Fusiform face area development correlates with development in higher-order social brain regions.

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] § Materials and methods › fMRI data analysis › Motion treatment ↔ scripts/05.motion_exclusions/mark_motion_exclusions.py, lines 142–217 · score 0.80 · composite motion, framewise displacement, standardised DVARS, rapidart, thresholds, workflow
[2] § Results › Development of FFA face response and functional connectivity ↔ scripts/FRIC_DevelopmentalChange.Rmd, lines 1614–1713 · score 0.76 · left STS, left amygdala, left MMPFC, right STS, right amygdala, right MMPFC
[3] § Results › Development of FFA face response and functional connectivity ↔ scripts/FRIC_FunctionalMaturityFFA.Rmd, lines 1209–1300 · score 0.75 · left STS, left amygdala, left MMPFC, right STS, right amygdala, right MMPFC
[4] § Materials and methods › fMRI data analysis › Region of interest (ROI) definition ↔ scripts/06.first_level/define_fROIs.py, lines 113–201 · score 0.71 · ROI definition, fROI, search space, ranking, maps, voxel
[5] § Materials and methods › Statistical analyses › Developmental change in functional responses of FFA, MMPFC, amygdala and STS ↔ scripts/FRIC_DevelopmentalChange.Rmd, lines 697–838 · score 0.58 · scene events, face events, S01, S12, interaction, hemisphere
[6] § Materials and methods › Statistical analyses › Associations between functional maturity of FFA and its functional connectivity to MMPFC, amygdala and STS ↔ scripts/FRIC_FunctionalMaturityFFA.Rmd, lines 304–429 · score 0.56 · preregistered linear mixed, independent variable, functional maturity, reversed, right FFA, interaction
[7] § Materials and methods › fMRI data analysis › Inter-region correlations (i.e., functional connectivity) ↔ scripts/FRIC_MRIvariables.Rmd, lines 717–776 · score 0.53 · inter region correlations, Pearson correlations, ROIs, STS, MMPFC, FFA
[8] § Materials and methods › fMRI data analysis › Timecourse extraction ↔ scripts/06.first_level/firstlevel_pipeline.py, lines 205–257 · score 0.52 · aCompCor, outlier volumes, regressed, filtered
[9] § Materials and methods › fMRI data analysis › Region of interest (ROI) definition ↔ scripts/06.first_level/extract_stats.py, lines 20–63 · score 0.51 · localiser task, beliefs, fROI, split, events, model
[10] § Materials and methods › fMRI data analysis › Functional maturity ↔ scripts/FRIC_MRIvariables.Rmd, lines 209–309 · score 0.51 · Pearson correlation, fROIs, scored, child, adult, STS

