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MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning

Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we develop...

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Autores principales: Xiao, Dongsheng, Forys, Brandon J., Vanni, Matthieu P., Murphy, Timothy H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514445/
https://www.ncbi.nlm.nih.gov/pubmed/34645817
http://dx.doi.org/10.1038/s41467-021-26255-2
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author Xiao, Dongsheng
Forys, Brandon J.
Vanni, Matthieu P.
Murphy, Timothy H.
author_facet Xiao, Dongsheng
Forys, Brandon J.
Vanni, Matthieu P.
Murphy, Timothy H.
author_sort Xiao, Dongsheng
collection PubMed
description Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.
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spelling pubmed-85144452021-10-29 MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning Xiao, Dongsheng Forys, Brandon J. Vanni, Matthieu P. Murphy, Timothy H. Nat Commun Article Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis. Nature Publishing Group UK 2021-10-13 /pmc/articles/PMC8514445/ /pubmed/34645817 http://dx.doi.org/10.1038/s41467-021-26255-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Xiao, Dongsheng
Forys, Brandon J.
Vanni, Matthieu P.
Murphy, Timothy H.
MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning
title MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning
title_full MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning
title_fullStr MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning
title_full_unstemmed MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning
title_short MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning
title_sort mesonet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8514445/
https://www.ncbi.nlm.nih.gov/pubmed/34645817
http://dx.doi.org/10.1038/s41467-021-26255-2
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