Biology subjects

Balsor, J. L.

Publications and source records attributed to Balsor, J. L..

4 recordsLinked to original sources

bioRxiv2022

An Optrode Array for Spatiotemporally Precise Large-Scale Optogenetic Stimulation of Deep Cortical Layers in Non-human Primates

Optogenetics has transformed studies of neural circuit function, but remains challenging to apply in large brains, such as those of non-human primates (NHPs). A major challenge is delivering intense, spatiotemporally precise, patterned photostimulation across large volumes in deep tissue. Such stimulation is critical, for example, to modulate selectively deep-layer corticocortical feedback projections. To address this unmet need, we have developed the Utah Optrode Array (UOA), a 10x10 glass needle waveguide array fabricated atop a novel opaque optical interposer then bonded to an electrically addressable LED array. In vivo experiments with the UOA demonstrated large-scale, spatiotemporally precise, activation of deep circuits in monkey cortex. Specifically, the UOA permitted both focal (confined to single layers/columns), and widespread (multiple layers/columns) optogenetic activation of deep layer neurons, simply by varying the number of activated LEDs and/or the irradiance. Thus, the UOA represents a powerful optoelectronic device for targeted manipulation of deep-layer circuits in NHP models.

neuroscience↗

bioRxiv2021

A Practical Guide to Sparse Clustering for Studying Molecular Development of the Human Brain

Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables (e.g. genes or proteins (p)) for each sample. This leads to a data structure that is high dimensional (p >> n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p >> n. Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparse K-means clustering (Robust Sparse K-means Clustering (RSKC)) that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brains development.

neuroscience↗

bioRxiv2019

Recovery of glutamatergic and GABAergic protein expression in visual cortex after monocular deprivation

A collection of glutamatergic and GABAergic proteins participate in regulating experience-dependent plasticity in the visual cortex (V1). Many studies have characterized changes to those proteins caused by monocular deprivation (MD) during the critical period (CP), but less is known about changes that occur when MD stops. We measured the effects of 3 types of visual experience after MD (n=24, 10 male and 14 female); reverse occlusion (RO), binocular deprivation (BD), or binocular vision, on the expression of synaptic proteins in V1 including glutamatergic and GABAergic receptor subunits. Synapsin expression was increased by RO but not affected by the other treatments. BD shifted the balance between glutamatergic and GABAergic receptor subunits to favor GABAA1. In contrast, BV shifted expression to favor the glutamatergic mechanisms by increasing NMDAR and decreasing GABAA1 subunits. None of the conditions returned normal expression levels to all of the proteins, but BV was the closest.

neuroscience↗

bioRxiv2019

A primer on high-dimensional data analysis workflows for studying visual cortex development and plasticity

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.

neuroscience↗