Paper

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The authors' code

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---
title: "FRIC_DevelopmentalChange"
author: "LJS"
date: "2025-01-14"
output: html_document
---
# Libraries
```{r setup, include=FALSE}
library("rlang") #toolbox
library("readr") #toolbox
library("dplyr") #data manipulation
library("purrr") #data manipulation
library("tidyr") #data manipulation
library("here") #toolbox
library("tidyverse") #data manipulation
library("ggpubr") #plotting
library("tidytext") #data manipulation
library("knitr") #rmarkdown
library("htmltools") #rmarkdown
library("markdown") #rmarkdown
library("httpuv") #rmarkdown
library("NCmisc") #toolbox
library("stringr") #data manipulation
library("reshape") #data manipulation
library("reshape2") #data manipulation
library("lme4") #regression models
library("lmerTest") #regression models
library("broom.mixed") #save regression results
library("car") #VIF
library("VIM") #visualise missing data
library("gridExtra") #combine plots
library("patchwork") #combine plots
```
# Face Responses in Childhood (FRIC) project
This script characterises age-related changes in functional maturity, response magnitude to face/scene events and functional connectivity of FFA, MMPFC, STS and amygdala, as well as age-related changes in lateralisation of face response in FFA.
Structure:
- Section I: Functional maturity measure (fm) i.e., similarity between children’s and adults’ timecourses’
- Section II: Magnitude of responses of each ROI at face/scene events
- Section III: Age effects on functional connectivity (fx) between regions
- Section IV: Age effects on lateralisation of face response (non-preregistered)
## Section I: Functional maturity measure (fm) i.e., similarity between children’s and adults’ timecourses’
### Read and tidy up data
```{r}
# Covariates
participants <- read_tsv(here("raw_data/covariates", "participants.tsv"), col_names=TRUE)
colnames(participants)[which(names(participants) == "participant_id")] <- "ID"
## Remove the word "pixar" and trim extra whitespace
participants$ID <- gsub("[sub-pixar-]","", participants$ID)
participants$ID <- trimws(participants$ID)
# Motion
motion <- read_tsv(here("raw_data/motion", "outlier_info.tsv"), col_names=TRUE)
colnames(motion)[which(names(motion) == "subject")] <- "ID"
## Remove the word "pixar" and trim extra whitespace
motion$ID <- gsub("[sub-pixar-]","", motion$ID)
motion$ID <- trimws(motion$ID)
# Fm
child_fm <- read_csv(here("processed_data", "child_MRIvariables.csv"), col_names=TRUE)
child_fm <- child_fm[,c(1:9)]
## Reshape the dataset to long format
long_data <- child_fm %>%
pivot_longer(cols = starts_with("zscored_fm_"),
names_to = c("hemisphere", "region"),
names_pattern = "zscored_fm_(l|r)(.*)", # Capture hemisphere and region
values_to = "fm") %>%
mutate(hemisphere = ifelse(hemisphere == "l", "left", "right")) # Convert l/r to left/right
# Merge age, motion and fm data
fm_data <- merge(long_data, participants[,c(1:2)], by="ID")
fm_data <- merge(fm_data, motion[,c(4,6)], by="ID")
colnames(fm_data) <- c("ID", "hemisphere", "region", "fm", "age", "meanFD")
# Ensure categorical variables are treated as factors
fm_data$ID <- as.factor(fm_data$ID)
fm_data$hemisphere <- as.factor(fm_data$hemisphere)
fm_data$region <- as.factor(fm_data$region)
# Scale predictors
fm_data[, c("age", "meanFD")] <- scale(fm_data[, c("age", "meanFD")])
```
### Models
With interaction effects
```{r}
# FFA
fm_data_FFA <- subset(fm_data, region == "FFA")
model_FFA <- lmer(fm ~ age*hemisphere + meanFD + (1 | ID), data = fm_data_FFA)
summary_FFA <- tidy(model_FFA)
# MMPFC
fm_data_MMPFC <- subset(fm_data, region == "MMPFC")
model_MMPFC <- lmer(fm ~ age*hemisphere + meanFD + (1 | ID), data = fm_data_MMPFC)
summary_MMPFC <- tidy(model_MMPFC)
# Amygdala
fm_data_amygdala <- subset(fm_data, region == "amygdala")
model_amygdala <- lmer(fm ~ age*hemisphere + meanFD + (1 | ID), data = fm_data_amygdala)
summary_amygdala <- tidy(model_amygdala)
# STS
fm_data_STS <- subset(fm_data, region == "STS")
model_STS <- lmer(fm ~ age*hemisphere + meanFD + (1 | ID), data = fm_data_STS)
summary_STS <- tidy(model_STS)
# Summary
all_summaries <- bind_rows(
summary_FFA %>% mutate(region = "FFA"),
summary_MMPFC %>% mutate(region = "MMPFC"),
summary_amygdala %>% mutate(region = "amygdala"),
summary_STS %>% mutate(region = "STS")
)
view(all_summaries)
```
Since interaction effects are non-sig, repeat models without interaction effects
```{r}
# FFA
fm_data_FFA <- subset(fm_data, region == "FFA")
model_FFA <- lmer(fm ~ age + hemisphere + meanFD + (1 | ID), data = fm_data_FFA)
summary_FFA <- tidy(model_FFA)
# MMPFC
fm_data_MMPFC <- subset(fm_data, region == "MMPFC")
model_MMPFC <- lmer(fm ~ age + hemisphere + meanFD + (1 | ID), data = fm_data_MMPFC)
summary_MMPFC <- tidy(model_MMPFC)
# Amygdala
fm_data_amygdala <- subset(fm_data, region == "amygdala")
model_amygdala <- lmer(fm ~ age + hemisphere + meanFD + (1 | ID), data = fm_data_amygdala)
summary_amygdala <- tidy(model_amygdala)
# STS
fm_data_STS <- subset(fm_data, region == "STS")
model_STS <- lmer(fm ~ age + hemisphere + meanFD + (1 | ID), data = fm_data_STS)
summary_STS <- tidy(model_STS)
# Save results
all_summaries <- bind_rows(
summary_FFA %>% mutate(region = "FFA"),
summary_MMPFC %>% mutate(region = "MMPFC"),
summary_amygdala %>% mutate(region = "amygdala"),
summary_STS %>% mutate(region = "STS")
) %>%
mutate(
estimate = round(estimate, 2),
std.error = round(std.error, 2),
statistic = round(statistic, 2),
df = round(df, 0),
p.value = if_else(
p.value < 0.001,
format(p.value, scientific = TRUE, digits = 4),
sprintf("%.3f", p.value)
)
)
write.csv(all_summaries, here("results/RQ1", "Fm_age.csv"), row.names = FALSE)
```
### Model plots
Age and functional maturity
```{r}
# Data wrangling
data_plot <- fm_data
data_plot$age <- NULL
data_plot$meanFD <- NULL
data_plot <- merge(data_plot, participants[,c(1:2)], by="ID")
colnames(data_plot) <- c("ID", "hemisphere", "region", "fm", "age")
data_plot_FFA <- subset(data_plot, region == "FFA")
data_plot_STS <- subset(data_plot, region == "STS")
data_plot_MMPFC <- subset(data_plot, region == "MMPFC")
data_plot_amygdala <- subset(data_plot, region == "amygdala")
# Colours
custom_colours_amygdala <- c("#E1AA9F", "#D34835")
custom_colours_MMPFC <- c("#87BACF", "#578CAD")
custom_colours_FFA <- c("#C39EDA", "#652975")
custom_colours_STS <- c("#FFF865", "#FFCE1B")
# FFA plot
plot_FFA <- ggplot(data_plot_FFA, aes(x = age, y = fm, color = hemisphere)) +
geom_point(alpha = 0.8, size = 2.5, shape = 19) +
geom_smooth(method = "lm", se = FALSE, linetype = "solid", linewidth = 2) +
scale_color_manual(values = custom_colours_FFA) +
labs(title = "",
x = "Age",
y = "",
color = "FFA Hemisphere") +
theme_minimal() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 12),
legend.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
strip.text = element_text(size = 14, face = "bold"),
strip.background = element_blank(),
axis.line.x = element_blank(),
axis.text.y = element_blank(),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold")) +
coord_cartesian(ylim = c(-3, 3)) +
annotate(
"text",
x = Inf, y = -Inf,
label = "Age effect: \nβ(SE)=0.26(0.07), p<0.001",
hjust = 1.05,
vjust = -0.5,
size = 4,
color = "black"
)
# MMPFC plot
plot_MMPFC <- ggplot(data_plot_MMPFC, aes(x = age, y = fm, color = hemisphere)) +
geom_point(alpha = 0.8, size = 2.8, shape = 19) +
geom_smooth(method = "lm", se = FALSE, linetype = "solid", linewidth = 2) +
scale_color_manual(values = custom_colours_MMPFC) +
labs(title = "",
x = "Age",
y = "Functional maturity",
color = "MMPFC Hemisphere") +
theme_minimal() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 12),
legend.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
strip.text = element_text(size = 14, face = "bold"),
strip.background = element_blank(),
axis.line.x = element_blank(),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold")) +
coord_cartesian(ylim = c(-3, 3)) +
annotate(
"text",
x = Inf, y = -Inf,
label = "Age effect: \nβ(SE)=0.35(0.07), p<0.001",
hjust = 1.05,
vjust = -0.5,
size = 4,
color = "black"
)
# Amygdala plot
plot_amygdala <- ggplot(data_plot_amygdala, aes(x = age, y = fm, color = hemisphere)) +
geom_point(alpha = 0.8, size = 2.8, shape = 19) +
geom_smooth(method = "lm", se = FALSE, linetype = "solid", linewidth = 2) +
scale_color_manual(values = custom_colours_amygdala) +
labs(
title = "",
x = "Age",
y = "",
color = "Amygdala Hemisphere"
) +
theme_minimal() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 12),
legend.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
strip.text = element_text(size = 14, face = "bold"),
strip.background = element_blank(),
axis.line.x = element_blank(),
axis.text.y = element_blank(),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold")
) +
coord_cartesian(ylim = c(-3, 3)) +
annotate(
"text",
x = Inf, y = -Inf,
label = "Age effect: \nβ(SE)=0.20(0.08), p=0.016",
hjust = 1.05,
vjust = -0.5,
size = 4,
color = "black"
)
# STS plot
plot_STS <- ggplot(data_plot_STS, aes(x = age, y = fm, color = hemisphere)) +
geom_point(alpha = 0.8, size = 2.5, shape = 19) +
geom_smooth(method = "lm", se = FALSE, linetype = "solid", linewidth = 2) +
scale_color_manual(values = custom_colours_STS) +
labs(title = "",
x = "Age",
y = "",
color = "STS Hemisphere") +
theme_minimal() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 12),
legend.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
strip.text = element_text(size = 14, face = "bold"),
strip.background = element_blank(),
axis.line.x = element_blank(),
axis.text.y = element_blank(),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold")) +
coord_cartesian(ylim = c(-3, 3)) +
annotate(
"text",
x = Inf, y = -Inf,
label = "Age effect: \nβ(SE)=0.24(0.07), p<0.001",
hjust = 1.05,
vjust = -0.5,
size = 4,
color = "black"
)
# Combine and save plots
fm_age <- (plot_MMPFC | plot_amygdala | plot_STS | plot_FFA ) +
plot_annotation(title = "Functional maturity across Age") &
theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"))
fm_age
ggsave(here("results/figures", "Fm_age.png"), plot = fm_age, width = 13, height = 5, units = "in", dpi = 300)
```
## Section II: Magnitude of responses of each ROI at face/scene events
### Read data
```{r}
# Events magnitude
child_eventsmagnitude <- read_csv(here("processed_data", "child_MRIvariables.csv"), col_names=TRUE)
child_eventsmagnitude <- child_eventsmagnitude[,c(1, 10:201)]
# Reshape the dataset to long format
long_data <- child_eventsmagnitude %>%
pivot_longer(
cols = -ID, # Exclude the ID column
names_to = c("region", "event"), # Split names into region and event
names_pattern = "([lr]?[^_]+)_(F\\d{2}|S\\d{2})", # Match region and event
values_to = "value" # The name of the new column for values
) %>%
mutate(
hemisphere = if_else(grepl("^l", region), "left", "right"), # Add hemisphere based on region prefix
region = gsub("^[lr]", "", region) # Remove hemisphere prefix from region
) %>%
pivot_wider(
names_from = "event", # Make "event" column headers
