Do You Match What I Match? A Systematic Review of Over 20 Years of the Emotional Face Matching Task Reveals Vast Inconsistencies in Design and Implementation.

Code ↔ Paper

10 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 10 matches

[1] § Method ↔ A_P_Activated_Regions_PgtC.Rmd, lines 55 to 69 · score 0.96 · Autism Spectrum Disorder, Borderline Personality Disorder, anxiety related disorders, Bipolar Disorder, Body, clinically · Report a mismatch
[2] § Method ↔ D_Age_Sex_Sample_Size.Rmd, lines 541 to 592 · score 0.81 · mood disorders, anxiety related, PMDD, ASD, BDD, BPD · Report a mismatch
[3] § Results › Study Samples › Sex/Gender ↔ D_Age_Sex_Sample_Size.Rmd, lines 541 to 592 · score 0.65 · mood disorders, anxiety related, log, neurodivergent, Age, Sex · Report a mismatch
[4] § Results › Study Samples › Sex/Gender ↔ A_P_Activated_Regions_PgtC.Rmd, lines 55 to 69 · score 0.63 · Borderline Personality Disorder, anxiety related disorders, Body, Gender, schizophrenia · Report a mismatch
[5] § Method › Data Extraction › Data Synthesis ↔ T_Task_Parameters.Rmd, lines 258 to 321 · score 0.63 · stimuli expressions, contempt, disgust, fearful, happy, sad · Report a mismatch
[6] § Results › Brain Regions Activated › Regions Activated—Control Samples › Frontal Regions ↔ A_C_Activated_Regions_C.Rmd, lines 1337 to 1439 · score 0.60 · ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, ventromedial, cingulate, brain, activated · Report a mismatch
[7] § Results › Brain Regions Activated › Regions Activated—Control Samples › Frontal Regions ↔ A_P_Activated_Regions_PgtC.Rmd, lines 1451 to 1551 · score 0.60 · ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, ventromedial, cingulate, brain, activated · Report a mismatch
[8] § Results › Brain Regions Activated › Regions Activated—Control Samples › Occipital Regions ↔ A_C_Activated_Regions_C.Rmd, lines 1337 to 1439 · score 0.53 · occipital lobe, cuneus, lingual, gyrus, cortex, brain · Report a mismatch
[9] § Results › Brain Regions Activated › Regions Activated—Control Samples › Occipital Regions ↔ A_P_Activated_Regions_PgtC.Rmd, lines 1451 to 1551 · score 0.53 · occipital lobe, cuneus, lingual, gyrus, cortex, brain · Report a mismatch
[10] § Results › Task Parameters › Face Stimuli—Expressions ↔ T_Task_Parameters.Rmd, lines 258 to 321 · score 0.52 · anger, contempt, disgust, fearful, happy, surprise · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

