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  This material is based upon work supported by the National Science Foundation under Grant Number 0713315 (Principal Investigator: Jing Hua). Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF)

 

NSF Project: Integrated Modeling and Learning of Multimodality Data across Subjects for Brain Disorder Study

 

Graphics and Imaging Lab

Wayne State University

 

Summary


With ever-improving imaging technologies and ever-increasing high-performance computational power, the complexity and scale of acquired brain imaging data have continued to grow at an explosive pace. Rapid advances in multimodality imaging technologies have significantly accelerated brain disorder studies by providing complementary information on many aspects of the human brain in the normal and diseased states. Capitalizing on the availability of large-scale data, we are now able to computationally integrate, index and model the brain functions across a large population for discovering more detailed understanding and more profound knowledge about complex biological interactions in the human brain. Based on our continuous research effort along this direction, this NSF project is developing a novel, rigorous theoretical framework based on Riemannian geometry, multivariate simplex splines, and statistical learning, which provides a basis for multimodality information integration and understanding across populations. Specifically, our research team will design a fundamental framework for advanced and integrated analysis of brain imaging data. It is expected that the developed, advanced informatics tools will allow the quantitative and integrative analysis of a variety of functional patterns and the relationships between anatomical and functional features in different datasets. The proposed computational framework has the potential to be applied across multiple areas of brain research as well as in clinical diagnosis.

 

 

Software Download

We are pleased to announce the first release of our software, BrainSpace version 1.0 (release date: April 09, 2009). You may browse our brief software manual (PDF Version) for more information regarding the BrainSpace. Detailed instructions regarding how to install and use the software will be provided upon registration.

The figure below shows the conformal brain surface model (Figure 1A and Figure 1B) facilitates accurate matching and registration among subjects in the canonical, spherical domain, hence supporting integrated cross-subject analysis of Positron Emission Tomography (PET) (molecular-level brain activity analysis) (Figure 1C), Diffusion Tensor Imaging (DTI) (neural fiber connectivity analysis) (Figure 1D), and Electroencephalography (EEG) (time-varying signal analysis) (Figure 1E) in computer-aided diagnosis of brain disorders. Figure 2 shows the basic processing pipeline in BrainSpace.

 

The software installation package and manual can be download free after a validated registration. Please follow this link for registration and download. The related publications is as follows. Please make proper references to one or more of the related articles if you use BrainSpace for your research and publications. If you have any question, suggestion, or comment, please contact us via ai2543@wayne.edu.

 

Selected, Related Publications:

  1. Jing Hua, Zhaoqiang Lai, Ming Dong, Xianfeng Gu, and Hong Qin, "Geodesic Distance-Weighted Shape Vector Image Diffusion," IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1643 - 1650, 2008. (Citing article)

  2. Yuanhong Li, Ming Dong, and Jing Hua, "Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 5, pp. 953 - 960, 2009.

  3. Yunhao Tan, Jing Hua, and Hong Qin, "Physically Based Modeling and Simulation with Dynamic Spherical Volumetric Simplex Splines," Computer-Aided Design, Vol. 41, 2009.

  4. Michael E. Behen, Otto Muzik, Anita S.D. Saporta, Benjamin J Wilson, Darshan Pai, Jing Hua and Harry T. Chugani, "Abnormal fronto-striatal connectivity in children with histories of early deprivation: A diffusion tensor imaging study," Brain Imaging and Behavior, Vol. 3, 2009.

  5. Guangyu Zou, Jing Hua, Ming Dong, and Hong Qin, "Surface Matching with Salient Keypoints in Geodesic Scale Space," Journal of Computer Animation and Virtual Worlds, Vol. 19, No. 3-4, pp. 399 - 410, 2008.

  6. Cui Lin, Shiyong Lu, Xuwei Liang, Jing Hua, and Otto Muzik, "Cocluster Analysis of Thalamo-Cortical Fiber Tracts Extracted from Diffusion Tensor MRI," International Journal of Data Mining and Bioinformatics, Vol. 2, No. 4, pp. 342 - 361, 2008.

  7. Darshan Pai, Otto Muzik, and Jing Hua, "Quantitative Analysis of Diffusion Tensor Images across Subjects Using Probabilistic Tractography," In Proceedings of IEEE International Conference on Image Processing (ICIP), 2008. (Citing article)

  8. Zhaoqiang Lai and Jing Hua, "3D Surface Matching and Registration through Shape Images," In Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2008.

  9. Guangyu Zou, Jing Hua, and Otto Muzik, "Non-rigid Surface Registration Using Spherical Thin-plate Splines," In Proceedings of the 10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Part I, LNCS 4791, pp. 367-374, 2007. (Citing article)

  10. Guangyu Zou, Jing Hua, and Ming Dong, "Integrative Information Visualization of Multimodality Neuroimaging Data," In Proceedings of the 15th Pacific Graphics Conference (PG), 2007.

  11. Guangyu Zou, Jing Hua, Xianfeng Gu, and Otto Muzik, "An Approach for Intersubject Analysis of 3D Brain Images based on Conformal Geometry," In Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 1193 - 1196, 2006. (Citing article)