LRG: Zooming out the brain scales

Title

Leaders

Tommaso Gili (X: @tommasogili)

Collaborators

No response

Brainhack Global 2023 Event

Brainhack Lucca

Project Description

Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organised over multiple scales linked to one another (Betzel and Bassett, 2017). Simple organising principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. Such a multiscale property can be appreciated by progressively coarse-graining the connected anatomical regions by changing the observation resolution. This project aims to apply a recently proposed graph coarse-graining method (the Laplacian Renormalization Group: Villegas et al., 2023) to the human connectome and identify the network density's role in identifying multiple connectivity scales.

Link to project repository/sources

No response

Goals for Brainhack Global

• To create a pipeline for an effective structural network sparsification, starting from probabilistic and deterministic tractography of multiple subjects. • To adapt the Laplacian Renormalization Group algorithm for the human connectome. • To establish the working limits of a non-geometric coarse-graining in terms of network density.

Good first issues

To define a strategy to create a consensus structural network from tractography.
To realise a filtration process that reduces the network's density, preserving the topology.
To extract a multiresolution description of the human connectome using the Laplacian Renormalization Group approach.

Communication channels

• Slack/Whatsapp/Email

Skills

• Familiarity with DTI and Tractography • General proficiency with Python/MATLAB/Bash • Network analysis • Data Visualisation and Communication

Onboarding documentation

No response

What will participants learn?

• To calculate a representative structural network across subjects. • To reduce a network density, preserving its topology. • To renormalise a network without any geometric constrain.

Data to use

Participants will receive probabilistic and deterministic tractography from healthy human DTI measurements.

Number of collaborators

3-5

Credit to collaborators

No response

Image

Leave this text if you don't have an image yet. Pict_Brain_LRG_BrainHack

Type

coding_methods, method_development, pipeline_development

Development status

1_basic structure

Topic

connectome, neural_networks, tractography

Tools

Jupyter, other

Programming language

Matlab, Python

Modalities

DWI

Git skills

0_no_git_skills

Anything else?

No response

Things to do after the project is submitted and ready to review.

[X] Add a comment below the main post of your issue saying: Hi @brainhacklucca my project is ready!
[X] Twitter-sized summary of your project pitch.

brainhacklucca / brainhacklucca.github.io