R Markdown · 1,592 lines · 79 KB · GPL-3.0 · 4 matches

---
title: "Activation_Patients_Controls"
author: "HannahSavage"
date: "2023-05-31"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#Set env
```{r, include = FALSE}
library(readxl)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(grid)
library(reshape)
library(scales)
library(sjmisc)
library(scatterpie)
library(showtext)
library(psych)
#library('qgraph')
library('data.table')
library('reshape2')
library('ade4')
library('viridis')
library(extrafont)
library(ggalluvial)
```
```{r}
#Select the columns that will be used
FgtC_P_Activated <- read_excel("/project/3022054.01/projects/linschl/efmt/review_update_2026/Data_extraction_update_2026_clean_contrasts.xlsx")
#colnames(FgtC_P_Activated)[143:150] = c( "FgtC_PgtC", "WB_Th", "ROI_Th")
FgtC_P_Activated <- FgtC_P_Activated %>%
dplyr:: rename(
FgtC_PgtC = `Patients_>_Controls:_Faces_>_Control_clean_ROI`,
WB_Th = `Whole_brain_threshold_coded...145`,
ROI_Th = `ROI_threshold_coded...146`
)
#FgtC_P_Activated_LONG <- gather(FgtC_P_Activated, Diagnosis, Present, c(6:16,18:28, 30:40) , factor_key=TRUE)
#FgtC_P_Activated <- filter(FgtC_P_Activated_LONG, Present==1)
```
```{r}
#How many samples have which clinical groups
print(paste0("ADHD: ", nrow(subset(FgtC_P_Activated, PG1_ADHD == 1 | PG2_ADHD == 1 | PG3_ADHD == 1))))
print(paste0("Anxiety Related Disorder: ", nrow(subset(FgtC_P_Activated, PG1_ARD == 1 | PG2_ARD == 1 | PG3_ARD == 1))))
print(paste0("Autism Spectrum Disorder: ", nrow(subset(FgtC_P_Activated, PG1_ASD == 1 | PG2_ASD == 1 | PG3_ASD == 1))))
print(paste0("Bipolar Disorder: ", nrow(subset(FgtC_P_Activated, PG1_BP == 1 | PG2_BP == 1 | PG3_BP == 1))))
print(paste0("Body Dismorphic Disorder: ", nrow(subset(FgtC_P_Activated, PG1_BDD == 1 | PG2_BDD == 1 | PG3_BDD == 1))))
print(paste0("Borderline Personality Disorder: ", nrow(subset(FgtC_P_Activated, PG1_BPD == 1 | PG2_BPD == 1 | PG3_BPD == 1))))
print(paste0("Gender Dyshoria: ", nrow(subset(FgtC_P_Activated, PG1_GD == 1 | PG2_GD == 1 | PG3_GD == 1))))
print(paste0("MDD: ", nrow(subset(FgtC_P_Activated, PG1_MDD == 1 | PG2_MDD == 1 | PG3_MDD == 1))))
print(paste0("OCD: ", nrow(subset(FgtC_P_Activated, PG1_OCD == 1 | PG2_OCD == 1 | PG3_OCD == 1))))
print(paste0("Schizophrenia: ", nrow(subset(FgtC_P_Activated, PG1_Schizophrenia == 1 | PG2_Schizophrenia == 1 | PG3_Schizophrenia == 1))))
print(paste0("SUD: ", nrow(subset(FgtC_P_Activated, PG1_SUD == 1 | PG2_SUD == 1 | PG3_SUD == 1))))
print(paste0("PMDD: ", nrow(subset(FgtC_P_Activated, PG1_PMDD == 1 | PG2_PMDD == 1 | PG3_PMDD == 1))))
```
```{r}
##SET DIAGNOSIS:
#Diagnosis <- 'ADHD'
#Diagnosis <- 'ARD'
#Diagnosis <- 'ASD'
#Diagnosis <- 'BP'
#Diagnosis <- 'BDD' ##NO STUDIES
#Diagnosis <- 'BPD'
#Diagnosis <- 'GD'
#Diagnosis <- 'MDD'
#Diagnosis <- 'OCD'
#Diagnosis <- 'Schizophrenia'
#Diagnosis <- 'SUD'
Diagnosis <- 'PMDD'
```
```{r}
if (Diagnosis == 'ADHD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_ADHD == 1 | PG2_ADHD == 1 | PG3_ADHD == 1)
} else if (Diagnosis == 'ARD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_ARD == 1 | PG2_ARD == 1 | PG3_ARD == 1)
} else if (Diagnosis == 'ASD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_ASD == 1 | PG2_ASD == 1 | PG3_ASD == 1)
} else if (Diagnosis == 'BDD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_BDD == 1 | PG2_BDD == 1 | PG3_BDD == 1)
} else if (Diagnosis == 'BP') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_BP == 1 | PG2_BP == 1 | PG3_BP == 1)
} else if (Diagnosis == 'BPD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_BPD == 1 | PG2_BPD == 1 | PG3_BPD == 1)
} else if (Diagnosis == 'GD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_GD == 1 | PG2_GD == 1 | PG3_GD == 1)
} else if (Diagnosis == 'MDD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_MDD == 1 | PG2_MDD == 1 | PG3_MDD == 1)
} else if (Diagnosis == 'OCD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_OCD == 1 | PG2_OCD == 1 | PG3_OCD == 1)
} else if (Diagnosis == 'PMDD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_PMDD == 1 | PG2_PMDD == 1 | PG3_SUD == 1)
} else if (Diagnosis == 'Schizophrenia') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_Schizophrenia == 1 | PG2_Schizophrenia == 1 | PG3_Schizophrenia == 1)
} else if (Diagnosis == 'SUD') {
FgtC_P_Activated <- subset(FgtC_P_Activated, PG1_SUD == 1 | PG2_SUD == 1 | PG3_SUD == 1)
}
```
#Select the columns that will be used
```{r}
FgtC_P_Activated <- select(FgtC_P_Activated, c('Article_Num','Publication_Year',
"ROI_regions",
"FgtC_PgtC",
"WB_Th",
"ROI_Th"))
colnames(FgtC_P_Activated) <- c("Article_Num", "Publication_Year", "ROI_regions", "FgtC_PgtC", "WB_Th", "ROI_Th")
```
#Remove extra strings and make all lower case
```{r}
FgtC_P_Activated[FgtC_P_Activated == "NA"] <- NA
FgtC_P_Activated <- FgtC_P_Activated %>%
mutate_at("ROI_regions", str_replace, "a priori: ", "")
FgtC_P_Activated <- FgtC_P_Activated %>%
mutate_at("ROI_regions", str_replace, "post hoc: ", "")
FgtC_P_Activated <- FgtC_P_Activated %>%
mutate_at("ROI_regions", str_replace, "data driven: ", "")
FgtC_P_Activated$ROI_regions <- tolower(FgtC_P_Activated$ROI_regions)
FgtC_P_Activated$FgtC_PgtC <- tolower(FgtC_P_Activated$FgtC_PgtC)
#Select the rows that will be used (only ones with patient groups)
#FgtC_P_Activated <- filter(FgtC_P_Activated, !is.na(PG1_Diagnosis_stand))
```
```{r}
#Create empty vector to store the total number of contrasts using each threshold:
Th_Counts <- vector(mode = "numeric", length = 9)
```
###THRESHOLD 1: ROI/SVC --> UNCORRECTED
#Th1: Get complete list of ROIs that were used
```{r}
#ROI Threshold 1: ROI/SVC - P uncorrected
FgtC_P_Specified_ROI1 <- filter(FgtC_P_Activated, ROI_Th == 1)
Th1_ID <- FgtC_P_Specified_ROI1[,c("Article_Num", "Publication_Year")]
Th_Counts[1] <- nrow(Th1_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI1 <- separate(FgtC_P_Specified_ROI1, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI1[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10")], id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th1: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI1 <- filter(FgtC_P_Activated, ROI_Th == 1)
FgtC_P_Activated_ROI1 <- separate(FgtC_P_Activated_ROI1, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
FgtC_P_Activated_ROI1 <- FgtC_P_Activated_ROI1 %>% mutate(across(where(is.character), str_trim))
```
#Th1: Code if an ROI was selected (Searched_ROI_Th1) and if it was reported as activated (Counts_ROI_Th1)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI1 <- select(FgtC_P_Specified_ROI1, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI1 <- select(FgtC_P_Activated_ROI1, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = ROI_list
#Create empty dataframe to store the counts
Counts_ROI_Th1 <- data.frame(matrix(ncol = length(ROI_list), nrow = nrow(FgtC_P_Activated_ROI1)))
Searched_ROI_Th1 <- data.frame(matrix(ncol = length(ROI_list), nrow = nrow(FgtC_P_Activated_ROI1)))
colnames(Counts_ROI_Th1) <- c(ROI_list)
colnames(Searched_ROI_Th1) <- c(ROI_list)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")
for (n in 1:nrow(FgtC_P_Activated_ROI1)){ #for each row of the ROI_1 df
#print(as.integer(n))
ColList <- names(FgtC_P_Specified_ROI1)[!is.na(FgtC_P_Specified_ROI1[n,])] #only select the ROIs that they said they were interested in
ROIs_Specified <- as.character(FgtC_P_Specified_ROI1[n, ColList])
for (c in ROIs_Specified){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], c)) { #If they searched for it, add a 1 to the the column in the df
#print(group)
Searched_ROI_Th1[n,group] <- (1)
if (sum(grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI1[n,AllActivatedCols])) >= 1){ #if they reported it was active, add a 1 to the column in the df
Counts_ROI_Th1[n,group] <- (1)}
}
}
}
}
#Mutate and put into a neat df (Th1)
Searched_ROI_Th1 <- mutate_all(Searched_ROI_Th1, function(x) as.numeric(as.character(x)))
Counts_ROI_Th1 <- mutate_all(Counts_ROI_Th1, function(x) as.numeric(as.character(x)))
Counts_ROI_Th1[,c("Article_Num", "Publication_Year")] <- Th1_ID[,c("Article_Num", "Publication_Year")]
Searched_ROI_Th1[,c("Article_Num", "Publication_Year")] <- Th1_ID[,c("Article_Num", "Publication_Year")]
Th1 <- data.frame(matrix(ncol = length(ROI_list), nrow = 2))
colnames(Th1) <- c(ROI_list)
row.names(Th1) <- c("Searched", "Activated")
Th1[1,] <- colSums(Searched_ROI_Th1, na.rm = TRUE)
Th1[2,] <- colSums(Counts_ROI_Th1, na.rm = TRUE)
Th1[3,] <- Th1[2,]/Th1[1,] * 100
#write.csv(Th1, "M:/Documents/Hariri_review/Data_Vis/Th1.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
rm(Headings)
rm(GroupList)
rm(ColList)
rm(ROIs_Specified)
rm(Th1_ID)
#rm(Headings_Active)
```
###THRESHOLD 2: ROI/SVC --> FDR corrected
#Th2: Get complete list of ROIs that were used
```{r}
#ROI Threshold 2: ROI/SVC --> FDR corrected
FgtC_P_Specified_ROI2 <- filter(FgtC_P_Activated, ROI_Th == 2)
Th2_ID <- FgtC_P_Specified_ROI2[,c("Article_Num", "Publication_Year")]
