_better) {
es$yi <- -es$yi
}
# Get the domain of this outcome
domain <- active_data$domain[1]
# Get technique, target, and other metadata from baseline
technique <- baseline$Technique[baseline$study_group_id == paste(study, active, sep="_")][1]
target <- baseline$Target[baseline$study_group_id == paste(study, active, sep="_")][1]
lateralisation <- baseline$Lateralisation[baseline$study_group_id == paste(study, active, sep="_")][1]
targeting_method <- baseline$Targeting_Method[baseline$study_group_id == paste(study, active, sep="_")][1]
total_time <- baseline$Total_Time[baseline$study_group_id == paste(study, active, sep="_")][1]
total_sessions <- baseline$Total_Sessions[baseline$study_group_id == paste(study, active, sep="_")][1]
session_frequency <- baseline$Session_Frequency[baseline$study_group_id == paste(study, active, sep="_")][1]
# Add to effect sizes dataframe
effect_sizes <- rbind(effect_sizes, data.frame(
study = study,
outcome = outcome,
domain = domain,
technique = technique,
target = target,
lateralisation = lateralisation,
targeting_method = targeting_method,
total_time = total_time,
total_sessions = total_sessions,
session_frequency = session_frequency,
yi = es$yi, # Effect size
vi = es$vi, # Variance
n1 = n1, # Sample size active
n2 = n2, # Sample size sham
lower_is_better = lower_is_better,
stringsAsFactors = FALSE
))
}
}
}
}
}
}
# Check the resulting effect sizes
cat("Total number of effect sizes calculated:", nrow(effect_sizes), "\n")## Total number of effect sizes calculated: 677Data Quality Check
Let’s check for missing values in key variables:
check_missing <- function(data) {
cat("Missing values in key variables:\n")
cat("technique:", sum(is.na(data$technique)), "missing out of", nrow(data), "\n")
cat("target:", sum(is.na(data$target)), "missing out of", nrow(data), "\n")
cat("lateralisation:", sum(is.na(data$lateralisation)), "missing out of", nrow(data), "\n")
cat("total_time:", sum(is.na(data$total_time)), "missing out of", nrow(data), "\n")
cat("total_sessions:", sum(is.na(data$total_sessions)), "missing out of", nrow(data), "\n")
cat("session_frequency:", sum(is.na(data$session_frequency)), "missing out of", nrow(data), "\n")
# Print unique values of technique to see what we're working with
cat("\nUnique values in technique variable:\n")
print(table(data$technique, useNA = "always"))
}Helper Functions for Subgroup Analyses
This function runs the subgroup analyses with error handling:
run_subgroup_analysis <- function(data, factor_var, factor_name, min_studies = 3) {
# First, remove NAs in the factor variable
data_clean <- data %>% filter(!is.na(!!sym(factor_var)))
cat("\nAnalyzing", factor_name, "with", nrow(data_clean), "valid cases\n")
# Get the unique levels of the factor
factor_levels <- unique(data_clean[[factor_var]])
cat("Found", length(factor_levels), "unique levels:", paste(factor_levels, collapse=", "), "\n")
# Create a list to store results for each subgroup
subgroup_results <- list()
# Create a dataframe to store results for the table
results_table <- data.frame(
Subgroup = character(),
k = integer(),
ES = numeric(),
CI_Lower = numeric(),
CI_Upper = numeric(),
p = numeric(),
I2 = numeric(),
stringsAsFactors = FALSE
)
# For each level of the factor, run a separate meta-analysis
for(level in factor_levels) {
# Filter data for this level
subgroup_data <- data_clean %>% filter(!!sym(factor_var) == level)
cat("Level:", level, "- Number of studies:", nrow(subgroup_data), "\n")
# Only analyze if we have enough studies
if(nrow(subgroup_data) >= min_studies) {
# Run the meta-analysis
tryCatch({
subgroup_model <- rma(yi, vi, data = subgroup_data, method = "REML")
# Store the results
subgroup_results[[as.character(level)]] <- subgroup_model
# Add to results table
results_table <- rbind(results_table, data.frame(
Subgroup = as.character(level),
k = subgroup_model$k,
ES = subgroup_model$b,
CI_Lower = subgroup_model$ci.lb,
CI_Upper = subgroup_model$ci.ub,
p = subgroup_model$pval,
I2 = (subgroup_model$tau2 / (subgroup_model$tau2 + subgroup_model$vt)) * 100,
stringsAsFactors = FALSE
))
}, error = function(e) {
cat("Error in subgroup", level, ":", e$message, "\n")
})
} else {
cat("Skipping", level, "- not enough studies\n")
}
}
# Check if we have any results
if(nrow(results_table) == 0) {
cat("No valid subgroups with enough studies found for", factor_name, "\n")
return(NULL)
}
# Run meta-regression to test for subgroup differences
if(length(unique(data_clean[[factor_var]])) > 1 && nrow(results_table) > 1) {
# Run meta-regression
tryCatch({
metareg_formula <- as.formula(paste("yi ~ factor(", factor_var, ")", sep=""))
metareg_model <- rma(metareg_formula, vi, data = data_clean, method = "REML")
# Print results
cat("\n\nMeta-regression for differences between", factor_name, "subgroups:\n")
print(summary(metareg_model))
}, error = function(e) {
cat("Error in meta-regression:", e$message, "\n")
metareg_model <- NULL
})
} else {
metareg_model <- NULL
}
# Format the results table
results_table$ES <- round(results_table$ES, 2)
results_table$CI_Lower <- round(results_table$CI_Lower, 2)
results_table$CI_Upper <- round(results_table$CI_Upper, 2)
results_table$p <- round(results_table$p, 3)
results_table$I2 <- round(results_table$I2, 1)
# Add significance markers
results_table$Sig <- ifelse(results_table$p < 0.001, "***",
ifelse(results_table$p < 0.01, "**",
ifelse(results_table$p < 0.05, "*", "")))
# Create effect size with CI column
results_table$Effect_Size <- paste0(results_table$ES, " [",
results_table$CI_Lower, ", ",
results_table$CI_Upper, "]",
results_table$Sig)
# Create a temporary table with columns we need for plotting
plot_table <- results_table %>%
select(Subgroup, ES, CI_Lower, CI_Upper, p)
# Reorder columns for display in the results table
results_table <- results_table %>%
select(Subgroup, k, Effect_Size, p, I2) %>%
rename(`Subgroup` = Subgroup,
`Number of Effect Sizes` = k,
`Effect Size [95% CI]` = Effect_Size,
`p-value` = p,
`I²` = I2)
# Create a forest plot for subgroups using the plot_table (not the renamed results_table)
plot_data <- plot_table %>%
mutate(
Significant = p < 0.05,
Subgroup = factor(Subgroup, levels = rev(Subgroup))
)
# Create the plot
p <- ggplot(plot_data, aes(x = ES, y = Subgroup, xmin = CI_Lower, xmax = CI_Upper,
color = Significant)) +
geom_point(size = 3) +
geom_errorbarh(height = 0.2) +
geom_vline(xintercept = 0, linetype = "dashed", color = "darkgray") +
scale_color_manual(values = c("FALSE" = "gray50", "TRUE" = "blue")) +
labs(title = paste("Effect of Neuromodulation by", factor_name),
x = "Hedges' g Effect Size",
y = "",
color = "Statistically\nSignificant") +
theme_minimal() +
theme(legend.position = "bottom")
# Print the table
cat("\n\nSubgroup Analysis for", factor_name, ":\n")
print(kable(results_table,
caption = paste("Summary of Results by", factor_name)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE))
# Display the plot
print(p)
# Return the results
return(list(table = results_table, plot = p, metareg = metareg_model))
}Data Preparation for Subgroup Analyses
Let’s clean the data and prepare it for our subgroup analyses:
# First make sure we replace empty strings with NA
effect_sizes <- effect_sizes %>%
mutate(across(where(is.character), ~na_if(., "")))
# Clean up technique names for better display
effect_sizes$technique <- toupper(effect_sizes$technique)
# Run the check for missing values
check_missing(effect_sizes)## Missing values in key variables:
## technique: 5 missing out of 677
## target: 5 missing out of 677
## lateralisation: 5 missing out of 677
## total_time: 5 missing out of 677
## total_sessions: 5 missing out of 677
## session_frequency: 5 missing out of 677
##
## Unique values in technique variable:
##
## CTBS DTMS HD-TDCS HF-RTMS ITBS LF-RTMS PRM-RTMS TACS
## 20 19 9 225 112 88 4 13
## TDCS <NA>
## 182 5Intervention Type Analysis
We’ll analyze the effect of different neuromodulation techniques:
##
##
## ** INTERVENTION TYPE ANALYSIS **##
## Analyzing Intervention Type with 672 valid cases
## Found 9 unique levels: LF-RTMS, HF-RTMS, ITBS, PRM-RTMS, TDCS, TACS, HD-TDCS, DTMS, CTBS
## Level: LF-RTMS - Number of studies: 88
## Level: HF-RTMS - Number of studies: 225
## Level: ITBS - Number of studies: 112
## Level: PRM-RTMS - Number of studies: 4
## Level: TDCS - Number of studies: 182
## Level: TACS - Number of studies: 13
## Level: HD-TDCS - Number of studies: 9
## Level: DTMS - Number of studies: 19
## Level: CTBS - Number of studies: 20
##
##
## Meta-regression for differences between Intervention Type subgroups:
##
## Mixed-Effects Model (k = 672; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -633.0810 1266.1619 1286.1619 1331.1297 1286.4994
##
## tau^2 (estimated amount of residual heterogeneity): 0.2285 (SE = 0.0186)
## tau (square root of estimated tau^2 value): 0.4780
## I^2 (residual heterogeneity / unaccounted variability): 71.17%
## H^2 (unaccounted variability / sampling variability): 3.47
## R^2 (amount of heterogeneity accounted for): 2.45%
##
## Test for Residual Heterogeneity:
## QE(df = 663) = 2092.4841, p-val < .0001
##
## Test of Moderators (coefficients 2:9):
## QM(df = 8) = 17.7426, p-val = 0.0232
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1079 0.1331 -0.8109 0.4174 -0.3687 0.1529
