Network-Quantifications

Network Quantification Code for Cortical Microconnectome Analysis

This repository provides MATLAB/CUDA code primarily associated with the following peer-reviewed article:

Kajiwara, M., Nomura, R., Goetze, F., Kawabata, M., Isomura, Y., Akutsu, T., & Shimono, M. (2021). Inhibitory neurons exhibit high controlling ability in the cortical microconnectome. PLOS Computational Biology, 17(4), e1008846. https://doi.org/10.1371/journal.pcbi.1008846

This repository is also related to the following whole-brain cortical microconnectome study:

Matsuda, K., Shirakami, A., Nakajima, R., Akutsu, T., & Shimono, M. (2023). Whole-Brain Evaluation of Cortical Microconnectomes. eNeuro, 10(10), ENEURO.0094-23.2023. https://doi.org/10.1523/ENEURO.0094-23.2023

The related eNeuro study extends cortical microconnectome analysis toward whole-brain evaluation across cortical region groups and examines regional nonuniformity in cortical functional microconnectomes.

Overview

These scripts reproduce figure-level analyses and network quantifications used to evaluate cortical microconnectome properties, including excitatory/inhibitory cell categories, k-core centrality, feedback vertex set-related analyses, degree distributions, firing-rate distributions, and connectivity-strength distributions.

Requirements

• MATLAB
• Statistics and Machine Learning Toolbox
• Signal Processing Toolbox

How to Run

1. Save all files in one directory.
2. Open MATLAB in that directory.
3. Add all subfolders to the MATLAB path:

addpath(genpath('./'));

4. Run the relevant figure script:

fig*

Example Script Functions

• fig5b.m: degree histograms / number of connections
• fig5ce.m: firing-rate histograms and connectivity-strength histograms for excitatory and inhibitory neurons
• fig5d.m: total numbers and relative ratios of excitatory and inhibitory neurons
• fig6ab.m: k-core analyses for excitatory and inhibitory neurons across cortical layers
• fig7ab.m: feedback vertex set-related analyses for excitatory and inhibitory neurons
• fig7c.m: dataset preparation for comparing high k-core nodes and feedback vertex set nodes

Citation

If you use this repository in any way that contributes to a publication, preprint, thesis, presentation, software tool, benchmark comparison, dataset analysis, or derivative codebase, please cite the peer-reviewed article below.

Please cite the paper if you:

• use the original MATLAB code
• modify or extend the code
• reuse part of the figure reproduction or network quantification workflow
• use the network metrics, k-core, FVS, or E/I comparison procedure
• use the dataset structure or preprocessing procedure
• build upon the idea of quantifying controlling ability in cortical microconnectome networks

Peer-reviewed article

For the scientific method, results, and interpretation, please cite:

Kajiwara, M., Nomura, R., Goetze, F., Kawabata, M., Isomura, Y., Akutsu, T., & Shimono, M. (2021). Inhibitory neurons exhibit high controlling ability in the cortical microconnectome. PLOS Computational Biology, 17(4), e1008846. https://doi.org/10.1371/journal.pcbi.1008846

Archived software release

The GitHub repository has been archived on Zenodo:

**Shimono, M. (2026). Network-Quantifications: Network quantification code for cortical microconnectome analysis. Zenodo. https://doi.org/10.5281/zenodo.xxxxxxxx**

Please cite the peer-reviewed article for the scientific method, results, and interpretation, and cite the Zenodo DOI when referring specifically to this archived software release.

Suggested Citation Sentence

This repository provides MATLAB code for reproducing network quantification and figure-level analyses of cortical microconnectome organization and excitatory/inhibitory controlling ability.