Th_Counts[2] <- nrow(Th2_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI2 <- separate(FgtC_P_Specified_ROI2, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10","Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20", "Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI2[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")],
id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th2: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI2 <- filter(FgtC_P_Activated, ROI_Th == 2)
FgtC_P_Activated_ROI2 <- separate(FgtC_P_Activated_ROI2, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
FgtC_P_Activated_ROI2 <- FgtC_P_Activated_ROI2 %>% mutate(across(where(is.character), str_trim))
```
#Th2: Code if an ROI was selected (Searched_ROI_Th1) and if it was reported as activated (Counts_ROI_Th1)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI2 <- select(FgtC_P_Specified_ROI2, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI2 <- select(FgtC_P_Activated_ROI2, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = ROI_list
#Create empty dataframe to store the counts
Counts_ROI_Th2 <- data.frame(matrix(ncol = length(ROI_list), nrow = nrow(FgtC_P_Activated_ROI2)))
Searched_ROI_Th2 <- data.frame(matrix(ncol = length(ROI_list), nrow = nrow(FgtC_P_Activated_ROI2)))
colnames(Counts_ROI_Th2) <- c(ROI_list)
colnames(Searched_ROI_Th2) <- c(ROI_list)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")
for (n in 1:nrow(FgtC_P_Specified_ROI2)){ #for each row of the ROI_1 df
#print(as.integer(n))
ColList <- names(FgtC_P_Specified_ROI2)[!is.na(FgtC_P_Specified_ROI2[n,])] #only select the ROIs that they said they were interested in
ROIs_Specified <- as.character(FgtC_P_Specified_ROI2[n, ColList])
for (c in ROIs_Specified){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], c)) { #If they searched for it, add a 1 to the the column in the df
#print(group)
Searched_ROI_Th2[n,group] <- (1)
if (sum(grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI2[n,AllActivatedCols])) >= 1){ #if they reported it was active, add a 1 to the column in the df
Counts_ROI_Th2[n,group] <- (1)}
}
}
}
}
#Mutate and put into a neat df (Th1)
Searched_ROI_Th2 <- mutate_all(Searched_ROI_Th2, function(x) as.numeric(as.character(x)))
Counts_ROI_Th2 <- mutate_all(Counts_ROI_Th2, function(x) as.numeric(as.character(x)))
Counts_ROI_Th2[,c("Article_Num", "Publication_Year")] <- Th2_ID[,c("Article_Num", "Publication_Year")]
Searched_ROI_Th2[,c("Article_Num", "Publication_Year")] <- Th2_ID[,c("Article_Num", "Publication_Year")]
Th2 <- data.frame(matrix(ncol = length(ROI_list), nrow = 2))
colnames(Th2) <- c(ROI_list)
row.names(Th2) <- c("Searched", "Activated")
Th2[1,] <- colSums(Searched_ROI_Th2, na.rm = TRUE)
Th2[2,] <- colSums(Counts_ROI_Th2, na.rm = TRUE)
Th2[3,] <- Th2[2,]/Th2[1,] * 100
#write.csv(Th2, "M:/Documents/Hariri_review/Data_Vis/Th2.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
rm(Headings)
rm(GroupList)
rm(ColList)
rm(ROIs_Specified)
rm(Th2_ID)
#rm(Headings_Active)
```
###THRESHOLD 3: ROI/SVC --> FWE corrected + Simulation
#Th3: Get complete list of ROIs that were used
```{r}
#ROI Threshold 3: ROI/SVC --> FWE corrected + Simulation
FgtC_P_Specified_ROI3 <- filter(FgtC_P_Activated, ROI_Th == 3)
Th3_ID <- FgtC_P_Specified_ROI3[,c("Article_Num", "Publication_Year")]
Th_Counts[3] <- nrow(Th3_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI3 <- separate(FgtC_P_Specified_ROI3, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10","Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20", "Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI3[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")],
id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th3: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI3 <- filter(FgtC_P_Activated, ROI_Th == 3)
FgtC_P_Activated_ROI3 <- separate(FgtC_P_Activated_ROI3, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40",
"Region41", "Region42", "Region43", "Region44", "Region45"), sep = ',')
FgtC_P_Activated_ROI3 <- FgtC_P_Activated_ROI3 %>% mutate(across(where(is.character), str_trim))
```
#Th3: Code if an ROI was selected (Searched_ROI_Th1) and if it was reported as activated (Counts_ROI_Th1)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI3 <- select(FgtC_P_Specified_ROI3, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI3 <- select(FgtC_P_Activated_ROI3, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = ROI_list
#Create empty dataframe to store the counts
Counts_ROI_Th3 <- data.frame(matrix(ncol = length(ROI_list), nrow = nrow(FgtC_P_Activated_ROI3)))
Searched_ROI_Th3 <- data.frame(matrix(ncol = length(ROI_list), nrow = nrow(FgtC_P_Activated_ROI3)))
colnames(Counts_ROI_Th3) <- c(ROI_list)
colnames(Searched_ROI_Th3) <- c(ROI_list)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40",
"Region41", "Region42", "Region43", "Region44", "Region45")
for (n in 1:nrow(FgtC_P_Specified_ROI3)){ #for each row of the ROI_1 df
#print(as.integer(n))
ColList <- names(FgtC_P_Specified_ROI3)[!is.na(FgtC_P_Specified_ROI3[n,])] #only select the ROIs that they said they were interested in
ROIs_Specified <- as.character(FgtC_P_Specified_ROI3[n, ColList])
for (c in ROIs_Specified){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], c)) { #If they searched for it, add a 1 to the the column in the df
#print(group)
Searched_ROI_Th3[n,group] <- (1)
if (sum(grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI3[n,AllActivatedCols])) >= 1){ #if they reported it was active, add a 1 to the column in the df
Counts_ROI_Th3[n,group] <- (1)}
}
}
}
}
#Mutate and put into a neat df (Th1)
Searched_ROI_Th3 <- mutate_all(Searched_ROI_Th3, function(x) as.numeric(as.character(x)))
Counts_ROI_Th3 <- mutate_all(Counts_ROI_Th3, function(x) as.numeric(as.character(x)))
Counts_ROI_Th3[,c("Article_Num", "Publication_Year")] <- Th3_ID[,c("Article_Num", "Publication_Year")]
Searched_ROI_Th3[,c("Article_Num", "Publication_Year")] <- Th3_ID[,c("Article_Num", "Publication_Year")]
Th3 <- data.frame(matrix(ncol = length(ROI_list), nrow = 2))
colnames(Th3) <- c(ROI_list)
row.names(Th3) <- c("Searched", "Activated")
Th3[1,] <- colSums(Searched_ROI_Th3, na.rm = TRUE)
Th3[2,] <- colSums(Counts_ROI_Th3, na.rm = TRUE)
Th3[3,] <- Th3[2,]/Th3[1,] * 100
#write.csv(Th3, "M:/Documents/Hariri_review/Data_Vis/Th3.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
rm(Headings)
rm(GroupList)
rm(ColList)
rm(ROIs_Specified)
rm(Th3_ID)
#rm(Headings_Active)
```
###THRESHOLD 4: ROI/SVC --> Other (unspecified multiple corrections)
#Th4: Get complete list of ROIs that were used
```{r}
#ROI Threshold 4: ROI/SVC --> Other (unspecified multiple corrections)
FgtC_P_Specified_ROI4 <- filter(FgtC_P_Activated, ROI_Th == 4)
Th4_ID <- FgtC_P_Specified_ROI4[,c("Article_Num", "Publication_Year")]
Th_Counts[4] <- nrow(Th4_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI4 <- separate(FgtC_P_Specified_ROI4, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI4[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")],
id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th4: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI4 <- filter(FgtC_P_Activated, ROI_Th == 4)
FgtC_P_Activated_ROI4 <- separate(FgtC_P_Activated_ROI4, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40"), sep = ',')
FgtC_P_Activated_ROI4 <- FgtC_P_Activated_ROI4 %>% mutate(across(where(is.character), str_trim))
```
#Th4: Code if an ROI was selected (Searched_ROI_Th4) and if it was reported as activated (Counts_ROI_Th4)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI4 <- select(FgtC_P_Specified_ROI4, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI4 <- select(FgtC_P_Activated_ROI4, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = ROI_list
#Create empty dataframe to store the counts
Counts_ROI_Th4 <- data.frame(matrix(ncol = length(ROI_list), nrow = nrow(FgtC_P_Activated_ROI4)))
Searched_ROI_Th4 <- data.frame(matrix(ncol = length(ROI_list), nrow = nrow(FgtC_P_Activated_ROI4)))
colnames(Counts_ROI_Th4) <- c(ROI_list)
colnames(Searched_ROI_Th4) <- c(ROI_list)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")
for (n in 1:nrow(FgtC_P_Specified_ROI4)){ #for each row of the ROI_1 df
#print(as.integer(n))
ColList <- names(FgtC_P_Specified_ROI4)[!is.na(FgtC_P_Specified_ROI4[n,])] #only select the ROIs that they said they were interested in
ROIs_Specified <- as.character(FgtC_P_Specified_ROI4[n, ColList])
for (c in ROIs_Specified){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], c)) { #If they searched for it, add a 1 to the the column in the df
#print(group)
Searched_ROI_Th4[n,group] <- (1)
if (sum(grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI4[n,AllActivatedCols])) >= 1){ #if they reported it was active, add a 1 to the column in the df
Counts_ROI_Th4[n,group] <- (1)}
}
}
}
}
#Mutate and put into a neat df (Th1)
Searched_ROI_Th4 <- mutate_all(Searched_ROI_Th4, function(x) as.numeric(as.character(x)))
Counts_ROI_Th4 <- mutate_all(Counts_ROI_Th4, function(x) as.numeric(as.character(x)))
Counts_ROI_Th4[,c("Article_Num", "Publication_Year")] <- Th4_ID[,c("Article_Num", "Publication_Year")]
