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Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI

Author

Listed:
  • Maged Goubran

    (Stanford University)

  • Christoph Leuze

    (Stanford University)

  • Brian Hsueh

    (Stanford University
    Stanford University)

  • Markus Aswendt

    (Stanford University)

  • Li Ye

    (Stanford University
    Stanford University)

  • Qiyuan Tian

    (Stanford University
    Stanford University)

  • Michelle Y. Cheng

    (Stanford University)

  • Ailey Crow

    (Stanford University)

  • Gary K. Steinberg

    (Stanford University)

  • Jennifer A. McNab

    (Stanford University)

  • Karl Deisseroth

    (Stanford University
    Stanford University
    Stanford University
    Stanford University)

  • Michael Zeineh

    (Stanford University)

Abstract

3D histology, slice-based connectivity atlases, and diffusion MRI are common techniques to map brain wiring. While there are many modality-specific tools to process these data, there is a lack of integration across modalities. We develop an automated resource that combines histologically cleared volumes with connectivity atlases and MRI, enabling the analysis of histological features across multiple fiber tracts and networks, and their correlation with in-vivo biomarkers. We apply our pipeline in a murine stroke model, demonstrating not only strong correspondence between MRI abnormalities and CLARITY-tissue staining, but also uncovering acute cellular effects in areas connected to the ischemic core. We provide improved maps of connectivity by quantifying projection terminals from CLARITY viral injections, and integrate diffusion MRI with CLARITY viral tracing to compare connectivity maps across scales. Finally, we demonstrate tract-level histological changes of stroke through this multimodal integration. This resource can propel investigations of network alterations underlying neurological disorders.

Suggested Citation

  • Maged Goubran & Christoph Leuze & Brian Hsueh & Markus Aswendt & Li Ye & Qiyuan Tian & Michelle Y. Cheng & Ailey Crow & Gary K. Steinberg & Jennifer A. McNab & Karl Deisseroth & Michael Zeineh, 2019. "Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI," Nature Communications, Nature, vol. 10(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13374-0
    DOI: 10.1038/s41467-019-13374-0
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    Cited by:

    1. Adam L Tyson & Charly V Rousseau & Christian J Niedworok & Sepiedeh Keshavarzi & Chryssanthi Tsitoura & Lee Cossell & Molly Strom & Troy W Margrie, 2021. "A deep learning algorithm for 3D cell detection in whole mouse brain image datasets," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-17, May.
    2. Kohei Otomo & Takaki Omura & Yuki Nozawa & Steven J. Edwards & Yukihiko Sato & Yuri Saito & Shigehiro Yagishita & Hitoshi Uchida & Yuki Watakabe & Kiyotada Naitou & Rin Yanai & Naruhiko Sahara & Satos, 2024. "descSPIM: an affordable and easy-to-build light-sheet microscope optimized for tissue clearing techniques," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    3. Jingtan Zhu & Xiaomei Liu & Zhang Liu & Yating Deng & Jianyi Xu & Kunxing Liu & Ruiying Zhang & Xizhi Meng & Peng Fei & Tingting Yu & Dan Zhu, 2024. "SOLID: minimizing tissue distortion for brain-wide profiling of diverse architectures," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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