A platform of digital brain using crowd power

  • Dongrong Xu
  • Fei Dai
  • Yue Lu


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.


Artificial intelligence Digital brain Synthesis reasoning Multi-source analogical generating Crowd wisdom Deducing Neuroimaging 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



Special thanks to Professor Yunhe Pan for the inspiring meeting and discussion in 2014, which initialized this work.


  1. Amunts K, Schleicher A, Bürgel U, et al., 1999. Broca’s region revisited: cytoarchitecture and intersubject variability. J Comp Neurol, 412(2):319–341.<319::AID-CNE10>3.0.CO;2-7CrossRefGoogle Scholar
  2. Arnold JB, Liow JS, Schaper KA, et al., 2001. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects. NeuroImage, 13(5):931–943. Scholar
  3. Aubert-Broche B, Evans AC, Collins L, 2006. A new improved version of the realistic digital brain phantom. NeuroImage, 32(1):138–145. Scholar
  4. Baars BJ, Gage NM, 2010. Cognition, Brain, and Consciousness: Introduction to Cognitive Neuroscience (2nd Ed.). Elsevier, p.591–616. Scholar
  5. 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
  6. Bansal R, Staib LH, Xu DR, et al., 2007. Statistical analyses of brain surfaces using gaussian random fields on 2-D manifolds. IEEE Trans Med Imag, 26(1):46–57. Scholar
  7. Bansal R, Staib LH, Laine AF, et al., 2012. Anatomical brain images alone can accurately diagnose chronic neuropsy chiatric illnesses. PloS ONE, 7(12):e50698. Scholar
  8. Bansal R, Hao XJ, Liu F, et al., 2013. The effects of changing water content, relaxation times, and tissue contrast on tissue segmentation and measures of cortical anatomy in MR images. Magn Reson Imag, 31(10):1709–1730. Scholar
  9. Bastin ME, 1999. Correction of eddy current-induced artefacts in diffusion tensor imaging using iterative crosscorrelation. Magn Reson Imag, 17(7):1011–1024. Scholar
  10. Bastin ME, 2001. On the use of the FLAIR technique to improve the correction of eddy current induced artefacts in MR diffusion tensor imaging. Magn Reson Imag, 19(7): 937–950. Scholar
  11. Bastin ME, Armitage PA, 2000. On the use of water phantom images to calibrate and correct eddy current induced artefacts in MR diffusion tensor imaging. Magn Reson Imag, 18(6):681–687. Scholar
  12. Belliveau JW, Kennedy DNJr, McKinstry RC, et al., 1991. Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 254(5032):716–719. Scholar
  13. Bohland JW, Bokil H, Allen CB, et al., 2009. The brain atlas concordance problem: quantitative comparison of anatomical parcellations. PLoS ONE, 4(9):e7200. Scholar
  14. Bradley MM, Sabatinelli D, Lang PJ, et al., 2003. Activation of the visual cortex in motivated attention. Behav Neurosci, 117(2):369–380. Scholar
  15. Brown RW, Cheng YCN, Haacke EM, et al., 2014. Magnetic Resonance Imaging: Physical Principles and Sequence Design (2nd Ed.). Wiley Blackwell, New York. Scholar
  16. Davatzikos C, 1996a. Nonlinear registration of brain images using deformable models. Proc Workshop on Mathematical Methods in Biomedical Image Analysis, p.94–103. Scholar
  17. Davatzikos C, 1996b. Spatial normalization of 3D brain images using deformable models. J Comput Assist Tomogr, 20(4):656–665. Scholar
  18. Davatzikos C, 1997. Spatial transformation and registration of brain images using elastically deformable models. Comput Vis Image Underst, 66(2):207–222. Scholar
  19. Davatzikos C, Genc A, Xu DR, et al., 2001. Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy. NeuroImage, 14(6):1361–1369. Scholar
  20. DeYoe EA, Carman GJ, Bandettini P, et al., 1996. Mapping striate and extrastriate visual areas in human cerebral cortex. PNAS, 93(6):2382–2386. Scholar
  21. Dubin M, Weissman M, Xu DR, et al., 2012. Identification of a circuit-based endophenotype for familial depression. Psych Res Neuroimag, 201(3):175–181. Scholar