Searched_ROI_Th4[,c("Article_Num", "Publication_Year")] <- Th4_ID[,c("Article_Num", "Publication_Year")]
Th4 <- data.frame(matrix(ncol = length(ROI_list), nrow = 2))
colnames(Th4) <- c(ROI_list)
row.names(Th4) <- c("Searched", "Activated")
Th4[1,] <- colSums(Searched_ROI_Th4, na.rm = TRUE)
Th4[2,] <- colSums(Counts_ROI_Th4, na.rm = TRUE)
Th4[3,] <- Th4[2,]/Th4[1,] * 100
#write.csv(Th4, "M:/Documents/Hariri_review/Data_Vis/Th4.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
rm(Headings)
rm(GroupList)
rm(ColList)
rm(ROIs_Specified)
rm(Th4_ID)
#rm(Headings_Active)
```
#Create a combined ROI count file to remove from whole brain results.
```{r}
Combined_ROIs <- bind_rows(Searched_ROI_Th1,Searched_ROI_Th2,Searched_ROI_Th3,Searched_ROI_Th4)
```
###THRESHOLD 5: WHOLE BRAIN --> Uncorrected
#Th5: Get complete list of ROIs that were used
```{r}
#ROI Threshold 5: WHOLE BRAIN --> Uncorrected
FgtC_P_Specified_ROI5 <- filter(FgtC_P_Activated, WB_Th == 5)
Th5_ID <- FgtC_P_Specified_ROI5[,c("Article_Num", "Publication_Year")]
Th_Counts[5] <- nrow(Th5_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI5 <- separate(FgtC_P_Specified_ROI5, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10","Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20", "Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI5[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")],
id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th5: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI5 <- filter(FgtC_P_Activated, WB_Th == 5)
FgtC_P_Activated_ROI5 <- separate(FgtC_P_Activated_ROI5, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40",
"Region41", "Region42", "Region43", "Region44", "Region45"), sep = ',')
FgtC_P_Activated_ROI5 <- FgtC_P_Activated_ROI5 %>% mutate(across(where(is.character), str_trim))
#make into long format
Headings_Active <- melt(FgtC_P_Activated_ROI5[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40",
"Region41", "Region42", "Region43", "Region44", "Region45")], id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings_Active$value <- str_trim(Headings_Active$value, side = c("both", "left", "right"))
Active_List <- sort(unique(Headings_Active$value))
```
#Th5: Code which regions were reported as activated (Counts_ROI_Th5)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI5 <- select(FgtC_P_Specified_ROI5, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI5 <- select(FgtC_P_Activated_ROI5, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = Active_List #Instead of searching within the Regions that were
#Create empty data frame to store the counts
Counts_ROI_Th5 <- data.frame(matrix(ncol = length(Active_List), nrow = nrow(FgtC_P_Activated_ROI5)))
colnames(Counts_ROI_Th5) <- c(Active_List)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")
for (n in 1:nrow(FgtC_P_Activated_ROI5)){ #for each row of the Activated df
#print(as.integer(n))
ColList <- names(FgtC_P_Activated_ROI5)[!is.na(FgtC_P_Activated_ROI5[n,])] #only select the Activated columns that have
for (c in ColList){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI5[n,c])) { #If they said it was activated add a 1 to the the column in the df
#print(group)
Counts_ROI_Th5[n,group] <- (1)
#if (sum(grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI5[n,AllActivatedCols])) >= 1){ #if they reported it was active, add a 1 to the column in the df
# Counts_ROI_Th5[n,group] <- (1)}
}
}
}
}
#Mutate and put into a neat df (Th5)
Counts_ROI_Th5 <- mutate_all(Counts_ROI_Th5, function(x) as.numeric(as.character(x)))
Counts_ROI_Th5[,c("Article_Num", "Publication_Year")] <- Th5_ID[,c("Article_Num", "Publication_Year")]
```
#Th5: Remove activated regions if they were from an ROI analysis (Combined_ROIs)
```{r}
Article_Nums <- Counts_ROI_Th5[,c("Article_Num")]
for (a in Article_Nums){
#print(paste0("Article_Num =", a))
if (a %in% Combined_ROIs$Article_Num){
r = which(Combined_ROIs$Article_Num %in% a)
q = which(Counts_ROI_Th5$Article_Num %in% a)
#print(paste0("It is row ", r, " of the Combined ROI file, and row ", q, "of the Th5 Counts"))
for (c in colnames(Combined_ROIs)){
if(is.na(Combined_ROIs[r,c])){ #If it is not empty in the ROI or the Counts list (i.e. they searched for it and found it)
next #Code as ROI
}
else {
#print(paste0(c, " is where they have searched for"))
Counts_ROI_Th5[q,c] <- "ROI"}
}
}
else {
#print("This study did not use an ROI")
}
}
#Save version with ROIs indicated
Counts_ROI_Th5_wROI <- Counts_ROI_Th5
#Remove ROIs and count
Counts_ROI_Th5[Counts_ROI_Th5 == "ROI"] <- NA
Counts_ROI_Th5<- mutate_all(Counts_ROI_Th5, function(x) as.numeric(as.character(x)))
Counts_ROI_Th5[,c("Article_Num", "Publication_Year")] <- Th5_ID[,c("Article_Num", "Publication_Year")] #Re-fill this in as some got removed during "ROI" removing
Th5 <- data.frame(matrix(ncol = length(Active_List), nrow = 2))
colnames(Th5) <- c(Active_List)
row.names(Th5) <- c("Applied", "Activated")
Th5[1,] <- nrow(FgtC_P_Specified_ROI5)
Th5[2,] <- colSums(Counts_ROI_Th5, na.rm = TRUE)
#Th5[2,] <- 1
Th5[3,] <- Th5[1,]/nrow(Counts_ROI_Th5) * 100
#write.csv(Th5, "M:/Documents/Hariri_review/Data_Vis/Th5.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
rm(Headings)
rm(GroupList)
rm(ColList)
rm(Th5_ID)
rm(Headings_Active)
```
###THRESHOLD 6: WHOLE BRAIN --> FDR corrected
#Th6: Get complete list of ROIs that were used
```{r}
#ROI Threshold 6: WHOLE BRAIN --> Uncorrected
FgtC_P_Specified_ROI6 <- filter(FgtC_P_Activated, WB_Th == 6)
Th6_ID <- FgtC_P_Specified_ROI6[,c("Article_Num", "Publication_Year")]
Th_Counts[6] <- nrow(Th6_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI6 <- separate(FgtC_P_Specified_ROI6, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10","Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20", "Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI6[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")],
id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th6: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI6 <- filter(FgtC_P_Activated, WB_Th == 6)
FgtC_P_Activated_ROI6 <- separate(FgtC_P_Activated_ROI6, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40"), sep = ',')
FgtC_P_Activated_ROI6 <- FgtC_P_Activated_ROI6 %>% mutate(across(where(is.character), str_trim))
#make into long format
Headings_Active <- melt(FgtC_P_Activated_ROI6[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")], id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings_Active$value <- str_trim(Headings_Active$value, side = c("both", "left", "right"))
Active_List <- sort(unique(Headings_Active$value))
```
#Th6: Code which regions were reported as activated (Counts_ROI_Th5)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI6 <- select(FgtC_P_Specified_ROI6, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI6 <- select(FgtC_P_Activated_ROI6, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = ROI_list
GroupList = Active_List #Instead of searching within the Regions that were
#Create empty data frame to store the counts
Counts_ROI_Th6 <- data.frame(matrix(ncol = length(Active_List), nrow = nrow(FgtC_P_Activated_ROI6)))
colnames(Counts_ROI_Th6) <- c(Active_List)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")
for (n in 1:nrow(FgtC_P_Activated_ROI6)){ #for each row of the Activated df
#print(as.integer(n))
ColList <- names(FgtC_P_Activated_ROI6)[!is.na(FgtC_P_Activated_ROI6[n,])] #only select the Activated columns that have
for (c in ColList){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI6[n,c])) { #If they said it was activated add a 1 to the the column in the df
#print(group)
Counts_ROI_Th6[n,group] <- (1)
}
}
}
}
#Mutate and put into a neat df (Th5)
Counts_ROI_Th6 <- mutate_all(Counts_ROI_Th6, function(x) as.numeric(as.character(x)))
Counts_ROI_Th6[,c("Article_Num", "Publication_Year")] <- Th6_ID[,c("Article_Num", "Publication_Year")]
```
#Th6: Remove activated regions if they were from an ROI analysis (Combined_ROIs)
```{r}
Article_Nums <- Counts_ROI_Th6[,c("Article_Num")]
for (a in Article_Nums){
#print(paste0("Article_Num =", a))
if (a %in% Combined_ROIs$Article_Num){
r = which(Combined_ROIs$Article_Num %in% a)
q = which(Counts_ROI_Th6$Article_Num %in% a)
#print(paste0("It is row ", r, " of the Combined ROI file, and row ", q, "of the Th6 Counts"))
for (c in colnames(Combined_ROIs)){
if(is.na(Combined_ROIs[r,c])){ #If it is not empty in the ROI or the Counts list (i.e. they searched for it and found it)
next #Code as ROI
}
else {
#print(paste0(c, " is where they have searched for"))
Counts_ROI_Th6[q,c] <- "ROI"}
}
}
else {
#print("This study did not use an ROI")
}
}
#Save version with ROIs indicated
Counts_ROI_Th6_wROI <- Counts_ROI_Th6
#Remove ROIs and count
Counts_ROI_Th6[Counts_ROI_Th6 == "ROI"] <- NA
Counts_ROI_Th6<- mutate_all(Counts_ROI_Th6, function(x) as.numeric(as.character(x)))
Counts_ROI_Th6[,c("Article_Num", "Publication_Year")] <- Th6_ID[,c("Article_Num", "Publication_Year")] #Re-fill this in as some got removed during "ROI" removing