values_from = "value" # Fill the values for each event
)
long_data <- long_data %>%
select(ID, hemisphere, region, F01, F02, F03, F04, F05, F06, F07, F08, F09, F10, F11, F12,
S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12)
# Merge age, motion and fm data
eventsmagnitude_data <- merge(long_data, participants[,c(1:2)], by="ID")
eventsmagnitude_data <- merge(eventsmagnitude_data, motion[,c(4,6)], by="ID")
colnames(eventsmagnitude_data) <- c("ID", "hemisphere", "region", "F01", "F02", "F03", "F04", "F05", "F06", "F07", "F08", "F09", "F10", "F11", "F12", "S01", "S02", "S03", "S04", "S05", "S06", "S07", "S08", "S09", "S10", "S11", "S12", "age", "meanFD")
# Scale predictors
eventsmagnitude_data[, c("age", "meanFD")] <- scale(eventsmagnitude_data[, c("age", "meanFD")])
# Ensure categorical variables are treated as factors
eventsmagnitude_data$ID <- as.factor(eventsmagnitude_data$ID)
eventsmagnitude_data$hemisphere <- as.factor(eventsmagnitude_data$hemisphere)
eventsmagnitude_data$region <- as.factor(eventsmagnitude_data$region)
```
### Models
FFA
```{r}
# Read data
eventsmagnitude_data_FFA <- subset(eventsmagnitude_data, region == "FFA")
## Face events
### Models with interaction effects
events <- paste0("F", sprintf("%02d", 1:12))
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age*hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_FFA)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_FFA_face_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) # Add the event name to each summary
}))
summaries_FFA_face_events$region <- "FFA"
summaries_FFA_face_events <- summaries_FFA_face_events %>%
mutate(sig = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
))
### Interaction effects are non-sig for all events, repeat models without interaction effects for all events
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age + hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_FFA)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_FFA_face_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
summaries_FFA_face_events$region <- "FFA"
summaries_FFA_face_events$p.value <- as.numeric(summaries_FFA_face_events$p.value)
summaries_FFA_face_events <- summaries_FFA_face_events %>%
mutate(sig = case_when(
p.value < 0.004 ~ "significant after correction",
TRUE ~ ""
))
## Scene events
### Models with interaction effects
events <- paste0("S", sprintf("%02d", 1:12)) #generates S01, S02, ..., S12
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age*hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_FFA)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_FFA_scene_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
summaries_FFA_scene_events$region <- "FFA"
summaries_FFA_scene_events <- summaries_FFA_scene_events %>%
mutate(sig = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
))
### Interaction effects are non-sig for all events, repeat models without interaction effects for all events
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age + hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_FFA)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_FFA_scene_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
summaries_FFA_scene_events$region <- "FFA"
summaries_FFA_scene_events$p.value <- as.numeric(summaries_FFA_scene_events$p.value)
summaries_FFA_scene_events <- summaries_FFA_scene_events %>%
mutate(sig = case_when(
p.value < 0.004 ~ "significant after correction",
TRUE ~ ""
))
```
MMPFC
```{r}
# Read data
eventsmagnitude_data_MMPFC <- subset(eventsmagnitude_data, region == "MMPFC")
## Face events
### Models with interaction effects
events <- paste0("F", sprintf("%02d", 1:12))
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age*hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_MMPFC)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_MMPFC_face_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
summaries_MMPFC_face_events$region <- "MMPFC"
summaries_MMPFC_face_events <- summaries_MMPFC_face_events %>%
mutate(sig = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
))
### Interaction effects are non-sig for all events but F05, F07, F10 and F12, repeat models without interaction effects for all events but F05, F06, F07, F10 and F12
events <- c("F01", "F02", "F03", "F04", "F06", "F08", "F09", "F11")
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age + hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_MMPFC)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_MMPFC_face_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
model_MMPFC_F05 <- lmer(F05 ~ age*hemisphere + meanFD + (1 | ID), data = eventsmagnitude_data_MMPFC)
summary_MMPFC_F05 <- tidy(model_MMPFC_F05)
summary_MMPFC_F05 <- summary_MMPFC_F05 %>% mutate(event = "F05")
model_MMPFC_F07 <- lmer(F07 ~ age*hemisphere + meanFD + (1 | ID), data = eventsmagnitude_data_MMPFC)
summary_MMPFC_F07 <- tidy(model_MMPFC_F07)
summary_MMPFC_F07 <- summary_MMPFC_F07 %>% mutate(event = "F07")
model_MMPFC_F10 <- lmer(F10 ~ age*hemisphere + meanFD + (1 | ID), data = eventsmagnitude_data_MMPFC)
summary_MMPFC_F10 <- tidy(model_MMPFC_F10)
summary_MMPFC_F10 <- summary_MMPFC_F10 %>% mutate(event = "F10")
model_MMPFC_F12 <- lmer(F12 ~ age*hemisphere + meanFD + (1 | ID), data = eventsmagnitude_data_MMPFC)
summary_MMPFC_F12 <- tidy(model_MMPFC_F12)
summary_MMPFC_F12 <- summary_MMPFC_F12 %>% mutate(event = "F12")
summaries_MMPFC_face_events <- bind_rows(summaries_MMPFC_face_events, summary_MMPFC_F05, summary_MMPFC_F07, summary_MMPFC_F10, summary_MMPFC_F12)
summaries_MMPFC_face_events$region <- "MMPFC"
summaries_MMPFC_face_events$p.value <- as.numeric(summaries_MMPFC_face_events$p.value)
summaries_MMPFC_face_events <- summaries_MMPFC_face_events %>%
mutate(sig = case_when(
p.value < 0.004 ~ "significant after correction",
TRUE ~ ""
))
## Scene events
### Models with interaction effects
events <- paste0("S", sprintf("%02d", 1:12)) #generates S01, S02, ..., S12
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age*hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_MMPFC)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_MMPFC_scene_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) # Add the event name to each summary
}))
summaries_MMPFC_scene_events$region <- "MMPFC"
summaries_MMPFC_scene_events <- summaries_MMPFC_scene_events %>%
mutate(sig = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
))
### Interaction effects are non-sig for all events, repeat models without interaction effects for all events
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age + hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_MMPFC)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_MMPFC_scene_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
summaries_MMPFC_scene_events$region <- "MMPFC"
summaries_MMPFC_scene_events$p.value <- as.numeric(summaries_MMPFC_scene_events$p.value)
summaries_MMPFC_scene_events <- summaries_MMPFC_scene_events %>%
mutate(sig = case_when(
p.value < 0.004 ~ "significant after correction",
TRUE ~ ""
))
```
Amygdala
```{r}
# Read data
eventsmagnitude_data_amygdala <- subset(eventsmagnitude_data, region == "amygdala")
## Face events
### Models with interaction effects
events <- paste0("F", sprintf("%02d", 1:12))
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age*hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_amygdala)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_amygdala_face_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) # Add the event name to each summary
}))
summaries_amygdala_face_events$region <- "amygdala"
summaries_amygdala_face_events <- summaries_amygdala_face_events %>%
mutate(sig = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
))
### Interaction effects are non-sig for all events but F04, repeat models without interaction effects for all events but F04
events <- c("F01", "F02", "F03", "F05", "F06", "F07", "F08", "F09", "F10", "F11", "F12")
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age + hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_amygdala)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_amygdala_face_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) # Add the event name to each summary
}))
model_amygdala_F04 <- lmer(F04 ~ age*hemisphere + meanFD + (1 | ID), data = eventsmagnitude_data_amygdala)
summary_amygdala_F04 <- tidy(model_amygdala_F04)
summary_amygdala_F04 <- summary_amygdala_F04 %>% mutate(event = "F04")
summaries_amygdala_face_events <- bind_rows(summaries_amygdala_face_events, summary_amygdala_F04)
summaries_amygdala_face_events$region <- "amygdala"
summaries_amygdala_face_events$p.value <- as.numeric(summaries_amygdala_face_events$p.value)
summaries_amygdala_face_events <- summaries_amygdala_face_events %>%
mutate(sig = case_when(
p.value < 0.004 ~ "significant after correction",
TRUE ~ ""
))
## Scene events
### Models with interaction effects
events <- paste0("S", sprintf("%02d", 1:12)) #generates S01, S02, ..., S12
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age*hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_amygdala)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_amygdala_scene_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
summaries_amygdala_scene_events$region <- "amygdala"
summaries_amygdala_scene_events <- summaries_amygdala_scene_events %>%
mutate(sig = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
))
### Interaction effects are non-sig for all events, repeat models without interaction effects for all events
model_summaries <- list()
for (event in events) {
# Create the formula dynamically
formula <- as.formula(paste(event, "~ age + hemisphere + meanFD + (1 | ID)"))
# Fit the model
model <- lmer(formula, data = eventsmagnitude_data_amygdala)
# Summarise the model
summary_tidy <- tidy(model)
# Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_amygdala_scene_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) # Add the event name to each summary
}))
summaries_amygdala_scene_events$region <- "amygdala"
summaries_amygdala_scene_events$p.value <- as.numeric(summaries_amygdala_scene_events$p.value)
summaries_amygdala_scene_events <- summaries_amygdala_scene_events %>%
mutate(sig = case_when(
p.value < 0.004 ~ "significant after correction",
TRUE ~ ""
))
```
STS
```{r}
# Read data
eventsmagnitude_data_STS <- subset(eventsmagnitude_data, region == "STS")
## Face events
### Models with interaction effects
events <- paste0("F", sprintf("%02d", 1:12))
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age*hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_STS)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_STS_face_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) # Add the event name to each summary
}))
summaries_STS_face_events$region <- "STS"
summaries_STS_face_events <- summaries_STS_face_events %>%
mutate(sig = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
))
### Interaction effects are non-sig for all events but F09, repeat models without interaction effects for all events but F09
events <- c("F01", "F02", "F03", "F04", "F05", "F06", "F07", "F08", "F10", "F11", "F12")
