skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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},
issn = {1471-2105},
number = S6,
volume = 18,
place = {United States},
year = {2017},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 3 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Mapping Change in Large Networks
journal, January 2010


Exploratory spatio-temporal visualization: an analytical review
journal, December 2003

  • Andrienko, Natalia; Andrienko, Gennady; Gatalsky, Peter
  • Journal of Visual Languages & Computing, Vol. 14, Issue 6
  • DOI: 10.1016/S1045-926X(03)00046-6

ConnectedCharts: Explicit Visualization of Relationships between Data Graphics
journal, June 2012


Intrusion and misuse detection in large-scale systems
journal, January 2002

  • Erbacher, R. F.; Walker, K. L.; Frincke, D. A.
  • IEEE Computer Graphics and Applications, Vol. 22, Issue 1
  • DOI: 10.1109/38.974517

Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data
journal, January 2002


Toward a Deeper Understanding of the Role of Interaction in Information Visualization
journal, November 2007

  • Yi, Ji Soo; Kang, Youn ah; Stasko, John
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 13, Issue 6
  • DOI: 10.1109/TVCG.2007.70515

Methods for Visual Understanding of Hierarchical System Structures
journal, January 1981

  • Sugiyama, Kozo; Tagawa, Shojiro; Toda, Mitsuhiko
  • IEEE Transactions on Systems, Man, and Cybernetics, Vol. 11, Issue 2
  • DOI: 10.1109/TSMC.1981.4308636

Navigating Clustered Graphs Using Force-Directed Methods
journal, January 2000

  • Eades, Peter; Lin Huang, Mao
  • Journal of Graph Algorithms and Applications, Vol. 4, Issue 3
  • DOI: 10.7155/jgaa.00029

Space, time and visual analytics
journal, October 2010

  • Andrienko, Gennady; Andrienko, Natalia; Demsar, Urska
  • International Journal of Geographical Information Science, Vol. 24, Issue 10
  • DOI: 10.1080/13658816.2010.508043

Visualizing the Evolution of Communities in Dynamic Graphs: Visualizing the Evolution of Communities in Dynamic Graphs
journal, November 2014

  • Vehlow, C.; Beck, F.; Auwärter, P.
  • Computer Graphics Forum, Vol. 34, Issue 1
  • DOI: 10.1111/cgf.12512

NeuralAct: A Tool to Visualize Electrocortical (ECoG) Activity on a Three-Dimensional Model of the Cortex
journal, November 2014


Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures
journal, April 2007


Spatial and temporal visualisation techniques for crash analysis
journal, November 2011

  • Plug, Charlotte; Xia, Jianhong (Cecilia); Caulfield, Craig
  • Accident Analysis & Prevention, Vol. 43, Issue 6
  • DOI: 10.1016/j.aap.2011.05.007

Scaling Effects and Spatio-Temporal Multilevel Dynamics in Epileptic Seizures
journal, February 2012


A Task Taxonomy for Network Evolution Analysis
journal, March 2014

  • Jae-wook Ahn, ; Plaisant, Catherine; Shneiderman, Ben
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 20, Issue 3
  • DOI: 10.1109/TVCG.2013.238

Visualizing the Evolution of Community Structures in Dynamic Social Networks
journal, June 2011


Small MultiPiles: Piling Time to Explore Temporal Patterns in Dynamic Networks
journal, June 2015

  • Bach, B.; Henry-Riche, N.; Dwyer, T.
  • Computer Graphics Forum, Vol. 34, Issue 3
  • DOI: 10.1111/cgf.12615

Spatial and temporal relationships of electrocorticographic alpha and gamma activity during auditory processing
journal, August 2014


Functional Network Organization of the Human Brain
journal, November 2011


Community detection in graphs
journal, February 2010


Dynamic reconfiguration of frontal brain networks during executive cognition in humans
journal, August 2015

  • Braun, Urs; Schäfer, Axel; Walter, Henrik
  • Proceedings of the National Academy of Sciences, Vol. 112, Issue 37
  • DOI: 10.1073/pnas.1422487112

Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans
journal, April 2009


Modular Brain Networks
journal, January 2016


ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps
journal, June 2003


Dynamic functional connectivity: Promise, issues, and interpretations
journal, October 2013


Envisioning Information
journal, January 1991


Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity
journal, July 2017

  • Murugesan, Sugeerth; Bouchard, Kristopher; Brown, Jesse A.
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 14, Issue 4
  • DOI: 10.1109/TCBB.2016.2564970