Th6 <- data.frame(matrix(ncol = length(Active_List), nrow = 2))
colnames(Th6) <- c(Active_List)
row.names(Th6) <- c("Applied", "Activated")
Th6[1,] <- nrow(FgtC_P_Specified_ROI6)
Th6[2,] <- colSums(Counts_ROI_Th6, na.rm = TRUE)
#Th6[2,] <- 2
Th6[3,] <- Th6[1,]/nrow(Counts_ROI_Th6) * 100
#write.csv(Th6, "M:/Documents/Hariri_review/Data_Vis/Th6.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
rm(Headings)
rm(GroupList)
rm(ColList)
rm(Th6_ID)
rm(Headings_Active)
```
###THRESHOLD 7: Whole brain --> FWE corrected + Simulation
#Th7: Get complete list of ROIs that were used
```{r}
#ROI Threshold 5: WHOLE BRAIN --> Uncorrected
FgtC_P_Specified_ROI7 <- filter(FgtC_P_Activated, WB_Th == 7)
Th7_ID <- FgtC_P_Specified_ROI7[,c("Article_Num", "Publication_Year")]
Th_Counts[7] <- nrow(Th7_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI7 <- separate(FgtC_P_Specified_ROI7, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10","Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20", "Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI7[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")],
id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th7: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI7 <- filter(FgtC_P_Activated, WB_Th == 7)
FgtC_P_Activated_ROI7 <- separate(FgtC_P_Activated_ROI7, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40"), sep = ',')
FgtC_P_Activated_ROI7 <- FgtC_P_Activated_ROI7 %>% mutate(across(where(is.character), str_trim))
#make into long format
Headings_Active <- melt(FgtC_P_Activated_ROI7[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")], id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings_Active$value <- str_trim(Headings_Active$value, side = c("both", "left", "right"))
Active_List <- sort(unique(Headings_Active$value))
```
#Th7: Code which regions were reported as activated (Counts_ROI_Th5)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI7 <- select(FgtC_P_Specified_ROI7, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI7 <- select(FgtC_P_Activated_ROI7, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = ROI_list
GroupList = Active_List #Instead of searching within the Regions that were
#Create empty data frame to store the counts
Counts_ROI_Th7 <- data.frame(matrix(ncol = length(Active_List), nrow = nrow(FgtC_P_Activated_ROI7)))
colnames(Counts_ROI_Th7) <- c(Active_List)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")
for (n in 1:nrow(FgtC_P_Activated_ROI7)){ #for each row of the Activated df
#print(as.integer(n))
ColList <- names(FgtC_P_Activated_ROI7)[!is.na(FgtC_P_Activated_ROI7[n,])] #only select the Activated columns that have
for (c in ColList){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI7[n,c])) { #If they said it was activated add a 1 to the the column in the df
#print(group)
Counts_ROI_Th7[n,group] <- (1)
}
}
}
}
#Mutate and put into a neat df (Th5)
Counts_ROI_Th7 <- mutate_all(Counts_ROI_Th7, function(x) as.numeric(as.character(x)))
Counts_ROI_Th7[,c("Article_Num", "Publication_Year")] <- Th7_ID[,c("Article_Num", "Publication_Year")]
```
#Th7: Remove activated regions if they were from an ROI analysis (Combined_ROIs)
```{r}
Article_Nums <- Counts_ROI_Th7[,c("Article_Num")]
for (a in Article_Nums){
#print(paste0("Article_Num =", a))
if (a %in% Combined_ROIs$Article_Num){
r = which(Combined_ROIs$Article_Num %in% a)
q = which(Counts_ROI_Th7$Article_Num %in% a)
#print(paste0("It is row ", r, " of the Combined ROI file, and row ", q, "of the Th7 Counts"))
for (c in colnames(Combined_ROIs)){
if(is.na(Combined_ROIs[r,c])){ #If it is not empty in the ROI or the Counts list (i.e. they searched for it and found it)
next #Code as ROI
}
else {
#print(paste0(c, " is where they have searched for"))
Counts_ROI_Th7[q,c] <- "ROI"}
}
}
else {
#print("This study did not use an ROI")
}
}
#Save version with ROIs indicated
Counts_ROI_Th7_wROI <- Counts_ROI_Th7
#Remove ROIs and count
Counts_ROI_Th7[Counts_ROI_Th7 == "ROI"] <- NA
Counts_ROI_Th7<- mutate_all(Counts_ROI_Th7, function(x) as.numeric(as.character(x)))
Counts_ROI_Th7[,c("Article_Num", "Publication_Year")] <- Th7_ID[,c("Article_Num", "Publication_Year")] #Re-fill this in as some got removed during "ROI" removing
Th7 <- data.frame(matrix(ncol = length(Active_List), nrow = 2))
colnames(Th7) <- c(Active_List)
row.names(Th7) <- c("Applied", "Activated")
Th7[1,] <- nrow(FgtC_P_Specified_ROI7)
Th7[2,] <- colSums(Counts_ROI_Th7, na.rm = TRUE)
#Th7[2,] <- 2
Th7[3,] <- Th7[1,]/nrow(Counts_ROI_Th7) * 100
#write.csv(Th7, "M:/Documents/Hariri_review/Data_Vis/Th7.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
rm(Headings)
rm(GroupList)
rm(ColList)
rm(Th7_ID)
rm(Headings_Active)
```
###THRESHOLD 8: Whole brain --> Other (unspecified multiple corrections)
#Th8: Get complete list of ROIs that were used
```{r}
#ROI Threshold 5: WHOLE BRAIN --> Uncorrected
FgtC_P_Specified_ROI8 <- filter(FgtC_P_Activated, WB_Th == 8)
Th8_ID <- FgtC_P_Specified_ROI8[,c("Article_Num", "Publication_Year")]
Th_Counts[8] <- nrow(Th8_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI8 <- separate(FgtC_P_Specified_ROI8, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10","Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20", "Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI8[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")],
id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th8: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI8 <- filter(FgtC_P_Activated, WB_Th == 8)
FgtC_P_Activated_ROI8 <- separate(FgtC_P_Activated_ROI8, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40"), sep = ',')
FgtC_P_Activated_ROI8 <- FgtC_P_Activated_ROI8 %>% mutate(across(where(is.character), str_trim))
#make into long format
Headings_Active <- melt(FgtC_P_Activated_ROI8[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")], id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings_Active$value <- str_trim(Headings_Active$value, side = c("both", "left", "right"))
Active_List <- sort(unique(Headings_Active$value))
```
#Th8: Code which regions were reported as activated (Counts_ROI_Th5)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI8 <- select(FgtC_P_Specified_ROI8, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI8 <- select(FgtC_P_Activated_ROI8, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = ROI_list
GroupList = Active_List #Instead of searching within the Regions that were
#Create empty data frame to store the counts
Counts_ROI_Th8 <- data.frame(matrix(ncol = length(Active_List), nrow = nrow(FgtC_P_Activated_ROI8)))
colnames(Counts_ROI_Th8) <- c(Active_List)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")
for (n in 1:nrow(FgtC_P_Activated_ROI8)){ #for each row of the Activated df
#print(as.integer(n))
ColList <- names(FgtC_P_Activated_ROI8)[!is.na(FgtC_P_Activated_ROI8[n,])] #only select the Activated columns that have
for (c in ColList){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI8[n,c])) { #If they said it was activated add a 1 to the the column in the df
#print(group)
Counts_ROI_Th8[n,group] <- (1)
}
}
}
}
#Mutate and put into a neat df (Th5)
Counts_ROI_Th8 <- mutate_all(Counts_ROI_Th8, function(x) as.numeric(as.character(x)))
Counts_ROI_Th8[,c("Article_Num", "Publication_Year")] <- Th8_ID[,c("Article_Num", "Publication_Year")]
```
#Th8: Remove activated regions if they were from an ROI analysis (Combined_ROIs)
```{r}
Article_Nums <- Counts_ROI_Th8[,c("Article_Num")]
for (a in Article_Nums){
#print(paste0("Article_Num =", a))
if (a %in% Combined_ROIs$Article_Num){
r = which(Combined_ROIs$Article_Num %in% a)
q = which(Counts_ROI_Th8$Article_Num %in% a)
#print(paste0("It is row ", r, " of the Combined ROI file, and row ", q, "of the Th8 Counts"))
for (c in colnames(Combined_ROIs)){
if(is.na(Combined_ROIs[r,c])){ #If it is not empty in the ROI or the Counts list (i.e. they searched for it and found it)
next #Code as ROI
}
else {
#print(paste0(c, " is where they have searched for"))
Counts_ROI_Th8[q,c] <- "ROI"}
}
}
else {
#print("This study did not use an ROI")
}
}
#Save version with ROIs indicated
Counts_ROI_Th8_wROI <- Counts_ROI_Th8
#Remove ROIs and count
Counts_ROI_Th8[Counts_ROI_Th8 == "ROI"] <- NA
Counts_ROI_Th8<- mutate_all(Counts_ROI_Th8, function(x) as.numeric(as.character(x)))
Counts_ROI_Th8[,c("Article_Num", "Publication_Year")] <- Th8_ID[,c("Article_Num", "Publication_Year")] #Re-fill this in as some got removed during "ROI" removing
Th8 <- data.frame(matrix(ncol = length(Active_List), nrow = 2))
colnames(Th8) <- c(Active_List)
row.names(Th8) <- c("Applied", "Activated")
Th8[1,] <- nrow(FgtC_P_Activated_ROI8)
Th8[2,] <- colSums(Counts_ROI_Th8, na.rm = TRUE)
#Th8[2,] <- 2
Th8[3,] <- Th8[1,]/nrow(Counts_ROI_Th8) * 100
#write.csv(Th8, "M:/Documents/Hariri_review/Data_Vis/Th8.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
rm(Headings)
rm(GroupList)
rm(ColList)
rm(Th8_ID)
rm(Headings_Active)
```
###THRESHOLD 9: Other
#Th9: Get complete list of ROIs that were used
```{r}
