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Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages
We report improved automated open-source methodology for head-fixed mesoscale cortical imaging and/or behavioral training of home cage mice using Raspberry Pi-based hardware. Staged partial and probabilistic restraint allows mice to adjust to self-initiated headfixation over 3 weeks’ time with ~50%...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332290/ https://www.ncbi.nlm.nih.gov/pubmed/32412409 http://dx.doi.org/10.7554/eLife.55964 |
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author | Murphy, Timothy H Michelson, Nicholas J Boyd, Jamie D Fong, Tony Bolanos, Luis A Bierbrauer, David Siu, Teri Balbi, Matilde Bolanos, Federico Vanni, Matthieu LeDue, Jeff M |
author_facet | Murphy, Timothy H Michelson, Nicholas J Boyd, Jamie D Fong, Tony Bolanos, Luis A Bierbrauer, David Siu, Teri Balbi, Matilde Bolanos, Federico Vanni, Matthieu LeDue, Jeff M |
author_sort | Murphy, Timothy H |
collection | PubMed |
description | We report improved automated open-source methodology for head-fixed mesoscale cortical imaging and/or behavioral training of home cage mice using Raspberry Pi-based hardware. Staged partial and probabilistic restraint allows mice to adjust to self-initiated headfixation over 3 weeks’ time with ~50% participation rate. We support a cue-based behavioral licking task monitored by a capacitive touch-sensor water spout. While automatically head-fixed, we acquire spontaneous, movement-triggered, or licking task-evoked GCaMP6 cortical signals. An analysis pipeline marked both behavioral events, as well as analyzed brain fluorescence signals as they relate to spontaneous and/or task-evoked behavioral activity. Mice were trained to suppress licking and wait for cues that marked the delivery of water. Correct rewarded go-trials were associated with widespread activation of midline and lateral barrel cortex areas following a vibration cue and delayed frontal and lateral motor cortex activation. Cortical GCaMP signals predicted trial success and correlated strongly with trial-outcome dependent body movements. |
format | Online Article Text |
id | pubmed-7332290 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-73322902020-07-13 Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages Murphy, Timothy H Michelson, Nicholas J Boyd, Jamie D Fong, Tony Bolanos, Luis A Bierbrauer, David Siu, Teri Balbi, Matilde Bolanos, Federico Vanni, Matthieu LeDue, Jeff M eLife Neuroscience We report improved automated open-source methodology for head-fixed mesoscale cortical imaging and/or behavioral training of home cage mice using Raspberry Pi-based hardware. Staged partial and probabilistic restraint allows mice to adjust to self-initiated headfixation over 3 weeks’ time with ~50% participation rate. We support a cue-based behavioral licking task monitored by a capacitive touch-sensor water spout. While automatically head-fixed, we acquire spontaneous, movement-triggered, or licking task-evoked GCaMP6 cortical signals. An analysis pipeline marked both behavioral events, as well as analyzed brain fluorescence signals as they relate to spontaneous and/or task-evoked behavioral activity. Mice were trained to suppress licking and wait for cues that marked the delivery of water. Correct rewarded go-trials were associated with widespread activation of midline and lateral barrel cortex areas following a vibration cue and delayed frontal and lateral motor cortex activation. Cortical GCaMP signals predicted trial success and correlated strongly with trial-outcome dependent body movements. eLife Sciences Publications, Ltd 2020-05-15 /pmc/articles/PMC7332290/ /pubmed/32412409 http://dx.doi.org/10.7554/eLife.55964 Text en © 2020, Murphy et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Murphy, Timothy H Michelson, Nicholas J Boyd, Jamie D Fong, Tony Bolanos, Luis A Bierbrauer, David Siu, Teri Balbi, Matilde Bolanos, Federico Vanni, Matthieu LeDue, Jeff M Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages |
title | Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages |
title_full | Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages |
title_fullStr | Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages |
title_full_unstemmed | Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages |
title_short | Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages |
title_sort | automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332290/ https://www.ncbi.nlm.nih.gov/pubmed/32412409 http://dx.doi.org/10.7554/eLife.55964 |
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