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Modeling the Functional Network for Spatial Navigation in the Human Brain

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The Budapest Reference Connectome Server v2.0.

Balázs Szalkai¹, Csaba Kerepesi¹, Bálint Varga¹

¹PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.

Neuroscience Letters

|April 12, 2015

Summary

Human brain connectomes vary, but common connections exist. The Budapest Reference Connectome Server v2.0 identifies these shared brain graph edges from MRI data, creating robust reference connectomes.

Keywords:

Consensus connectome Reference connectome connectome

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Area of Science:

Neuroscience
Graph Theory
Medical Imaging

Background:

Human brain connectomes are unique, lacking a single abstract representation.
Despite individual differences, common structural connections (edges) exist between cortical areas.

Purpose of the Study:

To develop a server that identifies and filters common graph edges from multiple human brain connectomes.
To provide downloadable, annotated reference connectome datasets.
To visualize consensus brain graphs in 3D.

Main Methods:

Utilized 96 MRI datasets from the Human Connectome Project.
Computed connectomes with 1015 vertices each.
Developed the Budapest Reference Connectome Server v2.0 for edge identification and filtering.
Generated graphs in CSV and GraphML formats with FreeSurfer anatomical annotations.

Main Results:

Generated consensus graphs representing common brain connections across individuals.
Enabled user-defined parameter settings for edge identification and filtering.
Provided interactive 3D visualization of the consensus brain graph.

Conclusions:

The server provides robust, reduced-error reference connectomes from independent MRI data.
These consensus graphs serve as valuable tools for understanding human brain connectivity.
The resource facilitates standardized analysis of brain networks.