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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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 s ame 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.

Original languageEnglish (US)
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
PublisherSpringer Verlag
Pages273-280
Number of pages8
Volume17
EditionPt 3
ISBN (Print)9783319104423
DOIs
StatePublished - Jan 1 2014
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: Sep 14 2014Sep 18 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8675 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston, MA
Period9/14/149/18/14

Fingerprint

Diffusion tensor imaging
Joint Modeling
Neurons
Mouse
Neuron
Brain
Tensor
Imaging
Connectivity
Tomography
Macros
Brain mapping
Photons
Modality
Photon
Imaging techniques
Medical imaging
Electric wiring
Spectral Clustering
Multiple Scales

Keywords

  • brain mapping
  • DTI
  • Multi-scale connectome
  • neuron tracer

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Chen, H., Zhao, Y., Zhang, T., Zhang, H., Kuang, H., Li, M., ... Liu, T. (2014). Construct and assess multimodal mouse brain connectomes via joint modeling of multi-scale DTI and neuron tracer data. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 17, pp. 273-280). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8675 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-319-10443-0_35

Construct and assess multimodal mouse brain connectomes via joint modeling of multi-scale DTI and neuron tracer data. / Chen, Hanbo; Zhao, Yu; Zhang, Tuo; Zhang, Hongmiao; Kuang, Hui; Li, Meng; Tsien, Joe Z.; Liu, Tianming.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 17 Pt 3. ed. Springer Verlag, 2014. p. 273-280 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8675 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chen, H, Zhao, Y, Zhang, T, Zhang, H, Kuang, H, Li, M, Tsien, JZ & Liu, T 2014, Construct and assess multimodal mouse brain connectomes via joint modeling of multi-scale DTI and neuron tracer data. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 17, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8675 LNCS, Springer Verlag, pp. 273-280, 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, Boston, MA, United States, 9/14/14. https://doi.org/10.1007/978-3-319-10443-0_35
Chen H, Zhao Y, Zhang T, Zhang H, Kuang H, Li M et al. Construct and assess multimodal mouse brain connectomes via joint modeling of multi-scale DTI and neuron tracer data. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 17. Springer Verlag. 2014. p. 273-280. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-319-10443-0_35
Chen, Hanbo ; Zhao, Yu ; Zhang, Tuo ; Zhang, Hongmiao ; Kuang, Hui ; Li, Meng ; Tsien, Joe Z. ; Liu, Tianming. / Construct and assess multimodal mouse brain connectomes via joint modeling of multi-scale DTI and neuron tracer data. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 17 Pt 3. ed. Springer Verlag, 2014. pp. 273-280 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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