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Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging
Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Bio-Protocol
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635841/ https://www.ncbi.nlm.nih.gov/pubmed/34909442 http://dx.doi.org/10.21769/BioProtoc.4221 |
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author | Arefin, Tanzil Mahmud Lee, Choong Heon White, Jordon D. Zhang, Jiangyang Kaffman, Arie |
author_facet | Arefin, Tanzil Mahmud Lee, Choong Heon White, Jordon D. Zhang, Jiangyang Kaffman, Arie |
author_sort | Arefin, Tanzil Mahmud |
collection | PubMed |
description | Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains. Graphic abstract: [Image: see text] Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm. |
format | Online Article Text |
id | pubmed-8635841 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Bio-Protocol |
record_format | MEDLINE/PubMed |
spelling | pubmed-86358412022-11-20 Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging Arefin, Tanzil Mahmud Lee, Choong Heon White, Jordon D. Zhang, Jiangyang Kaffman, Arie Bio Protoc Methods Article Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains. Graphic abstract: [Image: see text] Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm. Bio-Protocol 2021-11-20 /pmc/articles/PMC8635841/ /pubmed/34909442 http://dx.doi.org/10.21769/BioProtoc.4221 Text en ©Copyright Arefin et al. https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). |
spellingShingle | Methods Article Arefin, Tanzil Mahmud Lee, Choong Heon White, Jordon D. Zhang, Jiangyang Kaffman, Arie Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging |
title | Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging |
title_full | Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging |
title_fullStr | Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging |
title_full_unstemmed | Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging |
title_short | Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging |
title_sort | macroscopic structural and connectome mapping of the mouse brain using diffusion magnetic resonance imaging |
topic | Methods Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635841/ https://www.ncbi.nlm.nih.gov/pubmed/34909442 http://dx.doi.org/10.21769/BioProtoc.4221 |
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