#ROI Threshold 9: WHOLE BRAIN --> Uncorrected
FgtC_P_Specified_ROI9 <- filter(FgtC_P_Activated, WB_Th == 9)
Th9_ID <- FgtC_P_Specified_ROI9[,c("Article_Num", "Publication_Year")]
Th_Counts[9] <- nrow(Th9_ID)
###GET ROIs THAT STUDIES SPECIFIED:
#make columns based on the ROIs specified
FgtC_P_Specified_ROI9 <- separate(FgtC_P_Specified_ROI9, c(ROI_regions), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10","Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20", "Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30"), sep = ',')
#make into long format
Headings <- melt(FgtC_P_Specified_ROI9[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30")],
id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings$value <- str_trim(Headings$value, side = c("both", "left", "right"))
#make list of unique ROIs for the headings string matching columns/counts
ROI_list <- sort(unique(Headings$value))
```
#Th9: Split the reported activation
```{r}
###GET ROIs THAT STUDIES REPORTED AS ACTIVATED:
FgtC_P_Activated_ROI9 <- filter(FgtC_P_Activated, WB_Th == 9)
FgtC_P_Activated_ROI9 <- separate(FgtC_P_Activated_ROI9, c(FgtC_PgtC), into = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40"), sep = ',')
FgtC_P_Activated_ROI9 <- FgtC_P_Activated_ROI9 %>% mutate(across(where(is.character), str_trim))
#make into long format
Headings_Active <- melt(FgtC_P_Activated_ROI9[,c('Article_Num','Publication_Year',"Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")], id.vars=c('Article_Num','Publication_Year'), var='ROI_specified')
Headings_Active$value <- str_trim(Headings_Active$value, side = c("both", "left", "right"))
Active_List <- sort(unique(Headings_Active$value))
```
#Th9: Code which regions were reported as activated (Counts_ROI_Th5)
```{r}
#Remove extra columns from Specified + Activated
FgtC_P_Specified_ROI9 <- select(FgtC_P_Specified_ROI9, -c(Article_Num, Publication_Year, FgtC_PgtC, WB_Th, ROI_Th))
FgtC_P_Activated_ROI9 <- select(FgtC_P_Activated_ROI9, -c(Article_Num, Publication_Year, ROI_regions, WB_Th, ROI_Th))
GroupList = ROI_list
GroupList = Active_List #Instead of searching within the Regions that were
#Create empty data frame to store the counts
Counts_ROI_Th9 <- data.frame(matrix(ncol = length(Active_List), nrow = nrow(FgtC_P_Activated_ROI9)))
colnames(Counts_ROI_Th9) <- c(Active_List)
AllActivatedCols = c("Region1", "Region2", "Region3", "Region4", "Region5", "Region6", "Region7", "Region8", "Region9", "Region10",
"Region11", "Region12", "Region13", "Region14", "Region15", "Region16", "Region17", "Region18", "Region19", "Region20",
"Region21", "Region22", "Region23", "Region24", "Region25", "Region26", "Region27", "Region28", "Region29", "Region30",
"Region31", "Region32", "Region33", "Region34", "Region35", "Region36", "Region37", "Region38", "Region39", "Region40")
for (n in 1:nrow(FgtC_P_Activated_ROI9)){ #for each row of the Activated df
#print(as.integer(n))
ColList <- names(FgtC_P_Activated_ROI9)[!is.na(FgtC_P_Activated_ROI9[n,])] #only select the Activated columns that have
for (c in ColList){ #for the ROIs that they said they were interested in
#print(c)
for (group in GroupList){ #find the string match (column names based on all ROIs mentioned)
#print(group)
if (grepl(GroupList[which(GroupList == group)], FgtC_P_Activated_ROI9[n,c])) { #If they said it was activated add a 1 to the the column in the df
#print(group)
Counts_ROI_Th9[n,group] <- (1)
}
}
}
}
#Mutate and put into a neat df (Th5)
Counts_ROI_Th9 <- mutate_all(Counts_ROI_Th9, function(x) as.numeric(as.character(x)))
Counts_ROI_Th9[,c("Article_Num", "Publication_Year")] <- Th9_ID[,c("Article_Num", "Publication_Year")]
```
#Th9: Remove activated regions if they were from an ROI analysis (Combined_ROIs)
```{r}
Article_Nums <- Counts_ROI_Th9[,c("Article_Num")]
for (a in Article_Nums){
#print(paste0("Article_Num =", a))
if (a %in% Combined_ROIs$Article_Num){
r = which(Combined_ROIs$Article_Num %in% a)
q = which(Counts_ROI_Th9$Article_Num %in% a)
#print(paste0("It is row ", r, " of the Combined ROI file, and row ", q, "of the Th9 Counts"))
for (c in colnames(Combined_ROIs)){
if(is.na(Combined_ROIs[r,c])){ #If it is not empty in the ROI or the Counts list (i.e. they searched for it and found it)
next #Code as ROI
}
else {
#print(paste0(c, " is where they have searched for"))
Counts_ROI_Th9[q,c] <- "ROI"}
}
}
else {
#print("This study did not use an ROI")
}
}
#Save version with ROIs indicated
Counts_ROI_Th9_wROI <- Counts_ROI_Th9
#Remove ROIs and count
Counts_ROI_Th9[Counts_ROI_Th9 == "ROI"] <- NA
Counts_ROI_Th9<- mutate_all(Counts_ROI_Th9, function(x) as.numeric(as.character(x)))
Counts_ROI_Th9[,c("Article_Num", "Publication_Year")] <- Th9_ID[,c("Article_Num", "Publication_Year")] #Re-fill this in as some got removed during "ROI" removing
Th9 <- data.frame(matrix(ncol = length(Active_List), nrow = 2))
colnames(Th9) <- c(Active_List)
row.names(Th9) <- c("Applied", "Activated")
Th9[1,] <- nrow(FgtC_P_Activated_ROI9)
Th9[2,] <- colSums(Counts_ROI_Th9, na.rm = TRUE)
#Th9[2,] <- 1
Th9[3,] <- Th9[1,]/nrow(Counts_ROI_Th9) * 100
#write.csv(Th9, "M:/Documents/Hariri_review/Data_Vis/Th9.csv", row.names=TRUE)
```
```{r}
#Clean the workspace:
# Save the vector to a text file
write.table(Th_Counts, file = paste0("/project/3022054.01/projects/linschl/efmt/review_update_2026/02_results/patients_pgtc/Th_Counts_", Diagnosis ,".txt"), col.names = FALSE)
rm(Th_Counts)
rm(Headings)
rm(GroupList)
rm(ColList)
rm(Th9_ID)
rm(Headings_Active)
rm(Active_List)
```
```{r}
#Transform all ThX dataframes
Th1_t <- as.data.frame(t(Th1))
Th1_t$names <- rownames(Th1_t)
Th1_t <- Th1_t[order(Th1_t$Searched, Th1_t$Activated, decreasing=TRUE),]
Th1_t$names <- factor(Th1_t$names, levels = Th1_t$names)
names(Th1_t) <- c("Th1_Searched", "Th1_Activated", "Th1_Perc", "names")
Th2_t <- as.data.frame(t(Th2))
Th2_t$names <- rownames(Th2_t)
Th2_t <- Th2_t[order(Th2_t$Searched, Th2_t$Activated, decreasing=TRUE),]
Th2_t$names <- factor(Th2_t$names, levels = Th2_t$names)
names(Th2_t) <- c("Th2_Searched", "Th2_Activated", "Th2_Perc", "names")
Th3_t <- as.data.frame(t(Th3))
Th3_t$names <- rownames(Th3_t)
Th3_t <- Th3_t[order(Th3_t$Searched, Th3_t$Activated, decreasing=TRUE),]
Th3_t$names <- factor(Th3_t$names, levels = Th3_t$names)
names(Th3_t) <- c("Th3_Searched", "Th3_Activated", "Th3_Perc", "names")
Th4_t <- as.data.frame(t(Th4))
Th4_t$names <- rownames(Th4_t)
Th4_t <- Th4_t[order(Th4_t$Searched, Th4_t$Activated, decreasing=TRUE),]
Th4_t$names <- factor(Th4_t$names, levels = Th4_t$names)
names(Th4_t) <- c("Th4_Searched", "Th4_Activated", "Th4_Perc", "names")
Th5_t <- as.data.frame(t(Th5))
Th5_t$names <- rownames(Th5_t)
Th5_t <- Th5_t[order(Th5_t$Activated, decreasing=TRUE),]
Th5_t$names <- factor(Th5_t$names, levels = Th5_t$names)
names(Th5_t) <- c("Th5_Applied", "Th5_Activated", "Th5_Perc", "names")
Th6_t <- as.data.frame(t(Th6))
Th6_t$names <- rownames(Th6_t)
Th6_t <- Th6_t[order(Th6_t$Activated, decreasing=TRUE),]
Th6_t$names <- factor(Th6_t$names, levels = Th6_t$names)
names(Th6_t) <- c("Th6_Applied", "Th6_Activated", "Th6_Perc", "names")
Th7_t <- as.data.frame(t(Th7))
Th7_t$names <- rownames(Th7_t)
Th7_t <- Th7_t[order(Th7_t$Activated, decreasing=TRUE),]
Th7_t$names <- factor(Th7_t$names, levels = Th7_t$names)
names(Th7_t) <- c("Th7_Applied","Th7_Activated", "Th7_Perc", "names")
Th8_t <- as.data.frame(t(Th8))
Th8_t$names <- rownames(Th8_t)
Th8_t <- Th8_t[order(Th8_t$Activated, decreasing=TRUE),]
Th8_t$names <- factor(Th8_t$names, levels = Th8_t$names)
names(Th8_t) <- c("Th8_Applied","Th8_Activated", "Th8_Perc", "names")
Th9_t <- as.data.frame(t(Th9))
Th9_t$names <- rownames(Th9_t)
Th9_t <- Th9_t[order(Th9_t$Activated, decreasing=TRUE),]
Th9_t$names <- factor(Th9_t$names, levels = Th9_t$names)
names(Th9_t) <- c("Th9_Applied","Th9_Activated", "Th9_Perc", "names")
```
```{r}
#Merge them all together
list_df = list(Th1_t, Th2_t, Th3_t, Th4_t, Th5_t, Th6_t, Th7_t, Th8_t, Th9_t)
Total_counts <- list_df %>% reduce(full_join, by='names')
Total_counts <- Total_counts %>%
select(names, everything())
Total_counts <- Total_counts[order(Total_counts$names),]
Total_counts$names <- as.character(Total_counts$names)
write.csv(Total_counts, paste0("/project/3022054.01/projects/linschl/efmt/review_update_2026/02_results/patients_pgtc/Total_Counts_",Diagnosis,".csv"), row.names=TRUE)
```
```{r}
rm(list=(ls()[ls()!="Total_counts"]))
#rm(list = ls()[!ls() %in% c("Total_counts", "Diagnosis")])
#Diagnosis <- 'ADHD'
```
#Synthesising similar regions
```{r}
##SUBCORTICAL:
Amygdala <- as.data.frame(dplyr::filter(Total_counts, grepl('amygdala', names)))
#AAL 41+42
Thalamus <- as.data.frame(dplyr::filter(Total_counts, (grepl('thalamus', names) & !grepl('hypo', names)) | grepl('geniculate', names)))
#AAL 77 + 78 + lateral geniculate
Hypothalamus <- as.data.frame(dplyr::filter(Total_counts, grepl('thalamus', names) & grepl('hypo', names)))
#AAL NA
Hippocampus <- as.data.frame(dplyr::filter(Total_counts, grepl('hippocamp', names) & !grepl('para', names)))
#AAL 37 +38
