Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment
The easiest way to obtain the data used in our analysis is as follows:
First, clone this Github repository with the command:
git clone https://github.com/CoDe-Neuro/neonatal_dfc
You will need to download some additional datasets we made available via Zenodo.
cd neonatal_dfc
curl -o data/2020_07_MASTER_connectomes90_All_select.mat https://zenodo.org/record/7053984/files/2020_07_MASTER_connectomes90_All_select.mat\?download\=1
cd neonatal_dfc
curl -o data/2020_07_MASTER_connectomes90_All_select.mat https://zenodo.org/record/7053984/files/2020_07_MASTER_connectomes90_All_select.mat\?download\=1
-o data/df_clus_LEiDa_dim.csv https://zenodo.org/record/7053984/files/df_clus_LEiDa_dim.csv?download=1
-o data/df_kura.csv https://zenodo.org/record/7053984/files/df_kura.csv?download=1
-o data/df_LEiDa.csv https://zenodo.org/record/7053984/files/df_LEiDa.csv?download=1
First we need to install the dynfc libs.
pip install pip install dynfc==0.0.3a1
The following commands will obtain both LEiDA and KOP for every subject in the dataset.
cd src
for i in {1..390}
do
python run_bstates.py $i
python run_kuramoto.py $i
done
python run_pca.py 390 LEiDa 0
python run_bigClus.py 390 LEiDa 0
python merge_km.py 390 kura 0
To guarantee reproducibility and reduce issues involved in solving dependencies, all the routines available here run in a Docker container. Please download the last version of the software at their website: https://www.docker.com/
To run the Docker container with the defined settings
docker-compose up
All routines for the analyses in this article are available in the R Markdown document to ease reproduction and visualisation. Open the link http://localhost:8787 and then the file index.Rmd when the RStudio loads. The web page should work as a regular RStudio IDE.
When you are finished with your analysis stop the Docker container with the command:
docker-compose down
This project was funded by a Wellcome Trust Seed Award in Science [217316/Z/19/Z].
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