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

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 and Michael Zeineh ()
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Maged Goubran: Stanford University
Christoph Leuze: Stanford University
Brian Hsueh: Stanford University
Markus Aswendt: Stanford University
Li Ye: Stanford University
Qiyuan Tian: 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
Michael Zeineh: Stanford University

Nature Communications, 2019, vol. 10, issue 1, 1-17

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-13374-0

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