Code for Figures 3-4 based on Pearson’s correlations

Load libraries to produce figures:

In [1]:

# load libraries
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(patchwork)
library(ggplot2)
library(ggpubr)
library(neuroUp)

Set seed and create Figure 3a for the Feedback task DLPFC ROI:

In [2]:

# set seed
set.seed(1234)

# calculate difference mfg_learning vs. mfg_application
data_feedback <- feedback
data_feedback$dif <- data_feedback$mfg_learning - data_feedback$mfg_application

# Estimate correlations
feedback_fig <- estim_corr(data = data_feedback,
                           vars_of_interest = c("dif", "age"),
                           sample_size = 20:271, 
                           k = 1000, 
                           name = "A. Feedback DLPFC corr. with age")
# plot figure 3a
feedback_fig$fig_corr

Plot Figure 4a for the Feedback task DLPFC ROI:

In [3]:

# plot figure 4a
feedback_fig$fig_corr_nozero

Set seed and create Figure 3b for the Gambling task NAcc ROI:

In [4]:

# set seed
set.seed(1234)

# Estimate correlations
gambling_fig <- estim_corr(data = gambling, 
                           vars_of_interest = c("lnacc_self_winvsloss", 
                                                "age"), 
                           sample_size = 20:221, 
                           k = 1000, 
                           name = "B. Gambling NAcc corr. with age")

# plot figure 3b
gambling_fig$fig_corr

Plot Figure 4b for the Gambling task NAcc ROI:

In [5]:

# plot figure 4b
gambling_fig$fig_corr_nozero

Set seed and create Figure 3c for the Self-evaluations task mPFC ROI:

In [6]:

# set seed
set.seed(1234)

# calculate difference mpfc_self - mpfc_control
data_self <- self_eval
data_self$dif <- data_self$mpfc_self - data_self$mpfc_control

# Estimate correlations
selfeval_fig <- estim_corr(data = data_self,
                           vars_of_interest = c("dif",
                                                "age"),
                           sample_size = 20:149, 
                           k = 1000, 
                           name = "C. Self-evaluations mPFC corr. with age")

# plot figure 3c
selfeval_fig$fig_corr

Plot Figure 4c for the Self-evaluations task mPFC ROI:

In [7]:

# plot figure 4c
selfeval_fig$fig_corr_nozero

Set seed and create Figure 3D for the Gaining for self task NAcc ROI:

In [8]:

# set seed
set.seed(1234)

# calculate difference nacc_selfgain vs. nacc_bothnogain
data_vicar <- vicar_char
data_vicar$dif <- data_vicar$nacc_selfgain - data_vicar$nacc_bothnogain

# Estimate correlations
vicar_char_fig <- estim_corr(data = data_vicar, 
                             vars_of_interest = c("dif", 
                                                  "age"),
                             sample_size = 20:156, 
                             k = 1000, 
                             name = "D. Gaining self NAcc corr. with age")

# plot figure 3d
vicar_char_fig$fig_corr

Plot Figure 4d for the Gaining for self task NAcc ROI:

In [9]:

# plot figure 4d
vicar_char_fig$fig_corr_nozero

Plot a mock figure with similar aesthetics to create an overall ggplot legend.

The only purpose of this code chunk is to make a simple overall legend to show that in light blue a subset of the individual permutations is shown and in purple the overall values. The original legends with permutation numbers will not be shown in the overall figure to create a cleaner look. The approach taken here was to use a simple mock ggplot figure and then use the ggpubr package to isolate the legend and display the legend together with the 4 actual plots.

In [10]:

# make simple mock data to create overall legend
legend_data <- tibble(legend = c("10 permutations","Overall"), N = 1:2, scores = c(1.6, 1.8),
                      lower = c(.4, .6), upper = c(2.8, 3))
# factorize legend and N
legend_data$legend <- factor(legend_data$legend)
legend_data$N <- as.factor(legend_data$N)

