BrainNetVis: An Open-Access Tool to Effectively Quantify and Visualize Brain Networks

Eleni G Christodoulou,Ioannis G Tollis,Vangelis Sakkalis,Vassilis Tsiaras

doi:10.1155/2011/747290

Eleni G Christodoulou, Ioannis G Tollis + Show 2 more

Open Access

https://doi.org/10.1155/2011/747290

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Abstract

This paper presents BrainNetVis, a tool which serves brain network modelling and visualization, by providing both quantitative and qualitative network measures of brain interconnectivity. It emphasizes the needs that led to the creation of this tool by presenting similar works in the field and by describing how our tool contributes to the existing scenery. It also describes the methods used for the calculation of the graph metrics (global network metrics and vertex metrics), which carry the brain network information. To make the methods clear and understandable, we use an exemplar dataset throughout the paper, on which the calculations and the visualizations are performed. This dataset consists of an alcoholic and a control group of subjects.

Highlights

One of the major issues in neuroscience is to describe how different brain areas communicate with each other during perception, cognition, and action as well as during spontaneous activity in the default or resting state
We are using the data of a specific use case, consisting of alcoholic and control subjects, in order to provide concrete examples of use of the application
The inner brain connectivity is depicted as a graph; different sensor locations are visualized as nodes and their interconnections as edges

Summary

Introduction

One of the major issues in neuroscience is to describe how different brain areas communicate with each other during perception, cognition, and action as well as during spontaneous activity in the default or resting state. Two different approaches for capturing and localizing brain activity motifs have been proposed; univariate spectrum based analysis and functional connectivity analysis [1]. A network is formed by corresponding either brain areas or channels to vertices and by considering an edge between two vertices if and only if the estimated interdependence is above a threshold. The authors propose two alternative approaches in selecting a threshold value based either on group statistics between specific graphtheoretic measures of the populations under analysis [6] or utilizing a signal-based technique of selecting the optimal visualization threshold using surrogate (artificially generated ensemble of data aiming at revealing the most significantly coupled brain regions) datasets to correctly identify the most significant correlation patterns [7]. Using the network characterization, one can draw conclusions on the effect of illnesses or of cognitive loads on functional connectivity [6,7,8,9,10,11]

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