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

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 vol...

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Autores principales: Goubran, Maged, Leuze, Christoph, Hsueh, Brian, Aswendt, Markus, Ye, Li, Tian, Qiyuan, Cheng, Michelle Y., Crow, Ailey, Steinberg, Gary K., McNab, Jennifer A., Deisseroth, Karl, Zeineh, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890789/
https://www.ncbi.nlm.nih.gov/pubmed/31796741
http://dx.doi.org/10.1038/s41467-019-13374-0
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author Goubran, Maged
Leuze, Christoph
Hsueh, Brian
Aswendt, Markus
Ye, Li
Tian, Qiyuan
Cheng, Michelle Y.
Crow, Ailey
Steinberg, Gary K.
McNab, Jennifer A.
Deisseroth, Karl
Zeineh, Michael
author_facet Goubran, Maged
Leuze, Christoph
Hsueh, Brian
Aswendt, Markus
Ye, Li
Tian, Qiyuan
Cheng, Michelle Y.
Crow, Ailey
Steinberg, Gary K.
McNab, Jennifer A.
Deisseroth, Karl
Zeineh, Michael
author_sort Goubran, Maged
collection PubMed
description 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.
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spelling pubmed-68907892019-12-05 Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI Goubran, Maged Leuze, Christoph Hsueh, Brian Aswendt, Markus Ye, Li Tian, Qiyuan Cheng, Michelle Y. Crow, Ailey Steinberg, Gary K. McNab, Jennifer A. Deisseroth, Karl Zeineh, Michael Nat Commun Article 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. Nature Publishing Group UK 2019-12-03 /pmc/articles/PMC6890789/ /pubmed/31796741 http://dx.doi.org/10.1038/s41467-019-13374-0 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Goubran, Maged
Leuze, Christoph
Hsueh, Brian
Aswendt, Markus
Ye, Li
Tian, Qiyuan
Cheng, Michelle Y.
Crow, Ailey
Steinberg, Gary K.
McNab, Jennifer A.
Deisseroth, Karl
Zeineh, Michael
Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI
title Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI
title_full Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI
title_fullStr Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI
title_full_unstemmed Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI
title_short Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI
title_sort multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890789/
https://www.ncbi.nlm.nih.gov/pubmed/31796741
http://dx.doi.org/10.1038/s41467-019-13374-0
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