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Neurobridge integrates functional and anatomical neural connectivity to design dynamic models of the cerebral cortex. Using graph theory and machine learning, it models brain region interactions and validates these models with real biological data to explore brain connectivity.

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Neurobridge: Bridging Functional and Anatomical Neural Connectivity through Dynamical Modeling

Overview

The neurobridge project aims to investigate an approach to reconcile structural and functional neural connectivity to design dynamical models of the cerebral cortex. The goal is to use both functional and anatomical data to create a model that can simulate the dynamics of the brain and provide insights into its connectivity.

This project involves using both graph theory and machine learning tools to analyze brain connectivity data and apply dynamic modeling techniques to represent how different regions of the brain interact with each other.

Objectives

  • Import and pre-process biological data on structural and functional connectivity.
  • Design a dynamical network model to reproduce biological data features.
  • Validate the designed model.

Approach

The project will use existing tools and algorithms for connectivity analysis:

  1. Peixoto's clustering algorithm using the graph-tool library.
  2. Baruzzi's method for integrating structural data and functional data.

The dataset provided contains two matrices:

  • Functional Matrix (matrices.mat): A correlation matrix between 116 brain regions (116x116) across 30 patients (300 sessions in total).
  • Connectivity Matrix (matrices_HNU1.mat): A structural matrix representing the number of nerve bundles connecting the regions.

References

  • Baruzzi, V., Lodi, M., Sorrentino, F., & Storace, M. (2023). Bridging functional and anatomical neural connectivity through cluster synchronization. Scientific Reports, 13, 22430. DOI: 10.1038/s41598-023-49746-2
  • Peixoto, T.P. (2022). Ordered community detection in directed networks. Phys. Rev. E, 106(2), 024305.

Requirements

This project uses Python and Conda for environment management. The required dependencies are listed below.

Dependencies:

  • graph-tool (for graph-based algorithms)

Setting Up the Environment:

  1. Create a new Conda environment using the environment.yml file:

    conda env create -f environment.yml
  2. Activate the environment:

    conda activate neurobridge

Usage

TBD

Documentation

  • The meeting logs and additional documentation can be found in the docs/ folder, including:
    • log.md: A log of all meeting discussions, with a detailed introduction to the project.

Future Improvements

TBD

License

TBD

About

Neurobridge integrates functional and anatomical neural connectivity to design dynamic models of the cerebral cortex. Using graph theory and machine learning, it models brain region interactions and validates these models with real biological data to explore brain connectivity.

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