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Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis

Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and qu...

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Autores principales: Grein, Stephan, Qi, Guanxiao, Queisser, Gillian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333680/
https://www.ncbi.nlm.nih.gov/pubmed/32676020
http://dx.doi.org/10.3389/fncom.2020.00042
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author Grein, Stephan
Qi, Guanxiao
Queisser, Gillian
author_facet Grein, Stephan
Qi, Guanxiao
Queisser, Gillian
author_sort Grein, Stephan
collection PubMed
description Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the “mass” of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.
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spelling pubmed-73336802020-07-15 Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis Grein, Stephan Qi, Guanxiao Queisser, Gillian Front Comput Neurosci Neuroscience Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the “mass” of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows. Frontiers Media S.A. 2020-06-26 /pmc/articles/PMC7333680/ /pubmed/32676020 http://dx.doi.org/10.3389/fncom.2020.00042 Text en Copyright © 2020 Grein, Qi and Queisser. 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
Grein, Stephan
Qi, Guanxiao
Queisser, Gillian
Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis
title Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis
title_full Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis
title_fullStr Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis
title_full_unstemmed Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis
title_short Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis
title_sort density visualization pipeline: a tool for cellular and network density visualization and analysis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333680/
https://www.ncbi.nlm.nih.gov/pubmed/32676020
http://dx.doi.org/10.3389/fncom.2020.00042
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AT queissergillian densityvisualizationpipelineatoolforcellularandnetworkdensityvisualizationandanalysis