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A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex

Many neural mechanisms regulate experience-dependent plasticity in the visual cortex (V1), and new techniques for quantifying large numbers of proteins or genes are transforming how plasticity is studied into the era of big data. With those large data sets comes the challenge of extracting biologica...

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Autores principales: Balsor, Justin L., Ahuja, Dezi, Jones, David G., Murphy, Kathryn M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7482673/
https://www.ncbi.nlm.nih.gov/pubmed/33192303
http://dx.doi.org/10.3389/fncel.2020.00245
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author Balsor, Justin L.
Ahuja, Dezi
Jones, David G.
Murphy, Kathryn M.
author_facet Balsor, Justin L.
Ahuja, Dezi
Jones, David G.
Murphy, Kathryn M.
author_sort Balsor, Justin L.
collection PubMed
description Many neural mechanisms regulate experience-dependent plasticity in the visual cortex (V1), and new techniques for quantifying large numbers of proteins or genes are transforming how plasticity is studied into the era of big data. With those large data sets comes the challenge of extracting biologically meaningful results about visual plasticity from data-driven analytical methods designed for high-dimensional data. In other areas of neuroscience, high-information content methodologies are revealing more subtle aspects of neural development and individual variations that give rise to a richer picture of brain disorders. We have developed an approach for studying V1 plasticity that takes advantage of the known functions of many synaptic proteins for regulating visual plasticity. We use that knowledge to rebrand protein measurements into plasticity features and combine those into a plasticity phenotype. Here, we provide a primer for analyzing experience-dependent plasticity in V1 using example R code to identify high-dimensional changes in a group of proteins. We describe using PCA to classify high-dimensional plasticity features and use them to construct a plasticity phenotype. In the examples, we show how to use this analytical framework to study and compare experience-dependent development and plasticity of V1 and apply the plasticity phenotype to translational research questions. We include an R package “PlasticityPhenotypes” that aggregates the coding packages and custom code written in RStudio to construct and analyze plasticity phenotypes.
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spelling pubmed-74826732020-11-12 A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex Balsor, Justin L. Ahuja, Dezi Jones, David G. Murphy, Kathryn M. Front Cell Neurosci Neuroscience Many neural mechanisms regulate experience-dependent plasticity in the visual cortex (V1), and new techniques for quantifying large numbers of proteins or genes are transforming how plasticity is studied into the era of big data. With those large data sets comes the challenge of extracting biologically meaningful results about visual plasticity from data-driven analytical methods designed for high-dimensional data. In other areas of neuroscience, high-information content methodologies are revealing more subtle aspects of neural development and individual variations that give rise to a richer picture of brain disorders. We have developed an approach for studying V1 plasticity that takes advantage of the known functions of many synaptic proteins for regulating visual plasticity. We use that knowledge to rebrand protein measurements into plasticity features and combine those into a plasticity phenotype. Here, we provide a primer for analyzing experience-dependent plasticity in V1 using example R code to identify high-dimensional changes in a group of proteins. We describe using PCA to classify high-dimensional plasticity features and use them to construct a plasticity phenotype. In the examples, we show how to use this analytical framework to study and compare experience-dependent development and plasticity of V1 and apply the plasticity phenotype to translational research questions. We include an R package “PlasticityPhenotypes” that aggregates the coding packages and custom code written in RStudio to construct and analyze plasticity phenotypes. Frontiers Media S.A. 2020-08-27 /pmc/articles/PMC7482673/ /pubmed/33192303 http://dx.doi.org/10.3389/fncel.2020.00245 Text en Copyright © 2020 Balsor, Ahuja, Jones and Murphy. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Balsor, Justin L.
Ahuja, Dezi
Jones, David G.
Murphy, Kathryn M.
A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex
title A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex
title_full A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex
title_fullStr A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex
title_full_unstemmed A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex
title_short A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex
title_sort primer on constructing plasticity phenotypes to classify experience-dependent development of the visual cortex
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7482673/
https://www.ncbi.nlm.nih.gov/pubmed/33192303
http://dx.doi.org/10.3389/fncel.2020.00245
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