Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, Weber, Gunther H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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
_version_ 1783244050388746240
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
work_keys_str_mv AT murugesansugeerth multiscalevisualanalysisoftimevaryingelectrocorticographydataviaclusteringofbrainregions
AT bouchardkristofer multiscalevisualanalysisoftimevaryingelectrocorticographydataviaclusteringofbrainregions
AT changedward multiscalevisualanalysisoftimevaryingelectrocorticographydataviaclusteringofbrainregions
AT doughertymax multiscalevisualanalysisoftimevaryingelectrocorticographydataviaclusteringofbrainregions
AT hamannbernd multiscalevisualanalysisoftimevaryingelectrocorticographydataviaclusteringofbrainregions
AT weberguntherh multiscalevisualanalysisoftimevaryingelectrocorticographydataviaclusteringofbrainregions