Parahippocampus <- as.data.frame(dplyr::filter(Total_counts, grepl('hippocamp', names) & grepl('para', names)))
#AAL 39 + 40
Basal_Ganglia <- as.data.frame(dplyr::filter(Total_counts, grepl('basal ganglia', names) | grepl('lentiform', names)| grepl('pallidus', names ) | grepl('putamen', names) | grepl('accumbens', names) | grepl('substantia', names) | grepl('caudate', names) | grepl('pallidum', names)))
#AAL 73+79 75+76 71 + 72 (Putamen, pallidum, caudate, lentiform gyrus, nucleus accumbens)
#Cerebellum/Brain stem
Cerebellum <- as.data.frame(dplyr::filter(Total_counts, grepl('cerebel', names) | grepl('vermis', names) | grepl('declive', names)))
#AAL 91-116
Brain_stem <- as.data.frame(dplyr::filter(Total_counts, grepl('stem', names) | grepl('pons', names) | grepl('midbrain', names)))
#AAL NA
#OCCIPITAL:
Occipital_V1 <- as.data.frame(dplyr::filter(Total_counts, (grepl('occipital', names) & (grepl('pole', names)) | (grepl('visual', names)& !grepl('extra',names))| grepl('calcarine', names)) | (grepl('striate', names) & !grepl('extra',names))))
Occipital_V1 <- rbind(Occipital_V1, subset(Total_counts, names =="occipital lobe" | names == "l occipital"| names == "r occipital"| names == "l occipital cortex"| names == "r occipital cortex"| names == "l occipital lobe"| names == "r occipital lobe" | names == "r occipital gyrus"| names == "l occipital gyrus") )
#AAL 43 + 44
Occipital_Secondary <- as.data.frame(dplyr::filter(Total_counts, (grepl('occipital', names) & grepl('superior', names)) | (grepl('occipital', names) & (grepl('medial', names) | grepl('middle', names) | grepl('lateral', names))) | (grepl('occipital', names) & (grepl('inferior', names) | grepl('face', names)))| grepl('extra', names)))
#AAL 49 + 50 #AAL 51 + 52 #AAL 53 + 54
Occipital_Secondary <- rbind(Occipital_Secondary, subset(Total_counts, names =="occipital lobe" | names == "l occipital"| names == "r occipital"| names == "l occipital cortex"| names == "r occipital cortex"| names == "l occipital lobe"| names == "r occipital lobe") )
Occipital_Cuneus <- as.data.frame(dplyr::filter(Total_counts, grepl('cune', names) & !grepl('pre',names)))
#AAL 51 + 52
Lingual <- as.data.frame(dplyr::filter(Total_counts, grepl('lingual', names)))
#AAL 47 + 48
#Occipital_TOTAL <- as.data.frame(dplyr::filter(Total_counts, (grepl('occipital', names) | grepl('visual', names) | ((grepl('cune', names)) & !grepl('pre', names)) | grepl('striate', names))))
#TEMPORAL: ##CHECKED 31/5
Temporal_Superior <- as.data.frame(dplyr::filter(Total_counts, grepl('temporal', names) & grepl('superior', names) & !grepl('pole',names)))
#AAL 81 + 82
Temporal_Superior <- rbind(Temporal_Superior, subset(Total_counts, names == "r temporal lobe" | names == "l temporal lobe" | names == "r temporal gyrus"))
#Add unspecific terms
Temporal_Pole <- as.data.frame(dplyr::filter(Total_counts, grepl('temporal', names) & grepl('pole',names)))
#AAL 83 + 84
Temporal_Middle <- as.data.frame(dplyr::filter(Total_counts, (grepl('temporal', names) & (grepl('medial', names) | grepl('middle', names)) | grepl('entorhinal', names) )))
#AAL 85 + 86 + Entorhinal
Temporal_Middle <- rbind(Temporal_Middle, subset(Total_counts, names == "r temporal lobe" | names == "l temporal lobe" | names == "r temporal gyrus"))
Temporal_Inferior <- as.data.frame(dplyr::filter(Total_counts, grepl('temporal', names) & grepl('inferior', names)))
#AAL 89 + 90
Temporal_Inferior <- rbind(Temporal_Inferior, subset(Total_counts, names == "r temporal lobe" | names == "l temporal lobe" | names == "r temporal gyrus"))
#Temporal_TOTAL <- as.data.frame(dplyr::filter(Total_counts, grepl('temporal', names)))
Fusiform <- as.data.frame(dplyr::filter(Total_counts, grepl('fusiform', names)))
#AAL 55 + 56
#PARIETAL:
Parietal_Superior <- as.data.frame(dplyr::filter(Total_counts, grepl('parietal', names) & grepl('superior', names)))
#AAL 59 + 60
Parietal_Inferior <- as.data.frame(dplyr::filter(Total_counts, grepl('parietal', names) & grepl('inferior', names)| grepl('angular ', names)))
#AAL 65 + 66 (Angular Gyrus) + 61 + 62 (Inf. Parietal Gyrus)
Parietal_Precuneus <- as.data.frame(dplyr::filter(Total_counts, grepl('cune', names) & grepl('pre',names)))
#AAL 67 + 68
#INSULA
Insula <- as.data.frame(dplyr::filter(Total_counts, grepl('insula', names)))
#AAL 29 + 30
Claustrum <- as.data.frame(dplyr::filter(Total_counts, grepl('claustrum', names)))
#AAL NA
#FRONTAL:
Frontal_dlPFC <- as.data.frame(dplyr::filter(Total_counts, (grepl('frontal', names) & grepl('superior', names) & !grepl('orbi', names) & !grepl('medi', names)) | (grepl('frontal', names) & (grepl('middle', names) & !grepl('orbi', names)) & !grepl('vent', names) | grepl('dorsolateral', names) | grepl('eye',names))))
#AAL 3 + 4 7 + 8 (frontal eye fields, dorsolateral prefrontal cortex, medial frontal gyrus)
Frontal_dlPFC <- rbind(Frontal_dlPFC, subset(Total_counts, names == "l anterior prefrontal cortex" | names == "r anterior prefrontal cortex" | names == "l frontal cortex" | names == "r frontal cortex" | names == "l prefrontal cortex" | names == "r prefrontal cortex"))
#Add unspecific terms
Frontal_vlPFC <- as.data.frame(dplyr::filter(Total_counts, grepl('frontal', names) & ((grepl('inferior', names) & !grepl('orbito', names) | grepl('ventrolateral', names)))))
#AAL 11-16 (ventrolateral prefrontal cortex, inferior frontal gyrus)
Frontal_vlPFC <- rbind(Frontal_vlPFC, subset(Total_counts, names == "l anterior prefrontal cortex" | names == "r anterior prefrontal cortex" | names == "l frontal cortex" | names == "r frontal cortex" | names == "l prefrontal cortex" | names == "r prefrontal cortex"))
Frontal_dmPFC <- as.data.frame(dplyr::filter(Total_counts, (grepl('frontal', names) & ((grepl('superior', names) & !grepl('orbi', names) & grepl('medial', names))|(grepl('dors', names) & !grepl('lateral', names))) | (grepl('supplementary', names) & grepl('pre', names)))|grepl('premotor', names) | grepl('precentral', names)))
#AAL 23 + 24 (frontal superior medial, presupplementary motor area, #AAL 19 + 20 - supplementary motor area, precentral gyrus, premotor cortex)
Frontal_dmPFC <- rbind(Frontal_dmPFC, subset(Total_counts, names == "l anterior prefrontal cortex" | names == "r anterior prefrontal cortex" | names == "l frontal cortex" | names == "r frontal cortex" | names == "l prefrontal cortex" | names == "r prefrontal cortex"| names == "l medial frontal cortex" | names == "r medial frontal cortex" | names == "medial frontal cortex" | names == "r medial prefrontal cortex" | names == "l medial prefrontal cortex" ))
Frontal_vmPFC <- as.data.frame(dplyr::filter(Total_counts, (grepl('frontal', names) & ((grepl('vent', names) & grepl('medi', names)) | grepl('pole', names)) | grepl('rectal', names)) | (grepl('cingul', names) & grepl("genu", names)) | grepl('subcallosal', names)))
#AAL ventromedial pfc and subgenual cingulate
Frontal_vmPFC <- rbind(Frontal_vmPFC, subset(Total_counts, names == "l anterior prefrontal cortex" | names == "r anterior prefrontal cortex" | names == "l frontal cortex" | names == "r frontal cortex" | names == "l prefrontal cortex" | names == "r prefrontal cortex"| names == "l medial frontal cortex" | names == "r medial frontal cortex" | names == "medial frontal cortex" | names == "r medial prefrontal cortex" | names == "l medial prefrontal cortex" ))
#Subgenual_Cingulate <- as.data.frame(dplyr::filter(Total_counts, (grepl('cingul', names) & grepl("genu", names)) | grepl('subcallosal', names)))
Cingulate<- as.data.frame(dplyr::filter(Total_counts, grepl('cingul', names) & !grepl("genu", names)))
#AAL 31 - 36 (All regions - acc, mcc, pcc)
Orbito_Frontal_TOTAL <- as.data.frame(dplyr::filter(Total_counts, grepl('orbito', names)))
NOSIGDIF <- as.data.frame(dplyr::filter(Total_counts, grepl('nsd', names)))
```
```{r}
REGIONS <- (setdiff(ls(), c("Total_counts", "REGIONS","r", "df","df2", "df2L", "df2R", "Regions_Activated", "Regions_Activated_L", "Regions_Activated_R")))
#CHECKING_counts <- REGIONS %>% reduce(full_join, by='names')
```
```{r}
#COMBINED LEFT AND RIGHT
Regions_Activated <- data.frame()
r = 1 #increasing row numbers
usednames <- c()
for (df in REGIONS){
#print(df)
#Get the DF
.val <- get(df)
df2 <- as.data.frame(.val)
#Add the colSum to the new DF
Regions_Activated[r,1] <- as.character(df)
Regions_Activated[r,2:28] <- colSums(df2[,2:28], na.rm = TRUE)
r= r+1
usednames <- c(usednames, df2$names)
}
colnames(Regions_Activated) <- c("Region", "Th1_Searched", "Th1_Activated", "Th1_Perc", "Th2_Searched", "Th2_Activated", "Th2_Perc","Th3_Searched", "Th3_Activated", "Th3_Perc", "Th4_Searched", "Th4_Activated", "Th4_Perc", "Th5_Applied", "Th5_Activated", "Th5_Perc","Th6_Applied", "Th6_Activated", "Th6_Perc", "Th7_Applied","Th7_Activated", "Th7_Perc", "Th8_Applied","Th8_Activated", "Th8_Perc", "Th9_Applied","Th9_Activated", "Th9_Perc")
write.csv(Regions_Activated, paste0("/project/3022054.01/projects/linschl/efmt/review_update_2026/02_results/patients_pgtc/Regions_Activated_PMDD.csv"), row.names=TRUE) # BP
```
```{r}
UNUSED_names <- setdiff(usednames, Total_counts$names)
```