## factor(technique)DTMS 0.3653 0.1946 1.8775 0.0605 -0.0161 0.7466
## factor(technique)HD-TDCS 0.3848 0.2397 1.6052 0.1085 -0.0851 0.8547
## factor(technique)HF-RTMS 0.3198 0.1385 2.3085 0.0210 0.0483 0.5913
## factor(technique)ITBS 0.3667 0.1438 2.5502 0.0108 0.0849 0.6485
## factor(technique)LF-RTMS 0.4548 0.1472 3.0891 0.0020 0.1662 0.7433
## factor(technique)PRM-RTMS 0.3552 0.3337 1.0644 0.2871 -0.2988 1.0092
## factor(technique)TACS 0.2964 0.2246 1.3199 0.1869 -0.1437 0.7366
## factor(technique)TDCS 0.2047 0.1403 1.4587 0.1447 -0.0704 0.4798
##
## intrcpt
## factor(technique)DTMS .
## factor(technique)HD-TDCS
## factor(technique)HF-RTMS *
## factor(technique)ITBS *
## factor(technique)LF-RTMS **
## factor(technique)PRM-RTMS
## factor(technique)TACS
## factor(technique)TDCS
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for Intervention Type :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by Intervention Type</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> LF-RTMS </td>
## <td style="text-align:right;"> 88 </td>
## <td style="text-align:left;"> 0.34 [0.2, 0.49]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 79.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> HF-RTMS </td>
## <td style="text-align:right;"> 225 </td>
## <td style="text-align:left;"> 0.21 [0.14, 0.28]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 70.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> ITBS </td>
## <td style="text-align:right;"> 112 </td>
## <td style="text-align:left;"> 0.27 [0.11, 0.42]** </td>
## <td style="text-align:right;"> 0.001 </td>
## <td style="text-align:right;"> 87.7 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> PRM-RTMS </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.25 [-0.12, 0.62] </td>
## <td style="text-align:right;"> 0.190 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> TDCS </td>
## <td style="text-align:right;"> 182 </td>
## <td style="text-align:left;"> 0.08 [0.03, 0.14]** </td>
## <td style="text-align:right;"> 0.002 </td>
## <td style="text-align:right;"> 21.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt5 </td>
## <td style="text-align:left;"> TACS </td>
## <td style="text-align:right;"> 13 </td>
## <td style="text-align:left;"> 0.23 [0, 0.47]* </td>
## <td style="text-align:right;"> 0.048 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt6 </td>
## <td style="text-align:left;"> HD-TDCS </td>
## <td style="text-align:right;"> 9 </td>
## <td style="text-align:left;"> 0.22 [0.02, 0.41]* </td>
## <td style="text-align:right;"> 0.030 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt7 </td>
## <td style="text-align:left;"> DTMS </td>
## <td style="text-align:right;"> 19 </td>
## <td style="text-align:left;"> 0.24 [0.07, 0.42]** </td>
## <td style="text-align:right;"> 0.006 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt8 </td>
## <td style="text-align:left;"> CTBS </td>
## <td style="text-align:right;"> 20 </td>
## <td style="text-align:left;"> -0.14 [-0.57, 0.28] </td>
## <td style="text-align:right;"> 0.510 </td>
## <td style="text-align:right;"> 88.7 </td>
## </tr>
## </tbody>
## </table>
Target Region Analysis
Next, we examine the effect of different brain targets:
##
##
## ** TARGET REGION ANALYSIS **##
## Analyzing Target Region with 672 valid cases
## Found 34 unique levels: L-TPJ, Bi-TPJ, Bi-DLPFC, Cereb-Vermis, L-DLPFC, Bi-DMPFC, L-DLPFC_L-TPJ, L-FC/L-PC_CPz/FCz, L-STS, L-DMPFC, L-DLPFC_R-SORB, Bi-DLPFC_Bi-TPJ, L-DLPFC_R-DLPFC, R-ORBF, DLPFC, L-M1, TPJ, R-DLPFC, R-DLPFC_L-Orbit, L-PC, L-LPC, L-DLPFC_R-ORBF, Bi-PFC, L-DLPFC_Cz, Bi-Insula, fMRI-TC, L_DLPFC, fMRI-TPC, R-IPL, L-SMA, R-DLPFC_L-TPJ, L-DLPFC_L-PFC, L-DLPFC/L-TPJ_Cz, L-DLPFC_R-ORB
## Level: L-TPJ - Number of studies: 45
## Level: Bi-TPJ - Number of studies: 12
## Level: Bi-DLPFC - Number of studies: 45
## Level: Cereb-Vermis - Number of studies: 36
## Level: L-DLPFC - Number of studies: 232
## Level: Bi-DMPFC - Number of studies: 1
## Skipping Bi-DMPFC - not enough studies
## Level: L-DLPFC_L-TPJ - Number of studies: 71
## Level: L-FC/L-PC_CPz/FCz - Number of studies: 3
## Level: L-STS - Number of studies: 1
## Skipping L-STS - not enough studies
## Level: L-DMPFC - Number of studies: 3
## Level: L-DLPFC_R-SORB - Number of studies: 10
## Level: Bi-DLPFC_Bi-TPJ - Number of studies: 4
## Level: L-DLPFC_R-DLPFC - Number of studies: 32
## Level: R-ORBF - Number of studies: 24
## Level: DLPFC - Number of studies: 6
## Level: L-M1 - Number of studies: 5
## Level: TPJ - Number of studies: 4
## Level: R-DLPFC - Number of studies: 14
## Level: R-DLPFC_L-Orbit - Number of studies: 2
## Skipping R-DLPFC_L-Orbit - not enough studies
## Level: L-PC - Number of studies: 5
## Level: L-LPC - Number of studies: 8
## Level: L-DLPFC_R-ORBF - Number of studies: 48
## Level: Bi-PFC - Number of studies: 9
## Level: L-DLPFC_Cz - Number of studies: 6
## Level: Bi-Insula - Number of studies: 3
## Level: fMRI-TC - Number of studies: 3
## Level: L_DLPFC - Number of studies: 3
## Level: fMRI-TPC - Number of studies: 2
## Skipping fMRI-TPC - not enough studies
## Level: R-IPL - Number of studies: 6
## Level: L-SMA - Number of studies: 5
## Level: R-DLPFC_L-TPJ - Number of studies: 12
## Level: L-DLPFC_L-PFC - Number of studies: 5
## Level: L-DLPFC/L-TPJ_Cz - Number of studies: 4
## Level: L-DLPFC_R-ORB - Number of studies: 3
##
##
## Meta-regression for differences between Target Region subgroups:
##
## Mixed-Effects Model (k = 672; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -593.9482 1187.8965 1257.8965 1413.9383 1262.0825
##
## tau^2 (estimated amount of residual heterogeneity): 0.2103 (SE = 0.0179)
## tau (square root of estimated tau^2 value): 0.4586
## I^2 (residual heterogeneity / unaccounted variability): 69.52%
## H^2 (unaccounted variability / sampling variability): 3.28
## R^2 (amount of heterogeneity accounted for): 10.19%
##
## Test for Residual Heterogeneity:
## QE(df = 638) = 1927.4497, p-val < .0001
##
## Test of Moderators (coefficients 2:34):
## QM(df = 33) = 78.6316, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.1239 0.0933 1.3284 0.1840 -0.0589
## factor(target)Bi-DLPFC_Bi-TPJ 0.5546 0.3369 1.6461 0.0998 -0.1058
## factor(target)Bi-DMPFC -0.0459 0.6879 -0.0667 0.9468 -1.3942
## factor(target)Bi-Insula 0.4986 0.3859 1.2922 0.1963 -0.2577
## factor(target)Bi-PFC 0.0420 0.2137 0.1967 0.8441 -0.3768
## factor(target)Bi-TPJ -0.1271 0.1854 -0.6854 0.4931 -0.4905
## factor(target)Cereb-Vermis -0.1317 0.1291 -1.0201 0.3077 -0.3846
## factor(target)DLPFC 0.4471 0.2336 1.9138 0.0556 -0.0108
## factor(target)fMRI-TC -0.3721 0.3595 -1.0351 0.3006 -1.0768
## factor(target)fMRI-TPC -0.2334 0.4053 -0.5759 0.5647 -1.0278
## factor(target)L_DLPFC 0.0144 0.3618 0.0399 0.9682 -0.6946
## factor(target)L-DLPFC 0.1644 0.1003 1.6394 0.1011 -0.0322
## factor(target)L-DLPFC_Cz 0.0703 0.2725 0.2580 0.7964 -0.4638
## factor(target)L-DLPFC_L-PFC 0.0431 0.2493 0.1728 0.8628 -0.4455
## factor(target)L-DLPFC_L-TPJ -0.0449 0.1173 -0.3825 0.7021 -0.2748
## factor(target)L-DLPFC_R-DLPFC -0.0055 0.1342 -0.0412 0.9672 -0.2685
## factor(target)L-DLPFC_R-ORB -0.1945 0.3386 -0.5743 0.5657 -0.8582
## factor(target)L-DLPFC_R-ORBF 0.0003 0.1242 0.0026 0.9979 -0.2432
## factor(target)L-DLPFC_R-SORB -0.1635 0.2039 -0.8017 0.4227 -0.5631
## factor(target)L-DLPFC/L-TPJ_Cz -0.0634 0.3193 -0.1986 0.8426 -0.6893
## factor(target)L-DMPFC -0.1944 0.3538 -0.5496 0.5826 -0.8878
## factor(target)L-FC/L-PC_CPz/FCz 0.4586 0.3427 1.3381 0.1808 -0.2131
## factor(target)L-LPC -0.3844 0.2103 -1.8275 0.0676 -0.7966
## factor(target)L-M1 0.1599 0.2667 0.5994 0.5489 -0.3629
## factor(target)L-PC 0.2111 0.2958 0.7137 0.4754 -0.3687
## factor(target)L-SMA 0.8873 0.2697 3.2897 0.0010 0.3587
## factor(target)L-STS -0.2319 0.5366 -0.4322 0.6656 -1.2835
## factor(target)L-TPJ 0.3598 0.1306 2.7542 0.0059 0.1038
## factor(target)R-DLPFC -0.0629 0.1712 -0.3673 0.7134 -0.3984
## factor(target)R-DLPFC_L-Orbit 0.0020 0.4207 0.0048 0.9962 -0.8225
## factor(target)R-DLPFC_L-TPJ -0.3385 0.2525 -1.3404 0.1801 -0.8334
## factor(target)R-IPL -0.2196 0.2984 -0.7360 0.4618 -0.8045
## factor(target)R-ORBF 0.2826 0.1395 2.0260 0.0428 0.0092
## factor(target)TPJ -0.8814 0.2969 -2.9686 0.0030 -1.4634
## ci.ub
## intrcpt 0.3068
## factor(target)Bi-DLPFC_Bi-TPJ 1.2150 .
## factor(target)Bi-DMPFC 1.3024
## factor(target)Bi-Insula 1.2549
## factor(target)Bi-PFC 0.4608
## factor(target)Bi-TPJ 0.2363
## factor(target)Cereb-Vermis 0.1213
## factor(target)DLPFC 0.9050 .
## factor(target)fMRI-TC 0.3325
## factor(target)fMRI-TPC 0.5610
## factor(target)L_DLPFC 0.7235
## factor(target)L-DLPFC 0.3609
## factor(target)L-DLPFC_Cz 0.6044
## factor(target)L-DLPFC_L-PFC 0.5317
## factor(target)L-DLPFC_L-TPJ 0.1851
## factor(target)L-DLPFC_R-DLPFC 0.2574
## factor(target)L-DLPFC_R-ORB 0.4692
## factor(target)L-DLPFC_R-ORBF 0.2438
## factor(target)L-DLPFC_R-SORB 0.2362
## factor(target)L-DLPFC/L-TPJ_Cz 0.5625
## factor(target)L-DMPFC 0.4989
## factor(target)L-FC/L-PC_CPz/FCz 1.1302
## factor(target)L-LPC 0.0279 .
## factor(target)L-M1 0.6827
## factor(target)L-PC 0.7910
## factor(target)L-SMA 1.4160 **
## factor(target)L-STS 0.8198
## factor(target)L-TPJ 0.6159 **
## factor(target)R-DLPFC 0.2727
## factor(target)R-DLPFC_L-Orbit 0.8266
## factor(target)R-DLPFC_L-TPJ 0.1564
## factor(target)R-IPL 0.3653
## factor(target)R-ORBF 0.5561 *
## factor(target)TPJ -0.2995 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for Target Region :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by Target Region</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> L-TPJ </td>
## <td style="text-align:right;"> 45 </td>
## <td style="text-align:left;"> 0.46 [0.19, 0.72]** </td>
## <td style="text-align:right;"> 0.001 </td>
## <td style="text-align:right;"> 81.1 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> Bi-TPJ </td>
## <td style="text-align:right;"> 12 </td>
## <td style="text-align:left;"> 0.01 [-0.16, 0.18] </td>
## <td style="text-align:right;"> 0.938 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> Bi-DLPFC </td>
## <td style="text-align:right;"> 45 </td>
## <td style="text-align:left;"> 0.12 [-0.05, 0.29] </td>
## <td style="text-align:right;"> 0.166 </td>
## <td style="text-align:right;"> 47.4 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> Cereb-Vermis </td>
## <td style="text-align:right;"> 36 </td>
## <td style="text-align:left;"> -0.01 [-0.1, 0.08] </td>
## <td style="text-align:right;"> 0.842 </td>
## <td style="text-align:right;"> 1.1 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> L-DLPFC </td>
## <td style="text-align:right;"> 232 </td>
## <td style="text-align:left;"> 0.29 [0.21, 0.37]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 77.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt5 </td>
## <td style="text-align:left;"> L-DLPFC_L-TPJ </td>
## <td style="text-align:right;"> 71 </td>
## <td style="text-align:left;"> 0.05 [-0.03, 0.12] </td>
## <td style="text-align:right;"> 0.251 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt6 </td>
## <td style="text-align:left;"> L-FC/L-PC_CPz/FCz </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.58 [0.2, 0.97]** </td>
## <td style="text-align:right;"> 0.003 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt7 </td>
## <td style="text-align:left;"> L-DMPFC </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> -0.07 [-0.49, 0.35] </td>
## <td style="text-align:right;"> 0.743 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt8 </td>
## <td style="text-align:left;"> L-DLPFC_R-SORB </td>
## <td style="text-align:right;"> 10 </td>
## <td style="text-align:left;"> -0.04 [-0.41, 0.33] </td>
## <td style="text-align:right;"> 0.829 </td>
## <td style="text-align:right;"> 68.6 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt9 </td>
## <td style="text-align:left;"> Bi-DLPFC_Bi-TPJ </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.72 [-0.25, 1.7] </td>
## <td style="text-align:right;"> 0.147 </td>
## <td style="text-align:right;"> 79.1 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt10 </td>
## <td style="text-align:left;"> L-DLPFC_R-DLPFC </td>
## <td style="text-align:right;"> 32 </td>
## <td style="text-align:left;"> 0.08 [0, 0.17] </td>
## <td style="text-align:right;"> 0.064 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt11 </td>
## <td style="text-align:left;"> R-ORBF </td>
## <td style="text-align:right;"> 24 </td>
## <td style="text-align:left;"> 0.4 [0.25, 0.55]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 65.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt12 </td>
## <td style="text-align:left;"> DLPFC </td>
## <td style="text-align:right;"> 6 </td>
## <td style="text-align:left;"> 0.57 [0.15, 0.99]** </td>
## <td style="text-align:right;"> 0.008 </td>
## <td style="text-align:right;"> 76.4 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt13 </td>
## <td style="text-align:left;"> L-M1 </td>
## <td style="text-align:right;"> 5 </td>
## <td style="text-align:left;"> 0.28 [0, 0.56] </td>
## <td style="text-align:right;"> 0.050 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt14 </td>
## <td style="text-align:left;"> TPJ </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> -1 [-3.18, 1.17] </td>
## <td style="text-align:right;"> 0.365 </td>
## <td style="text-align:right;"> 98.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt15 </td>
## <td style="text-align:left;"> R-DLPFC </td>
## <td style="text-align:right;"> 14 </td>
## <td style="text-align:left;"> 0.07 [-0.12, 0.26] </td>
## <td style="text-align:right;"> 0.480 </td>
## <td style="text-align:right;"> 43.8 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt16 </td>
## <td style="text-align:left;"> L-PC </td>
## <td style="text-align:right;"> 5 </td>
## <td style="text-align:left;"> 0.44 [-2.81, 3.68] </td>
## <td style="text-align:right;"> 0.793 </td>
## <td style="text-align:right;"> 98.8 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt17 </td>
## <td style="text-align:left;"> L-LPC </td>
## <td style="text-align:right;"> 8 </td>
## <td style="text-align:left;"> -0.26 [-0.6, 0.08] </td>
## <td style="text-align:right;"> 0.137 </td>
## <td style="text-align:right;"> 69.8 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt18 </td>
## <td style="text-align:left;"> L-DLPFC_R-ORBF </td>
## <td style="text-align:right;"> 48 </td>
## <td style="text-align:left;"> 0.12 [0, 0.25]* </td>
## <td style="text-align:right;"> 0.046 </td>
## <td style="text-align:right;"> 41.