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

• Published in: BMC bioinformatics Vol. 18; no. Suppl 6; p. 236
• Main Authors: Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, Weber, Gunther H
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.06.2017; BioMed Central; BMC
• Subjects: 60 APPLIED LIFE SCIENCES; Algorithms; Brain - diagnostic imaging; Brain - physiopathology; Brain imaging; Cluster Analysis; Clustering; Computational biology; Computational Biology - methods; Electrocorticography; Electrocorticography - methods; Epilepsy; Epilepsy - physiopathology; Graph visualization; Humans; Innovations; Medical research; Mutli-scale analysis; Neuroimaging; Neuroinformatics; Software; Spatio-temporal graphs; Unsupervised learning; Visual analysis; Brain imaging; Mutli-scale analysis; Epilepsy; Neuroinformatics; Visual analysis; Graph visualization; Spatio-temporal graphs; Clustering; Unsupervised learning; Electrocorticography
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1633-9

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.