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Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions
BACKGROUND: There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5471943/ https://www.ncbi.nlm.nih.gov/pubmed/28617218 http://dx.doi.org/10.1186/s12859-017-1633-9 |
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author | Murugesan, Sugeerth Bouchard, Kristofer Chang, Edward Dougherty, Max Hamann, Bernd Weber, Gunther H. |
author_facet | Murugesan, Sugeerth Bouchard, Kristofer Chang, Edward Dougherty, Max Hamann, Bernd Weber, Gunther H. |
author_sort | Murugesan, Sugeerth |
collection | PubMed |
description | BACKGROUND: There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. RESULTS: We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our system detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system’s effectiveness. CONCLUSION: ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1633-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5471943 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54719432017-06-19 Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions Murugesan, Sugeerth Bouchard, Kristofer Chang, Edward Dougherty, Max Hamann, Bernd Weber, Gunther H. BMC Bioinformatics Research BACKGROUND: There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. RESULTS: We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our system detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system’s effectiveness. CONCLUSION: ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1633-9) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-06 /pmc/articles/PMC5471943/ /pubmed/28617218 http://dx.doi.org/10.1186/s12859-017-1633-9 Text en © The Regents of the University of California 2017 Open Access This manuscript has been authored by an author at Lawrence Berkeley National Laboratory under Contract No. DE-AC02-05CH11231 with the U.S. Department of Energy. The U.S. Government retains, and the publisher, by accepting the article for publication, acknowledges, that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. Additionally, this is an Open Access article distributed under the terms of the Creative Commons 4.0 Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Murugesan, Sugeerth Bouchard, Kristofer Chang, Edward Dougherty, Max Hamann, Bernd Weber, Gunther H. Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions |
title | Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions |
title_full | Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions |
title_fullStr | Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions |
title_full_unstemmed | Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions |
title_short | Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions |
title_sort | multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5471943/ https://www.ncbi.nlm.nih.gov/pubmed/28617218 http://dx.doi.org/10.1186/s12859-017-1633-9 |
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