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt19 </td>
## <td style="text-align:left;"> Bi-PFC </td>
## <td style="text-align:right;"> 9 </td>
## <td style="text-align:left;"> 0.17 [-0.06, 0.39] </td>
## <td style="text-align:right;"> 0.156 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt20 </td>
## <td style="text-align:left;"> L-DLPFC_Cz </td>
## <td style="text-align:right;"> 6 </td>
## <td style="text-align:left;"> 0.19 [-0.36, 0.74] </td>
## <td style="text-align:right;"> 0.495 </td>
## <td style="text-align:right;"> 61.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt21 </td>
## <td style="text-align:left;"> Bi-Insula </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.62 [0.1, 1.14]* </td>
## <td style="text-align:right;"> 0.019 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt22 </td>
## <td style="text-align:left;"> fMRI-TC </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> -0.25 [-0.69, 0.19] </td>
## <td style="text-align:right;"> 0.270 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt23 </td>
## <td style="text-align:left;"> L_DLPFC </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.14 [-0.31, 0.58] </td>
## <td style="text-align:right;"> 0.547 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt24 </td>
## <td style="text-align:left;"> R-IPL </td>
## <td style="text-align:right;"> 6 </td>
## <td style="text-align:left;"> -0.09 [-0.51, 0.32] </td>
## <td style="text-align:right;"> 0.660 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt25 </td>
## <td style="text-align:left;"> L-SMA </td>
## <td style="text-align:right;"> 5 </td>
## <td style="text-align:left;"> 1.03 [0.25, 1.82]* </td>
## <td style="text-align:right;"> 0.010 </td>
## <td style="text-align:right;"> 86.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt26 </td>
## <td style="text-align:left;"> R-DLPFC_L-TPJ </td>
## <td style="text-align:right;"> 12 </td>
## <td style="text-align:left;"> -0.21 [-0.59, 0.17] </td>
## <td style="text-align:right;"> 0.278 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt27 </td>
## <td style="text-align:left;"> L-DLPFC_L-PFC </td>
## <td style="text-align:right;"> 5 </td>
## <td style="text-align:left;"> 0.17 [-0.04, 0.38] </td>
## <td style="text-align:right;"> 0.117 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt28 </td>
## <td style="text-align:left;"> L-DLPFC/L-TPJ_Cz </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.06 [-0.33, 0.46] </td>
## <td style="text-align:right;"> 0.765 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt29 </td>
## <td style="text-align:left;"> L-DLPFC_R-ORB </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> -0.07 [-0.45, 0.32] </td>
## <td style="text-align:right;"> 0.725 </td>
## <td style="text-align:right;"> 7.0 </td>
## </tr>
## </tbody>
## </table>
Lateralization Analysis
We analyze the effect of stimulation lateralization:
##
##
## ** LATERALIZATION ANALYSIS **##
## Analyzing Lateralization with 672 valid cases
## Found 5 unique levels: Uni-L, Bi-Seq, Bi-Sim, Uni-R, Uni-fMRI-L/R
## Level: Uni-L - Number of studies: 504
## Level: Bi-Seq - Number of studies: 55
## Level: Bi-Sim - Number of studies: 55
## Level: Uni-R - Number of studies: 56
## Level: Uni-fMRI-L/R - Number of studies: 2
## Skipping Uni-fMRI-L/R - not enough studies
##
##
## Meta-regression for differences between Lateralization subgroups:
##
## Mixed-Effects Model (k = 672; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -641.0461 1282.0923 1294.0923 1321.1090 1294.2196
##
## tau^2 (estimated amount of residual heterogeneity): 0.2328 (SE = 0.0188)
## tau (square root of estimated tau^2 value): 0.4825
## I^2 (residual heterogeneity / unaccounted variability): 71.56%
## H^2 (unaccounted variability / sampling variability): 3.52
## R^2 (amount of heterogeneity accounted for): 0.59%
##
## Test for Residual Heterogeneity:
## QE(df = 667) = 2134.3155, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 5.1930, p-val = 0.2681
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.0820 0.0839 0.9769 0.3286 -0.0825
## factor(lateralisation)Bi-Sim 0.0146 0.1149 0.1268 0.8991 -0.2106
## factor(lateralisation)Uni-fMRI-L/R -0.1915 0.4170 -0.4593 0.6461 -1.0088
## factor(lateralisation)Uni-L 0.1430 0.0879 1.6257 0.1040 -0.0294
## factor(lateralisation)Uni-R 0.1173 0.1156 1.0148 0.3102 -0.1093
## ci.ub
## intrcpt 0.2465
## factor(lateralisation)Bi-Sim 0.2397
## factor(lateralisation)Uni-fMRI-L/R 0.6258
## factor(lateralisation)Uni-L 0.3154
## factor(lateralisation)Uni-R 0.3439
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for Lateralization :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by Lateralization</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> Uni-L </td>
## <td style="text-align:right;"> 504 </td>
## <td style="text-align:left;"> 0.23 [0.17, 0.28]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 76.3 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> Bi-Seq </td>
## <td style="text-align:right;"> 55 </td>
## <td style="text-align:left;"> 0.07 [-0.05, 0.18] </td>
## <td style="text-align:right;"> 0.244 </td>
## <td style="text-align:right;"> 22.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> Bi-Sim </td>
## <td style="text-align:right;"> 55 </td>
## <td style="text-align:left;"> 0.06 [-0.03, 0.15] </td>
## <td style="text-align:right;"> 0.184 </td>
## <td style="text-align:right;"> 15.4 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> Uni-R </td>
## <td style="text-align:right;"> 56 </td>
## <td style="text-align:left;"> 0.23 [0.11, 0.35]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 55.6 </td>
## </tr>
## </tbody>
## </table>
Treatment Duration Analysis
We categorize and analyze the effect of total treatment time:
# Create categories for total time
effect_sizes <- effect_sizes %>%
mutate(time_category = case_when(
total_time < 100 ~ "Very Short (<100 min)",
total_time >= 100 & total_time < 300 ~ "Short (100-300 min)",
total_time >= 300 & total_time < 600 ~ "Medium (300-600 min)",
total_time >= 600 ~ "Long (>600 min)",
TRUE ~ NA_character_
))
cat("\n\n** TREATMENT TIME ANALYSIS **\n")##
##
## ** TREATMENT TIME ANALYSIS **##
## Analyzing Total Treatment Time with 672 valid cases
## Found 4 unique levels: Short (100-300 min), Very Short (<100 min), Medium (300-600 min), Long (>600 min)
## Level: Short (100-300 min) - Number of studies: 271
## Level: Very Short (<100 min) - Number of studies: 135
## Level: Medium (300-600 min) - Number of studies: 199
## Level: Long (>600 min) - Number of studies: 67
##
##
## Meta-regression for differences between Total Treatment Time subgroups:
##
## Mixed-Effects Model (k = 672; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -640.5327 1281.0655 1291.0655 1313.5869 1291.1561
##
## tau^2 (estimated amount of residual heterogeneity): 0.2323 (SE = 0.0188)
## tau (square root of estimated tau^2 value): 0.4820
## I^2 (residual heterogeneity / unaccounted variability): 71.49%
## H^2 (unaccounted variability / sampling variability): 3.51
## R^2 (amount of heterogeneity accounted for): 0.83%
##
## Test for Residual Heterogeneity:
## QE(df = 668) = 2132.2541, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.0736, p-val = 0.0696
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.1526 0.0710 2.1505 0.0315
## factor(time_category)Medium (300-600 min) 0.1227 0.0819 1.4978 0.1342
## factor(time_category)Short (100-300 min) 0.0483 0.0799 0.6042 0.5457
## factor(time_category)Very Short (<100 min) -0.0443 0.0873 -0.5073 0.6119
## ci.lb ci.ub
## intrcpt 0.0135 0.2916 *
## factor(time_category)Medium (300-600 min) -0.0379 0.2834
## factor(time_category)Short (100-300 min) -0.1084 0.2050
## factor(time_category)Very Short (<100 min) -0.2154 0.1268
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for Total Treatment Time :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by Total Treatment Time</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> Short (100-300 min) </td>
## <td style="text-align:right;"> 271 </td>
## <td style="text-align:left;"> 0.2 [0.13, 0.26]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 62.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> Very Short (<100 min) </td>
## <td style="text-align:right;"> 135 </td>
## <td style="text-align:left;"> 0.1 [-0.03, 0.23] </td>
## <td style="text-align:right;"> 0.131 </td>
## <td style="text-align:right;"> 83.7 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> Medium (300-600 min) </td>
## <td style="text-align:right;"> 199 </td>
## <td style="text-align:left;"> 0.28 [0.19, 0.36]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 76.6 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> Long (>600 min) </td>
## <td style="text-align:right;"> 67 </td>
## <td style="text-align:left;"> 0.15 [0.06, 0.24]** </td>
## <td style="text-align:right;"> 0.001 </td>
## <td style="text-align:right;"> 33.1 </td>
## </tr>
## </tbody>
## </table>
# Meta-regression with time as a continuous variable
time_metareg <- tryCatch({
rma(yi, vi, mods = ~ total_time, data = effect_sizes %>% filter(!is.na(total_time)), method = "REML")
}, error = function(e) {
cat("Error in time meta-regression:", e$message, "\n")
return(NULL)
})
if(!is.null(time_metareg)) {
cat("\n\nMeta-regression for Total Treatment Time (continuous):\n")
print(summary(time_metareg))
# Plot a bubble plot for time vs effect size
ggplot(effect_sizes %>% filter(!is.na(total_time)),
aes(x = total_time, y = yi, size = 1/sqrt(vi))) +
geom_hline(yintercept = 0, linetype = "dashed", color = "darkgray") +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", se = TRUE, color = "blue") +
labs(title = "Relationship Between Treatment Time and Effect Size",
x = "Total Treatment Time (minutes)",
y = "Hedges' g Effect Size",
size = "Precision\n(1/SE)") +
theme_minimal()
}##
##
## Meta-regression for Total Treatment Time (continuous):
##
## Mixed-Effects Model (k = 672; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -643.7424 1287.4847 1293.4847 1307.0066 1293.5208
##
## tau^2 (estimated amount of residual heterogeneity): 0.2336 (SE = 0.0188)
## tau (square root of estimated tau^2 value): 0.4834
## I^2 (residual heterogeneity / unaccounted variability): 71.64%
## H^2 (unaccounted variability / sampling variability): 3.53
## R^2 (amount of heterogeneity accounted for): 0.25%
##
## Test for Residual Heterogeneity:
## QE(df = 670) = 2152.1119, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.1814, p-val = 0.1397
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1570 0.0372 4.2263 <.0001 0.0842 0.2299 ***
## total_time 0.0002 0.0001 1.4770 0.1397 -0.0001 0.0004
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation: size.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
Session Frequency Analysis
We analyze the effect of session frequency:
##
##
## ** SESSION FREQUENCY ANALYSIS **frequency_results <- run_subgroup_analysis(
effect_sizes,
"session_frequency",
"Session Frequency"
)##
## Analyzing Session Frequency with 672 valid cases
## Found 2 unique levels: high, low
## Level: high - Number of studies: 167
## Level: low - Number of studies: 505
##
##
## Meta-regression for differences between Session Frequency subgroups:
##
## Mixed-Effects Model (k = 672; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -642.8603 1285.7206 1291.7206 1305.2424 1291.7566
##
## tau^2 (estimated amount of residual heterogeneity): 0.2321 (SE = 0.0188)
## tau (square root of estimated tau^2 value): 0.4818
## I^2 (residual heterogeneity / unaccounted variability): 71.50%
## H^2 (unaccounted variability / sampling variability): 3.51
## R^2 (amount of heterogeneity accounted for): 0.91%
##
## Test for Residual Heterogeneity:
## QE(df = 670) = 2141.1712, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.9577, p-val = 0.0467
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1212 0.0458 2.6459 0.0081 0.0314 0.2111
## factor(session_frequency)low 0.1051 0.0528 1.9894 0.0467 0.0016 0.2087
##
## intrcpt **
## factor(session_frequency)low *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for Session Frequency :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by Session Frequency</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> high </td>
## <td style="text-align:right;"> 167 </td>
## <td style="text-align:left;"> 0.12 [0.05, 0.19]** </td>
## <td style="text-align:right;"> 0.001 </td>
## <td style="text-align:right;"> 52.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> low </td>
## <td style="text-align:right;"> 505 </td>
## <td style="text-align:left;"> 0.23 [0.17, 0.28]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 74.9 </td>
## </tr>
## </tbody>
## </table>
Number of Sessions Analysis
We categorize and analyze the effect of the number of treatment sessions:
# Create categories for number of sessions
effect_sizes <- effect_sizes %>%
mutate(sessions_category = case_when(
total_sessions == 1 ~ "Single Session",
total_sessions > 1 & total_sessions <= 5 ~ "2-5 Sessions",
total_sessions > 5 & total_sessions <= 10 ~ "6-10 Sessions",
total_sessions > 10 & total_sessions <= 20 ~ "11-20 Sessions",
total_sessions > 20 ~ "More than 20 Sessions",
TRUE ~ NA_character_
))
cat("\n\n** NUMBER OF SESSIONS ANALYSIS **\n")##
##
## ** NUMBER OF SESSIONS ANALYSIS **sessions_results <- run_subgroup_analysis(
effect_sizes,
"sessions_category",
"Number of Sessions"
)##
## Analyzing Number of Sessions with 672 valid cases
## Found 5 unique levels: 11-20 Sessions, Single Session, 6-10 Sessions, More than 20 Sessions, 2-5 Sessions
## Level: 11-20 Sessions - Number of studies: 340
## Level: Single Session - Number of studies: 14
## Level: 6-10 Sessions - Number of studies: 199
## Level: More than 20 Sessions - Number of studies: 92
## Level: 2-5 Sessions - Number of studies: 27
##
##
## Meta-regression for differences between Number of Sessions subgroups:
##
## Mixed-Effects Model (k = 672; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -628.3011 1256.6023 1268.6023 1295.6190 1268.7295
