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A generative growth model for thalamocortical axonal branching in primary visual cortex

Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically...

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Autores principales: Kassraian-Fard, Pegah, Pfeiffer, Michael, Bauer, Roman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7018004/
https://www.ncbi.nlm.nih.gov/pubmed/32053598
http://dx.doi.org/10.1371/journal.pcbi.1007315
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author Kassraian-Fard, Pegah
Pfeiffer, Michael
Bauer, Roman
author_facet Kassraian-Fard, Pegah
Pfeiffer, Michael
Bauer, Roman
author_sort Kassraian-Fard, Pegah
collection PubMed
description Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically encoded instructions must be available during cortical development. Within a generative model, we investigate growth rules for axonal branching patterns in cat area 17, originating from the lateral geniculate nucleus of the thalamus. This target area of synaptic connections is characterized by extensive ramifications and a high bouton density, characteristics thought to preserve the spatial resolution of receptive fields and to enable connections for the ocular dominance columns. We compare individual and global statistics, such as a newly introduced length-weighted asymmetry index and the global segment-length distribution, of generated and biological branching patterns as the benchmark for growth rules. We show that the proposed model surpasses the statistical accuracy of the Galton-Watson model, which is the most commonly employed model for biological growth processes. In contrast to the Galton-Watson model, our model can recreate the log-normal segment-length distribution of the experimental dataset and is considerably more accurate in recreating individual axonal morphologies. To provide a biophysical interpretation for statistical quantifications of the axonal branching patterns, the generative model is ported into the physically accurate simulation framework of Cx3D. In this 3D simulation environment we demonstrate how the proposed growth process can be formulated as an interactive process between genetic growth rules and chemical cues in the local environment.
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spelling pubmed-70180042020-02-26 A generative growth model for thalamocortical axonal branching in primary visual cortex Kassraian-Fard, Pegah Pfeiffer, Michael Bauer, Roman PLoS Comput Biol Research Article Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically encoded instructions must be available during cortical development. Within a generative model, we investigate growth rules for axonal branching patterns in cat area 17, originating from the lateral geniculate nucleus of the thalamus. This target area of synaptic connections is characterized by extensive ramifications and a high bouton density, characteristics thought to preserve the spatial resolution of receptive fields and to enable connections for the ocular dominance columns. We compare individual and global statistics, such as a newly introduced length-weighted asymmetry index and the global segment-length distribution, of generated and biological branching patterns as the benchmark for growth rules. We show that the proposed model surpasses the statistical accuracy of the Galton-Watson model, which is the most commonly employed model for biological growth processes. In contrast to the Galton-Watson model, our model can recreate the log-normal segment-length distribution of the experimental dataset and is considerably more accurate in recreating individual axonal morphologies. To provide a biophysical interpretation for statistical quantifications of the axonal branching patterns, the generative model is ported into the physically accurate simulation framework of Cx3D. In this 3D simulation environment we demonstrate how the proposed growth process can be formulated as an interactive process between genetic growth rules and chemical cues in the local environment. Public Library of Science 2020-02-13 /pmc/articles/PMC7018004/ /pubmed/32053598 http://dx.doi.org/10.1371/journal.pcbi.1007315 Text en © 2020 Kassraian-Fard et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kassraian-Fard, Pegah
Pfeiffer, Michael
Bauer, Roman
A generative growth model for thalamocortical axonal branching in primary visual cortex
title A generative growth model for thalamocortical axonal branching in primary visual cortex
title_full A generative growth model for thalamocortical axonal branching in primary visual cortex
title_fullStr A generative growth model for thalamocortical axonal branching in primary visual cortex
title_full_unstemmed A generative growth model for thalamocortical axonal branching in primary visual cortex
title_short A generative growth model for thalamocortical axonal branching in primary visual cortex
title_sort generative growth model for thalamocortical axonal branching in primary visual cortex
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7018004/
https://www.ncbi.nlm.nih.gov/pubmed/32053598
http://dx.doi.org/10.1371/journal.pcbi.1007315
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