Li, HuangFang, ShiaofenGoni, JoaquinContreras, Joey A.Liang, YanhuaCai, ChengtaoWest, John D.Risacher, Shannon L.Wang, YangSporns, OlafSaykin, Andrew J.Shen, Li2017-05-232017-05-232015-08Li, H., Fang, S., Goni, J., Contreras, J. A., Liang, Y., Cai, C., … Shen, L. (2015). Integrated Visualization of Human Brain Connectome Data. Brain Informatics and Health : 8th International Conference, BIH 2015, London, UK, August 30-September 2, 2015 : Proceedings / Yike Guo, Karl Friston, Aldo Faisal, Sean Hill, Hanchuan Peng (eds.). BIH (Conference) (8th : 2015 : London, ., 9250, 295–305. http://doi.org/10.1007/978-3-319-23344-4_29https://hdl.handle.net/1805/12708Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases.en-USPublisher PolicyBrain connectomeMRIDTIfMRIVisualizationArticle