##
## tau^2 (estimated amount of residual heterogeneity): 0.2189 (SE = 0.0180)
## tau (square root of estimated tau^2 value): 0.4678
## I^2 (residual heterogeneity / unaccounted variability): 70.29%
## H^2 (unaccounted variability / sampling variability): 3.37
## R^2 (amount of heterogeneity accounted for): 6.56%
##
## Test for Residual Heterogeneity:
## QE(df = 667) = 2057.9858, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 31.3649, p-val < .0001
##
## Model Results:
##
## estimate se zval
## intrcpt 0.3093 0.0313 9.8884
## factor(sessions_category)2-5 Sessions -0.2069 0.1210 -1.7091
## factor(sessions_category)6-10 Sessions -0.2676 0.0523 -5.1136
## factor(sessions_category)More than 20 Sessions -0.1132 0.0662 -1.7101
## factor(sessions_category)Single Session -0.4333 0.1609 -2.6935
## pval ci.lb ci.ub
## intrcpt <.0001 0.2480 0.3705 ***
## factor(sessions_category)2-5 Sessions 0.0874 -0.4441 0.0304 .
## factor(sessions_category)6-10 Sessions <.0001 -0.3702 -0.1650 ***
## factor(sessions_category)More than 20 Sessions 0.0872 -0.2430 0.0165 .
## factor(sessions_category)Single Session 0.0071 -0.7485 -0.1180 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for Number of Sessions :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by Number of Sessions</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> 11-20 Sessions </td>
## <td style="text-align:right;"> 340 </td>
## <td style="text-align:left;"> 0.31 [0.24, 0.39]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 81.4 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> Single Session </td>
## <td style="text-align:right;"> 14 </td>
## <td style="text-align:left;"> -0.09 [-0.3, 0.12] </td>
## <td style="text-align:right;"> 0.401 </td>
## <td style="text-align:right;"> 25.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> 6-10 Sessions </td>
## <td style="text-align:right;"> 199 </td>
## <td style="text-align:left;"> 0.03 [-0.02, 0.08] </td>
## <td style="text-align:right;"> 0.195 </td>
## <td style="text-align:right;"> 18.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> More than 20 Sessions </td>
## <td style="text-align:right;"> 92 </td>
## <td style="text-align:left;"> 0.2 [0.11, 0.28]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 52.7 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> 2-5 Sessions </td>
## <td style="text-align:right;"> 27 </td>
## <td style="text-align:left;"> 0.11 [-0.07, 0.29] </td>
## <td style="text-align:right;"> 0.241 </td>
## <td style="text-align:right;"> 37.7 </td>
## </tr>
## </tbody>
## </table>
# Meta-regression with sessions as a continuous variable
sessions_metareg <- tryCatch({
rma(yi, vi, mods = ~ total_sessions, data = effect_sizes %>% filter(!is.na(total_sessions)), method = "REML")
}, error = function(e) {
cat("Error in sessions meta-regression:", e$message, "\n")
return(NULL)
})
if(!is.null(sessions_metareg)) {
cat("\n\nMeta-regression for Total Number of Sessions (continuous):\n")
print(summary(sessions_metareg))
# Plot a bubble plot for sessions vs effect size
ggplot(effect_sizes %>% filter(!is.na(total_sessions)),
aes(x = total_sessions, y = yi, size = 1/sqrt(vi))) +
geom_hline(yintercept = 0, linetype = "dashed", color = "darkgray") +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", se = TRUE, color = "blue") +
labs(title = "Relationship Between Number of Sessions and Effect Size",
x = "Total Number of Sessions",
y = "Hedges' g Effect Size",
size = "Precision\n(1/SE)") +
theme_minimal()
}##
##
## Meta-regression for Total Number of Sessions (continuous):
##
## Mixed-Effects Model (k = 672; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -642.3875 1284.7750 1290.7750 1304.2969 1290.8111
##
## tau^2 (estimated amount of residual heterogeneity): 0.2320 (SE = 0.0188)
## tau (square root of estimated tau^2 value): 0.4817
## I^2 (residual heterogeneity / unaccounted variability): 71.49%
## H^2 (unaccounted variability / sampling variability): 3.51
## R^2 (amount of heterogeneity accounted for): 0.94%
##
## Test for Residual Heterogeneity:
## QE(df = 670) = 2142.7155, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.9308, p-val = 0.0264
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1071 0.0478 2.2422 0.0249 0.0135 0.2007 *
## total_sessions 0.0052 0.0024 2.2205 0.0264 0.0006 0.0098 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation: size.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
Domain-Specific Analyses
Function to run analyses on specific symptom domains:
# Function to run subgroup analyses for a specific symptom domain
run_domain_subgroup_analyses <- function(domain_name) {
# Filter for this domain
domain_data <- effect_sizes %>% filter(domain == domain_name)
if(nrow(domain_data) >= 10) { # Only run if we have enough studies
cat("\n\n## Subgroup Analyses for", gsub("_", " ", domain_name), "Domain\n\n")
# Technique analysis
cat("\n### Intervention Type Analysis for", gsub("_", " ", domain_name), "\n")
technique_results <- run_subgroup_analysis(
domain_data,
"technique",
paste(gsub("_", " ", domain_name), "- Intervention Type"),
min_studies = 2 # Reduce minimum for domain-specific analyses
)
# Target analysis
cat("\n### Target Region Analysis for", gsub("_", " ", domain_name), "\n")
target_results <- run_subgroup_analysis(
domain_data,
"target",
paste(gsub("_", " ", domain_name), "- Target Region"),
min_studies = 2
)
# Sessions analysis
cat("\n### Treatment Sessions Analysis for", gsub("_", " ", domain_name), "\n")
sessions_results <- run_subgroup_analysis(
domain_data,
"sessions_category",
paste(gsub("_", " ", domain_name), "- Number of Sessions"),
min_studies = 2
)
} else {
cat("\n\nInsufficient data for subgroup analyses in the", gsub("_", " ", domain_name), "domain\n")
}
}Now we run the domain-specific analyses:
# Run subgroup analyses for key domains
domains_to_analyze <- c("positive_symptoms", "negative_symptoms", "total_psychopathology", "global_cognition")
for(domain in domains_to_analyze) {
cat("\n\n** DOMAIN-SPECIFIC ANALYSIS FOR", toupper(domain), "**\n")
run_domain_subgroup_analyses(domain)
}##
##
## ** DOMAIN-SPECIFIC ANALYSIS FOR POSITIVE_SYMPTOMS **
##
##
## ## Subgroup Analyses for positive symptoms Domain
##
##
## ### Intervention Type Analysis for positive symptoms
##
## Analyzing positive symptoms - Intervention Type with 118 valid cases
## Found 9 unique levels: LF-RTMS, HF-RTMS, ITBS, PRM-RTMS, TDCS, HD-TDCS, DTMS, CTBS, TACS
## Level: LF-RTMS - Number of studies: 32
## Level: HF-RTMS - Number of studies: 29
## Level: ITBS - Number of studies: 11
## Level: PRM-RTMS - Number of studies: 3
## Level: TDCS - Number of studies: 26
## Level: HD-TDCS - Number of studies: 2
## Level: DTMS - Number of studies: 2
## Level: CTBS - Number of studies: 9
## Level: TACS - Number of studies: 4
##
##
## Meta-regression for differences between positive symptoms - Intervention Type subgroups:
##
## Mixed-Effects Model (k = 118; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -128.6486 257.2971 277.2971 304.2106 279.5420
##
## tau^2 (estimated amount of residual heterogeneity): 0.4387 (SE = 0.0773)
## tau (square root of estimated tau^2 value): 0.6623
## I^2 (residual heterogeneity / unaccounted variability): 80.25%
## H^2 (unaccounted variability / sampling variability): 5.06
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 109) = 442.6867, p-val < .0001
##
## Test of Moderators (coefficients 2:9):
## QM(df = 8) = 6.6929, p-val = 0.5701
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3403 0.2459 -1.3839 0.1664 -0.8222 0.1416
## factor(technique)DTMS 0.6380 0.5951 1.0722 0.2836 -0.5283 1.8044
## factor(technique)HD-TDCS 0.6186 0.5962 1.0376 0.2995 -0.5500 1.7872
## factor(technique)HF-RTMS 0.4888 0.2819 1.7341 0.0829 -0.0637 1.0413
## factor(technique)ITBS 0.5974 0.3312 1.8037 0.0713 -0.0517 1.2465
## factor(technique)LF-RTMS 0.6994 0.2807 2.4913 0.0127 0.1492 1.2497
## factor(technique)PRM-RTMS 0.5204 0.5065 1.0274 0.3042 -0.4723 1.5130
## factor(technique)TACS 0.3530 0.4743 0.7441 0.4568 -0.5767 1.2826
## factor(technique)TDCS 0.4875 0.2889 1.6871 0.0916 -0.0788 1.0538
##
## intrcpt
## factor(technique)DTMS
## factor(technique)HD-TDCS
## factor(technique)HF-RTMS .
## factor(technique)ITBS .
## factor(technique)LF-RTMS *
## factor(technique)PRM-RTMS
## factor(technique)TACS
## factor(technique)TDCS .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for positive symptoms - Intervention Type :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by positive symptoms - Intervention Type</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> LF-RTMS </td>
## <td style="text-align:right;"> 32 </td>
## <td style="text-align:left;"> 0.36 [0.02, 0.69]* </td>
## <td style="text-align:right;"> 0.036 </td>
## <td style="text-align:right;"> 85.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> HF-RTMS </td>
## <td style="text-align:right;"> 29 </td>
## <td style="text-align:left;"> 0.14 [0.03, 0.26]* </td>
## <td style="text-align:right;"> 0.016 </td>
## <td style="text-align:right;"> 8.4 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> ITBS </td>
## <td style="text-align:right;"> 11 </td>
## <td style="text-align:left;"> 0.26 [-0.2, 0.72] </td>
## <td style="text-align:right;"> 0.273 </td>
## <td style="text-align:right;"> 85.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> PRM-RTMS </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.18 [-0.26, 0.62] </td>
## <td style="text-align:right;"> 0.418 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> TDCS </td>
## <td style="text-align:right;"> 26 </td>
## <td style="text-align:left;"> 0.12 [-0.03, 0.27] </td>
## <td style="text-align:right;"> 0.114 </td>
## <td style="text-align:right;"> 17.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt5 </td>
## <td style="text-align:left;"> HD-TDCS </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.24 [-0.19, 0.67] </td>
## <td style="text-align:right;"> 0.270 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt6 </td>
## <td style="text-align:left;"> DTMS </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.24 [-0.27, 0.75] </td>
## <td style="text-align:right;"> 0.348 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt7 </td>
## <td style="text-align:left;"> CTBS </td>
## <td style="text-align:right;"> 9 </td>
## <td style="text-align:left;"> -0.39 [-1.35, 0.57] </td>
## <td style="text-align:right;"> 0.422 </td>
## <td style="text-align:right;"> 95.8 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt8 </td>
## <td style="text-align:left;"> TACS </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.01 [-0.44, 0.45] </td>
## <td style="text-align:right;"> 0.975 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
## ### Target Region Analysis for positive symptoms
##
## Analyzing positive symptoms - Target Region with 118 valid cases
## Found 27 unique levels: L-TPJ, Bi-TPJ, Bi-DLPFC, Cereb-Vermis, L-DLPFC_L-TPJ, L-DLPFC, L-STS, L-DMPFC, Bi-DLPFC_Bi-TPJ, L-DLPFC_R-DLPFC, R-ORBF, DLPFC, L-M1, TPJ, R-DLPFC, Bi-PFC, L-DLPFC_R-SORB, Bi-Insula, fMRI-TC, L-DLPFC_R-ORBF, L_DLPFC, fMRI-TPC, R-IPL, L-SMA, R-DLPFC_L-TPJ, L-DLPFC_L-PFC, L-DLPFC/L-TPJ_Cz
## Level: L-TPJ - Number of studies: 23
## Level: Bi-TPJ - Number of studies: 6
## Level: Bi-DLPFC - Number of studies: 4
## Level: Cereb-Vermis - Number of studies: 3
## Level: L-DLPFC_L-TPJ - Number of studies: 19
## Level: L-DLPFC - Number of studies: 30
## Level: L-STS - Number of studies: 1
## Skipping L-STS - not enough studies
## Level: L-DMPFC - Number of studies: 1
## Skipping L-DMPFC - not enough studies
## Level: Bi-DLPFC_Bi-TPJ - Number of studies: 1
## Skipping Bi-DLPFC_Bi-TPJ - not enough studies
## Level: L-DLPFC_R-DLPFC - Number of studies: 2
## Level: R-ORBF - Number of studies: 2
## Level: DLPFC - Number of studies: 1
## Skipping DLPFC - not enough studies
## Level: L-M1 - Number of studies: 2
## Level: TPJ - Number of studies: 4
## Level: R-DLPFC - Number of studies: 1
## Skipping R-DLPFC - not enough studies
## Level: Bi-PFC - Number of studies: 1
## Skipping Bi-PFC - not enough studies
## Level: L-DLPFC_R-SORB - Number of studies: 1
## Skipping L-DLPFC_R-SORB - not enough studies
## Level: Bi-Insula - Number of studies: 1
## Skipping Bi-Insula - not enough studies
## Level: fMRI-TC - Number of studies: 2
## Level: L-DLPFC_R-ORBF - Number of studies: 3
## Level: L_DLPFC - Number of studies: 1
## Skipping L_DLPFC - not enough studies
## Level: fMRI-TPC - Number of studies: 2
## Level: R-IPL - Number of studies: 1
## Skipping R-IPL - not enough studies
## Level: L-SMA - Number of studies: 1
## Skipping L-SMA - not enough studies
## Level: R-DLPFC_L-TPJ - Number of studies: 2
## Level: L-DLPFC_L-PFC - Number of studies: 1
## Skipping L-DLPFC_L-PFC - not enough studies
## Level: L-DLPFC/L-TPJ_Cz - Number of studies: 2
##
##
## Meta-regression for differences between positive symptoms - Target Region subgroups:
##
## Mixed-Effects Model (k = 118; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -107.4613 214.9226 270.9226 341.2267 297.1161
##
## tau^2 (estimated amount of residual heterogeneity): 0.4425 (SE = 0.0853)
## tau (square root of estimated tau^2 value): 0.6652
## I^2 (residual heterogeneity / unaccounted variability): 80.40%
## H^2 (unaccounted variability / sampling variability): 5.10
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 91) = 370.7548, p-val < .0001
##
## Test of Moderators (coefficients 2:27):
## QM(df = 26) = 25.8135, p-val = 0.4734
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.5578 0.4030 1.3841 0.1663 -0.2321
## factor(target)Bi-DLPFC_Bi-TPJ -0.0902 0.8898 -0.1014 0.9193 -1.8341
## factor(target)Bi-Insula -0.0309 0.9010 -0.0343 0.9727 -1.7968
## factor(target)Bi-PFC -0.6051 0.8522 -0.7100 0.4777 -2.2753
## factor(target)Bi-TPJ -0.6892 0.5027 -1.3708 0.1704 -1.6745
## factor(target)Cereb-Vermis -0.5330 0.5836 -0.9132 0.3611 -1.6768
## factor(target)DLPFC -0.4033 0.8159 -0.4943 0.6211 -2.0025
## factor(target)fMRI-TC -0.8664 0.6779 -1.2781 0.2012 -2.1951
## factor(target)fMRI-TPC -0.6675 0.6588 -1.0132 0.3110 -1.9588
## factor(target)L_DLPFC -0.7266 0.8719 -0.8334 0.4046 -2.4355
## factor(target)L-DLPFC -0.4497 0.4251 -1.0577 0.2902 -1.2829
## factor(target)L-DLPFC_L-PFC -0.3430 0.8134 -0.4217 0.6733 -1.9372
## factor(target)L-DLPFC_L-TPJ -0.3934 0.4406 -0.8927 0.3720 -1.2570
## factor(target)L-DLPFC_R-DLPFC -0.2464 0.6659 -0.3700 0.7114 -1.5516
## factor(target)L-DLPFC_R-ORBF -0.5878 0.5969 -0.9849 0.3247 -1.7577
## factor(target)L-DLPFC_R-SORB -0.5233 0.8260 -0.6335 0.5264 -2.1422
## factor(target)L-DLPFC/L-TPJ_Cz -0.5711 0.6818 -0.8375 0.4023 -1.9074
## factor(target)L-DMPFC -0.6375 0.8625 -0.7392 0.4598 -2.3280
## factor(target)L-M1 -0.2236 0.6596 -0.3390 0.7346 -1.5163
## factor(target)L-SMA 1.2757 0.8567 1.4891 0.1365 -0.4034
## factor(target)L-STS -0.6658 0.8208 -0.8111 0.4173 -2.2745
## factor(target)L-TPJ 0.0188 0.4346 0.0433 0.9654 -0.8329
## factor(target)R-DLPFC -1.1290 0.8598 -1.3130 0.1892 -2.8142
## factor(target)R-DLPFC_L-TPJ -0.7783 0.7937 -0.9805 0.3268 -2.3340
## factor(target)R-IPL -0.2843 0.9355 -0.3039 0.7612 -2.1179
## factor(target)R-ORBF 0.1275 0.6388 0.1995 0.8418 -1.1246
## factor(target)TPJ -1.4164 0.5486 -2.5818 0.0098 -2.4916
## ci.ub
## intrcpt 1.3477
## factor(target)Bi-DLPFC_Bi-TPJ 1.6537
## factor(target)Bi-Insula 1.7351
## factor(target)Bi-PFC 1.0652
## factor(target)Bi-TPJ 0.2962
## factor(target)Cereb-Vermis 0.6109
## factor(target)DLPFC 1.1958
## factor(target)fMRI-TC 0.4623
## factor(target)fMRI-TPC 0.6238
## factor(target)L_DLPFC 0.9822
## factor(target)L-DLPFC 0.3836
## factor(target)L-DLPFC_L-PFC 1.2513
## factor(target)L-DLPFC_L-TPJ 0.4703
## factor(target)L-DLPFC_R-DLPFC 1.0588
## factor(target)L-DLPFC_R-ORBF 0.5820
## factor(target)L-DLPFC_R-SORB 1.0956
## factor(target)L-DLPFC/L-TPJ_Cz 0.7653
## factor(target)L-DMPFC 1.0529
## factor(target)L-M1 1.0692
## factor(target)L-SMA 2.9549
## factor(target)L-STS 0.9430
## factor(target)L-TPJ 0.8706
## factor(target)R-DLPFC 0.5563
## factor(target)R-DLPFC_L-TPJ 0.7774
## factor(target)R-IPL 1.5493
## factor(target)R-ORBF 1.3795
## factor(target)TPJ -0.3411 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for positive symptoms - Target Region :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by positive symptoms - Target Region</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> L-TPJ </td>
## <td style="text-align:right;"> 23 </td>
## <td style="text-align:left;"> 0.57 [0.15, 0.99]** </td>
## <td style="text-align:right;"> 0.008 </td>
## <td style="text-align:right;"> 85.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> Bi-TPJ </td>
## <td style="text-align:right;"> 6 </td>
## <td style="text-align:left;"> -0.12 [-0.41, 0.17] </td>
## <td style="text-align:right;"> 0.415 </td>
## <td style="text-align:right;"> 28.7 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> Bi-DLPFC </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.65 [-0.74, 2.04] </td>
## <td style="text-align:right;"> 0.360 </td>
## <td style="text-align:right;"> 90.2 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> Cereb-Vermis </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.02 [-0.32, 0.35] </td>
## <td style="text-align:right;"> 0.914 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> L-DLPFC_L-TPJ </td>
## <td style="text-align:right;"> 19 </td>
## <td style="text-align:left;"> 0.15 [-0.1, 0.39] </td>
## <td style="text-align:right;"> 0.241 </td>
## <td style="text-align:right;"> 55.4 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt5 </td>
## <td style="text-align:left;"> L-DLPFC </td>
## <td style="text-align:right;"> 30 </td>
## <td style="text-align:left;"> 0.12 [-0.03, 0.28] </td>
## <td style="text-align:right;"> 0.115 </td>
## <td style="text-align:right;"> 52.4 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt6 </td>
## <td style="text-align:left;"> L-DLPFC_R-DLPFC </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.32 [-0.13, 0.78] </td>
## <td style="text-align:right;"> 0.161 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt7 </td>
## <td style="text-align:left;"> R-ORBF </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.68 [0.38, 0.99]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt8 </td>
## <td style="text-align:left;"> L-M1 </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.33 [-0.31, 0.98] </td>
## <td style="text-align:right;"> 0.313 </td>
## <td style="text-align:right;"> 52.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt9 </td>
## <td style="text-align:left;"> TPJ </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> -1 [-3.18, 1.17] </td>
## <td style="text-align:right;"> 0.365 </td>
## <td style="text-align:right;"> 98.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt10 </td>
## <td style="text-align:left;"> fMRI-TC </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> -0.31 [-0.85, 0.23] </td>
## <td style="text-align:right;"> 0.263 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt11 </td>
## <td style="text-align:left;"> L-DLPFC_R-ORBF </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> -0.02 [-0.42, 0.39] </td>
## <td style="text-align:right;"> 0.938 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt12 </td>
## <td style="text-align:left;"> fMRI-TPC </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> -0.11 [-0.56, 0.34] </td>
## <td style="text-align:right;"> 0.638 </td>
## <td style="text-align:right;"> 5.6 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt13 </td>
## <td style="text-align:left;"> R-DLPFC_L-TPJ </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> -0.19 [-1.15, 0.77] </td>
## <td style="text-align:right;"> 0.701 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt14 </td>
## <td style="text-align:left;"> L-DLPFC/L-TPJ_Cz </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> -0.01 [-0.57, 0.55] </td>
## <td style="text-align:right;"> 0.963 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
## ### Treatment Sessions Analysis for positive symptoms
##
## Analyzing positive symptoms - Number of Sessions with 118 valid cases
## Found 4 unique levels: 11-20 Sessions, 6-10 Sessions, More than 20 Sessions, 2-5 Sessions
## Level: 11-20 Sessions - Number of studies: 61
## Level: 6-10 Sessions - Number of studies: 38
## Level: More than 20 Sessions - Number of studies: 11
## Level: 2-5 Sessions - Number of studies: 8
##
##
## Meta-regression for differences between positive symptoms - Number of Sessions subgroups:
##
## Mixed-Effects Model (k = 118; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -133.3652 266.7304 276.7304 290.4114 277.2859
