The NiMARE software paper, as a Jupyter Book.
To view the built book, see https://doi.org/10.55458/neurolibre.00007.
In order to execute the book's code, you will need install all of the Python libraries that are required.
The necessary requirements and associated versions are available in binder/requirements.txt.
You can install them with the following:
pip install -r binder/requirements.txt
The data files necessary to execute the code in this book are located at https://drive.google.com/uc?id=1LgscyPqnka163hu5mdJ1X7UvX3IvXDJ2 in a zip file.
You can either download these files to a data/ folder at the same level as content/, or you can rely on repo2data to download the files automatically during the book build.
To build:
jupyter-book build content/The book is configured to rely on the pre-generated cache (execute_notebooks is set to "cache").
If you want to build from scratch, then you can either change that setting in content/_config.yml or you can run jupyter-book clean content/ before building.
To create the amygdala mask, I did:
from nilearn import datasets, image
atlas = datasets.fetch_atlas_harvard_oxford("sub-maxprob-thr25-2mm")
amyg_val = atlas["labels"].index("Right Amygdala")
amygdala_mask = image.math_img(f"img == {amyg_val}", img=atlas["maps"])
amygdala_mask.to_filename(os.path.join(DATA_DIR, "amygdala_roi.nii.gz"))This is just the parameter estimate map from the DerSimonianLaird meta-analysis.
While most of the figures in this manuscript are produced by the executed code, a few of them were manually created with Google Drawings. Here are the links for those figures.
Figure 0: https://docs.google.com/drawings/d/1SMJL6x5UEkr6PjeKPXsh_qG1LXQaQj-ex1Dyjyi5LNY/edit
Figure 1: https://docs.google.com/drawings/d/1qhToDmOCbvpgpqQPH8RxGaOSox4BhKNlSM9hUdMsP-4/edit
Figure 2: https://docs.google.com/drawings/d/1u9xfy8KlThtiK8QuW0t9uyMu_DP32S9W6QcvurSFC2s/edit