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

Authors :
Markus Aswendt
Maged Goubran
Michael Zeineh
Christoph Leuze
Karl Deisseroth
Gary K. Steinberg
Li Ye
Brian Hsueh
Qiyuan Tian
Jennifer A. McNab
Michelle Y. Cheng
Ailey K. Crow
Source :
Nature Communications, Vol 10, Iss 1, Pp 1-17 (2019), Nature Communications
Publication Year :
2019
Publisher :
Nature Publishing Group, 2019.

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.<br />Many approaches exist to process data from individual imaging modalities, but integrating them is challenging. The authors develop an automated resource that enables co-registered network- and tract-level analysis of macroscopic in-vivo imaging and microscopic imaging of cleared tissue.

Details

Language :
English
ISSN :
20411723
Volume :
10
Issue :
1
Database :
OpenAIRE
Journal :
Nature Communications
Accession number :
edsair.doi.dedup.....6546e9a92eef626947fc260103c413bf
Full Text :
https://doi.org/10.1038/s41467-019-13374-0