##
## tau^2 (estimated amount of residual heterogeneity): 0.4235 (SE = 0.0735)
## tau (square root of estimated tau^2 value): 0.6508
## I^2 (residual heterogeneity / unaccounted variability): 79.82%
## H^2 (unaccounted variability / sampling variability): 4.96
## R^2 (amount of heterogeneity accounted for): 0.90%
##
## Test for Residual Heterogeneity:
## QE(df = 114) = 451.4017, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 3.8890, p-val = 0.2737
##
## Model Results:
##
## estimate se zval
## intrcpt 0.2811 0.0951 2.9559
## factor(sessions_category)2-5 Sessions -0.3435 0.2844 -1.2078
## factor(sessions_category)6-10 Sessions -0.2644 0.1563 -1.6911
## factor(sessions_category)More than 20 Sessions -0.0020 0.2356 -0.0084
## pval ci.lb ci.ub
## intrcpt 0.0031 0.0947 0.4674 **
## factor(sessions_category)2-5 Sessions 0.2271 -0.9010 0.2139
## factor(sessions_category)6-10 Sessions 0.0908 -0.5708 0.0420 .
## factor(sessions_category)More than 20 Sessions 0.9933 -0.4638 0.4598
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for positive symptoms - Number of Sessions :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by positive symptoms - Number of Sessions</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> 11-20 Sessions </td>
## <td style="text-align:right;"> 61 </td>
## <td style="text-align:left;"> 0.28 [0.09, 0.48]** </td>
## <td style="text-align:right;"> 0.005 </td>
## <td style="text-align:right;"> 82.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> 6-10 Sessions </td>
## <td style="text-align:right;"> 38 </td>
## <td style="text-align:left;"> 0.02 [-0.26, 0.29] </td>
## <td style="text-align:right;"> 0.902 </td>
## <td style="text-align:right;"> 82.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> More than 20 Sessions </td>
## <td style="text-align:right;"> 11 </td>
## <td style="text-align:left;"> 0.3 [0.09, 0.51]** </td>
## <td style="text-align:right;"> 0.006 </td>
## <td style="text-align:right;"> 34.7 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> 2-5 Sessions </td>
## <td style="text-align:right;"> 8 </td>
## <td style="text-align:left;"> -0.08 [-0.34, 0.18] </td>
## <td style="text-align:right;"> 0.553 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
##
## ** DOMAIN-SPECIFIC ANALYSIS FOR NEGATIVE_SYMPTOMS **
##
##
## ## Subgroup Analyses for negative symptoms Domain
##
##
## ### Intervention Type Analysis for negative symptoms
##
## Analyzing negative symptoms - Intervention Type with 106 valid cases
## Found 8 unique levels: LF-RTMS, HF-RTMS, ITBS, TDCS, TACS, HD-TDCS, DTMS, CTBS
## Level: LF-RTMS - Number of studies: 18
## Level: HF-RTMS - Number of studies: 33
## Level: ITBS - Number of studies: 18
## Level: TDCS - Number of studies: 23
## Level: TACS - Number of studies: 4
## Level: HD-TDCS - Number of studies: 3
## Level: DTMS - Number of studies: 4
## Level: CTBS - Number of studies: 3
##
##
## Meta-regression for differences between negative symptoms - Intervention Type subgroups:
##
## Mixed-Effects Model (k = 106; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -100.4297 200.8595 218.8595 242.1242 220.9049
##
## tau^2 (estimated amount of residual heterogeneity): 0.2780 (SE = 0.0581)
## tau (square root of estimated tau^2 value): 0.5272
## I^2 (residual heterogeneity / unaccounted variability): 71.83%
## H^2 (unaccounted variability / sampling variability): 3.55
## R^2 (amount of heterogeneity accounted for): 11.77%
##
## Test for Residual Heterogeneity:
## QE(df = 98) = 314.1667, p-val < .0001
##
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 14.6539, p-val = 0.0407
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0928 0.3685 0.2517 0.8012 -0.6295 0.8151
## factor(technique)DTMS 0.2724 0.4983 0.5467 0.5846 -0.7042 1.2490
## factor(technique)HD-TDCS 0.2090 0.5387 0.3880 0.6980 -0.8467 1.2647
## factor(technique)HF-RTMS 0.4913 0.3844 1.2780 0.2013 -0.2621 1.2447
## factor(technique)ITBS 0.6499 0.3976 1.6346 0.1021 -0.1294 1.4293
## factor(technique)LF-RTMS 0.1344 0.3992 0.3367 0.7364 -0.6480 0.9168
## factor(technique)TACS 0.2928 0.4955 0.5909 0.5546 -0.6784 1.2640
## factor(technique)TDCS 0.0159 0.3929 0.0405 0.9677 -0.7541 0.7859
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for negative symptoms - Intervention Type :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by negative symptoms - Intervention Type</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> LF-RTMS </td>
## <td style="text-align:right;"> 18 </td>
## <td style="text-align:left;"> 0.29 [0.13, 0.45]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> HF-RTMS </td>
## <td style="text-align:right;"> 33 </td>
## <td style="text-align:left;"> 0.59 [0.34, 0.84]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 82.1 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> ITBS </td>
## <td style="text-align:right;"> 18 </td>
## <td style="text-align:left;"> 0.72 [0.28, 1.17]** </td>
## <td style="text-align:right;"> 0.002 </td>
## <td style="text-align:right;"> 89.3 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> TDCS </td>
## <td style="text-align:right;"> 23 </td>
## <td style="text-align:left;"> 0.08 [-0.06, 0.23] </td>
## <td style="text-align:right;"> 0.251 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> TACS </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.41 [0.04, 0.79]* </td>
## <td style="text-align:right;"> 0.032 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt5 </td>
## <td style="text-align:left;"> HD-TDCS </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.18 [-0.21, 0.58] </td>
## <td style="text-align:right;"> 0.368 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt6 </td>
## <td style="text-align:left;"> DTMS </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.34 [-0.06, 0.73] </td>
## <td style="text-align:right;"> 0.093 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt7 </td>
## <td style="text-align:left;"> CTBS </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.07 [-0.29, 0.44] </td>
## <td style="text-align:right;"> 0.693 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
## ### Target Region Analysis for negative symptoms
##
## Analyzing negative symptoms - Target Region with 106 valid cases
## Found 28 unique levels: L-TPJ, Bi-TPJ, Bi-DLPFC, Cereb-Vermis, L-DLPFC, Bi-DMPFC, L-DLPFC_L-TPJ, L-FC/L-PC_CPz/FCz, L-DMPFC, Bi-DLPFC_Bi-TPJ, L-DLPFC_R-DLPFC, R-ORBF, DLPFC, L-M1, R-DLPFC, R-DLPFC_L-Orbit, Bi-PFC, L-DLPFC_R-SORB, Bi-Insula, fMRI-TC, L-DLPFC_R-ORBF, L_DLPFC, R-IPL, L-SMA, R-DLPFC_L-TPJ, L-DLPFC_L-PFC, L-DLPFC/L-TPJ_Cz, L-DLPFC_R-ORB
## Level: L-TPJ - Number of studies: 8
## Level: Bi-TPJ - Number of studies: 3
## Level: Bi-DLPFC - Number of studies: 9
## Level: Cereb-Vermis - Number of studies: 4
## Level: L-DLPFC - Number of studies: 39
## Level: Bi-DMPFC - Number of studies: 1
## Skipping Bi-DMPFC - not enough studies
## Level: L-DLPFC_L-TPJ - Number of studies: 10
## Level: L-FC/L-PC_CPz/FCz - Number of studies: 2
## Level: L-DMPFC - Number of studies: 1
## Skipping L-DMPFC - not enough studies
## Level: Bi-DLPFC_Bi-TPJ - Number of studies: 2
## Level: L-DLPFC_R-DLPFC - Number of studies: 2
## Level: R-ORBF - Number of studies: 2
## Level: DLPFC - Number of studies: 1
## Skipping DLPFC - not enough studies
## Level: L-M1 - Number of studies: 1
## Skipping L-M1 - not enough studies
## Level: R-DLPFC - Number of studies: 1
## Skipping R-DLPFC - not enough studies
## Level: R-DLPFC_L-Orbit - Number of studies: 2
## Level: Bi-PFC - Number of studies: 1
## Skipping Bi-PFC - not enough studies
## Level: L-DLPFC_R-SORB - Number of studies: 2
## Level: Bi-Insula - Number of studies: 1
## Skipping Bi-Insula - not enough studies
## Level: fMRI-TC - Number of studies: 1
## Skipping fMRI-TC - not enough studies
## Level: L-DLPFC_R-ORBF - Number of studies: 4
## Level: L_DLPFC - Number of studies: 2
## Level: R-IPL - Number of studies: 1
## Skipping R-IPL - not enough studies
## Level: L-SMA - Number of studies: 2
## Level: R-DLPFC_L-TPJ - Number of studies: 1
## Skipping R-DLPFC_L-TPJ - not enough studies
## Level: L-DLPFC_L-PFC - Number of studies: 1
## Skipping L-DLPFC_L-PFC - not enough studies
## Level: L-DLPFC/L-TPJ_Cz - Number of studies: 1
## Skipping L-DLPFC/L-TPJ_Cz - not enough studies
## Level: L-DLPFC_R-ORB - Number of studies: 1
## Skipping L-DLPFC_R-ORB - not enough studies
##
##
## Meta-regression for differences between negative symptoms - Target Region subgroups:
##
## Mixed-Effects Model (k = 106; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -82.9655 165.9310 223.9310 292.2756 260.1810
##
## tau^2 (estimated amount of residual heterogeneity): 0.3126 (SE = 0.0708)
## tau (square root of estimated tau^2 value): 0.5591
## I^2 (residual heterogeneity / unaccounted variability): 74.49%
## H^2 (unaccounted variability / sampling variability): 3.92
## R^2 (amount of heterogeneity accounted for): 0.78%
##
## Test for Residual Heterogeneity:
## QE(df = 78) = 271.3771, p-val < .0001
##
## Test of Moderators (coefficients 2:28):
## QM(df = 27) = 27.7324, p-val = 0.4249
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.3725 0.2347 1.5869 0.1125 -0.0876
## factor(target)Bi-DLPFC_Bi-TPJ 0.4822 0.5725 0.8423 0.3996 -0.6398
## factor(target)Bi-DMPFC -0.2945 0.7886 -0.3734 0.7088 -1.8401
## factor(target)Bi-Insula 0.4536 0.7647 0.5932 0.5530 -1.0452
## factor(target)Bi-PFC -0.0204 0.7006 -0.0291 0.9768 -1.3937
## factor(target)Bi-TPJ -0.2697 0.4432 -0.6085 0.5428 -1.1383
## factor(target)Cereb-Vermis -0.3205 0.3929 -0.8157 0.4147 -1.0906
## factor(target)DLPFC 0.2870 0.6570 0.4368 0.6623 -1.0007
## factor(target)fMRI-TC -0.5006 0.7197 -0.6955 0.4867 -1.9112
## factor(target)L_DLPFC -0.0800 0.5383 -0.1485 0.8819 -1.1349
## factor(target)L-DLPFC 0.4140 0.2574 1.6083 0.1078 -0.0905
## factor(target)L-DLPFC_L-PFC -0.3170 0.6513 -0.4868 0.6264 -1.5935
## factor(target)L-DLPFC_L-TPJ -0.3136 0.3197 -0.9811 0.3266 -0.9401
## factor(target)L-DLPFC_R-DLPFC -0.0225 0.5215 -0.0432 0.9655 -1.0446
## factor(target)L-DLPFC_R-ORB -0.3380 0.6886 -0.4909 0.6235 -1.6876
## factor(target)L-DLPFC_R-ORBF -0.2512 0.4149 -0.6054 0.5449 -1.0643
## factor(target)L-DLPFC_R-SORB -0.5538 0.5003 -1.1069 0.2683 -1.5345
## factor(target)L-DLPFC/L-TPJ_Cz -0.3517 0.7280 -0.4831 0.6291 -1.7786
## factor(target)L-DMPFC -0.3974 0.7118 -0.5584 0.5766 -1.7924
## factor(target)L-FC/L-PC_CPz/FCz 0.1987 0.5189 0.3830 0.7017 -0.8183
## factor(target)L-M1 -0.3613 0.6839 -0.5283 0.5973 -1.7017
## factor(target)L-SMA -0.2614 0.5075 -0.5150 0.6065 -1.2561
## factor(target)L-TPJ -0.2574 0.3404 -0.7560 0.4496 -0.9246
## factor(target)R-DLPFC -0.3069 0.7049 -0.4353 0.6633 -1.6885
## factor(target)R-DLPFC_L-Orbit -0.2465 0.5239 -0.4704 0.6381 -1.2734
## factor(target)R-DLPFC_L-TPJ -0.9132 0.8845 -1.0324 0.3019 -2.6467
## factor(target)R-IPL -0.1332 0.7984 -0.1668 0.8675 -1.6981
## factor(target)R-ORBF 0.1651 0.4851 0.3403 0.7336 -0.7857
## ci.ub
## intrcpt 0.8326
## factor(target)Bi-DLPFC_Bi-TPJ 1.6042
## factor(target)Bi-DMPFC 1.2511
## factor(target)Bi-Insula 1.9524
## factor(target)Bi-PFC 1.3528
## factor(target)Bi-TPJ 0.5989
## factor(target)Cereb-Vermis 0.4496
## factor(target)DLPFC 1.5747
## factor(target)fMRI-TC 0.9100
## factor(target)L_DLPFC 0.9750
## factor(target)L-DLPFC 0.9185
## factor(target)L-DLPFC_L-PFC 0.9595
## factor(target)L-DLPFC_L-TPJ 0.3129
## factor(target)L-DLPFC_R-DLPFC 0.9995
## factor(target)L-DLPFC_R-ORB 1.0116
## factor(target)L-DLPFC_R-ORBF 0.5620
## factor(target)L-DLPFC_R-SORB 0.4268
## factor(target)L-DLPFC/L-TPJ_Cz 1.0753
## factor(target)L-DMPFC 0.9976
## factor(target)L-FC/L-PC_CPz/FCz 1.2157
## factor(target)L-M1 0.9791
## factor(target)L-SMA 0.7333
## factor(target)L-TPJ 0.4099
## factor(target)R-DLPFC 1.0748
## factor(target)R-DLPFC_L-Orbit 0.7804
## factor(target)R-DLPFC_L-TPJ 0.8204
## factor(target)R-IPL 1.4317
## factor(target)R-ORBF 1.1158
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for negative symptoms - Target Region :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by negative symptoms - Target Region</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> L-TPJ </td>
