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

BMC Bioinformatics

Research

Kristofer Bouchard¹  Max Dougherty¹  Gunther H. Weber²  Sugeerth Murugesan²  Bernd Hamann³  Edward Chang⁴ 

[1] Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, 94720, Berkeley, CA, USA;Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, 94720, Berkeley, CA, USA;Department of Computer Science, University of California, One Shields Avenue, 95616, Davis, CA, USA;Department of Computer Science, University of California, One Shields Avenue, 95616, Davis, CA, USA;Department of Neurological Surgery, UCSF, 505 Parnassus Ave, 94143, San Francisco, CA, USA;

关键词: Electrocorticography;    Clustering;    Spatio-temporal graphs;    Unsupervised learning;    Neuroinformatics;    Epilepsy;    Visual analysis;    Brain imaging;    Graph visualization;    Mutli-scale analysis;   

DOI  :  10.1186/s12859-017-1633-9

来源: Springer

【 摘 要 】

BackgroundThere 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.ResultsWe 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.ConclusionECoG 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.

【 授权许可】

CC BY   
© The Regents of the University of California 2017

【 图 表 】

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Fig. 1