highspeed-masks/code/docs/highspeed-masks-docs.Rmd

```{r, echo=FALSE, message=FALSE, include=FALSE}
if (!requireNamespace("pacman")) install.packages("pacman")
packages_cran <- c("here")
pacman::p_load(char = packages_cran)
if (basename(here::here()) == "highspeed"){
  path_root = here::here("highspeed-masks")
} else {
  path_root = here::here()
}
```

## Feature selection: Anatomical masks

### Overview

As described in the paper, we used a feature selection approach that combined binarized anatomical ROIs with functional ROIs based on first-level GLMs.

#### Data availability

The data is freely available from https://github.com/lnnrtwttkhn/highspeed-masks and https://gin.g-node.org/lnnrtwttkhn/highspeed-masks.

#### License

The dataset is licensed under Creative Commons Attribution-ShareAlike 4.0.
Please see https://creativecommons.org/licenses/by-sa/4.0/ for details.

### Creating binary anatomical masks using `highspeed-masks.py`

We created binarized anatomical masks of occipito-temporal cortex and hippocampus based on the participant-specific Freesurfer parcellation using a Nipype workflow:

```{python, echo=TRUE, code=readLines(file.path(path_root, "code", "masks", "highspeed-masks.py")), eval=FALSE, python.reticulate=FALSE}
```

### Plotting masked data using `highspeed-masks-plot.py`

We generated some plots of the data using the following code:

```{python, echo=TRUE, code=readLines(file.path(path_root, "code", "masks", "highspeed-masks-plot.py")), eval=FALSE, python.reticulate=FALSE}
```

### Software: Required packages

The `requirements.txt` file lists the required packages which can be installed e.g., using `pip install -r requirements.txt`

```{bash, echo=TRUE, code=readLines(file.path(path_root, "requirements.txt")), eval=FALSE}
```