## <td style="text-align:right;"> 8 </td>
## <td style="text-align:left;"> 0.2 [-0.06, 0.46] </td>
## <td style="text-align:right;"> 0.135 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> Bi-TPJ </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.09 [-0.27, 0.46] </td>
## <td style="text-align:right;"> 0.611 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> Bi-DLPFC </td>
## <td style="text-align:right;"> 9 </td>
## <td style="text-align:left;"> 0.37 [-0.07, 0.81] </td>
## <td style="text-align:right;"> 0.103 </td>
## <td style="text-align:right;"> 61.1 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> Cereb-Vermis </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.05 [-0.23, 0.33] </td>
## <td style="text-align:right;"> 0.718 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> L-DLPFC </td>
## <td style="text-align:right;"> 39 </td>
## <td style="text-align:left;"> 0.79 [0.52, 1.05]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 86.3 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt5 </td>
## <td style="text-align:left;"> L-DLPFC_L-TPJ </td>
## <td style="text-align:right;"> 10 </td>
## <td style="text-align:left;"> 0.09 [-0.13, 0.31] </td>
## <td style="text-align:right;"> 0.437 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt6 </td>
## <td style="text-align:left;"> L-FC/L-PC_CPz/FCz </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.57 [0.1, 1.04]* </td>
## <td style="text-align:right;"> 0.018 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt7 </td>
## <td style="text-align:left;"> Bi-DLPFC_Bi-TPJ </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.96 [-1.46, 3.37] </td>
## <td style="text-align:right;"> 0.438 </td>
## <td style="text-align:right;"> 92.2 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt8 </td>
## <td style="text-align:left;"> L-DLPFC_R-DLPFC </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.3 [-0.25, 0.84] </td>
## <td style="text-align:right;"> 0.283 </td>
## <td style="text-align:right;"> 24.6 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt9 </td>
## <td style="text-align:left;"> R-ORBF </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.54 [0.23, 0.84]** </td>
## <td style="text-align:right;"> 0.001 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt10 </td>
## <td style="text-align:left;"> R-DLPFC_L-Orbit </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.13 [-0.37, 0.62] </td>
## <td style="text-align:right;"> 0.618 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt11 </td>
## <td style="text-align:left;"> L-DLPFC_R-SORB </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> -0.18 [-0.57, 0.21] </td>
## <td style="text-align:right;"> 0.360 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt12 </td>
## <td style="text-align:left;"> L-DLPFC_R-ORBF </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> 0.08 [-0.31, 0.47] </td>
## <td style="text-align:right;"> 0.674 </td>
## <td style="text-align:right;"> 7.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt13 </td>
## <td style="text-align:left;"> L_DLPFC </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.29 [-0.26, 0.84] </td>
## <td style="text-align:right;"> 0.297 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt14 </td>
## <td style="text-align:left;"> L-SMA </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.11 [-0.54, 0.76] </td>
## <td style="text-align:right;"> 0.739 </td>
## <td style="text-align:right;"> 58.2 </td>
## </tr>
## </tbody>
## </table>
##
## ### Treatment Sessions Analysis for negative symptoms
##
## Analyzing negative symptoms - Number of Sessions with 106 valid cases
## Found 4 unique levels: 11-20 Sessions, 6-10 Sessions, More than 20 Sessions, 2-5 Sessions
## Level: 11-20 Sessions - Number of studies: 62
## Level: 6-10 Sessions - Number of studies: 29
## Level: More than 20 Sessions - Number of studies: 11
## Level: 2-5 Sessions - Number of studies: 4
##
##
## Meta-regression for differences between negative symptoms - Number of Sessions subgroups:
##
## Mixed-Effects Model (k = 106; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -104.4877 208.9755 218.9755 232.1003 219.6005
##
## tau^2 (estimated amount of residual heterogeneity): 0.2850 (SE = 0.0579)
## tau (square root of estimated tau^2 value): 0.5339
## I^2 (residual heterogeneity / unaccounted variability): 72.58%
## H^2 (unaccounted variability / sampling variability): 3.65
## R^2 (amount of heterogeneity accounted for): 9.53%
##
## Test for Residual Heterogeneity:
## QE(df = 102) = 336.9933, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 10.3778, p-val = 0.0156
##
## Model Results:
##
## estimate se zval
## intrcpt 0.5520 0.0819 6.7393
## factor(sessions_category)2-5 Sessions -0.6457 0.3482 -1.8542
## factor(sessions_category)6-10 Sessions -0.4151 0.1482 -2.8001
## factor(sessions_category)More than 20 Sessions -0.0433 0.2024 -0.2137
## pval ci.lb ci.ub
## intrcpt <.0001 0.3914 0.7125 ***
## factor(sessions_category)2-5 Sessions 0.0637 -1.3283 0.0368 .
## factor(sessions_category)6-10 Sessions 0.0051 -0.7057 -0.1246 **
## factor(sessions_category)More than 20 Sessions 0.8308 -0.4400 0.3535
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for negative symptoms - Number of Sessions :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by negative symptoms - Number of Sessions</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> 11-20 Sessions </td>
## <td style="text-align:right;"> 62 </td>
## <td style="text-align:left;"> 0.56 [0.36, 0.75]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 82.7 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> 6-10 Sessions </td>
## <td style="text-align:right;"> 29 </td>
## <td style="text-align:left;"> 0.14 [0.01, 0.27]* </td>
## <td style="text-align:right;"> 0.035 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> More than 20 Sessions </td>
## <td style="text-align:right;"> 11 </td>
## <td style="text-align:left;"> 0.51 [0.21, 0.82]** </td>
## <td style="text-align:right;"> 0.001 </td>
## <td style="text-align:right;"> 65.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> 2-5 Sessions </td>
## <td style="text-align:right;"> 4 </td>
## <td style="text-align:left;"> -0.11 [-0.51, 0.29] </td>
## <td style="text-align:right;"> 0.598 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
##
## ** DOMAIN-SPECIFIC ANALYSIS FOR TOTAL_PSYCHOPATHOLOGY **
##
##
## ## Subgroup Analyses for total psychopathology Domain
##
##
## ### Intervention Type Analysis for total psychopathology
##
## Analyzing total psychopathology - Intervention Type with 72 valid cases
## Found 9 unique levels: ITBS, LF-RTMS, PRM-RTMS, TDCS, TACS, HF-RTMS, DTMS, CTBS, HD-TDCS
## Level: ITBS - Number of studies: 10
## Level: LF-RTMS - Number of studies: 10
## Level: PRM-RTMS - Number of studies: 1
## Skipping PRM-RTMS - not enough studies
## Level: TDCS - Number of studies: 17
## Level: TACS - Number of studies: 3
## Level: HF-RTMS - Number of studies: 24
## Level: DTMS - Number of studies: 3
## Level: CTBS - Number of studies: 3
## Level: HD-TDCS - Number of studies: 1
## Skipping HD-TDCS - not enough studies
##
##
## Meta-regression for differences between total psychopathology - Intervention Type subgroups:
##
## Mixed-Effects Model (k = 72; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -58.8186 117.6372 137.6372 159.0686 141.8680
##
## tau^2 (estimated amount of residual heterogeneity): 0.2422 (SE = 0.0647)
## tau (square root of estimated tau^2 value): 0.4921
## I^2 (residual heterogeneity / unaccounted variability): 70.23%
## H^2 (unaccounted variability / sampling variability): 3.36
## R^2 (amount of heterogeneity accounted for): 6.92%
##
## Test for Residual Heterogeneity:
## QE(df = 63) = 197.1374, p-val < .0001
##
## Test of Moderators (coefficients 2:9):
## QM(df = 8) = 10.1498, p-val = 0.2547
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1731 0.3514 0.4927 0.6222 -0.5156 0.8619
## factor(technique)DTMS 0.1500 0.5078 0.2954 0.7677 -0.8452 1.1452
## factor(technique)HD-TDCS 0.0860 0.6501 0.1323 0.8948 -1.1881 1.3601
## factor(technique)HF-RTMS 0.1751 0.3715 0.4715 0.6373 -0.5529 0.9032
## factor(technique)ITBS 0.6480 0.3997 1.6212 0.1050 -0.1354 1.4314
## factor(technique)LF-RTMS 0.5003 0.4023 1.2436 0.2137 -0.2882 1.2887
## factor(technique)PRM-RTMS 0.2688 0.7087 0.3793 0.7045 -1.1202 1.6578
## factor(technique)TACS 0.1417 0.5132 0.2761 0.7825 -0.8641 1.1475
## factor(technique)TDCS -0.0131 0.3822 -0.0343 0.9726 -0.7622 0.7360
##
## intrcpt
## factor(technique)DTMS
## factor(technique)HD-TDCS
## factor(technique)HF-RTMS
## factor(technique)ITBS
## factor(technique)LF-RTMS
## factor(technique)PRM-RTMS
## factor(technique)TACS
## factor(technique)TDCS
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for total psychopathology - Intervention Type :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by total psychopathology - Intervention Type</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> ITBS </td>
## <td style="text-align:right;"> 10 </td>
## <td style="text-align:left;"> 0.82 [0.37, 1.28]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 80.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> LF-RTMS </td>
## <td style="text-align:right;"> 10 </td>
## <td style="text-align:left;"> 0.65 [0.19, 1.11]** </td>
## <td style="text-align:right;"> 0.005 </td>
## <td style="text-align:right;"> 77.6 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> TDCS </td>
## <td style="text-align:right;"> 17 </td>
## <td style="text-align:left;"> 0.14 [-0.04, 0.32] </td>
## <td style="text-align:right;"> 0.134 </td>
## <td style="text-align:right;"> 13.2 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> TACS </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.36 [-0.1, 0.82] </td>
## <td style="text-align:right;"> 0.120 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> HF-RTMS </td>
## <td style="text-align:right;"> 24 </td>
## <td style="text-align:left;"> 0.35 [0.09, 0.62]* </td>
## <td style="text-align:right;"> 0.010 </td>
## <td style="text-align:right;"> 80.2 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt5 </td>
## <td style="text-align:left;"> DTMS </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.31 [-0.13, 0.75] </td>
## <td style="text-align:right;"> 0.165 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt6 </td>
## <td style="text-align:left;"> CTBS </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.2 [-0.17, 0.57] </td>
## <td style="text-align:right;"> 0.285 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
## ### Target Region Analysis for total psychopathology
##
## Analyzing total psychopathology - Target Region with 72 valid cases
## Found 25 unique levels: L-DLPFC, L-TPJ, L-DLPFC_L-TPJ, L-FC/L-PC_CPz/FCz, Cereb-Vermis, L-DMPFC, Bi-DLPFC, Bi-DLPFC_Bi-TPJ, L-DLPFC_R-DLPFC, R-ORBF, DLPFC, L-M1, R-DLPFC, Bi-PFC, L-DLPFC_R-SORB, L-DLPFC_Cz, Bi-Insula, L-DLPFC_R-ORBF, R-IPL, Bi-TPJ, L-SMA, R-DLPFC_L-TPJ, L-DLPFC_L-PFC, L-DLPFC/L-TPJ_Cz, L-DLPFC_R-ORB
## Level: L-DLPFC - Number of studies: 28
## Level: L-TPJ - Number of studies: 7
## Level: L-DLPFC_L-TPJ - Number of studies: 7
## Level: L-FC/L-PC_CPz/FCz - Number of studies: 1
## Skipping L-FC/L-PC_CPz/FCz - not enough studies
## Level: Cereb-Vermis - Number of studies: 3
## Level: L-DMPFC - Number of studies: 1
## Skipping L-DMPFC - not enough studies
## Level: Bi-DLPFC - Number of studies: 2
## Level: Bi-DLPFC_Bi-TPJ - Number of studies: 1
## Skipping Bi-DLPFC_Bi-TPJ - not enough studies
## Level: L-DLPFC_R-DLPFC - Number of studies: 2
## Level: R-ORBF - Number of studies: 2
## Level: DLPFC - Number of studies: 1
## Skipping DLPFC - not enough studies
## Level: L-M1 - Number of studies: 1
## Skipping L-M1 - not enough studies
## Level: R-DLPFC - Number of studies: 1
## Skipping R-DLPFC - not enough studies
## Level: Bi-PFC - Number of studies: 1
## Skipping Bi-PFC - not enough studies
## Level: L-DLPFC_R-SORB - Number of studies: 1
## Skipping L-DLPFC_R-SORB - not enough studies
## Level: L-DLPFC_Cz - Number of studies: 2
## Level: Bi-Insula - Number of studies: 1
## Skipping Bi-Insula - not enough studies
## Level: L-DLPFC_R-ORBF - Number of studies: 3
## Level: R-IPL - Number of studies: 1
## Skipping R-IPL - not enough studies
## Level: Bi-TPJ - Number of studies: 1
## Skipping Bi-TPJ - not enough studies
## Level: L-SMA - Number of studies: 1
## Skipping L-SMA - not enough studies
## Level: R-DLPFC_L-TPJ - Number of studies: 1
## Skipping R-DLPFC_L-TPJ - not enough studies
## Level: L-DLPFC_L-PFC - Number of studies: 1
## Skipping L-DLPFC_L-PFC - not enough studies
## Level: L-DLPFC/L-TPJ_Cz - Number of studies: 1
## Skipping L-DLPFC/L-TPJ_Cz - not enough studies
## Level: L-DLPFC_R-ORB - Number of studies: 1
## Skipping L-DLPFC_R-ORB - not enough studies
##
##
## Meta-regression for differences between total psychopathology - Target Region subgroups:
##
## Mixed-Effects Model (k = 72; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -46.6681 93.3361 145.3361 193.4400 215.5361
##
## tau^2 (estimated amount of residual heterogeneity): 0.2929 (SE = 0.0853)
## tau (square root of estimated tau^2 value): 0.5412
## I^2 (residual heterogeneity / unaccounted variability): 74.87%
## H^2 (unaccounted variability / sampling variability): 3.98
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 47) = 174.5570, p-val < .0001
