Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment

Download

The easiest way to obtain the data used in our analysis is as follows:

Github repository

First, clone this Github repository with the command:

git clone https://github.com/CoDe-Neuro/neonatal_dfc

Additional data

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 

⚠️ If you do not want to (or cannot) perform the BOLD signal analyses in Python, you can use the following command and jump directly to the figures and stats sections (in R).

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

BOLD signal LEiDA and KOP analyses (Python)

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

⚠️ This can take a while to calculate. Consider running in parallel to reduce time consumed.

python run_pca.py 390 LEiDa 0
python run_bigClus.py 390 LEiDa 0
python merge_km.py 390 kura 0

⚠️ The routines above should demand a considerable amount of RAM.

Figures + stats (R)

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.

⚠️ The login and password are 'rstudio' and 'letmein', respectively.

⚠️ We performed several permutation tests (n = 10,000) which can take a while to run.

When you are finished with your analysis stop the Docker container with the command:

docker-compose down

Funding

This project was funded by a Wellcome Trust Seed Award in Science [217316/Z/19/Z].

License

MIT License

Copyright (c) 2022

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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