A platform of digital brain using crowd power
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A powerful platform of digital brain is proposed using crowd wisdom for brain research, based on the computational artificial intelligence model of synthesis reasoning and multi-source analogical generating. The design of the platform aims to make it a comprehensive brain database, a brain phantom generator, a brain knowledge base, and an intelligent assistant for research on neurological and psychiatric diseases and brain development. Using big data, crowd wisdom, and high performance computers may significantly enhance the capability of the platform. Preliminary achievements along this track are reported.
KeywordsArtificial intelligence Digital brain Synthesis reasoning Multi-source analogical generating Crowd wisdom Deducing Neuroimaging
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Special thanks to Professor Yunhe Pan for the inspiring meeting and discussion in 2014, which initialized this work.
- Baars BJ, Gage NM, 2010. Cognition, Brain, and Consciousness: Introduction to Cognitive Neuroscience (2nd Ed.). Elsevier, p.591–616. https://doi.org/10.1016/B978-0-12-375070-9.00021-8Google Scholar
- Bansal R, Xu D, Peterson BS, 2005. Eigen function based coregistration of diffusion tensor images to anatomical magnetic resonance images. Proc Int Soc Magn Reson Med, 13:2332.Google Scholar
- Fan LY, 2013. Development of Artifact-Free Imaging System and fMRI Research Paradigm for Creative Thinking in an MR-Compatible Environment. MS Thesis, East China Normal University, Shanghai, China (in Chinese).Google Scholar
- Fan LY, Fan XF, Luo WC, et al., 2014. An explorative fMRI study of human creative thinking using: a specially designed iCAD system. Acta Psychol Sin, 46(4):427–436 (in Chinese). https://doi.org/10.3724/SP.J.1041.2014.00427Google Scholar
- Liu F, Peterson B, Duan Y, et al., 2006. Fast spin echo for T2 quantification at 3T. Proc 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, p.2404.Google Scholar
- Lorenzi M, Ayache N, Frisoni G, et al., 2010. 4D registration of serial brain’s MR images: a robust measure of changes applied to Alzheimer’s disease. Miccai Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data.Google Scholar
- Maguire EA, 2001. Neuroimaging, memory and the human hippocampus. Rev Neurol, 157(8-9 Pt 1):791–794.Google Scholar
- Ng HP, Ong SH, Foong KWC, et al., 2006. Medical image segmentation using k-means clustering and improved Watershed algorithm. IEEE Southwest Symp on Image Analysis and Interpretation, p.61–65. https://doi.org/10.1109/SSIAI.2006.1633722Google Scholar
- Pan YH, 1996. The synthesis reasoning. Patt Recogn Artif Intell, 9(3):201–208 (in Chinese).Google Scholar
- Pan YH, 1997. Intelligent CAD Methodology and Modeling. Science Press, Beijing, China (in Chinese).Google Scholar
- Shen DG, Sundar H, Xue Z, et al., 2005. Consistent estimation of cardiac motions by 4D image registration. LNCS, 3750: 902–910. https://doi.org/10.1007/11566489_111Google Scholar
- Wen Y, Peterson BS, Xu DR, 2013. A highly accurate, optical flow-based algorithm for nonlinear spatial normalization of diffusion tensor images. Int Joint Conf on Neural Networks, p.1–8. https://doi.org/10.1109/IJCNN.2013.6706989Google Scholar
- Xu DR, 1995. A Study of Analogical Generation of Image in Designing, in Computer Science. PhD Thesis, Zhejiang University, Hangzhou, China, p.120 (in Chinese).Google Scholar
- Xu DR, 1998. Automated analogical design of newspaper page layout. Chin J Comput, 21(12):1066–1073 (in Chinese). https://doi.org/10.3321/j.issn:0254-4164.1998.12.002Google Scholar
- Xu DR, Pan YH, 1995. Generation-oriented analogy reasoning. Sci China, 38(9):150–167Google Scholar