##
## Test of Moderators (coefficients 2:25):
## QM(df = 24) = 17.5423, p-val = 0.8246
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.0948 0.4946 0.1916 0.8480 -0.8745
## factor(target)Bi-DLPFC_Bi-TPJ 0.5277 0.8533 0.6185 0.5363 -1.1446
## factor(target)Bi-Insula 0.4246 0.8627 0.4922 0.6226 -1.2662
## factor(target)Bi-PFC 0.0992 0.8120 0.1222 0.9028 -1.4923
## factor(target)Bi-TPJ 0.1368 0.7783 0.1758 0.8605 -1.3886
## factor(target)Cereb-Vermis -0.0491 0.6150 -0.0798 0.9364 -1.2544
## factor(target)DLPFC 0.3450 0.7743 0.4456 0.6559 -1.1726
## factor(target)L-DLPFC 0.4240 0.5090 0.8331 0.4048 -0.5736
## factor(target)L-DLPFC_Cz 0.6047 0.6952 0.8699 0.3844 -0.7578
## factor(target)L-DLPFC_L-PFC 0.1644 0.7710 0.2132 0.8312 -1.3467
## factor(target)L-DLPFC_L-TPJ 0.0561 0.5542 0.1013 0.9193 -1.0301
## factor(target)L-DLPFC_R-DLPFC 0.4168 0.6723 0.6200 0.5353 -0.9008
## factor(target)L-DLPFC_R-ORB -0.5490 0.8041 -0.6827 0.4948 -2.1250
## factor(target)L-DLPFC_R-ORBF -0.1721 0.6232 -0.2762 0.7824 -1.3935
## factor(target)L-DLPFC_R-SORB -0.4514 0.7849 -0.5751 0.5652 -1.9898
## factor(target)L-DLPFC/L-TPJ_Cz 0.1534 0.8373 0.1833 0.8546 -1.4876
## factor(target)L-DMPFC -0.2017 0.8225 -0.2452 0.8063 -1.8138
## factor(target)L-FC/L-PC_CPz/FCz 0.5103 0.8085 0.6312 0.5279 -1.0743
## factor(target)L-M1 0.1443 0.7989 0.1806 0.8567 -1.4215
## factor(target)L-SMA 1.5005 0.8107 1.8508 0.0642 -0.0885
## factor(target)L-TPJ 0.6036 0.5577 1.0823 0.2791 -0.4894
## factor(target)R-DLPFC -0.0320 0.8165 -0.0392 0.9688 -1.6323
## factor(target)R-DLPFC_L-TPJ -0.2256 0.9513 -0.2371 0.8126 -2.0900
## factor(target)R-IPL -0.1156 0.8974 -0.1288 0.8975 -1.8744
## factor(target)R-ORBF 0.8748 0.6456 1.3550 0.1754 -0.3906
## ci.ub
## intrcpt 1.0641
## factor(target)Bi-DLPFC_Bi-TPJ 2.2001
## factor(target)Bi-Insula 2.1154
## factor(target)Bi-PFC 1.6907
## factor(target)Bi-TPJ 1.6622
## factor(target)Cereb-Vermis 1.1562
## factor(target)DLPFC 1.8627
## factor(target)L-DLPFC 1.4216
## factor(target)L-DLPFC_Cz 1.9672
## factor(target)L-DLPFC_L-PFC 1.6754
## factor(target)L-DLPFC_L-TPJ 1.1424
## factor(target)L-DLPFC_R-DLPFC 1.7344
## factor(target)L-DLPFC_R-ORB 1.0271
## factor(target)L-DLPFC_R-ORBF 1.0493
## factor(target)L-DLPFC_R-SORB 1.0870
## factor(target)L-DLPFC/L-TPJ_Cz 1.7945
## factor(target)L-DMPFC 1.4104
## factor(target)L-FC/L-PC_CPz/FCz 2.0949
## factor(target)L-M1 1.7100
## factor(target)L-SMA 3.0895 .
## factor(target)L-TPJ 1.6967
## factor(target)R-DLPFC 1.5683
## factor(target)R-DLPFC_L-TPJ 1.6389
## factor(target)R-IPL 1.6433
## factor(target)R-ORBF 2.1401
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for total psychopathology - Target Region :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by total psychopathology - Target Region</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> L-DLPFC </td>
## <td style="text-align:right;"> 28 </td>
## <td style="text-align:left;"> 0.53 [0.25, 0.8]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 83.5 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> L-TPJ </td>
## <td style="text-align:right;"> 7 </td>
## <td style="text-align:left;"> 0.67 [0.05, 1.29]* </td>
## <td style="text-align:right;"> 0.035 </td>
## <td style="text-align:right;"> 77.3 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> L-DLPFC_L-TPJ </td>
## <td style="text-align:right;"> 7 </td>
## <td style="text-align:left;"> 0.17 [-0.08, 0.41] </td>
## <td style="text-align:right;"> 0.183 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> Cereb-Vermis </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.05 [-0.32, 0.42] </td>
## <td style="text-align:right;"> 0.787 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt4 </td>
## <td style="text-align:left;"> Bi-DLPFC </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.1 [-0.52, 0.71] </td>
## <td style="text-align:right;"> 0.759 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt5 </td>
## <td style="text-align:left;"> L-DLPFC_R-DLPFC </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.47 [0.01, 0.92]* </td>
## <td style="text-align:right;"> 0.046 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt6 </td>
## <td style="text-align:left;"> R-ORBF </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.97 [0.66, 1.28]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt7 </td>
## <td style="text-align:left;"> L-DLPFC_Cz </td>
## <td style="text-align:right;"> 2 </td>
## <td style="text-align:left;"> 0.69 [0.1, 1.27]* </td>
## <td style="text-align:right;"> 0.021 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt8 </td>
## <td style="text-align:left;"> L-DLPFC_R-ORBF </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> -0.09 [-0.5, 0.31] </td>
## <td style="text-align:right;"> 0.651 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
## ### Treatment Sessions Analysis for total psychopathology
##
## Analyzing total psychopathology - Number of Sessions with 72 valid cases
## Found 4 unique levels: 11-20 Sessions, 6-10 Sessions, 2-5 Sessions, More than 20 Sessions
## Level: 11-20 Sessions - Number of studies: 40
## Level: 6-10 Sessions - Number of studies: 22
## Level: 2-5 Sessions - Number of studies: 3
## Level: More than 20 Sessions - Number of studies: 7
##
##
## Meta-regression for differences between total psychopathology - Number of Sessions subgroups:
##
## Mixed-Effects Model (k = 72; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -62.4936 124.9871 134.9871 146.0847 135.9549
##
## tau^2 (estimated amount of residual heterogeneity): 0.2410 (SE = 0.0619)
## tau (square root of estimated tau^2 value): 0.4910
## I^2 (residual heterogeneity / unaccounted variability): 70.46%
## H^2 (unaccounted variability / sampling variability): 3.39
## R^2 (amount of heterogeneity accounted for): 7.36%
##
## Test for Residual Heterogeneity:
## QE(df = 68) = 222.8489, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 7.0591, p-val = 0.0700
##
## Model Results:
##
## estimate se zval
## intrcpt 0.5583 0.0942 5.9254
## factor(sessions_category)2-5 Sessions -0.6641 0.3742 -1.7745
## factor(sessions_category)6-10 Sessions -0.3536 0.1651 -2.1414
## factor(sessions_category)More than 20 Sessions -0.2593 0.2354 -1.1013
## pval ci.lb ci.ub
## intrcpt <.0001 0.3736 0.7429 ***
## factor(sessions_category)2-5 Sessions 0.0760 -1.3976 0.0694 .
## factor(sessions_category)6-10 Sessions 0.0322 -0.6772 -0.0300 *
## factor(sessions_category)More than 20 Sessions 0.2708 -0.7206 0.2021
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for total psychopathology - Number of Sessions :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by total psychopathology - Number of Sessions</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> 11-20 Sessions </td>
## <td style="text-align:right;"> 40 </td>
## <td style="text-align:left;"> 0.56 [0.35, 0.77]*** </td>
## <td style="text-align:right;"> 0.000 </td>
## <td style="text-align:right;"> 79.9 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> 6-10 Sessions </td>
## <td style="text-align:right;"> 22 </td>
## <td style="text-align:left;"> 0.2 [0.05, 0.35]* </td>
## <td style="text-align:right;"> 0.011 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> 2-5 Sessions </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> -0.11 [-0.55, 0.33] </td>
## <td style="text-align:right;"> 0.615 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt3 </td>
## <td style="text-align:left;"> More than 20 Sessions </td>
## <td style="text-align:right;"> 7 </td>
## <td style="text-align:left;"> 0.3 [-0.13, 0.73] </td>
## <td style="text-align:right;"> 0.169 </td>
## <td style="text-align:right;"> 74.9 </td>
## </tr>
## </tbody>
## </table>
##
##
## ** DOMAIN-SPECIFIC ANALYSIS FOR GLOBAL_COGNITION **
##
##
## ## Subgroup Analyses for global cognition Domain
##
##
## ### Intervention Type Analysis for global cognition
##
## Analyzing global cognition - Intervention Type with 25 valid cases
## Found 6 unique levels: HF-RTMS, LF-RTMS, TDCS, ITBS, TACS, HD-TDCS
## Level: HF-RTMS - Number of studies: 10
## Level: LF-RTMS - Number of studies: 1
## Skipping LF-RTMS - not enough studies
## Level: TDCS - Number of studies: 6
## Level: ITBS - Number of studies: 6
## Level: TACS - Number of studies: 1
## Skipping TACS - not enough studies
## Level: HD-TDCS - Number of studies: 1
## Skipping HD-TDCS - not enough studies
##
##
## Meta-regression for differences between global cognition - Intervention Type subgroups:
##
## Mixed-Effects Model (k = 25; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -5.9095 11.8190 25.8190 32.4300 36.0008
##
## tau^2 (estimated amount of residual heterogeneity): 0.0307 (SE = 0.0370)
## tau (square root of estimated tau^2 value): 0.1751
## I^2 (residual heterogeneity / unaccounted variability): 26.38%
## H^2 (unaccounted variability / sampling variability): 1.36
## R^2 (amount of heterogeneity accounted for): 30.15%
##
## Test for Residual Heterogeneity:
## QE(df = 19) = 22.7010, p-val = 0.2508
##
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 7.1219, p-val = 0.2117
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0376 0.2964 0.1267 0.8991 -0.5434 0.6185
## factor(technique)HF-RTMS 0.2582 0.3135 0.8234 0.4103 -0.3563 0.8727
## factor(technique)ITBS -0.0463 0.3292 -0.1407 0.8881 -0.6914 0.5988
## factor(technique)LF-RTMS 0.6108 0.4074 1.4993 0.1338 -0.1877 1.4093
## factor(technique)TACS -0.3713 0.6389 -0.5811 0.5612 -1.6235 0.8810
## factor(technique)TDCS 0.0630 0.3366 0.1872 0.8515 -0.5967 0.7227
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for global cognition - Intervention Type :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by global cognition - Intervention Type</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> HF-RTMS </td>
## <td style="text-align:right;"> 10 </td>
## <td style="text-align:left;"> 0.29 [0.05, 0.53]* </td>
## <td style="text-align:right;"> 0.017 </td>
## <td style="text-align:right;"> 50.3 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> TDCS </td>
## <td style="text-align:right;"> 6 </td>
## <td style="text-align:left;"> 0.1 [-0.18, 0.37] </td>
## <td style="text-align:right;"> 0.491 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> ITBS </td>
## <td style="text-align:right;"> 6 </td>
## <td style="text-align:left;"> -0.01 [-0.25, 0.22] </td>
## <td style="text-align:right;"> 0.913 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
## ### Target Region Analysis for global cognition
##
## Analyzing global cognition - Target Region with 25 valid cases
## Found 10 unique levels: Bi-DLPFC, L-DLPFC, R-ORBF, L-DLPFC_R-DLPFC, L-PC, L-LPC, R-DLPFC, L-DLPFC_L-TPJ, L-DLPFC_R-ORBF, L-DLPFC_L-PFC
## Level: Bi-DLPFC - Number of studies: 1
## Skipping Bi-DLPFC - not enough studies
## Level: L-DLPFC - Number of studies: 12
## Level: R-ORBF - Number of studies: 1
## Skipping R-ORBF - not enough studies
## Level: L-DLPFC_R-DLPFC - Number of studies: 1
## Skipping L-DLPFC_R-DLPFC - not enough studies
## Level: L-PC - Number of studies: 1
## Skipping L-PC - not enough studies
## Level: L-LPC - Number of studies: 1
## Skipping L-LPC - not enough studies
## Level: R-DLPFC - Number of studies: 1
## Skipping R-DLPFC - not enough studies
## Level: L-DLPFC_L-TPJ - Number of studies: 3
## Level: L-DLPFC_R-ORBF - Number of studies: 3
## Level: L-DLPFC_L-PFC - Number of studies: 1
## Skipping L-DLPFC_L-PFC - not enough studies
##
##
## Meta-regression for differences between global cognition - Target Region subgroups:
##
## Mixed-Effects Model (k = 25; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -5.7322 11.4644 33.4644 41.2529 121.4644
##
## tau^2 (estimated amount of residual heterogeneity): 0.0431 (SE = 0.0463)
## tau (square root of estimated tau^2 value): 0.2077
## I^2 (residual heterogeneity / unaccounted variability): 33.88%
## H^2 (unaccounted variability / sampling variability): 1.51
## R^2 (amount of heterogeneity accounted for): 1.73%
##
## Test for Residual Heterogeneity:
## QE(df = 15) = 20.8890, p-val = 0.1404
##
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 7.4156, p-val = 0.5939
