Traditionally, myelin is viewed as insulation around axons, however, more recent studies have shown it also plays an important role in plasticity, axonal metabolism, and neuroimmune signaling. Myelin is a complex multi-protein structure composed of hundreds of proteins, with Myelin Basic Protein (MBP) being the most studied. MBP has two families: Classic-MBP that is necessary for activity driven compaction of myelin around axons, and Golli-MBP that is found in neurons, oligodendrocytes, and T-cells. Furthermore, Golli-MBP has been called a “molecular link” between the nervous and immune systems. In visual cortex specifically, myelin proteins interact with immune processes to affect experience-dependent plasticity. We studied myelin in human visual cortex using Western blotting to quantify Classic- and Golli-MBP expression in post-mortem tissue samples ranging in age from 20 days to 80 years. We found that Classic- and Golli-MBP have different patterns of change across the lifespan. Classic-MBP gradually increases to 42 years and then declines into aging. Golli-MBP has early developmental changes that are coincident with milestones in visual system sensitive period, and gradually increases into aging. There are three stages in the balance between Classic- and Golli-MBP expression, with Golli-MBP dominating early, then shifting to Classic-MBP, and back to Golli-MBP in aging. Also Golli-MBP has a wave of high inter-individual variability during childhood. These results about cortical MBP expression are timely because they compliment recent advances in MRI techniques that produce high resolution maps of cortical myelin in normal and diseased brain. In addition, the unique pattern of Golli-MBP expression across the lifespan suggests that it supports high levels of neuroimmune interaction in cortical development and in aging.

Abnormal visual experience during childhood often leads to amblyopia, with strong links to binocular dysfunction that can include poor acuity in both eyes, especially in central vision. In animal models of amblyopia, the non-deprived eye is often considered normal and what limits binocular acuity. This leaves open the question whether monocular deprivation (MD) induces binocular dysfunction similar to what is found in amblyopia. In previous studies of MD cats, we found a loss of excitatory receptors restricted to the central visual field representation in visual cortex (V1), including both eyes' columns. This led us to ask two questions about the effects of MD: how quickly are receptors lost in V1? and is there an impact on binocular acuity? We found that just a few hours of MD caused a rapid loss of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor proteins across all of V1. But after a few days of MD, there was recovery in the visual periphery, leaving a loss of AMPA receptors only in the central region of V1. We reared animals with early MD followed by a long period of binocular vision and found binocular acuity deficits that were greatest in the central visual field. Our results suggest that the greater binocular acuity deficits in the central visual field are driven in part by the long-term loss of AMPA receptors in the central region of V1.

Monocular deprivation (MD) during the critical period (CP) has enduring effects on visual acuity and the functioning of the visual cortex (V1). This experience-dependent plasticity has become a model for studying the mechanisms, especially glutamatergic and GABAergic receptors, that regulate amblyopia. Less is known, however, about treatment-induced changes to those receptors and if those changes differentiate treatments that support the recovery of acuity versus persistent acuity deficits. Here, we use an animal model to explore the effects of 3 visual treatments started during the CP (n=24, 10 male and 14 female): binocular vision (BV) that promotes good acuity versus reverse occlusion (RO) and binocular deprivation (BD) that causes persistent acuity deficits. We measured the recovery of a collection of glutamatergic and GABAergic receptor subunits in the V1 and modeled recovery of kinetics for NMDAR and GABAAR. There was a complex pattern of protein changes that prompted us to develop an unbiased data-driven approach for these high-dimensional data analyses to identify plasticity features and construct plasticity phenotypes. Cluster analysis of the plasticity phenotypes suggests that BV supports adaptive plasticity while RO and BD promote a maladaptive pattern. The RO plasticity phenotype appeared more similar to adults with a high expression of GluA2, and the BD phenotypes were dominated by GABAAα1, highlighting that multiple plasticity phenotypes can underlie persistent poor acuity. After 2-4 days of BV, the plasticity phenotypes resembled normals, but only one feature, the GluN2A:GluA2 balance, returned to normal levels. Perhaps, balancing Hebbian (GluN2A) and homeostatic (GluA2) mechanisms is necessary for the recovery of vision.

The mammalian sensory neocortex consists of hierarchically organized areas reciprocally connected via feedforward (FF) and feedback (FB) circuits. Several theories of hierarchical computation ascribe the bulk of the computational work of the cortex to looped FF-FB circuits between pairs of cortical areas. However, whether such corticocortical loops exist remains unclear. In higher mammals, individual FF-projection neurons send afferents almost exclusively to a single higher-level area. However, it is unclear whether FB-projection neurons show similar area-specificity, and whether they influence FF-projection neurons directly or indirectly. Using viral-mediated monosynaptic circuit tracing in macaque primary visual cortex (V1), we show that V1 neurons sending FF projections to area V2 receive monosynaptic FB inputs from V2, but not other V1-projecting areas. We also find monosynaptic FB-to-FB neuron contacts as a second motif of FB connectivity. Our results support the existence of FF-FB loops in primate cortex, and suggest that FB can rapidly and selectively influence the activity of incoming FF signals.

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.

ABSTRACTThe mammalian sensory neocortex consists of hierarchically organized areas reciprocally connected via feedforward (FF) and feedback (FB) circuits. Several theories of hierarchical computation ascribe the bulk of the computational work of the cortex to looped FF-FB circuits between pairs of cortical areas. However, whether such corticocortical loops exist remains unclear. In higher mammals, FF projections send afferents almost exclusively to a single higher-level area. However, it is unclear whether FB projections show similar area-specificity, and whether they influence FF-projection neurons directly or indirectly. Using viral-mediated monosynaptic circuit tracing in macaque visual cortex, we find that neurons sending FF projections to a higher-level area receive monosynaptic FB inputs exclusively from that area. We also find monosynaptic FB-to-FB neuron contacts as a second motif of FB connectivity. Our results support the existence of FF-FB loops in primate cortex, and suggest that FB can rapidly and selectively influence the activity of incoming FF signals.

New techniques for quantifying large numbers of proteins or genes are transforming the study of plasticity mechanisms in visual cortex (V1) into the era of big data. With those changes comes the challenge of applying new analytical methods designed for high-dimensional data.Studies of V1, however, can take advantage of the known functions that many proteins have in regulating experience-dependent plasticity to facilitate linking big data analyses with neurobiological functions. Here we discuss two workflows and provide example R code for analyzing high-dimensional changes in a group of proteins (or genes) using two data sets. The first data set includes 7 neural proteins, 9 visual conditions, and 3 regions in V1 from an animal model for amblyopia. The second data set includes 23 neural proteins and 31 ages (20d-80yrs) from human post-mortem samples of V1. Each data set presents different challenges and we describe using PCA, tSNE, and various clustering algorithms including sparse high-dimensional clustering. Also, we describe a new approach for identifying high-dimensional features and using them to construct a plasticity phenotype that identifies neurobiological differences among clusters. We include an R package "v1hdexplorer" that aggregates the various coding packages and custom visualization scripts written in R Studio.

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