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Mesoscale brain explorer, a flexible python-based image analysis and visualization tool
Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sen...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society of Photo-Optical Instrumentation Engineers
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438099/ https://www.ncbi.nlm.nih.gov/pubmed/28560240 http://dx.doi.org/10.1117/1.NPh.4.3.031210 |
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author | Haupt, Dirk Vanni, Matthieu P. Bolanos, Federico Mitelut, Catalin LeDue, Jeffrey M. Murphy, Tim H. |
author_facet | Haupt, Dirk Vanni, Matthieu P. Bolanos, Federico Mitelut, Catalin LeDue, Jeffrey M. Murphy, Tim H. |
author_sort | Haupt, Dirk |
collection | PubMed |
description | Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE. |
format | Online Article Text |
id | pubmed-5438099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Society of Photo-Optical Instrumentation Engineers |
record_format | MEDLINE/PubMed |
spelling | pubmed-54380992018-05-19 Mesoscale brain explorer, a flexible python-based image analysis and visualization tool Haupt, Dirk Vanni, Matthieu P. Bolanos, Federico Mitelut, Catalin LeDue, Jeffrey M. Murphy, Tim H. Neurophotonics Pioneers in Neurophotonics: Special Section Honoring Professor Amiram Grinvald Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE. Society of Photo-Optical Instrumentation Engineers 2017-05-19 2017-07 /pmc/articles/PMC5438099/ /pubmed/28560240 http://dx.doi.org/10.1117/1.NPh.4.3.031210 Text en © The Authors. https://creativecommons.org/licenses/by/3.0/ Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
spellingShingle | Pioneers in Neurophotonics: Special Section Honoring Professor Amiram Grinvald Haupt, Dirk Vanni, Matthieu P. Bolanos, Federico Mitelut, Catalin LeDue, Jeffrey M. Murphy, Tim H. Mesoscale brain explorer, a flexible python-based image analysis and visualization tool |
title | Mesoscale brain explorer, a flexible python-based image analysis and visualization tool |
title_full | Mesoscale brain explorer, a flexible python-based image analysis and visualization tool |
title_fullStr | Mesoscale brain explorer, a flexible python-based image analysis and visualization tool |
title_full_unstemmed | Mesoscale brain explorer, a flexible python-based image analysis and visualization tool |
title_short | Mesoscale brain explorer, a flexible python-based image analysis and visualization tool |
title_sort | mesoscale brain explorer, a flexible python-based image analysis and visualization tool |
topic | Pioneers in Neurophotonics: Special Section Honoring Professor Amiram Grinvald |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438099/ https://www.ncbi.nlm.nih.gov/pubmed/28560240 http://dx.doi.org/10.1117/1.NPh.4.3.031210 |
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