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age + hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_STS)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_STS_face_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
model_STS_F09 <- lmer(F09 ~ age*hemisphere + meanFD + (1 | ID), data = eventsmagnitude_data_STS)
summary_STS_F09 <- tidy(model_STS_F09)
summary_STS_F09 <- summary_STS_F09 %>% mutate(event = "F09")
summaries_STS_face_events <- bind_rows(summaries_STS_face_events, summary_STS_F09)
summaries_STS_face_events$region <- "STS"
summaries_STS_face_events$p.value <- as.numeric(summaries_STS_face_events$p.value)
summaries_STS_face_events <- summaries_STS_face_events %>%
mutate(sig = case_when(
p.value < 0.004 ~ "significant after correction",
TRUE ~ ""
))
## Scene events
### Models with interaction effects
events <- paste0("S", sprintf("%02d", 1:12)) #generates S01, S02, ..., S12
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age*hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_STS)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_STS_scene_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
summaries_STS_scene_events$region <- "STS"
summaries_STS_scene_events <- summaries_STS_scene_events %>%
mutate(sig = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
))
### Interaction effects are non-sig for all events but S02 and S08, repeat models without interaction effects for all events except S02 and S08
events <- c("S01", "S03", "S04", "S05", "S06", "S07", "S09", "S10", "S11", "S12")
model_summaries <- list()
for (event in events) {
#### Create the formula dynamically
formula <- as.formula(paste(event, "~ age + hemisphere + meanFD + (1 | ID)"))
#### Fit the model
model <- lmer(formula, data = eventsmagnitude_data_STS)
#### Summarise the model
summary_tidy <- tidy(model)
#### Store the summary in the list with the event name as the key
model_summaries[[event]] <- summary_tidy
}
summaries_STS_scene_events <- do.call(rbind, lapply(names(model_summaries), function(event) {
model_summaries[[event]] %>% mutate(event = event) #add the event name to each summary
}))
model_STS_S02 <- lmer(S02 ~ age*hemisphere + meanFD + (1 | ID), data = eventsmagnitude_data_STS)
summary_STS_S02 <- tidy(model_STS_S02)
summary_STS_S02 <- summary_STS_S02 %>% mutate(event = "S02")
model_STS_S08 <- lmer(S08 ~ age*hemisphere + meanFD + (1 | ID), data = eventsmagnitude_data_STS)
summary_STS_S08 <- tidy(model_STS_S08)
summary_STS_S08 <- summary_STS_S08 %>% mutate(event = "S08")
summaries_STS_scene_events <- bind_rows(summaries_STS_scene_events, summary_STS_S02, summary_STS_S08)
summaries_STS_scene_events$region <- "STS"
summaries_STS_scene_events$p.value <- as.numeric(summaries_STS_scene_events$p.value)
summaries_STS_scene_events <- summaries_STS_scene_events %>%
mutate(sig = case_when(
p.value < 0.004 ~ "significant after correction",
TRUE ~ ""
))
```
Save results
```{r}
all_summaries <- bind_rows(
summaries_FFA_face_events,
summaries_FFA_scene_events,
summaries_MMPFC_face_events,
summaries_MMPFC_scene_events,
summaries_amygdala_face_events,
summaries_amygdala_scene_events,
summaries_STS_face_events,
summaries_STS_scene_events
)
all_summaries$p.value <- as.numeric(all_summaries$p.value)
event_order <- c(
"F01","F09","F11","F04","F10","F12","F07","F08","F06","F05","F02","F03",
"S07","S02","S03","S04","S12","S11","S10","S06","S08","S05","S09","S01"
)
all_summaries <- all_summaries %>%
mutate(
across(c(estimate, std.error, statistic, df), ~ round(.x, 2)),
p.value = if_else(p.value < 0.001,
format(p.value, scientific = TRUE, digits = 4),
sprintf("%.3f", p.value)),
event = factor(event, levels = event_order)
) %>%
arrange(region, event)
write.csv(all_summaries, here("results/RQ1", "Eventmagnitude_age.csv"), row.names = FALSE)
```
### Model plots
Timecourses and events
```{r}
# Data wrangling
TCs_total <- read_csv(here("processed_data", "TCs_total.csv"), col_names=TRUE)
data_plot <- merge(TCs_total, participants[,c(1,3,4)], by="ID")
data_plot <- data_plot %>%
filter(Child_Adult == "child")
data_plot$Child_Adult <- NULL
data_plot <- data_plot %>%
group_by(time, AgeGroup) %>%
summarise(
avg_lamygdala = mean(lamygdala, na.rm = TRUE),
avg_ramygdala = mean(ramygdala, na.rm = TRUE),
avg_lFFA = mean(lFFA, na.rm = TRUE),
avg_rFFA = mean(rFFA, na.rm = TRUE),
avg_lMMPFC = mean(lMMPFC, na.rm = TRUE),
avg_rMMPFC = mean(rMMPFC, na.rm = TRUE),
avg_lSTS = mean(lSTS, na.rm = TRUE),
avg_rSTS = mean(rSTS, na.rm = TRUE), .groups = "drop"
)
avg_adult_TCs_trad_fROIs <- read.csv(here("raw_data/timecourses", "TCs_trad_adult_fROIs.csv"), header = TRUE, dec = ".", sep = ",")
avg_adult_TCs_trad_fROIs <- avg_adult_TCs_trad_fROIs %>%
mutate(time = time - 1)
TCs_amygdala <- read.csv(here("raw_data/timecourses", "TCs_amygdala.csv"), header = TRUE, dec = ".", sep = ",")
TCs_amygdala$sub <- gsub("[pixar]", "", TCs_amygdala$sub)
TCs_amygdala$sub <- trimws(TCs_amygdala$sub)
TCs_amygdala$run <- NULL
colnames(TCs_amygdala) <- c("time", "ID", "lamygdala", "ramygdala")
adult_list <- row.names(participants)[which(participants[["Child_Adult"]] == "adult")]
adult_TCs_amygdala <- TCs_amygdala %>%
filter(ID %in% adult_list)
avg_adult_TCs_amygdala <- adult_TCs_amygdala %>%
group_by(time) %>%
summarise(
avg_lamygdala = mean(lamygdala, na.rm = TRUE),
avg_ramygdala = mean(ramygdala, na.rm = TRUE)
)
avg_adult_TCs <- merge(avg_adult_TCs_amygdala, avg_adult_TCs_trad_fROIs, by="time")
avg_adult_TCs$AgeGroup <- "adult"
avg_adult_TCs <- avg_adult_TCs[, c("time", "AgeGroup", "avg_lamygdala", "avg_ramygdala", "avg_lFFA", "avg_rFFA", "avg_lMMPFC", "avg_rMMPFC", "avg_lSTS", "avg_rSTS")]
data_plot <- rbind(data_plot, avg_adult_TCs)
colnames(data_plot) <- c("time", "AgeGroup", "lamygdala", "ramygdala", "lFFA", "rFFA", "lMMPFC", "rMMPFC", "lSTS", "rSTS")
data_plot <- data_plot %>%
pivot_longer(
cols = c(lamygdala, ramygdala, lFFA, rFFA, lMMPFC, rMMPFC, lSTS, rSTS),
names_to = "Temp",
values_to = "value"
) %>%
## Separate the "Temp" column into "Hemisphere" and "Region"
mutate(
hemisphere = if_else(str_starts(Temp, "l"), "left", "right"),
region = case_when(
str_detect(Temp, "amygdala") ~ "amygdala",
str_detect(Temp, "FFA") ~ "FFA",
str_detect(Temp, "MMPFC") ~ "MMPFC",
str_detect(Temp, "STS") ~ "STS",
TRUE ~ NA_character_
)
) %>%
select(-Temp) #drop the temporary column
data_plot_FFA <- subset(data_plot, region == "FFA")
data_plot_MMPFC <- subset(data_plot, region == "MMPFC")
data_plot_amygdala <- subset(data_plot, region == "amygdala")
data_plot_STS <- subset(data_plot, region == "STS")
events_TRs <- read_csv(here("processed_data", "events_TRs.csv"), col_names=TRUE)
events_TRs <- events_TRs[,c(1:3)]
custom_colours <- c("#D34835", "#DDA86A", "#83AC66", "#87BACF", "#6989F3", "#652975")
# Figure in main text
Plot_rFFA <- ggplot(
subset(data_plot_FFA, hemisphere == "right")
) +
geom_line(aes(x = time, y = value, color = AgeGroup, group = AgeGroup),
size = 1.2, alpha = 1) +
geom_hline(yintercept = 0, color = "grey", linewidth = 0.8) +
geom_text(
data = subset(
data_plot_FFA,
hemisphere == "right" &
time %in% c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,
60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110,
115, 120, 125, 130, 135, 140, 145, 150, 155,
160, 165, 170)
),
aes(x = time, y = -0.07, label = time),
vjust = 0,
size = 3,
color = "black",
angle = 90
) +
theme_classic() +
scale_color_manual(values = custom_colours) +
scale_y_continuous(limits = c(-1.1, 1.1)) +
scale_x_continuous(
breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,
60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110,
115, 120, 125, 130, 135, 140, 145, 150, 155,
160, 165, 170),
labels = NULL
) +
labs(
title = "Right FFA",
y = "Response magnitude",
color = "Age group"
) +
theme(
legend.position = "bottom",
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 16),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.line.y = element_line(linewidth = 0.8, color = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 16, face = "bold")
)
## Add the shaded rectangles for events
Plot_rFFA <- Plot_rFFA +
geom_rect(data = events_TRs,
aes(xmin = onset, xmax = end, ymin = -1.1, ymax = 1.1, fill = trial_type),
alpha = 0.3, color = NA) +
scale_fill_manual(values = c("faces" = "#F9B6C7", "scenes" = "#3B7D23")) +
guides(fill = guide_legend(title = "Event type"), title.theme = element_text(face = "bold"))
print(Plot_rFFA)
## Save plot
ggsave(here("results/figures", "TCs_rFFA.png"), plot = Plot_rFFA, width = 15, height = 5, units = "in", dpi = 300)
# Supplementary figure
## FFA Timecourses, including events
### Plot
Plot_FFA <- ggplot(data_plot_FFA) +
geom_line(aes(x = time, y = value, color = AgeGroup, group = AgeGroup), size = 1.2, alpha = 1) +
geom_hline(yintercept = 0, color = "grey", linewidth = 0.8) +
geom_text(data = data_plot_FFA[data_plot_FFA$time %in% c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170),],
aes(x = time, y = -0.07, label = time), vjust = 0, size = 3, color = "black", angle = 90) +
facet_wrap(~ hemisphere, scales = "free_y") +
theme_classic() +
scale_color_manual(values = custom_colours) +
scale_y_continuous(limits = c(-1.1, 1.1)) +
scale_x_continuous(
breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170),
labels = NULL
) +
labs(
title = "FFA",
y = "Response magnitude",
color = "Age group"
) +
theme(
strip.text = element_text(size = 16),
strip.background = element_blank(),
legend.position = "bottom",
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 16),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.line.y = element_line(linewidth = 0.8, color = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 16, face = "bold")
)
### Add the shaded rectangles for events
Plot_FFA <- Plot_FFA +
geom_rect(data = events_TRs,
aes(xmin = onset, xmax = end, ymin = -1.1, ymax = 1.1, fill = trial_type),
alpha = 0.3, color = NA) +
scale_fill_manual(values = c("faces" = "#F9B6C7", "scenes" = "#3B7D23")) +
guides(fill = guide_legend(title = "Event type"), title.theme = element_text(face = "bold"))
print(Plot_FFA)
## MMPFC Timecourses, including events
Plot_MMPFC <- ggplot(data_plot_MMPFC) +
geom_line(aes(x = time, y = value, color = AgeGroup, group = AgeGroup), size = 1.2, alpha = 1) +
geom_hline(yintercept = 0, color = "grey", linewidth = 0.8) +
geom_text(data = data_plot_MMPFC[data_plot_MMPFC$time %in% c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170),],
aes(x = time, y = -0.07, label = time), vjust = 0, size = 3, color = "black", angle = 90) +
facet_wrap(~ hemisphere, scales = "free_y") +
theme_classic() +
scale_color_manual(values = custom_colours) +
scale_y_continuous(limits = c(-1.1, 1.1)) +
scale_x_continuous(
breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170),
labels = NULL
) +
labs(
title = "MMPFC",
y = "Response magnitude",
color = "Age group"
) +
theme(
strip.text = element_text(size = 16),
strip.background = element_blank(),
legend.position = "bottom",
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 16),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.line.y = element_line(linewidth = 0.8, color = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 16, face = "bold")
