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

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

doi:10.1155/2011/747290

Cited by 5 publications

(4 citation statements)

References 37 publications

(46 reference statements)

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“…Communicability functions have been incorporated into some computational tools for the analysis of complex networks. For instance, a Hub Objects Analyzer (Hubba) (Lin et al, 2008) designed as a web-based service for exploring important nodes in an interactome network and BrainNetVis (Christodoulou et al, 2011), a tool for both quantitative and qualitative network measures of brain interconnectivity, incorporate the subgraph centrality as a standard measure for the analysis of nodes in networks. A more general computational toolbox, CONTEST (Taylor and Higham, 2009), contains a series of Matlab utilities for generating and analyzing various types of networks and incorporates several communicability-based functions for the analysis of complex networks.…”

Section: A Analytical Resultsmentioning

confidence: 99%

The physics of communicability in complex networks

2012

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a b s t r a c tA fundamental problem in the study of complex networks is to provide quantitative measures of correlation and information flow between different parts of a system. To this end, several notions of communicability have been introduced and applied to a wide variety of real-world networks in recent years. Several such communicability functions are reviewed in this paper. It is emphasized that communication and correlation in networks can take place through many more routes than the shortest paths, a fact that may not have been sufficiently appreciated in previously proposed correlation measures. In contrast to these, the communicability measures reviewed in this paper are defined by taking into account all possible routes between two nodes, assigning smaller weights to longer ones. This point of view naturally leads to the definition of communicability in terms of matrix functions, such as the exponential, resolvent, and hyperbolic functions, in which the matrix argument is either the adjacency matrix or the graph Laplacian associated with the network.Considerable insight on communicability can be gained by modeling a network as a system of oscillators and deriving physical interpretations, both classical and quantummechanical, of various communicability functions. Applications of communicability measures to the analysis of complex systems are illustrated on a variety of biological, physical and social networks. The last part of the paper is devoted to a review of the notion of locality in complex networks and to computational aspects that by exploiting sparsity can greatly reduce the computational efforts for the calculation of communicability functions for large networks.

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Section: A Analytical Resultsmentioning

confidence: 99%

The physics of communicability in complex networks

2012

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“…Christodoulou et al . [CSTT11] present BrainNetVis to serve brain network modelling and visualization by providing both quantitative and qualitative network measures of brain interconnectivity. Xia et al .…”

Section: Related Workmentioning

confidence: 99%

“…In addition, several other useful tools for visualizing brain networks have been introduced. Christodoulou et al [CSTT11] present BrainNetVis to serve brain network modelling and visualization by providing both quantitative and qualitative network measures of brain interconnectivity. Xia et al [XWH13] introduce BrainNet Viewer to display brain surfaces, nodes, and edges as well as properties of the network.…”

Section: Related Workmentioning

confidence: 99%

Visual Exploration of Dynamic Multichannel EEG Coherence Networks

Gronde

Maurits

et al. 2018

Computer Graphics Forum

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Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline‐based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time‐annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks.

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“…Margulies et al 32 provided an overview of various frameworks for visualizing anatomical and functional connectivity in the human brain. Christodoulou et al presented BrainNetVis, 33 which effectively quantifies and visualizes brain networks. BrainNet Viewer, 34 developed by Xia et al, is a graphtheoretical network visualization toolbox which uses MATLAB to generate figures for brain connectomes.…”

Section: Related Work Brain/neuronal Connectivity Visualizationmentioning

confidence: 99%

SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks

Ma¹,

Forbes²,

Llano³

et al. 2016

jist

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Study of the behavior of individual members in communities of dynamic networks can help neuroscientists to understand how interactions between neurons in brain networks change over time. Visualization of those temporal features is challenging, especially for networks embedded within spatial structures, such as brain networks. In this article, the authors present the design of SwordPlots, an interactive multi-view visualization system to assist neuroscientists in their exploration of dynamic brain networks from multiple perspectives. Their visualization helps neuroscientists to understand how the functional behavior of the brain changes over time, how such behaviors are related to the spatial structure of the brain, and how communities of neurons with similar functionality evolve over time. To evaluate their application, they asked neuroscientists to use SwordPlots to examine four different mouse brain data sets. Based on feedback, their visualization design can provide neuroscientists with the ability to gain new insights into the properties of dynamic brain networks.

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

Cited by 5 publications

References 37 publications

The physics of communicability in complex networks

The physics of communicability in complex networks

Visual Exploration of Dynamic Multichannel EEG Coherence Networks

SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks

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