  22. Evans AC, 2006. The NIH MRI study of normal brain development. NeuroImage, 30(1):184–202. Scholar
  23. Fagiolo G, Waldman A, Hajnal JV, 2008. A simple procedure to improve FMRIb software library brain extraction tool performance. Br J Radiol, 81(963):250–251. Scholar
  24. 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
  25. 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). Scholar
  26. Hagmann P, Cammoun L, Gigandet X, et al., 2010. MR connectomics: principles and challenges. J Neurosci Methods, 194(1):34–45. Scholar
  27. Hagoort P, 2005. On broca, brain, and binding: a new framework. Trends Cogn Sci, 9(9):416–423. Scholar
  28. Hao XJ, Xu DR, Bansal R, et al., 2013. Multimodal magnetic resonance imaging: the coordinated use of multiple, mutually informative probes to understand brain structure and function. Human Brain Map, 34(2):253–271. Scholar
  29. Haselgrove JC, Moore JR, 1996. Correction for distortion of echo-planar images used to calculate the apparent diffusion coefficient. Magn Reson Med, 36(6):960–964. Scholar
  30. Hsu JL, Leemans A, Bai CH, et al., 2008. Gender differences and age-related white matter changes of the human brain: a diffusion tensor imaging study. NeuroImage, 39(2):566–577. Scholar
  31. Huster RJ, Westerhausen R, Kreuder F, et al., 2009. Hemispheric and gender related differences in the Midcingulum bundle: a DTI study. Human Brain Map, 30(2):383–391. Scholar
  32. Jack CR Jr, Bernstein MA, Fox NC, et al., 2008. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imag, 27(4):685–691. Scholar
  33. Jiang YW, Liu F, Fan MX, et al., 2017. Deducing magnetic resonance neuroimages based on knowledge from samples. Comput Med Imag Graph, 62:1–14. Scholar
  34. Kanungo T, Mount DM, Netanyahu NS, et al., 2002. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Patt Anal Mach Intell, 24(7):881–892. Scholar
  35. 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
  36. Liu F, Garland M, Duan YS, et al., 2008. Study of the development of fetal baboon brain using magnetic resonance imaging at 3 Tesla. NeuroImage, 40(1):148–159. Scholar
  37. Liu F, Garland M, Duan YS, et al., 2010. Techniques for in utero, longitudinal MRI of fetal brain development in baboons at 3 T. Methods, 50(3):147–156. Scholar
  38. Liu W, Liu XZ, Yang G, et al., 2012a. Improving the correction of eddy current-induced distortion in diffusion-weighted images by excluding signals from the cerebral spinal fluid. Comput Med Imag Graph, 36(7):542–551. Scholar
  39. Liu W, Liu XZ, He XF, et al., 2012b. Spatial normalization of diffusion tensor images with voxel-wise reconstruction of the diffusion gradient direction. Proc 2nd Int Conf on Multimodal Brain Image Analysis, p.134–146. Scholar
  40. Liu XZ, Yuan ZM, Zhu JM, et al., 2013. Medical image registration by combining global and local information: a chain-type diffeomorphic demons algorithm. Phys Med Biol, 58(23):8359–8378. Scholar
  41. 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
  42. Lynch G, 1979. Representations in the brain. Science, 204(4394):762. Scholar
  43. Maguire EA, 2001. Neuroimaging, memory and the human hippocampus. Rev Neurol, 157(8-9 Pt 1):791–794.Google Scholar
  44. Mak KK, Kong WY, Mak A, et al., 2013. Polymorphisms of the serotonin transporter gene and post-stroke depression: a meta-analysis. J Neurol Neurosurg Psych, 84(3):322–328. Scholar
  45. Michelucci P, Dickinson JL, 2016. The power of crowds. Science, 351(6268):32–33. Scholar
  46. Neeb H, Zilles K, Shah NJ, 2006. Fully-automated detection of cerebral water content changes: study of age-and genderrelated H2O patterns with quantitative MRI. NeuroImage, 29(3):910–922. Scholar
  47. 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. Scholar
  48. Nickel M, Murphy K, Tresp V, et al., 2016. A review of relational machine learning for knowledge graphs. Proc IEEE, 104(1):11–33. Scholar