)
### Add the shaded rectangles for events
Plot_MMPFC <- Plot_MMPFC +
geom_rect(data = events_TRs,
aes(xmin = onset, xmax = end, ymin = -1.1, ymax = 1.1, fill = trial_type),
alpha = 0.3, color = NA) +
scale_fill_manual(values = c("faces" = "#F9B6C7", "scenes" = "#3B7D23")) +
guides(fill = guide_legend(title = "Event type"), title.theme = element_text(face = "bold"))
### Get the 10th "faces" event (pink box)
pink_boxes <- events_TRs[events_TRs$trial_type == "faces",]
tenth_pink_box <- pink_boxes[10,] #get the 10th pink box
### Adjust the y position for the asterisk to stay within the valid range
Plot_MMPFC <- Plot_MMPFC +
geom_text(data = tenth_pink_box,
aes(x = (onset + end) / 2 - 1, y = 1.05, label = "*"), # Adjust y to fit within plot limits
size = 8, color = "black", fontface = "bold", vjust = 0)
print(Plot_MMPFC)
## Amygdala Timecourses, including events
Plot_amygdala <- ggplot(data_plot_amygdala) +
geom_line(aes(x = time, y = value, color = AgeGroup, group = AgeGroup), size = 1.2, alpha = 1) +
geom_hline(yintercept = 0, color = "grey", linewidth = 0.8) +
geom_text(data = data_plot_amygdala[data_plot_amygdala$time %in% c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170),],
aes(x = time, y = -0.07, label = time), vjust = 0, size = 3, color = "black", angle = 90) +
facet_wrap(~ hemisphere, scales = "free_y") +
theme_classic() +
scale_color_manual(values = custom_colours) +
scale_y_continuous(limits = c(-1.1, 1.1)) +
scale_x_continuous(
breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170),
labels = NULL
) +
labs(
title = "Amygdala",
y = "Response magnitude",
color = "Age group"
) +
theme(
strip.text = element_text(size = 16),
strip.background = element_blank(),
legend.position = "bottom",
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 16),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.line.y = element_line(linewidth = 0.8, color = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 16, face = "bold")
)
### Add the shaded rectangles for events
Plot_amygdala <- Plot_amygdala +
geom_rect(data = events_TRs,
aes(xmin = onset, xmax = end, ymin = -1.1, ymax = 1.1, fill = trial_type),
alpha = 0.3, color = NA) +
scale_fill_manual(values = c("faces" = "#F9B6C7", "scenes" = "#3B7D23")) +
guides(fill = guide_legend(title = "Event type"), title.theme = element_text(face = "bold"))
print(Plot_amygdala)
## STS Timecourses, including events
Plot_STS <- ggplot(data_plot_STS) +
geom_line(aes(x = time, y = value, color = AgeGroup, group = AgeGroup), size = 1.2, alpha = 1) +
geom_hline(yintercept = 0, color = "grey", linewidth = 0.8) +
geom_text(data = data_plot_STS[data_plot_STS$time %in% c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170),],
aes(x = time, y = -0.07, label = time), vjust = 0, size = 3, color = "black", angle = 90) +
facet_wrap(~ hemisphere, scales = "free_y") +
theme_classic() +
scale_color_manual(values = custom_colours) +
scale_y_continuous(limits = c(-1.1, 1.1)) +
scale_x_continuous(
breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170),
labels = NULL
) +
labs(
title = "STS",
y = "Response magnitude",
color = "Age group"
) +
theme(
strip.text = element_text(size = 16),
strip.background = element_blank(),
legend.position = "bottom",
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.x = element_text(size = 16),
axis.text.y = element_text(size = 16),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.line.y = element_line(linewidth = 0.8, color = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 16, face = "bold")
)
### Add the shaded rectangles for events
Plot_STS <- Plot_STS +
geom_rect(data = events_TRs,
aes(xmin = onset, xmax = end, ymin = -1.1, ymax = 1.1, fill = trial_type),
alpha = 0.3, color = NA) +
scale_fill_manual(values = c("faces" = "#F9B6C7", "scenes" = "#3B7D23")) +
guides(fill = guide_legend(title = "Event type"), title.theme = element_text(face = "bold"))
### Get the 10th "faces" event (pink box)
pink_boxes <- events_TRs[events_TRs$trial_type == "faces",]
fifth_pink_box <- pink_boxes[5,] #get the 10th pink box
### Adjust the y position for the asterisk to stay within the valid range
Plot_STS <- Plot_STS +
geom_text(data = fifth_pink_box,
aes(x = (onset + end) / 2 - 1, y = 1.05, label = "*"), # Adjust y to fit within plot limits
size = 8, color = "black", fontface = "bold", vjust = 0)
print(Plot_STS)
## Save plots
ggsave(here("results/figures", "TCs_FFA.png"), plot = Plot_FFA, width = 15, height = 5, units = "in", dpi = 300)
ggsave(here("results/figures", "TCs_MMPFC.png"), plot = Plot_MMPFC, width = 15, height = 5, units = "in", dpi = 300)
ggsave(here("results/figures", "TCs_amygdala.png"), plot = Plot_amygdala, width = 15, height = 5, units = "in", dpi = 300)
ggsave(here("results/figures", "TCs_STS.png"), plot = Plot_STS, width = 15, height = 5, units = "in", dpi = 300)
```
Age and response magnitude of MMPFC and STS to face events
Histogram
```{r}
# Data wrangling
data_plot <- merge(long_data, participants[,c(1,3)], by="ID")
data_plot$ID <- as.factor(data_plot$ID)
data_plot$hemisphere <- as.factor(data_plot$hemisphere)
data_plot$region <- as.factor(data_plot$region)
data_plot_MMPFC <- subset(data_plot, region == "MMPFC")
data_plot_STS <- subset(data_plot, region == "STS")
# Colours
custom_colours <- c("#D34835", "#DDA86A", "#83AC66", "#87BACF", "#6989F3", "#652975")
# Make individual plots for MMPFC and STS and events with significant age associations
## General plotting function
generate_plot <- function(data, feature, subtitle, show_legend = TRUE, y_label = NULL, legend_pos = "bottom") {
summary_data <- data %>%
filter(!is.na(.data[[feature]]) & !is.na(AgeGroup)) %>%
group_by(hemisphere, AgeGroup) %>%
summarise(
mean_value = mean(.data[[feature]], na.rm = TRUE),
se_value = sd(.data[[feature]], na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
ggplot(summary_data, aes(x = as.factor(AgeGroup), y = mean_value, fill = as.factor(AgeGroup))) +
geom_bar(stat = "identity", position = "stack", width = 1, alpha = 1) +
geom_errorbar(aes(ymin = mean_value - se_value, ymax = mean_value + se_value), width = 0) +
geom_hline(yintercept = 0, linetype = "solid", size = 1, color = "black") +
scale_fill_manual(values = custom_colours) +
labs(title = subtitle, x = "Age Group", y = y_label, fill = "Age Group") +
facet_wrap(~hemisphere) +
coord_cartesian(ylim = c(-1, 1)) +
theme_classic() +
theme(
strip.text = element_text(size = 14, face = "bold"),
strip.background= element_blank(),
legend.position = ifelse(show_legend, legend_pos, "right"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
axis.line.y = element_line(size = 0.8, color = "black")
)
}
## Generate plots with one reusable function
plot_F05 <- generate_plot(data_plot_MMPFC, "F05", "MMPFC response to F05", show_legend = TRUE, y_label = "Response magnitude")
plot_F10 <- generate_plot(data_plot_MMPFC, "F10", "STS response to F10", show_legend = TRUE)
## Combine plots with a common centered title and shared legend
final_plot <- (plot_F05 | plot_F10 ) +
plot_layout(ncol = 2, guides = "collect") +
plot_annotation(
title = ""
) &
theme(
legend.position = "bottom",
legend.text = element_text(size = 12),
legend.title = element_text(size = 12),
plot.title = element_text(size = 14, hjust = 0.5, face = "bold")
)
# Save plot
ggsave(here("results/figures", "Event_age_histogram.png"), plot = final_plot, width = 7, height = 5, units = "in", dpi = 300)
```
## Section III: Age effects on functional connectivity (Fx) between regions
### Read data
```{r}
# Covariates
participants <- read_tsv(here("raw_data/covariates", "participants.tsv"), col_names=TRUE)
colnames(participants)[which(names(participants) == "participant_id")] <- "ID"
## Remove the word "pixar" and trim extra whitespace
participants$ID <- gsub("[sub-pixar-]","", participants$ID)
participants$ID <- trimws(participants$ID)
# Motion
motion <- read_tsv(here("raw_data/motion", "outlier_info.tsv"), col_names=TRUE)
colnames(motion)[which(names(motion) == "subject")] <- "ID"
## Remove the word "pixar" and trim extra whitespace
motion$ID <- gsub("[sub-pixar-]","", motion$ID)
motion$ID <- trimws(motion$ID)
# Child fMRI variables
child_MRIvariables <- read_csv(here("processed_data", "child_MRIvariables.csv"), col_names=TRUE)
# Complete dataset
child_MRIvariables <- child_MRIvariables[,c(1, 202:229)]
child_MRIvariables <- merge(child_MRIvariables, participants[,c(1:2)], by="ID")
child_MRIvariables <- merge(child_MRIvariables, motion[,c(4,6)], by="ID")
child_MRIvariables <- child_MRIvariables %>%
rename_with(~ sub("^zscored_", "", .x), starts_with("zscored_"))
child_MRIvariables[, c("Age", "MeanFD")] <- scale(child_MRIvariables[, c("Age", "MeanFD")])
```
### Plot correlations between regions by age
```{r}
# Read data
TCs_total <- read_csv(here("processed_data", "TCs_total.csv"), col_names = TRUE)
TCs_total$time <- as.numeric(as.character(TCs_total$time))
data_plot <- merge(TCs_total, participants[, c("ID", "AgeGroup")], by = "ID")
data_plot <- data_plot %>%
filter(AgeGroup != "Adult")
# Rename timecourse columns
data_plot <- data_plot %>%
rename_with(~ c("left amygdala", "right amygdala", "left FFA", "right FFA", "left MMPFC", "right MMPFC", "left STS", "right STS"),
.cols = c("lamygdala", "ramygdala", "lFFA", "rFFA", "lMMPFC", "rMMPFC", "lSTS", "rSTS"))
# Define regions of interest
regions <- c("left amygdala", "right amygdala", "left FFA", "right FFA", "left MMPFC", "right MMPFC", "left STS", "right STS")
# Calculate correlation matrices per AgeGroup
age_groups <- unique(data_plot$AgeGroup)
correlation_by_agegroup <- map_df(age_groups, function(age) {
group_data <- data_plot[data_plot$AgeGroup == age, ]
seg1 <- group_data[group_data$time >= 1 & group_data$time <= 82, ]
seg2 <- group_data[group_data$time >= 87 & group_data$time <= 168, ]
mat1 <- cor(seg1[, regions], use = "pairwise.complete.obs")
mat2 <- cor(seg2[, regions], use = "pairwise.complete.obs")
mat_avg <- (mat1 + mat2) / 2
# Replace amygdala correlation with full-time tc version
amyg_cor <- cor(group_data[, c("left amygdala", "right amygdala")], use = "pairwise.complete.obs")[1, 2]
mat_avg["left amygdala", "right amygdala"] <- amyg_cor
mat_avg["right amygdala", "left amygdala"] <- amyg_cor
tibble(AgeGroup = age, cor_matrix = list(mat_avg))
})
# Convert matrices to long format for ggplot2
data_plot <- correlation_by_agegroup %>%
mutate(cor_matrix = map(cor_matrix, ~ melt(.x, varnames = c("Region1", "Region2")))) %>%
unnest(cor_matrix)
data_plot <- data_plot %>%
filter(Region1 != Region2)
# Order factor levels for consistent axis order
region_levels <- regions
data_plot$Region1 <- factor(data_plot$Region1, levels = region_levels)
data_plot$Region2 <- factor(data_plot$Region2, levels = region_levels)
# Identify upper triangle entries (for displaying numbers only)
data_plot <- data_plot %>%
mutate(is_upper = as.numeric(Region1) < as.numeric(Region2))
# Plot heatmap
heatmap_plot <- ggplot(data_plot, aes(x = Region1, y = Region2, fill = value)) +
geom_tile() +
geom_tile(data = data_plot %>% filter(is_upper), fill = "white", color = NA) +