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.2931 0.5065 0.5787 0.5628 -0.6996
## factor(target)L-DLPFC -0.0330 0.5162 -0.0639 0.9491 -1.0448
## factor(target)L-DLPFC_L-PFC -0.2555 0.5974 -0.4277 0.6688 -1.4263
## factor(target)L-DLPFC_L-TPJ -0.3052 0.6004 -0.5084 0.6112 -1.4820
## factor(target)L-DLPFC_R-DLPFC -0.0674 0.6141 -0.1097 0.9126 -1.2711
## factor(target)L-DLPFC_R-ORBF -0.2497 0.5513 -0.4530 0.6506 -1.3302
## factor(target)L-LPC -0.7054 0.6104 -1.1556 0.2478 -1.9017
## factor(target)L-PC -0.1436 0.6305 -0.2278 0.8198 -1.3794
## factor(target)R-DLPFC -0.2968 0.6047 -0.4908 0.6236 -1.4819
## factor(target)R-ORBF 0.3553 0.5891 0.6031 0.5465 -0.7994
## ci.ub
## intrcpt 1.2857
## factor(target)L-DLPFC 0.9789
## factor(target)L-DLPFC_L-PFC 0.9153
## factor(target)L-DLPFC_L-TPJ 0.8715
## factor(target)L-DLPFC_R-DLPFC 1.1363
## factor(target)L-DLPFC_R-ORBF 0.8308
## factor(target)L-LPC 0.4910
## factor(target)L-PC 1.0921
## factor(target)R-DLPFC 0.8884
## factor(target)R-ORBF 1.5100
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for global cognition - Target Region :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by global cognition - Target Region</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> L-DLPFC </td>
## <td style="text-align:right;"> 12 </td>
## <td style="text-align:left;"> 0.26 [0.05, 0.47]* </td>
## <td style="text-align:right;"> 0.015 </td>
## <td style="text-align:right;"> 43.7 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> L-DLPFC_L-TPJ </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> -0.01 [-0.6, 0.58] </td>
## <td style="text-align:right;"> 0.972 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> L-DLPFC_R-ORBF </td>
## <td style="text-align:right;"> 3 </td>
## <td style="text-align:left;"> 0.03 [-0.32, 0.39] </td>
## <td style="text-align:right;"> 0.857 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## </tbody>
## </table>
##
## ### Treatment Sessions Analysis for global cognition
##
## Analyzing global cognition - Number of Sessions with 25 valid cases
## Found 4 unique levels: 6-10 Sessions, More than 20 Sessions, 11-20 Sessions, 2-5 Sessions
## Level: 6-10 Sessions - Number of studies: 5
## Level: More than 20 Sessions - Number of studies: 8
## Level: 11-20 Sessions - Number of studies: 11
## Level: 2-5 Sessions - Number of studies: 1
## Skipping 2-5 Sessions - not enough studies
##
##
## Meta-regression for differences between global cognition - Number of Sessions subgroups:
##
## Mixed-Effects Model (k = 25; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -8.3757 16.7515 26.7515 31.9741 30.7515
##
## tau^2 (estimated amount of residual heterogeneity): 0.0516 (SE = 0.0414)
## tau (square root of estimated tau^2 value): 0.2271
## I^2 (residual heterogeneity / unaccounted variability): 38.66%
## H^2 (unaccounted variability / sampling variability): 1.63
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 21) = 32.0618, p-val = 0.0577
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.8558, p-val = 0.8361
##
## Model Results:
##
## estimate se zval
## intrcpt 0.2251 0.1075 2.0935
## factor(sessions_category)2-5 Sessions 0.1589 0.4514 0.3520
## factor(sessions_category)6-10 Sessions -0.1793 0.2283 -0.7854
## factor(sessions_category)More than 20 Sessions -0.0638 0.1682 -0.3794
## pval ci.lb ci.ub
## intrcpt 0.0363 0.0144 0.4359 *
## factor(sessions_category)2-5 Sessions 0.7249 -0.7259 1.0437
## factor(sessions_category)6-10 Sessions 0.4322 -0.6268 0.2682
## factor(sessions_category)More than 20 Sessions 0.7044 -0.3935 0.2658
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## Subgroup Analysis for global cognition - Number of Sessions :
## <table class="table table-striped table-hover table-condensed" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
## <caption>Summary of Results by global cognition - Number of Sessions</caption>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> Subgroup </th>
## <th style="text-align:right;"> Number of Effect Sizes </th>
## <th style="text-align:left;"> Effect Size [95% CI] </th>
## <th style="text-align:right;"> p-value </th>
## <th style="text-align:right;"> I² </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> intrcpt </td>
## <td style="text-align:left;"> 6-10 Sessions </td>
## <td style="text-align:right;"> 5 </td>
## <td style="text-align:left;"> 0.05 [-0.28, 0.38] </td>
## <td style="text-align:right;"> 0.767 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt1 </td>
## <td style="text-align:left;"> More than 20 Sessions </td>
## <td style="text-align:right;"> 8 </td>
## <td style="text-align:left;"> 0.16 [-0.03, 0.35] </td>
## <td style="text-align:right;"> 0.107 </td>
## <td style="text-align:right;"> 0.0 </td>
## </tr>
## <tr>
## <td style="text-align:left;"> intrcpt2 </td>
## <td style="text-align:left;"> 11-20 Sessions </td>
## <td style="text-align:right;"> 11 </td>
## <td style="text-align:left;"> 0.22 [-0.05, 0.49] </td>
## <td style="text-align:right;"> 0.118 </td>
## <td style="text-align:right;"> 64.8 </td>
## </tr>
## </tbody>
## </table>
Combined Results Summary
Finally, we create a comprehensive summary of all subgroup analyses:
# Create a combined summary of all subgroup analyses if results are available
combined_results <- list()
if(!is.null(technique_results)) combined_results$technique <- technique_results$table %>% mutate(Factor = "Intervention Type")
if(!is.null(target_results)) combined_results$target <- target_results$table %>% mutate(Factor = "Target Region")
if(!is.null(lateralization_results)) combined_results$lateral <- lateralization_results$table %>% mutate(Factor = "Lateralization")
if(!is.null(time_results)) combined_results$time <- time_results$table %>% mutate(Factor = "Total Treatment Time")
if(!is.null(frequency_results)) combined_results$freq <- frequency_results$table %>% mutate(Factor = "Session Frequency")
if(!is.null(sessions_results)) combined_results$sessions <- sessions_results$table %>% mutate(Factor = "Number of Sessions")
if(length(combined_results) > 0) {
# Combine all available results
combined_summary <- bind_rows(combined_results)
# Reorder columns
combined_summary <- combined_summary %>%
select(Factor, everything())
# Print the combined summary
kable(combined_summary,
caption = "Comprehensive Summary of All Subgroup Analyses") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE) %>%
pack_rows(index = table(combined_summary$Factor))
}| Factor | Subgroup | Number of Effect Sizes | Effect Size [95% CI] | p-value | I² | |
|---|---|---|---|---|---|---|
| Intervention Type | ||||||
| intrcpt…1 | Intervention Type | LF-RTMS | 88 | 0.34 [0.2, 0.49]*** | 0.000 | 79.0 |
| intrcpt1…2 | Intervention Type | HF-RTMS | 225 | 0.21 [0.14, 0.28]*** | 0.000 | 70.5 |
| intrcpt2…3 | Intervention Type | ITBS | 112 | 0.27 [0.11, 0.42]** | 0.001 | 87.7 |
| intrcpt3…4 | Intervention Type | PRM-RTMS | 4 | 0.25 [-0.12, 0.62] | 0.190 | 0.0 |
| intrcpt4…5 | Intervention Type | TDCS | 182 | 0.08 [0.03, 0.14]** | 0.002 | 21.9 |
| intrcpt5…6 | Intervention Type | TACS | 13 | 0.23 [0, 0.47]* | 0.048 | 0.0 |
| intrcpt6…7 | Intervention Type | HD-TDCS | 9 | 0.22 [0.02, 0.41]* | 0.030 | 0.0 |
| intrcpt7…8 | Intervention Type | DTMS | 19 | 0.24 [0.07, 0.42]** | 0.006 | 0.0 |
| intrcpt8…9 | Intervention Type | CTBS | 20 | -0.14 [-0.57, 0.28] | 0.510 | 88.7 |
| Lateralization | ||||||
| intrcpt…10 | Target Region | L-TPJ | 45 | 0.46 [0.19, 0.72]** | 0.001 | 81.1 |
| intrcpt1…11 | Target Region | Bi-TPJ | 12 | 0.01 [-0.16, 0.18] | 0.938 | 0.0 |
| intrcpt2…12 | Target Region | Bi-DLPFC | 45 | 0.12 [-0.05, 0.29] | 0.166 | 47.4 |
| intrcpt3…13 | Target Region | Cereb-Vermis | 36 | -0.01 [-0.1, 0.08] | 0.842 | 1.1 |
| Number of Sessions | ||||||
| intrcpt4…14 | Target Region | L-DLPFC | 232 | 0.29 [0.21, 0.37]*** | 0.000 | 77.9 |
| intrcpt5…15 | Target Region | L-DLPFC_L-TPJ | 71 | 0.05 [-0.03, 0.12] | 0.251 | 0.0 |
| intrcpt6…16 | Target Region | L-FC/L-PC_CPz/FCz | 3 | 0.58 [0.2, 0.97]** | 0.003 | 0.0 |
| intrcpt7…17 | Target Region | L-DMPFC | 3 | -0.07 [-0.49, 0.35] | 0.743 | 0.0 |
| intrcpt8…18 | Target Region | L-DLPFC_R-SORB | 10 | -0.04 [-0.41, 0.33] | 0.829 | 68.6 |
| Session Frequency | ||||||
| intrcpt9 | Target Region | Bi-DLPFC_Bi-TPJ | 4 | 0.72 [-0.25, 1.7] | 0.147 | 79.1 |
| intrcpt10 | Target Region | L-DLPFC_R-DLPFC | 32 | 0.08 [0, 0.17] | 0.064 | 0.0 |
| Target Region | ||||||
| intrcpt11 | Target Region | R-ORBF | 24 | 0.4 [0.25, 0.55]*** | 0.000 | 65.9 |
| intrcpt12 | Target Region | DLPFC | 6 | 0.57 [0.15, 0.99]** | 0.008 | 76.4 |
| intrcpt13 | Target Region | L-M1 | 5 | 0.28 [0, 0.56] | 0.050 | 0.0 |
| intrcpt14 | Target Region | TPJ | 4 | -1 [-3.18, 1.17] | 0.365 | 98.0 |
| intrcpt15 | Target Region | R-DLPFC | 14 | 0.07 [-0.12, 0.26] | 0.480 | 43.8 |
| intrcpt16 | Target Region | L-PC | 5 | 0.44 [-2.81, 3.68] | 0.793 | 98.8 |
| intrcpt17 | Target Region | L-LPC | 8 | -0.26 [-0.6, 0.08] | 0.137 | 69.8 |
| intrcpt18 | Target Region | L-DLPFC_R-ORBF | 48 | 0.12 [0, 0.25]* | 0.046 | 41.9 |
| intrcpt19 | Target Region | Bi-PFC | 9 | 0.17 [-0.06, 0.39] | 0.156 | 0.0 |
| intrcpt20 | Target Region | L-DLPFC_Cz | 6 | 0.19 [-0.36, 0.74] | 0.495 | 61.9 |
| intrcpt21 | Target Region | Bi-Insula | 3 | 0.62 [0.1, 1.14]* | 0.019 | 0.0 |
| intrcpt22 | Target Region | fMRI-TC | 3 | -0.25 [-0.69, 0.19] | 0.270 | 0.0 |
| intrcpt23 | Target Region | L_DLPFC | 3 | 0.14 [-0.31, 0.58] | 0.547 | 0.0 |
| intrcpt24 | Target Region | R-IPL | 6 | -0.09 [-0.51, 0.32] | 0.660 | 0.0 |
| intrcpt25 | Target Region | L-SMA | 5 | 1.03 [0.25, 1.82]* | 0.010 | 86.5 |
| intrcpt26 | Target Region | R-DLPFC_L-TPJ | 12 | -0.21 [-0.59, 0.17] | 0.278 | 0.0 |
| intrcpt27 | Target Region | L-DLPFC_L-PFC | 5 | 0.17 [-0.04, 0.38] | 0.117 | 0.0 |
| intrcpt28 | Target Region | L-DLPFC/L-TPJ_Cz | 4 | 0.06 [-0.33, 0.46] | 0.765 | 0.0 |
| intrcpt29 | Target Region | L-DLPFC_R-ORB | 3 | -0.07 [-0.45, 0.32] | 0.725 | 7.0 |
| intrcpt…40 | Lateralization | Uni-L | 504 | 0.23 [0.17, 0.28]*** | 0.000 | 76.3 |
| intrcpt1…41 | Lateralization | Bi-Seq | 55 | 0.07 [-0.05, 0.18] | 0.244 | 22.0 |
| intrcpt2…42 | Lateralization | Bi-Sim | 55 | 0.06 [-0.03, 0.15] | 0.184 | 15.4 |
| intrcpt3…43 | Lateralization | Uni-R | 56 | 0.23 [0.11, 0.35]*** | 0.000 | 55.6 |
| intrcpt…44 | Total Treatment Time | Short (100-300 min) | 271 | 0.2 [0.13, 0.26]*** | 0.000 | 62.5 |
| intrcpt1…45 | Total Treatment Time | Very Short (<100 min) | 135 | 0.1 [-0.03, 0.23] | 0.131 | 83.7 |
| intrcpt2…46 | Total Treatment Time | Medium (300-600 min) | 199 | 0.28 [0.19, 0.36]*** | 0.000 | 76.6 |
| intrcpt3…47 | Total Treatment Time | Long (>600 min) | 67 | 0.15 [0.06, 0.24]** | 0.001 | 33.1 |
| intrcpt…48 | Session Frequency | high | 167 | 0.12 [0.05, 0.19]** | 0.001 | 52.5 |
| intrcpt1…49 | Session Frequency | low | 505 | 0.23 [0.17, 0.28]*** | 0.000 | 74.9 |
| intrcpt…50 | Number of Sessions | 11-20 Sessions | 340 | 0.31 [0.24, 0.39]*** | 0.000 | 81.4 |
| Total Treatment Time | ||||||
| intrcpt1…51 | Number of Sessions | Single Session | 14 | -0.09 [-0.3, 0.12] | 0.401 | 25.9 |
| intrcpt2…52 | Number of Sessions | 6-10 Sessions | 199 | 0.03 [-0.02, 0.08] | 0.195 | 18.5 |
| intrcpt3…53 | Number of Sessions | More than 20 Sessions | 92 | 0.2 [0.11, 0.28]*** | 0.000 | 52.7 |
| intrcpt4…54 | Number of Sessions | 2-5 Sessions | 27 | 0.11 [-0.07, 0.29] | 0.241 | 37.7 |