The brain signals have both a linear and a nonlinear distribution, respectively. Furthermore, it has a great degree of dimension. In order to understand cognitive processes in the brain. It is vital for us to map out the flow of information in the brian. Or, to put it another way, we should attempt to assess functional connectivity in the brain.
- Ubuntu 18.04 [x86_64-pc-linux-gnu (64-bit)]
- Matlab 2020a
- Python 3.8
- R 4.0.3
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Download dependencies toolbox for Matlab/Python/R
The Matlab libraries are used to estimate conditional mutual information is listed as follows, GCMI.
Python libraries are used to download dataset and estimate multivariate mutual information are listed as follows, pyentropy-master Nilearn, RBIG, CorEx.
R packages used to visualize tree graph in this studies are listed as follows: factoextra, igraph, entropy, cluster, and gplots.
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Reproduce figs in paper
All of the figures can be replicated using the codes that have been provided. It is possible to plot the tree plot after you have saved the connection matrix and then utilized the previously stated R programs to do so.
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Some function used from here and you are welcome to join me in making contributions to the neuroscience-information-theory-python-toolbox project.
People who want to learn more about information theory in neuroscience might use this Python toolkit.
@article{LI202285,
title = {Functional connectivity inference from fMRI data using multivariate information measures},
journal = {Neural Networks},
volume = {146},
pages = {85-97},
year = {2022},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2021.11.016},
url = {https://www.sciencedirect.com/science/article/pii/S0893608021004445},
author = {Qiang Li}
}
@misc{QL22TC,
doi = {10.48550/ARXIV.2208.05770},
url = {https://arxiv.org/abs/2208.05770},
author = {Li, Qiang and Steeg, Greg Ver and Malo, Jesus},
keywords = {Neurons and Cognition (q-bio.NC), Probability (math.PR), FOS: Biological sciences, FOS: Biological sciences, FOS: Mathematics, FOS: Mathematics},
title = {Functional Connectivity in Visual Areas from Total Correlation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
@Article{e24121725,
AUTHOR = {Li, Qiang and Steeg, Greg Ver and Yu, Shujian and Malo, Jesus},
TITLE = {Functional Connectome of the Human Brain with Total Correlation},
JOURNAL = {Entropy},
VOLUME = {24},
YEAR = {2022},
NUMBER = {12},
ARTICLE-NUMBER = {1725},
URL = {https://www.mdpi.com/1099-4300/24/12/1725},
DOI = {10.3390/e24121725}
}