# plot data to produce legend
figure_legend <- ggplot2::ggplot(data = legend_data, 
                               ggplot2::aes(x = .data$N, 
                                            y = .data$scores,
                                            colour = .data$legend,
                                            size = .data$legend) ) +
  ggplot2::theme_classic() +
  ggplot2::geom_point(position = ggplot2::position_dodge(.8),
                      ggplot2::aes(x = .data$N, 
                                   y = .data$scores,
                                   colour = .data$legend,
                                   size = .data$legend)) +
  ggplot2::scale_size_manual(values = c(2, 4))  +
  ggplot2::geom_errorbar(ggplot2::aes(ymin = .data$lower, ymax = .data$upper),
                         linewidth = .5, 
                         position = ggplot2::position_dodge(.1)) +
  ggplot2::scale_linetype_manual(values = c(1, 6)) +
  ggplot2::scale_color_manual(values = c("#009E73", "#E69F00") ) +
  theme(legend.title=element_blank())

# use ggpubr get_legend to plot legend only
leg <- ggpubr::get_legend(figure_legend)

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

simple_legend <- ggpubr::as_ggplot(leg)

# show the simple overall legend
simple_legend

Plot Figure 3 (4 data sets combined):

In [11]:

# plot figure 3 using patchwork library
feedback_fig$fig_corr + theme(legend.position = "none") +
  gambling_fig$fig_corr + theme(legend.position = "none") + 
  selfeval_fig$fig_corr + theme(legend.position = "none") +
  vicar_char_fig$fig_corr + theme(legend.position = "none") +
  inset_element(simple_legend, 
                left = 1.7, bottom = 3.8, right = 0, top = 0, 
                on_top = T, align_to = 'full')

Estimates of Pearson’s correlation between age and the task effect for five different sample sizes (starting with \(N = 20\), then 1/5th parts of the total dataset). For each sample size 10 randomly chosen HDCI’s out of the 1000 HDCI’s computed are displayed (in green, permutation numbers used are displayed to the right of each subfigure). The average estimate with credible interval summarizing the 1000 HDCI’s for each sample size are plotted in orange. DLPFC = dorsolateral prefrontal cortex; mPFC = medial prefrontal cortex; NAcc = nucleus accumbens. Age is modeled as linearly increasing or decreasing.

Plot Figure 4 (4 data sets combined):

In [12]:

# plot figure 4 using patchwork library
feedback_fig$fig_corr_nozero + gambling_fig$fig_corr_nozero + selfeval_fig$fig_corr_nozero + vicar_char_fig$fig_corr_nozero

For each task, for five different sample sizes (starting with \(N = 20\), then 1/5th parts of the total dataset), the proportion of intervals not containing the value 0 is plotted in orange. Age is modeled as linearly increasing or decreasing.

Extract numbers to produce table 3:

In [13]:

# first extract tibble from results (select mean only)
feedback_sum <- as_tibble(feedback_fig$tbl_select) %>%
  dplyr::filter(permutation == "Overall")
gambling_sum <- as_tibble(gambling_fig$tbl_select) %>%
  dplyr::filter(permutation == "Overall")
selfeval_sum <- as_tibble(selfeval_fig$tbl_select) %>%
  dplyr::filter(permutation == "Overall")
gaining_sum <- as_tibble(vicar_char_fig$tbl_select) %>%
  dplyr::filter(permutation == "Overall")

Use numbers to produce a table in text:

task	brain region	\(n = 20\)	\(n = 2/5\)	\(n = 3/5\)	\(n = 4/5\)	\(N = total\)
Feedback	DLPFC	0.38 (-0.06, 0.69)	0.38 (0.16, 0.56), n = 70	0.37 (0.21, 0.52), n = 120	0.38 (0.24, 0.5), n = 170	0.38 (0.27, 0.47), N = 271
Gambling	NAcc	-0.02 (-0.44, 0.4)	-0.04 (-0.29, 0.21), n = 60	-0.04 (-0.23, 0.16), n = 100	-0.04 (-0.2, 0.13), n = 140	-0.04 (-0.17, 0.09), N = 221
Self-evaluations	mPFC	0.07 (-0.36, 0.48)	0.06 (-0.23, 0.34), n = 46	0.06 (-0.17, 0.29), n = 72	0.06 (-0.14, 0.25), n = 98	0.06 (-0.1, 0.22), N = 149
Gaining self	NAcc	-0.18 (-0.56, 0.26)	-0.17 (-0.44, 0.11), n = 47	-0.17 (-0.38, 0.06), n = 74	-0.17 (-0.35, 0.03), n = 101	-0.17 (-0.31, -0.01), N = 156