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. 2017. "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 = 2017,
month = 6
}

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

Save / Share:
  • Over the past decade, a number of experiments dealt with the problem of measuring the arrival direction distribution of cosmic rays, looking for information on the propagation mechanisms and the identification of their sources. Any deviation from the isotropy may be regarded to as a signature of unforeseen or unknown phenomena, mostly if well localized in the sky and occurring at low rigidity. It induced experimenters to search for excesses down to angular scales as narrow as 10 Degree-Sign , disclosing the issue of properly filtering contributions from wider structures. A solution commonly envisaged was based on time-average methods tomore » determine the reference value of cosmic-ray flux. Such techniques are nearly insensitive to signals wider than the time window in use, thus allowing us to focus the analysis on medium- and small-scale signals. Nonetheless, the signal often cannot be excluded in the calculation of the reference value, which induces systematic errors. The use of time-average methods recently revealed important discoveries about the medium-scale cosmic-ray anisotropy, present both in the northern and southern hemispheres. It is known that the excess (or deficit) is observed as less intense than in reality and that fake deficit zones are rendered around true excesses because of the absolute lack of knowledge a priori of which signal is true and which is not. This work is an attempt to critically review the use of time-average-based methods for observing extended features in the cosmic-ray arrival distribution pattern.« less
  • This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitatesmore » intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. Furthermore, to illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.« less
  • Here, we present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views—such as heat maps, node link diagrams, and anatomical views—using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parametersmore » gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. In order to demonstrate the utility of our tool, we also present two case studies—exploring progressive supranuclear palsy, as well as memory encoding and retrieval« less
  • Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less
  • Cited by 1