Here, we present the analysis steps that were used for the evaluation of the autohrf package based on the spatial working memory study discussed in the paper Purg, N., Demšar, J., & Repovš, G. (2022). autohrf – an R package for generating data-informed event models for general linear modeling of task-based fMRI data. Frontiers in Neuroimaging.
We start the analysis by loading relevant libraries and the fMRI data collected during the spatial working memory study.
# libraries
library(autohrf)
library(dplyr)
library(ggplot2)
library(magrittr)
# load the data
df <- swm
head(df)## roi t y
## 1 L_1 0 0.02712162
## 2 L_1 1 0.06248649
## 3 L_1 2 0.12908108
## 4 L_1 3 0.30183784
## 5 L_1 4 0.51691892
## 6 L_1 5 0.65970270The loaded data frame has 11520 observations, which correspond to the fMRI measurements for 360 different brain areas over 32 time points during a spatial working memory task trial. The fMRI data has been averaged for individual voxels within each region of interest (ROI), across individual task trials collected in one to three recording sessions per each participant, and across 37 participants. Each observation has three variables (roi, t, and y), where roi denotes the region of interest, t the time stamp and y the value of the BOLD signal.
To visualize the data we select six ROIs with different types of activity during the spatial working memory task and plot their mean activity during a task trial.
# prepare relevant ROIs for visualization
roisv <- c("R_V1", "R_V4", "R_FEF", "R_AIP", "R_POS1", "R_A1")
# view data of selected ROIs
df %>%
filter(roi %in% roisv) %>%
mutate(roi = factor(roi, levels=roisv)) %>%
ggplot(aes(t, y)) +
geom_line(size=0.8) +
facet_wrap(~ roi, nrow = 1)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.