geom_text(data = data_plot %>% filter(is_upper),
aes(label = sprintf("%.2f", value)), size = 2.8) +
facet_wrap(~ AgeGroup, nrow=1) +
scale_fill_gradient2(
low = "#D34835", high = "#87BACF", mid = "white",
midpoint = 0, limit = c(-0.80, 0.80),
name = "Correlation",
breaks = c(-0.80, -0.40, 0, 0.40, 0.80),
labels = c("-0.80", "-0.40", "0.00", "0.40", "0.80")
) +
theme_minimal() +
labs(
title = "",
x = "",
y = ""
) +
theme(
legend.position = "right",
axis.text.x = element_text(angle = 45, hjust = 1, face = "bold", color = "darkgrey", size = 13),
axis.text.y = element_text(face = "bold", color = "darkgrey", size = 13),
strip.text = element_text(size = 14),
plot.title = element_text(hjust = 0.5, size = 19),
plot.title.position = "plot"
)
# Display the plot
print(heatmap_plot)
# Save plot
ggsave(here("results/figures", "Fx_AgeGroup.png"), plot = heatmap_plot, width = 13, height = 4, units = "in", dpi = 300)
```
### Descriptive statistics: Mean FFA Fx values with ROIs (non-preregistered)
```{r}
# Read data
TCs_total <- read_csv(here("processed_data", "TCs_total.csv"), col_names = TRUE)
TCs_total$time <- as.numeric(as.character(TCs_total$time))
data_plot <- merge(TCs_total, participants[, c("ID", "AgeGroup")], by = "ID")
data_plot <- data_plot %>%
filter(AgeGroup != "Adult")
# Rename ROI columns
data_plot <- data_plot %>%
rename_with(
~ c("left amygdala", "right amygdala",
"left FFA", "right FFA",
"left MMPFC", "right MMPFC",
"left STS", "right STS"),
.cols = c("lamygdala", "ramygdala",
"lFFA", "rFFA",
"lMMPFC", "rMMPFC",
"lSTS", "rSTS")
)
# Define ROIs
regions <- c("left amygdala", "right amygdala",
"left FFA", "right FFA",
"left MMPFC", "right MMPFC",
"left STS", "right STS")
# Compute subject-level correlation matrices
cor_subject <- data_plot %>%
group_by(ID) %>%
group_map(~ {
seg1 <- .x %>% filter(time >= 1 & time <= 82)
seg2 <- .x %>% filter(time >= 87 & time <= 168)
mat1 <- cor(seg1[, regions], use = "pairwise.complete.obs")
mat2 <- cor(seg2[, regions], use = "pairwise.complete.obs")
mat_avg <- (mat1 + mat2) / 2
# Replace amygdala correlation with full-time tc version
amyg_cor <- cor(.x[, c("left amygdala", "right amygdala")], use = "pairwise.complete.obs")[1,2]
mat_avg["left amygdala", "right amygdala"] <- amyg_cor
mat_avg["right amygdala", "left amygdala"] <- amyg_cor
mat_avg
})
# Convert all matrices to long format
cor_long <- map_dfr(cor_subject, ~ as.data.frame(as.table(.x)) %>%
setNames(c("Region1", "Region2", "r")))
# Exclude diagonal (self-correlations)
cor_long <- cor_long %>%
filter(Region1 != Region2)
# Filter for left/right FFA and compute summary + t-tests
cor_summary_with_t <- cor_long %>%
filter(Region1 %in% c("right FFA", "left FFA")) %>%
group_by(Region1, Region2) %>%
summarise(
mean_r = mean(r, na.rm = TRUE),
sd_r = sd(r, na.rm = TRUE),
n = n(),
t_test = list(t.test(r, mu = 0, na.rm = TRUE)),
.groups = "drop"
) %>%
mutate(
mean_sd = sprintf("%.2f (%.2f)", mean_r, sd_r),
t_test = map(t_test, broom::tidy)
) %>%
unnest(t_test) %>%
mutate(
p.value = ifelse(p.value < 0.001,
format(p.value, scientific = TRUE, digits = 4),
sprintf("%.3f", p.value)),
t_stat = sprintf("%.2f", statistic),
df = sprintf("%.0f", parameter)
) %>%
select(Region1, Region2, mean_sd, t_stat, df, p.value) %>%
arrange(Region1, Region2)
# View final table
cor_summary_with_t
# Save the table
write_csv(cor_summary_with_t, here("results/RQ1", "FFA_fx_descriptive_table.csv"))
```
### Models (models including STS were not pre-registered)
#### Primary models
Linear models
```{r}
model_rlFFA <- lm(fx_rFFA_lFFA ~ Age + MeanFD, data = child_MRIvariables)
summary(model_rlFFA)
```
Linear-mixed-effect models
```{r}
# Make long data for rest of the models:
long_data <- child_MRIvariables %>%
pivot_longer(
cols = starts_with("fx_"),
names_to = c("Region1", "Region2"),
names_pattern = "fx_(.+)_(.+)",
values_to = "zscored_value"
)
long_data <- long_data %>%
mutate(
Hemisphere = ifelse(grepl("^r", Region2), "right", "left"),
Region2 = sub("^[rl]", "", Region2)
)
# Models with interaction effects
## rFFA and r/lMMPFC
model_data <- long_data %>%
filter(Region1 == "rFFA" & Region2 == "MMPFC")
model_rFFA_rlMMPFC <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rFFA_rlMMPFC)
## rFFA and r/lamygdala
model_data <- long_data %>%
filter(Region1 == "rFFA" & Region2 == "amygdala")
model_rFFA_rlamygdala <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rFFA_rlamygdala)
## rFFA and r/lSTS
model_data <- long_data %>%
filter(Region1 == "rFFA" & Region2 == "STS")
model_rFFA_rlSTS <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rFFA_rlSTS)
# No interaction effects are significant except for rFFA and r/lMMPFC, so models are run without interaction effects except for rFFA and r/lMMPFC
## rFFA and r/lMMPFC
model_data <- long_data %>%
filter(Region1 == "rFFA" & Region2 == "MMPFC")
model_rFFA_rlMMPFC <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rFFA_rlMMPFC)
## rFFA and r/lamygdala
model_data <- long_data %>%
filter(Region1 == "rFFA" & Region2 == "amygdala")
model_rFFA_rlamygdala <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rFFA_rlamygdala)
## rFFA and r/lSTS
model_data <- long_data %>%
filter(Region1 == "rFFA" & Region2 == "STS")
model_rFFA_rlSTS <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rFFA_rlSTS)
```
#### Secondary models
Linear models
```{r}
# rlMMPFC
model_rlMMPFC <- lm(fx_lMMPFC_rMMPFC ~ Age + MeanFD, data = child_MRIvariables)
summary(model_rlMMPFC)
# rlamygdala
model_rlamygdala <- lm(fx_lamygdala_ramygdala ~ Age + MeanFD, data = child_MRIvariables)
summary(model_rlamygdala)
# rlSTS
model_rlSTS <- lm(fx_lSTS_rSTS ~ Age + MeanFD, data = child_MRIvariables)
summary(model_rlSTS)
```
Linear-mixed-effect models
```{r}
# Models with interaction effects
## lFFA and r/lMMPFC
model_data <- long_data %>%
filter(Region1 == "lFFA" & Region2 == "MMPFC")
model_lFFA_rlMMPFC <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lFFA_rlMMPFC)
## lFFA and r/lamygdala
model_data <- long_data %>%
filter(Region1 == "lFFA" & Region2 == "amygdala")
model_lFFA_rlamygdala <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lFFA_rlamygdala)
## lFFA and r/lSTS
model_data <- long_data %>%
filter(Region1 == "lFFA" & Region2 == "STS")
model_lFFA_rlSTS <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lFFA_rlSTS)
## rMMPFC and r/lamygdala
model_data <- long_data %>%
filter(Region1 == "rMMPFC" & Region2 == "amygdala")
model_rMMPFC_rlamygdala <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rMMPFC_rlamygdala)
## rMMPFC and r/lSTS
model_data <- long_data %>%
filter(Region1 == "rMMPFC" & Region2 == "STS")
model_rMMPFC_rlSTS <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rMMPFC_rlSTS)
## lMMPFC and r/lamygdala
model_data <- long_data %>%
filter(Region1 == "lMMPFC" & Region2 == "amygdala")
model_lMMPFC_rlamygdala <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lMMPFC_rlamygdala)
## lMMPFC and r/lSTS
model_data <- long_data %>%
filter(Region1 == "lMMPFC" & Region2 == "STS")
model_lMMPFC_rlSTS <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lMMPFC_rlSTS)
## ramygdala and r/lSTS
model_data <- long_data %>%
filter(Region1 == "ramygdala" & Region2 == "STS")
model_ramygdala_rlSTS <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_ramygdala_rlSTS)
## lamygdala and r/lSTS
model_data <- long_data %>%
filter(Region1 == "lamygdala" & Region2 == "STS")
model_lamygdala_rlSTS <- lmer(
zscored_value ~ Age*Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lamygdala_rlSTS)
# No interaction effects are significant, so models are run without interaction effects
## lFFA and r/lMMPFC
model_data <- long_data %>%
filter(Region1 == "lFFA" & Region2 == "MMPFC")
model_lFFA_rlMMPFC <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lFFA_rlMMPFC)
## lFFA and r/lamygdala
model_data <- long_data %>%
filter(Region1 == "lFFA" & Region2 == "amygdala")
model_lFFA_rlamygdala <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lFFA_rlamygdala)
## lFFA and r/lSTS
model_data <- long_data %>%
filter(Region1 == "lFFA" & Region2 == "STS")
model_lFFA_rlSTS <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lFFA_rlSTS)
## rMMPFC and r/lamygdala
model_data <- long_data %>%
filter(Region1 == "rMMPFC" & Region2 == "amygdala")
model_rMMPFC_rlamygdala <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rMMPFC_rlamygdala)
## rMMPFC and r/lSTS
model_data <- long_data %>%
filter(Region1 == "rMMPFC" & Region2 == "STS")
model_rMMPFC_rlSTS <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_rMMPFC_rlSTS)
## lMMPFC and r/lamygdala
model_data <- long_data %>%
filter(Region1 == "lMMPFC" & Region2 == "amygdala")
model_lMMPFC_rlamygdala <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lMMPFC_rlamygdala)
## lMMPFC and r/lSTS
model_data <- long_data %>%
filter(Region1 == "lMMPFC" & Region2 == "STS")
model_lMMPFC_rlSTS <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lMMPFC_rlSTS)
## ramygdala and r/lSTS
model_data <- long_data %>%
filter(Region1 == "ramygdala" & Region2 == "STS")
model_ramygdala_rlSTS <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_ramygdala_rlSTS)
## lamygdala and r/lSTS
model_data <- long_data %>%
filter(Region1 == "lamygdala" & Region2 == "STS")
model_lamygdala_rlSTS <- lmer(
zscored_value ~ Age + Hemisphere + MeanFD + (1 | ID),
data = model_data
)
summary(model_lamygdala_rlSTS)
```
Save models
```{r}
# Create tidy summaries of all models
## Primary models
model_rlFFA_summary <- tidy(model_rlFFA) %>%
mutate(Model = "rlFFA", Label = "Primary")
model_rFFA_rlMMPFC_summary <- tidy(model_rFFA_rlMMPFC, effects = "fixed") %>%
mutate(Model = "rFFA_rlMMPFC", Label = "Primary")
model_rFFA_rlamygdala_summary <- tidy(model_rFFA_rlamygdala, effects = "fixed") %>%
mutate(Model = "rFFA_rlamygdala", Label = "Primary")
model_rFFA_rlSTS_summary <- tidy(model_rFFA_rlSTS, effects = "fixed") %>%
mutate(Model = "rFFA_rlSTS", Label = "Primary")
## Secondary models
model_lFFA_rlMMPFC_summary <- tidy(model_lFFA_rlMMPFC, effects = "fixed") %>%
mutate(Model = "lFFA_rlMMPFC", Label = "Secondary")
model_lFFA_rlamygdala_summary <- tidy(model_lFFA_rlamygdala, effects = "fixed") %>%
mutate(Model = "lFFA_rlamygdala", Label = "Secondary")
model_lFFA_rlSTS_summary <- tidy(model_lFFA_rlSTS, effects = "fixed") %>%
mutate(Model = "lFFA_rlSTS", Label = "Secondary")
model_rlMMPFC_summary <- tidy(model_rlMMPFC) %>%
mutate(Model = "rlMMPFC", Label = "Secondary")
model_rMMPFC_rlamygdala_summary <- tidy(model_rMMPFC_rlamygdala, effects = "fixed") %>%
mutate(Model = "rMMPFC_rlamygdala", Label = "Secondary")
model_rMMPFC_rlSTS_summary <- tidy(model_rMMPFC_rlSTS, effects = "fixed") %>%
mutate(Model = "rMMPFC_rlSTS", Label = "Secondary")
model_lMMPFC_rlamygdala_summary <- tidy(model_lMMPFC_rlamygdala, effects = "fixed") %>%
mutate(Model = "lMMPFC_rlamygdala", Label = "Secondary")
model_lMMPFC_rlSTS_summary <- tidy(model_lMMPFC_rlSTS, effects = "fixed") %>%
mutate(Model = "lMMPFC_rlSTS", Label = "Secondary")
model_rlamygdala_summary <- tidy(model_rlamygdala, effects = "fixed") %>%
mutate(Model = "rlamygdala", Label = "Secondary")
