{"title":"CereVA - Visual Analysis of Functional Brain Connectivity","authors":"M. D. Ridder, Karsten Klein, Jinman Kim","doi":"10.5220/0005305901310138","DOIUrl":null,"url":null,"abstract":"We present CereVA, a web-based interface for the visual analysis of brain activity data. CereVA combines 2D and 3D visualizations and allows the user to interactively explore and compare brain activity data sets. The web-based interface combines several linked graphical representations of the network data, allowing for tight integration of different visualizations. The data is presented in the anatomical context within a 3D volume rendering, by node-link visualizations of connectivity networks, and by a matrix view of the data. In addition, our approach provides graph-theoretical analysis of the connectivity networks. Our solution supports several analysis tasks, including the comparison of connectivity networks, the analysis of correlation patterns, and the aggregation of networks, e.g. over a population.","PeriodicalId":326087,"journal":{"name":"International Conference on Information Visualization Theory and Applications","volume":"391 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Information Visualization Theory and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0005305901310138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
We present CereVA, a web-based interface for the visual analysis of brain activity data. CereVA combines 2D and 3D visualizations and allows the user to interactively explore and compare brain activity data sets. The web-based interface combines several linked graphical representations of the network data, allowing for tight integration of different visualizations. The data is presented in the anatomical context within a 3D volume rendering, by node-link visualizations of connectivity networks, and by a matrix view of the data. In addition, our approach provides graph-theoretical analysis of the connectivity networks. Our solution supports several analysis tasks, including the comparison of connectivity networks, the analysis of correlation patterns, and the aggregation of networks, e.g. over a population.