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BrainNetVis:一种可有效量化和可视化脑网络的开放获取工具。

BrainNetVis: an open-access tool to effectively quantify and visualize brain networks.

机构信息

Institute of Computer Science, Foundation for Research and Technology-Hellas, FORTH, Heraklion, Greece.

出版信息

Comput Intell Neurosci. 2011;2011:747290. doi: 10.1155/2011/747290. Epub 2011 Mar 14.


DOI:10.1155/2011/747290
PMID:21461404
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3065033/
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.

摘要

本文提出了 BrainNetVis,这是一种用于大脑网络建模和可视化的工具,提供了大脑连通性的定量和定性网络度量。它通过展示该领域的类似作品并描述我们的工具如何为现有场景做出贡献,强调了创建该工具的需求。它还描述了用于计算图形指标(全局网络指标和顶点指标)的方法,这些指标承载着大脑网络信息。为了使方法清晰易懂,我们在整篇文章中使用了一个示例数据集,在该数据上执行计算和可视化。该数据集由酒精组和对照组的受试者组成。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/4852f82c629a/CIN2011-747290.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/2181546aaf10/CIN2011-747290.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/bda76e068e8f/CIN2011-747290.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/348a197630d1/CIN2011-747290.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/df851080003d/CIN2011-747290.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/20ac48c8060b/CIN2011-747290.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/4852f82c629a/CIN2011-747290.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/2181546aaf10/CIN2011-747290.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/bda76e068e8f/CIN2011-747290.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/348a197630d1/CIN2011-747290.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/df851080003d/CIN2011-747290.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/20ac48c8060b/CIN2011-747290.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcfe/3065033/4852f82c629a/CIN2011-747290.006.jpg

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BrainNetVis: an open-access tool to effectively quantify and visualize brain networks.

Comput Intell Neurosci. 2011-3-14

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引用本文的文献

[1]
Data-driven visualization of multichannel EEG coherence networks based on community structure analysis.

Appl Netw Sci. 2018

本文引用的文献

[1]
Applied strategies towards EEG/MEG biomarker identification in clinical and cognitive research.

Biomark Med. 2011-2

[2]
Functional connectivity networks in the autistic and healthy brain assessed using Granger causality.

Annu Int Conf IEEE Eng Med Biol Soc. 2010

[3]
Finding stationary subspaces in multivariate time series.

Phys Rev Lett. 2009-11-20

[4]
Complex network measures of brain connectivity: uses and interpretations.

Neuroimage. 2009-10-9

[5]
Complex modular structure of large-scale brain networks.

Chaos. 2009-6

[6]
Assessment of linear and nonlinear synchronization measures for analyzing EEG in a mild epileptic paradigm.

IEEE Trans Inf Technol Biomed. 2009-7

[7]
Complex brain networks: graph theoretical analysis of structural and functional systems.

Nat Rev Neurosci. 2009-3

[8]
Brain network analysis from high-resolution EEG recordings by the application of theoretical graph indexes.

IEEE Trans Neural Syst Rehabil Eng. 2008-10

[9]
Optimal brain network synchrony visualization: application in an alcoholism paradigm.

Annu Int Conf IEEE Eng Med Biol Soc. 2007

[10]
Time-significant wavelet coherence for the evaluation of schizophrenic brain activity using a graph theory approach.

Conf Proc IEEE Eng Med Biol Soc. 2006

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