model_ramygdala_rlSTS_summary <- tidy(model_ramygdala_rlSTS, effects = "fixed") %>%
mutate(Model = "ramygdala_rlSTS", Label = "Secondary")
model_lamygdala_rlSTS_summary <- tidy(model_lamygdala_rlSTS, effects = "fixed") %>%
mutate(Model = "lamygdala_rlSTS", Label = "Secondary")
model_rlSTS_summary <- tidy(model_rlSTS, effects = "fixed") %>%
mutate(Model = "rlSTS", Label = "Secondary")
# Combine all summaries into one data frame
all_model_summaries <- bind_rows(
model_rlFFA_summary,
model_rFFA_rlMMPFC_summary,
model_rFFA_rlamygdala_summary,
model_rFFA_rlSTS_summary,
model_lFFA_rlMMPFC_summary,
model_lFFA_rlamygdala_summary,
model_lFFA_rlSTS_summary,
model_rlMMPFC_summary,
model_lMMPFC_rlamygdala_summary,
model_lMMPFC_rlSTS_summary,
model_rMMPFC_rlamygdala_summary,
model_rMMPFC_rlSTS_summary,
model_rlamygdala_summary,
model_lamygdala_rlSTS_summary,
model_ramygdala_rlSTS_summary,
model_rlSTS_summary
) %>%
mutate(
estimate = round(estimate, 2),
std.error = round(std.error, 2),
statistic = round(statistic, 2),
df = round(df, 0),
p.value = if_else(
p.value < 0.001,
format(p.value, scientific = TRUE, digits = 4),
sprintf("%.3f", p.value)
))
# Save the combined summaries to a CSV file
write.csv(all_model_summaries, here("results/RQ1", "Fx_age.csv"), row.names = FALSE)
```
### Model plots
```{r}
# Data wrangling
data_plot <- read_csv(here("processed_data", "child_MRIvariables.csv"), col_names=TRUE)
data_plot <- data_plot[,c(1, 202:229)]
data_plot <- merge(data_plot, participants[,c(1:2)], by="ID")
data_plot <- merge(data_plot, motion[,c(4,6)], by="ID")
data_plot <- data_plot %>%
rename_with(~ sub("^zscored_", "", .x), starts_with("zscored_"))
data_plot <- data_plot %>%
pivot_longer(
cols = starts_with("fx_"),
names_to = c("Region1", "Region2"),
names_pattern = "fx_(.+)_(.+)",
values_to = "zscored_value"
)
data_plot <- data_plot %>%
mutate(
Hemisphere = ifelse(grepl("^r", Region2), "right", "left"),
Region2 = sub("^[rl]", "", Region2)
)
data_plot_rFFA_MMPFC <- data_plot %>%
filter(Region2 == "MMPFC", Region1 == "rFFA")
data_plot_lFFA_MMPFC <- data_plot %>%
filter(Region2 == "MMPFC", Region1 == "lFFA")
data_plot_rFFA_amygdala <- data_plot %>%
filter(Region2 == "amygdala", Region1 == "rFFA")
data_plot_lFFA_amygdala <- data_plot %>%
filter(Region2 == "amygdala", Region1 == "lFFA")
data_plot_rFFA_STS <- data_plot %>%
filter(Region2 == "STS", Region1 == "rFFA")
data_plot_lFFA_STS <- data_plot %>%
filter(Region2 == "STS", Region1 == "lFFA")
# Colours
custom_colours_amygdala <- c("#E1AA9F", "#D34835")
custom_colours_MMPFC <- c("#87BACF", "#578CAD")
custom_colours_FFA <- c("#C39EDA", "#652975")
custom_colours_STS <- c("#FFF865", "#FFCE1B")
# Define a base theme to avoid repetition
base_theme <- theme_minimal() +
theme(
strip.text = element_text(size = 14, face = "bold"),
strip.background = element_blank(),
axis.line.x = element_blank(),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14, face = "bold"),
plot.title = element_text(size = 14, hjust = 0.5, face = "bold"),
legend.text = element_text(size = 12),
legend.title = element_text(size = 12, face = "bold")
)
# Function to create ggplots efficiently
create_plot <- function(data, color_values, legend_title, x_label = "", y_label = "", legend_position = "none", remove_y_axis_labels = FALSE) {
plot <- ggplot(data, aes(x = Age, y = zscored_value, color = Hemisphere)) +
geom_point(alpha = 0.8, size = 2.8, shape = 19) +
geom_smooth(method = "lm", se = FALSE, linetype = "solid", linewidth = 2) +
scale_color_manual(values = color_values) +
labs(x = x_label, y = y_label, color = legend_title) +
base_theme +
theme(legend.position = legend_position) +
coord_cartesian(ylim = c(-4, 3)) +
scale_x_continuous(breaks = c(3, 6, 9, 12))
if (remove_y_axis_labels) {
plot <- plot + theme(
axis.title.y = element_blank(),
axis.text.y = element_blank()
)
}
return(plot)
}
# Create all plots using the function
plot_lFFA_MMPFC <- create_plot(data_plot_lFFA_MMPFC, custom_colours_MMPFC, "MMPFC Hemisphere", "", "Functional connectivity between\n left FFA and region")
plot_lFFA_amygdala <- create_plot(data_plot_lFFA_amygdala, custom_colours_amygdala, "Amygdala Hemisphere", "Age", legend_position = "bottom", remove_y_axis_labels = TRUE)
plot_lFFA_STS <- create_plot(data_plot_lFFA_STS, custom_colours_STS, "STS Hemisphere", "", legend_position = "bottom", remove_y_axis_labels = TRUE)
plot_rFFA_MMPFC <- create_plot(data_plot_rFFA_MMPFC, custom_colours_MMPFC, "MMPFC Hemisphere", "", "Functional connectivity between\n right FFA and region", legend_position = "bottom") +
annotate(
"text",
x = Inf, y = -Inf, # bottom right corner
label = "Age x Hemisphere (right) effect: \nβ(SE)=0.16(0.07), p=0.016", # your annotation
hjust = 1.05, # slightly >1 for proper right alignment
vjust = -0.5, # nudges text up from bottom
size = 4,
color = "black"
)
plot_rFFA_amygdala <- create_plot(data_plot_rFFA_amygdala, custom_colours_amygdala, "Amygdala Hemisphere", "Age", remove_y_axis_labels = TRUE)
plot_rFFA_STS <- create_plot(data_plot_rFFA_STS, custom_colours_STS, "STS Hemisphere", "", remove_y_axis_labels = TRUE)
# Combine plots into a single row
fx_rFFA_age <- (
plot_rFFA_MMPFC | plot_rFFA_amygdala | plot_rFFA_STS
) +
plot_layout(
ncol = 3,
widths = rep(1, 3),
guides = "collect"
) +
plot_annotation(
title = "Functional Connectivity Across Age",
theme = theme(
plot.title = element_text(size = 14, hjust = 0.5, face = "bold")
)
) &
theme(legend.position = "bottom")
fx_rFFA_age
fx_lFFA_age <- (
plot_lFFA_MMPFC | plot_lFFA_amygdala | plot_lFFA_STS ) +
plot_layout(
ncol = 3,
widths = rep(1, 3),
guides = "collect"
) +
plot_annotation(
title = "Functional Connectivity Across Age",
theme = theme(
plot.title = element_text(size = 14, hjust = 0.5, face = "bold")
)
) &
theme(legend.position = "bottom")
fx_lFFA_age
# Save plots
ggsave(here("results/figures", "Fx_rFFA_age.png"), plot = fx_rFFA_age, width = 10, height = 5, units = "in", dpi = 300)
ggsave(here("results/figures", "Fx_lFFA_age.png"), plot = fx_lFFA_age, width = 10, height = 5, units = "in", dpi = 300)
```
## Section IV: Age effects on lateralisation of face response (non-preregistered)
### Descriptive statistics: Mean LI values
```{r}
# Read data
child_LI <- read_csv(here("processed_data", "child_MRIvariables.csv"), col_names=TRUE)
child_LI <- child_LI[,c(1, 230:231)]
# Define variables to analyse
li_columns <- c("lateralisation_index_0.05", "lateralisation_index_0.1")
# Compute sunnary + t-tests
li_summary <- li_columns %>%
lapply(function(col_name) {
values <- child_LI[[col_name]]
# Compute mean and SD
mean_val <- mean(values, na.rm = TRUE)
sd_val <- sd(values, na.rm = TRUE)
# One-sample t-test against 0
t_test <- t.test(values, mu = 0, na.rm = TRUE)
# Format p-value
p_val <- ifelse(t_test$p.value < 0.001,
format(t_test$p.value, scientific = TRUE, digits = 3),
sprintf("%.3f", t_test$p.value))
# Create output row
tibble(
Measure = col_name,
mean_sd = sprintf("%.2f (%.2f)", mean_val, sd_val),
t_stat = sprintf("%.2f", t_test$statistic),
df = sprintf("%.0f", t_test$parameter),
p_value = p_val
)
}) %>%
bind_rows() # Combine into a single table
# View final table
li_summary
# Save the table
write_csv(li_summary, here("results/RQ1", "LI_descriptive_table.csv"))
```
### Age effects on lateralisation of face response
#### Read data
```{r}
# Covariates
participants <- read_tsv(here("raw_data/covariates", "participants.tsv"), col_names=TRUE)
colnames(participants)[which(names(participants) == "participant_id")] <- "ID"
## Remove the word "pixar" and trim extra whitespace
participants$ID <- gsub("[sub-pixar-]","", participants$ID)
participants$ID <- trimws(participants$ID)
# Motion
motion <- read_tsv(here("raw_data/motion", "outlier_info.tsv"), col_names=TRUE)
colnames(motion)[which(names(motion) == "subject")] <- "ID"
## Remove the word "pixar" and trim extra whitespace
motion$ID <- gsub("[sub-pixar-]","", motion$ID)
motion$ID <- trimws(motion$ID)
# Child fMRI variables
child_LI <- read_csv(here("processed_data", "child_MRIvariables.csv"), col_names=TRUE)
# Complete dataset
child_LI <- child_LI[,c(1, 230:231)]
child_LI <- merge(child_LI, participants[,c(1:2)], by="ID")
child_LI <- merge(child_LI, motion[,c(4,6)], by="ID")
child_LI <- child_LI %>%
rename_with(~ sub("^zscored_", "", .x), starts_with("zscored_"))
child_LI[, c("lateralisation_index_0.05","lateralisation_index_0.1", "Age", "MeanFD")] <- scale(child_LI[, c("lateralisation_index_0.05","lateralisation_index_0.1", "Age", "MeanFD")])
```
#### Models
##### Threshold 0.05
```{r}
model_LI_0.05_age <- lm(lateralisation_index_0.05 ~ Age + MeanFD, data = child_LI)
summary_LI_0.05_age <- tidy(model_LI_0.05_age)
```
##### Threshold 0.1
```{r}
model_LI_0.1_age <- lm(lateralisation_index_0.1 ~ Age + MeanFD, data = child_LI)
summary_LI_0.1_age <- tidy(model_LI_0.1_age)
```
##### Save models
```{r}
all_summaries <- bind_rows(
summary_LI_0.05_age %>% mutate(threshold = "0.05"),
summary_LI_0.1_age %>% mutate(threshold = "0.1")
) %>%
mutate(
p.value = as.numeric(p.value), # ensure numeric
estimate = round(estimate, 2),
std.error = round(std.error, 2),
statistic = round(statistic, 2),
p.value = if_else(
p.value < 0.001,
format(p.value, scientific = TRUE, digits = 4),
sprintf("%.3f", p.value)
)
)
write.csv(all_summaries, here("results/RQ1", "LI_age.csv"), row.names = FALSE)
```
#### Model plots
LI and age
```{r}
# Read data
child_LI <- read_csv(here("processed_data", "child_MRIvariables.csv"), col_names=TRUE)
child_LI <- child_LI[,c(1, 230:231)]
child_LI <- merge(child_LI, participants[,c(1:2)], by="ID")
child_LI <- merge(child_LI, motion[,c(4,6)], by="ID")
child_LI <- child_LI %>%
rename_with(~ sub("^zscored_", "", .x), starts_with("zscored_"))
# LI of children at threshold p<0.05
## Plot
LI_age_children <- ggplot(
child_LI,
aes(x = Age, y = lateralisation_index_0.05)
) +
geom_point(color = "#9463A7", alpha = 0.8, size = 2.9, shape = 19) +
geom_smooth(method = "lm", se = FALSE, color = "#9463A7", linewidth = 2) +
labs(
title = "Lateralisation Index of FFA Across Age",
x = "Age",
y = "Lateralisation Index (p<0.05)"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 16, face = "bold"),
axis.title.y = element_text(size = 16, face = "bold"),
axis.text = element_text(size = 12)
)
ggsave(here("results/figures", "LI_0.05_age.png"), plot = LI_age_children, width = 5, height = 5, units = "in", dpi = 300)
# LI of children at threshold p<0.01
LI_age_children <- ggplot(
child_LI,
aes(x = Age, y = lateralisation_index_0.1)
) +
geom_point(color = "#9463A7", alpha = 0.8, size = 2.9, shape = 19) +
geom_smooth(method = "lm", se = FALSE, color = "#9463A7", linewidth = 2) +
labs(
title = "Lateralisation Index of FFA Across Age",
x = "Age",
y = "Lateralisation Index (p<0.10)"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 16, face = "bold"),
axis.title.y = element_text(size = 16, face = "bold"),
axis.text = element_text(size = 12)
)
ggsave(here("results/figures", "LI_0.1_age.png"), plot = LI_age_children, width = 5, height = 5, units = "in", dpi = 300)
```
```{r}
markdownToHTML("FRIC_DevelopmentalChange.Rmd",output ="FRIC_DevelopmentalChange.html")
```

