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Title: Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions

Abstract

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. 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. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identifymore » 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.« less

Authors:
 [1];  [2];  [3];  [2];  [4];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Davis, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. UCSF, San Francisco, CA (United States)
  4. Univ. of California, Davis, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1379884
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
BMC Bioinformatics
Additional Journal Information:
Journal Volume: 18; Journal Issue: S6; Journal ID: ISSN 1471-2105
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Electrocorticography; Clustering; Spatio-temporal graphs; Unsupervised learning; Neuroinformatics; Epilepsy; Visual analysis; Brain imaging; Graph visualization; Mutli-scale analysis

Citation Formats

Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, and Weber, Gunther H. Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions. United States: N. p., 2017. Web. doi:10.1186/s12859-017-1633-9.
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. United States. doi:10.1186/s12859-017-1633-9.
Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, and Weber, Gunther H. Tue . "Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions". United States. doi:10.1186/s12859-017-1633-9. https://www.osti.gov/servlets/purl/1379884.
@article{osti_1379884,
title = {Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions},
author = {Murugesan, Sugeerth and Bouchard, Kristofer and Chang, Edward and Dougherty, Max and Hamann, Bernd and Weber, Gunther H.},
abstractNote = {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. 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. 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.},
doi = {10.1186/s12859-017-1633-9},
journal = {BMC Bioinformatics},
number = S6,
volume = 18,
place = {United States},
year = {Tue Jun 06 00:00:00 EDT 2017},
month = {Tue Jun 06 00:00:00 EDT 2017}
}

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