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A weighted communicability measure applied to complex brain networks.

Jonathan J Crofts¹, Desmond J Higham

¹University of Strathclyde, Glasgow G1 1XH, UK. ra.jcro@maths.strath.ac.uk

Journal of the Royal Society, Interface

|January 15, 2009

Summary

A new network communicability measure successfully differentiates brain connectivity in patients versus controls. This method analyzes white matter tracts using magnetic resonance imaging, revealing subtle biological features in brain networks.

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Area of Science:

Neuroscience
Network Science
Medical Imaging

Background:

Non-invasive magnetic resonance imaging (MRI) provides detailed anatomical data on human brain white matter tracts.
Understanding global connectivity patterns is crucial for diagnosing neurological conditions.

Purpose of the Study:

To introduce and validate a novel weighted network communicability measure.
To assess the measure's efficacy in distinguishing between diseased patients and healthy controls based on brain connectivity.

Main Methods:

Utilized magnetic resonance imaging (MRI) to acquire brain anatomical data.
Applied a new weighted network communicability approach to analyze real-valued connectivity data without discretization.
Compared network properties between patient and control groups.

Main Results:

The new communicability measure successfully differentiated local and global connectivity differences between patients and controls.
The method extracted biologically relevant features not evident in raw connectivity data.
Demonstrated the advantage of using real-valued connectivity data over binarized matrices.

Conclusions:

The developed network communicability measure offers a sensitive tool for analyzing brain connectivity.
This approach enhances the non-invasive study of white matter tracts in neurological disorders.
Highlights the potential of network science in advancing experimental neuroscience and diagnostics.