Advertisement

Construct and Assess Multimodal Mouse Brain Connectomes via Joint Modeling of Multi-scale DTI and Neuron Tracer Data

  • Hanbo Chen
  • Yu Zhao
  • Tuo Zhang
  • Hongmiao Zhang
  • Hui Kuang
  • Meng Li
  • Joe Z. Tsien
  • Tianming Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Mapping the neuronal wiring diagrams in the brain at multiple spatial scales has been one of the major brain mapping objectives. Macro-scale medical imaging modalities such as diffusion tensor imaging (DTI) and meso-scale biological imaging such as serial two-photon tomography have emerged as the prominent tools to reveal structural connectivity patterns at multiple scales. However, a significant gap that whether/how DTI data and microscopic data are correlated with each other for the same species of mammalian brains, e.g., mouse brains, has been rarely explored. To bridge this knowledge gap, this work aims to construct multi-modal mouse brain connectomes via joint modeling of macro-scale DTI data and meso-scale neuronal tracing data. Specifically, the high-resolution DTI data and its streamline tractography result are mapped to the Allen Mouse Brain Atlas, in which the high-density axonal projections were already mapped by microscopic serial two-photon tomography. Then, multi-modal connectomes were constructed and the multi-view spectral clustering method is employed to assess consistent and discrepant connectivity patterns across the multi-scale multi-modal connectomes. Experimental results demonstrated the importance of fusing multimodal, multi-scale imaging modalities for structural connectivity and connectome mapping.

Keywords

Multi-scale connectome DTI neuron tracer brain mapping 

References

  1. 1.
    He, B., Coleman, T., Genin, G.M., Glover, G., Hu, X., Johnson, N., Liu, T., Makeig, S., Sajda, P., Ye, K.: Grand challenges in mapping the human brain: NSF workshop report. IEEE Trans. Biomed. Eng. 60, 2983–2992 (2013)CrossRefGoogle Scholar
  2. 2.
    ACD Interim Report: Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Working Group (2013) Google Scholar
  3. 3.
    Sotiropoulos, S.N., Jbabdi, S., Xu, J., Andersson, J.L., Moeller, S., Auerbach, E.J., Glasser, M.F., Hernandez, M., Sapiro, G., Jenkinson, M., Feinberg, D.A., Yacoub, E., Lenglet, C., Van Essen, D.C., Ugurbil, K., Behrens, T.E.J.: Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 80, 125–143 (2013)CrossRefGoogle Scholar
  4. 4.
    Oh, S.W., et al.: A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014)CrossRefGoogle Scholar
  5. 5.
    Ragan, T., Kadiri, L.R., Venkataraju, K.U., Bahlmann, K., Sutin, J., Taranda, J., Arganda-Carreras, I., Kim, Y., Seung, H.S., Osten, P.: Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat. Methods 9, 255–258 (2012)CrossRefGoogle Scholar
  6. 6.
    Zhang, J., van Zijl, P.C.M., Mori, S.: Three-dimensional diffusion tensor magnetic resonance microimaging of adult mouse brain and hippocampus. Neuroimage 15, 892–901 (2002)CrossRefGoogle Scholar
  7. 7.
    Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001)CrossRefGoogle Scholar
  8. 8.
    Jiang, H., van Zijl, P.C.M., Kim, J., Pearlson, G.D., Mori, S.: DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput. Methods Programs Biomed. 81, 106–116 (2006)CrossRefGoogle Scholar
  9. 9.
    Liu, T., Nie, J., Tarokh, A., Guo, L., Wong, S.T.C.: Reconstruction of central cortical surface from brain MRI images: method and application. Neuroimage 40, 991–1002 (2008)CrossRefGoogle Scholar
  10. 10.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15, 850–863 (1993)CrossRefGoogle Scholar
  11. 11.
    Chen, H., Li, K., Zhu, D., Jiang, X., Yuan, Y., Lv, P., Zhang, T., Guo, L., Shen, D., Liu, T.: Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. IEEE Trans. Med. Imaging. 32, 1576–1586 (2013)CrossRefGoogle Scholar
  12. 12.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hanbo Chen
    • 1
  • Yu Zhao
    • 1
  • Tuo Zhang
    • 1
    • 2
  • Hongmiao Zhang
    • 3
  • Hui Kuang
    • 3
  • Meng Li
    • 3
  • Joe Z. Tsien
    • 3
  • Tianming Liu
    • 1
  1. 1.Cortical Architecture Imaging and Discovery Laboratory, Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensUSA
  2. 2.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Brain and Behavior Discovery InstituteMedical College of Georgia at Georgia Regents UniversityUSA

Personalised recommendations