  49. Packard MG, White NM, 1991. Dissociation of hippocampus and caudate nucleus memory systems by posttraining intracerebral injection of dopamine agonists. Behav Neurosci, 105(2):295–306. Scholar
  50. Pan YH, 1996. The synthesis reasoning. Patt Recogn Artif Intell, 9(3):201–208 (in Chinese).Google Scholar
  51. Pan YH, 1997. Intelligent CAD Methodology and Modeling. Science Press, Beijing, China (in Chinese).Google Scholar
  52. Peterson BS, Warnera V, Bansal R, et al., 2009. Cortical thinning in persons at increased familial risk for major depression. PNAS, 106(15):6273–6278. Scholar
  53. Plessen KJ, Grüner R, Lundervold A, et al., 2006. Reduced white matter connectivity in the corpus callosum of children with Tourette syndrome. J Child Psychol Psych, 47(10):1013–1022. Scholar
  54. Rhodes G, Brennan S, Carey S, 1987. Identification and ratings of caricatures: implications for mental representations of faces. Cogn Psychol, 19(4):473–497. Scholar
  55. Schreibmann E, Thorndyke B, Li TF, et al., 2008. Fourdimensional image registration for image-guided radiotherapy. Int J Radiat Oncol Biol Phys, 71(2):578–586. Scholar
  56. Shapiro ML, Eichenbaum H, 1999. Hippocampus as a memory map: Synaptic plasticity and memory encoding by hippocampal neurons. Hippocampus, 9(4):365–384.<365::AID-HIPO4>3.0.CO;2-TCrossRefGoogle Scholar
  57. Shen DG, Davatzikos C, 2002. HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imag, 21(11):1421–1439. Scholar
  58. Shen DG, Davatzikos C, 2003. Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration. NeuroImage, 18(1):28–41. Scholar
  59. Shen DG, Davatzikos C, 2004. Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warping. NeuroImage, 21(4):1508–1517. Scholar
  60. Shen DG, Sundar H, Xue Z, et al., 2005. Consistent estimation of cardiac motions by 4D image registration. LNCS, 3750: 902–910. Scholar
  61. Sowell ER, Peterson BS, Kan E, et al., 2007. Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cerebr Cort, 17(7):1550–1560. Scholar
  62. Sowell ER, Kan E, Yoshii J, et al., 2008. Thinning of sensorimotor cortices in children with tourette syndrome. Nat Neurosci, 11(6):637–639. Scholar
  63. Sporns O, 2011. The human connectome: a complex network. Ann New York Acad Sci, 1224(1):109–125. Scholar
  64. Squire LR, 1992. Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans. Psychol Rev, 99(2):195–231.CrossRefGoogle Scholar
  65. Toga AW, Clark KA, Thompson PM, et al., 2012. Mapping the human connectome. Neurosurgery, 71(1):1–5. Scholar
  66. van Essen DC, Smith SM, Barch DM, et al., 2013. The WU-Minn Human Connectome Project: an overview. NeuroImage, 80:62–79. Scholar
  67. van Hecke W, Sijbers J, de Backer S, et al., 2009. On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods. NeuroImage, 46(3):692–707. Scholar
  68. 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. Scholar
  69. 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
  70. Xu DR, 1998. Automated analogical design of newspaper page layout. Chin J Comput, 21(12):1066–1073 (in Chinese). Scholar
  71. Xu DR, Pan YH, 1995. Generation-oriented analogy reasoning. Sci China, 38(9):150–167Google Scholar
  72. Xu DR, Mori S, Shen DG, et al., 2003. Spatial normalization of diffusion tensor fields. Magn Reson Med, 50(1):175–182. Scholar
  73. Xu DR, Hao XJ, Bansal R, et al., 2008. Seamless warping of diffusion tensor fields. IEEE Trans Med Imag, 27(3):285–299. Scholar
  74. Zhuang JC, Hrabe J, Kangarlu A, et al., 2006. Correction of eddy-current distortions in diffusion tensor images using the known directions and strengths of diffusion gradients. J Magn Reson Imag, 24(5):1188–1193. Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Columbia University & New York State Psychiatric InstituteNew YorkUSA
  2. 2.Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina

Personalised recommendations