FRIC_DevelopmentalChange.Rmd at commit 9982f13, no license · at the source

Overview

Authors: Lorena Jiménez-Sánchez¹, Melissa Thye¹, Hilary Richardson¹

ORCID iDs: Lorena Jiménez-Sánchez

Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK

Institutions: University of Edinburgh (United Kingdom)

Journal: Developmental cognitive neuroscience, volume 80, article 101765

Dates: received 12 March 2026; accepted 16 June 2026; published online 18 June 2026; in print August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.dcn.2026.101765 · PMID 42322775 · PMCID PMC13312595 · OpenAlex W7165176499

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism)

Methods: Statistics, Preprocessing, Connectivity, fMRI & imaging

Keywords: Face selectivity, development, social cognition, fusiform face area, paediatric fMRI

MeSH: Amygdala*, Facial Recognition*, Prefrontal Cortex*, Social Perception*, Temporal Lobe*, Adult, Brain Mapping, Child, Child, Preschool, Cross-Sectional Studies, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)

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

Funding: University of Edinburgh (108890/Z/15/Z); Wellcome Trust (108890/Z/15/Z)

Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

The fusiform face area (FFA) preferentially responds to faces within the first months of life. One hypothesis is that higher-order social responses in middle medial prefrontal cortex (MMPFC) or face responses in superior temporal sulcus (STS) drive the development of face-selective responses in FFA, with right-hemisphere dominance in FFA eventually arising from lateralised connections to these regions. Another hypothesis proposes an innate face template in the amygdala guides attention to face-like shapes. This study opportunistically examined the development of the FFA, MMPFC, STS, and amygdala in childhood using an open cross-sectional movie-viewing fMRI dataset with 3–12-year-olds (N = 117, M = 6.77 years) and adults (N = 33, M = 24.77 years). We tested for correlations between FFA development and development in MMPFC, STS, and amygdala on the premise that associations between these regions may be observable even in children, and such associations could constrain hypotheses and analytic approaches in future studies with infants. First, we measured functional maturity- i.e., how similar each child’s response to the movie was to an adult average response timecourse. In all regions, older children’s responses were more adult-like. Next, we tested whether FFA maturity correlated with functional connectivity with, or functional maturity of, MMPFC, STS, or amygdala. Children with more mature right FFA responses showed stronger right FFA-right MMPFC connectivity. Children with more mature FFA responses also had more mature STS responses, bilaterally. This study provides preliminary evidence that FFA co-develops with higher-order social brain regions.

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.

hrichardsonlab/fmri-analysis

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 15a4d88696a4daa910c428d636a1d5ad90c581d5, 10 September 2026

Languages: Python (24), Shell (18), R (1)

Size: 310 files, 43 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, 1 notebook

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

Tools: NumPy (24 files), pandas (24 files), Nipype (14 files), Nilearn (11 files), NiBabel (9 files), SciPy (8 files), FSL (6 files), PyBIDS (5 files), fMRIPrep (4 files), Dcm2Bids (2 files), FreeSurfer (2 files), ANTs (1 file), Matplotlib (1 file), MRIQC (1 file), scikit-learn (1 file), tedana (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

44 files

scripts/00.setup/setup_project.sh, Shell, 113 lines
scripts/00.setup/symlink_singularities.sh, Shell, 62 lines
scripts/01.bids/bids_conversion.sh, Shell, 261 lines
scripts/01.bids/deface_data.sh, Shell, 117 lines
scripts/02.mriqc/run_mriqc.sh, Shell, 133 lines
scripts/03.freesurfer/run_freesurfer.sh, Shell, 156 lines
scripts/04.fmriprep/denoise_echos.py, Python, 258 lines
scripts/04.fmriprep/run_fmriprep.sh, Shell, 201 lines
scripts/04.fmriprep/run_tedana.sh, Shell, 188 lines
scripts/05.motion_exclusions/check_data.sh, Shell, 112 lines
scripts/05.motion_exclusions/concat_brain_masks.py, Python, 138 lines
scripts/05.motion_exclusions/generate_scanfiles.sh, Shell, 139 lines
scripts/05.motion_exclusions/mark_motion_exclusions.py, Python, 262 lines, 1 match
scripts/06.first_level/calc_psc.py, Python, 339 lines
scripts/06.first_level/combine_runs.py, Python, 485 lines
scripts/06.first_level/convert_surface.py, Python, 238 lines
scripts/06.first_level/define_fROIs.py, Python, 309 lines, 1 match
scripts/06.first_level/delete_firstlevel_outputs.sh, Shell, 120 lines
scripts/06.first_level/extract_stats.py, Python, 566 lines, 1 match
scripts/06.first_level/firstlevel_pipeline.py, Python, 1,001 lines, 1 match
scripts/06.first_level/generate_eventfiles.sh, Shell, 182 lines
scripts/06.first_level/process_freesurfer_ROI.py, Python, 159 lines
scripts/06.first_level/run_first-level.sh, Shell, 158 lines
scripts/06.first_level/timecourse_pipeline.py, Python, 1,028 lines
scripts/07.second_level/label_clusters.py, Python, 315 lines
scripts/07.second_level/reverse_correlation.py, Python, 264 lines
scripts/07.second_level/run_second-level.sh, Shell, 144 lines
scripts/07.second_level/secondlevel_pipeline.py, Python, 517 lines
scripts/08.multivariate_analyses/calc_noise_ceiling.py, Python, 328 lines
scripts/08.multivariate_analyses/check_fold_reliability.py, Python, 406 lines
scripts/08.multivariate_analyses/check_roi_reliability.py, Python, 350 lines
scripts/08.multivariate_analyses/compute_neural_rdms.py, Python, 866 lines
scripts/08.multivariate_analyses/correlate_rdms.py, Python, 301 lines
scripts/08.multivariate_analyses/run_multivariate.sh, Shell, 137 lines
scripts/misc/clean_environment.sh, Shell, 53 lines
scripts/misc/compile_rsa_stats.py, Python, 88 lines
scripts/misc/compile_stats.py, Python, 104 lines
scripts/misc/compile_timecourses.py, Python, 127 lines
scripts/misc/get_outlier_info.sh, Shell, 134 lines
scripts/misc/get_run_info.py, Python, 117 lines
scripts/misc/interpolate_timecourses.Rmd, R, 123 lines
scripts/misc/resample_ROIs.py, Python, 111 lines
scripts/misc/run_py-script.sh, Shell, 93 lines
README.md, Text, 35 lines

At the source: github.com/hrichardsonlab/fmri-analysis

LorenaJS/FRIC

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 9982f13ac4e7399ce88801d7264b89bb0c45139c, 22 May 2026

Languages: R (5)

Size: 18 files, 5 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, 5 notebooks

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

Tools: tidyverse (5 files), ggpubr (4 files), patchwork (4 files), broom (3 files), car (3 files), lme4 (3 files), lmerTest (3 files), reshape2 (3 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

6 files

scripts/FRIC_BaselineCharacteristics.Rmd, R, 420 lines
scripts/FRIC_DevelopmentalChange.Rmd, R, 2,474 lines, 2 matches
scripts/FRIC_FunctionalMaturityFFA.Rmd, R, 1,473 lines, 2 matches
scripts/FRIC_LateralisationIndexFFA.Rmd, R, 1,808 lines
scripts/FRIC_MRIvariables.Rmd, R, 1,207 lines, 2 matches
README.md, Text, 41 lines

At the source: github.com/LorenaJS/FRIC

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
48 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

Datasets cited

openneuro:ds000228, at OpenNeuro; found in the text, “Materials and methods”

Data availability

The movie fMRI data analysed during the current study (originally collected by Richardson et al., 2018) are publicly available on OpenNeuro (https://openneuro.org/datasets/ds000228). Further details about the fMRI analysis pipeline, and publicly available code, can be found at https://github.com/hrichardsonlab/fmri-analysis. Average timecourses from the referent adult populations, used as regressors in this study, are available through OSF (https://osf.io/7a8w5/ for FFA and LOC, and https://osf.io/mxkag/ for MMPFC and S2). Search spaces are also available through OSF (https://osf.io/mxkag/ for MMPFC and S2 split by hemisphere, and left FFA defined by flipping the right FFA search space). Data and scripts used for this analysis can be accessed at https://github.com/LorenaJS/FRIC.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 5 keywords, 15 MeSH terms, 2 funders, 78 references.

Cite

This paper

Jiménez-Sánchez, L., Thye, M., & Richardson, H. (2026). Fusiform face area development correlates with development in higher-order social brain regions. Developmental cognitive neuroscience, 80, 101765. https://doi.org/10.1016/j.dcn.2026.101765

BibTeX

@article{jimenezsanchez2026fusiform, author = {Jiménez-Sánchez, Lorena and Thye, Melissa and Richardson, Hilary}, title = {{Fusiform face area development correlates with development in higher-order social brain regions}}, journal = {Developmental cognitive neuroscience}, year = {2026}, month = jun, volume = {80}, pages = {101765}, publisher = {Elsevier}, issn = {1878-9293}, doi = {10.1016/j.dcn.2026.101765}, url = {https://doi.org/10.1016/j.dcn.2026.101765}, pmid = {42322775}, pmcid = {PMC13312595} }

RIS

TY - JOUR AU - Jiménez-Sánchez, Lorena AU - Thye, Melissa AU - Richardson, Hilary TI - Fusiform face area development correlates with development in higher-order social brain regions T2 - Developmental cognitive neuroscience J2 - Dev Cogn Neurosci PY - 2026 DA - 2026/06/18 VL - 80 SP - 101765 SN - 1878-9293 PB - Elsevier DO - 10.1016/j.dcn.2026.101765 UR - https://doi.org/10.1016/j.dcn.2026.101765 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.dcn.2026.101765", "type": "article-journal", "title": "Fusiform face area development correlates with development in higher-order social brain regions", "container-title": "Developmental cognitive neuroscience", "author": [ { "family": "Jiménez-Sánchez", "given": "Lorena" }, { "family": "Thye", "given": "Melissa" }, { "family": "Richardson", "given": "Hilary" } ], "container-title-short": "Dev Cogn Neurosci", "volume": "80", "page": "101765", "DOI": "10.1016/j.dcn.2026.101765", "PMID": "42322775", "PMCID": "PMC13312595", "ISSN": "1878-9293", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.dcn.2026.101765", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 18 ] ] } }

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

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