AbstractMany 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 and using that to rebrand the results of high-dimensional analyses 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 the plasticity phenotype can be visualized and used to identify neurobiological features in V1 that change during development or after different visual rearing conditions. We include an R package “v1hdexplorer” that aggregates the various coding packages and custom visualization scripts written in R Studio.
Global impairment and therapeutic restoration of visual plasticity mechanisms after a localized cortical stroke