A_P_Activated_Regions_PgtC.Rmd at commit 7873b81, under GPL-3.0 · at the source

Overview

Authors: Hannah S Savage^1,2,3, Linda Schlüter^1,2, Roberta Russo¹, Imogen Leaning^1,2, Christian F Beckmann^1,2,4, Andre F Marquand^1,2

ORCID iDs: Linda Schlüter, Imogen Leaning

Institutions: Radboud University Nijmegen (Netherlands); Radboud University Medical Center (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands); University College London (United Kingdom); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom)

Journal: Human brain mapping, volume 47, issue 14, article e70636

Dates: received 6 May 2025; accepted 20 August 2026; published online 21 September 2026; in print October 2026

Type: Review · Language: English

License: CC BY

Identifiers: DOI 10.1002/hbm.70636 · PMID 42768741 · PMCID PMC13594551 · OpenAlex W7213986787

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: human (organism), cognitive (subfield)

Methods: fMRI & imaging

MeSH: Brain*, Brain Mapping*, Emotions*, Facial Expression*, Facial Recognition*, Humans (* major topic)

Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: European Research Council (101001118)

Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

We performed a systematic review of over 20 years of literature that implemented a variant of the emotional face matching task (EFMT). We aimed to quantify the heterogeneity in tasks used across studies, and to summarise the regions reported as activated when participants match the target face stimuli, as compared to matching the control stimuli, within and between patients with a mental health diagnosis and unaffected controls. Our results highlight the vast heterogeneity in all aspects of task design and implementation across studies. Our high‐level synthesis of brain regions activated broadly confirms the involvement of the amygdala during emotional face matching, but shows that an increase in amygdala activation is inconsistent both within and across mental health conditions. We further highlight increased activation within the cingulate cortex as a transdiagnostic alteration (vs. controls) during emotional face matching. We introduce solutions to address the challenge of heterogeneity, including a standard version of the EFMT and applicable statistical methods, and provide recommendations for the field to progress our understanding of emotional face processing.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

predictive-clinical-neuroscience/EFMT_Review

License: GPL-3.0

State: the link answers, verified on 11 November 2026

Evidence: files inventoried

Commit: 7873b814b8f92e278a7a399351c3a3d6da4473cc, 22 July 2026

Languages: R (14)

Size: 21 files, 14 scripts

Software Heritage: not archived

Found in: “Data and Code Availability”

Holds: README, license file, 14 notebooks

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

Tools: reshape2 (14 files), ggplot2 (13 files), psych (13 files), tidyverse (13 files), data.table (8 files), cowplot (2 files)

Availability: 2 checks, the latest on 11 November 2026: the link answers

11 November 2026: the link answers
1 October 2026: the link answers

16 files

A_C_Activated_Regions_C.Rmd, R, 1,531 lines, 2 matches
A_C_Plots_Brain_Thresholds.Rmd, R, 184 lines
A_P_Activated_Regions_PgtC.Rmd, R, 1,592 lines, 4 matches
A_P_Plots_Brain_Patients_Combined.Rmd, R, 550 lines
A_P_Plots_Brain_Thresholds_Patients.Rmd, R, 222 lines
D_Age_Sex_Sample_Size.Rmd, R, 596 lines, 2 matches
D_Number_Size_Groups.Rmd, R, 161 lines
D_Patient_Group_Split.Rmd, R, 98 lines
QC_summary_plot.Rmd, R, 84 lines
T_Processing_Software.Rmd, R, 314 lines
T_Scanners.Rmd, R, 232 lines
T_Scanning_Parameters.Rmd, R, 176 lines
T_Task_Names.Rmd, R, 194 lines
T_Task_Parameters.Rmd, R, 548 lines, 2 matches
LICENSE, License, 674 lines
README.md, Text, 40 lines

At the source: github.com/predictive-clinical-neuroscience/EFMT_Review

Hannah-Savage/Standard_EFMT

License: none: the authors keep all their rights

State: the link answers, verified on 11 November 2026

Evidence: files inventoried

Commit: bcda5239ea616b5f691126336bc184a1cbb9b043, 22 November 2023

Languages: Python (7)

Size: 201 files, 7 scripts

Software Heritage: not archived

Found in: the text, “Recommendations”

Holds: README

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

Tools: PsychoPy (5 files), NumPy (2 files)

Availability: 2 checks, the latest on 11 November 2026: the link answers

11 November 2026: the link answers
1 October 2026: the link answers

8 files

Hariri/Hariri.py, Python, 216 lines
Hariri/__init__.py, Python, 1 line
Hariri/schedules/convert_schedules.py, Python, 47 lines
StimTool.py, Python, 157 lines
StimToolLib.py, Python, 746 lines
__init__.py, Python, 1 line
sound_test.py, Python, 25 lines
README.md, Text, 3 lines

At the source: github.com/Hannah-Savage/Standard_EFMT

laureate-institute-for-brain-research/HARIRI-Task

License: none: the authors keep all their rights

State: the link answers, verified on 11 November 2026

Evidence: files inventoried

Commit: f7aa1d36d9a963762cc7ac56deafb985813b03ec, 11 February 2022

Languages: Python (7)

Size: 190 files, 7 scripts

Software Heritage: not archived

Found in: the acknowledgements

Holds: README

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

Tools: PsychoPy (5 files), NumPy (2 files)

Availability: 2 checks, the latest on 11 November 2026: the link answers

11 November 2026: the link answers
1 October 2026: the link answers

8 files

Hariri/Hariri.py, Python, 216 lines
Hariri/__init__.py, Python, 1 line
Hariri/schedules/convert_schedules.py, Python, 47 lines
StimTool.py, Python, 157 lines
StimToolLib.py, Python, 740 lines
__init__.py, Python, 1 line
sound_test.py, Python, 25 lines
README.md, Text, 4 lines

At the source: github.com/laureate-institute-for-brain-research/HARIRI-Task

The paper's code and data availability statement is in the Data section.

Tracing map

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

What the map holds:

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

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

Data

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

Data Availability Statement

The data extraction table and all code to run the analysis and generate the plots in the manuscript can be found at https://github.com/predictive‐clinical‐neuroscience/EFMT_Review (https://github.com/predictive-clinical-neuroscience/EFMT_Review). The standardised version of the EFMT is available at https://github.com/Hannah‐Savage/Standard_EFMT (https://github.com/Hannah-Savage/Standard_EFMT).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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

Version 1, 1 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 MeSH terms, 1 funder, 59 references.

Cite

This paper

Savage, H. S., Schlüter, L., Russo, R., Leaning, I., Beckmann, C. F., & Marquand, A. F. (2026). Do You Match What I Match? A Systematic Review of Over 20 Years of the Emotional Face Matching Task Reveals Vast Inconsistencies in Design and Implementation. Human brain mapping, 47(14), e70636. https://doi.org/10.1002/hbm.70636

BibTeX

@article{savage2026do, author = {Savage, Hannah S and Schlüter, Linda and Russo, Roberta and Leaning, Imogen and Beckmann, Christian F and Marquand, Andre F}, title = {{Do You Match What I Match? A Systematic Review of Over 20 Years of the Emotional Face Matching Task Reveals Vast Inconsistencies in Design and Implementation}}, journal = {Human brain mapping}, year = {2026}, month = oct, volume = {47}, number = {14}, pages = {e70636}, publisher = {Wiley}, issn = {1065-9471}, doi = {10.1002/hbm.70636}, url = {https://doi.org/10.1002/hbm.70636}, pmid = {42768741}, pmcid = {PMC13594551} }

RIS

TY - JOUR AU - Savage, Hannah S AU - Schlüter, Linda AU - Russo, Roberta AU - Leaning, Imogen AU - Beckmann, Christian F AU - Marquand, Andre F TI - Do You Match What I Match? A Systematic Review of Over 20 Years of the Emotional Face Matching Task Reveals Vast Inconsistencies in Design and Implementation T2 - Human brain mapping J2 - Hum Brain Mapp PY - 2026 DA - 2026/10/01 VL - 47 IS - 14 SP - e70636 SN - 1065-9471 PB - Wiley DO - 10.1002/hbm.70636 UR - https://doi.org/10.1002/hbm.70636 LA - en ER -

CSL-JSON

{ "id": "10.1002/hbm.70636", "type": "article-journal", "title": "Do You Match What I Match? A Systematic Review of Over 20 Years of the Emotional Face Matching Task Reveals Vast Inconsistencies in Design and Implementation", "container-title": "Human brain mapping", "author": [ { "family": "Savage", "given": "Hannah S" }, { "family": "Schlüter", "given": "Linda" }, { "family": "Russo", "given": "Roberta" }, { "family": "Leaning", "given": "Imogen" }, { "family": "Beckmann", "given": "Christian F" }, { "family": "Marquand", "given": "Andre F" } ], "container-title-short": "Hum Brain Mapp", "volume": "47", "issue": "14", "page": "e70636", "DOI": "10.1002/hbm.70636", "PMID": "42768741", "PMCID": "PMC13594551", "ISSN": "1065-9471", "publisher": "Wiley", "URL": "https://doi.org/10.1002/hbm.70636", "language": "en", "issued": { "date-parts": [ [ 2026, 10, 1 ] ] } }

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

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown

<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.1002/hbm.70636.svg)](https://openscicode.org/badge/doi/10.1002/hbm.70636)
<!-- oscr-badge:end -->

reStructuredText

.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.1002/hbm.70636.svg
   :target: https://openscicode.org/badge/doi/10.1002/hbm.70636
   :alt: OSCR

.. oscr-badge:end

HTML

<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.1002/hbm.70636"><img src="https://openscicode.org/badge/doi/10.1002/hbm.70636.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.

Do You Match What I Match? A Systematic Review of Over 20 Years of the Emotional Face Matching Task Reveals Vast Inconsistencies in Design and Implementation.

Code ↔ Paper

Paper

The authors' code

Overview

Abstract

Repositories

predictive-clinical-neuroscience/EFMT_Review

Hannah-Savage/Standard_EFMT

laureate-institute-for-brain-research/HARIRI-Task

Tracing map

Data

Data Availability Statement

Versions

Version 1, 1 October 2026: the first record

Cite

This paper

Similar papers

Contribute

Claim this paper

Correct its record

Validate its tracing map

Add the badge to the code's README

Request its removal

Discussion, reproductions, activity