Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions
Abstract 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 neura...
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| Format: | Article |
| Language: | English |
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BMC
2017-06-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1633-9 |
| _version_ | 1819291111911325696 |
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| author | Sugeerth Murugesan Kristofer Bouchard Edward Chang Max Dougherty Bernd Hamann Gunther H. Weber |
| author_facet | Sugeerth Murugesan Kristofer Bouchard Edward Chang Max Dougherty Bernd Hamann Gunther H. Weber |
| author_sort | Sugeerth Murugesan |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-24T03:33:27Z |
| format | Article |
| id | doaj.art-7e947c30d7ac4b0fa75f07c066eb85fe |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-24T03:33:27Z |
| publishDate | 2017-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-7e947c30d7ac4b0fa75f07c066eb85fe2022-12-21T17:17:07ZengBMCBMC Bioinformatics1471-21052017-06-0118S611510.1186/s12859-017-1633-9Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regionsSugeerth Murugesan0Kristofer Bouchard1Edward Chang2Max Dougherty3Bernd Hamann4Gunther H. Weber5Computational Research Division, Lawrence Berkeley National LaboratoryComputational Research Division, Lawrence Berkeley National LaboratoryDepartment of Neurological Surgery, UCSFComputational Research Division, Lawrence Berkeley National LaboratoryDepartment of Computer Science, University of CaliforniaComputational Research Division, Lawrence Berkeley National LaboratoryAbstract 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.http://link.springer.com/article/10.1186/s12859-017-1633-9ElectrocorticographyClusteringSpatio-temporal graphsUnsupervised learningNeuroinformaticsEpilepsy |
| spellingShingle | Sugeerth Murugesan Kristofer Bouchard Edward Chang Max Dougherty Bernd Hamann Gunther H. Weber Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions BMC Bioinformatics Electrocorticography Clustering Spatio-temporal graphs Unsupervised learning Neuroinformatics Epilepsy |
| 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 | Electrocorticography Clustering Spatio-temporal graphs Unsupervised learning Neuroinformatics Epilepsy |
| url | http://link.springer.com/article/10.1186/s12859-017-1633-9 |
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