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- ```{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}
- ```
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