Li, HuangFang, ShiaofenContreras, Joey A.West, John D.Risacher, Shannon L.Wang, YangSporns, OlafSaykin, Andrew J.Goñi, JoaquínShen, Li2018-06-082018-06-082017-08-23Li, H., Fang, S., Contreras, J. A., West, J. D., Risacher, S. L., Wang, Y., … Shen, L. (2017). Brain explorer for connectomic analysis. Brain Informatics, 4(4), 253–269. https://doi.org/10.1007/s40708-017-0071-92198-4018https://hdl.handle.net/1805/16445Visualization plays a vital role in the analysis of multimodal 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 anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. 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-USAttribution 3.0 United StatesBrain connectomeDiffusion tensor imagingFunctional magnetic resonance imagingMagnetic resonance imagingVisualizationArticle