Cargando…
The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model
We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses,...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4461825/ https://www.ncbi.nlm.nih.gov/pubmed/26113811 http://dx.doi.org/10.3389/fnana.2015.00076 |
_version_ | 1782375563033313280 |
---|---|
author | Aćimović, Jugoslava Mäki-Marttunen, Tuomo Linne, Marja-Leena |
author_facet | Aćimović, Jugoslava Mäki-Marttunen, Tuomo Linne, Marja-Leena |
author_sort | Aćimović, Jugoslava |
collection | PubMed |
description | We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses, node degree, and the effective radius, the maximal distance between two neurons expected to form at least one synapse. We related these quantities to the network connectivity described using standard measures from graph theory, such as motif counts, clustering coefficient, minimal path length, and small-world coefficient. These measures are used in a neuroscience context to study phenomena from synaptic connectivity in the small neuronal networks to large scale functional connectivity in the cortex. For these measures we provide analytical solutions that clearly relate different model properties. Neurites that sparsely cover space lead to a small effective radius. If the effective radius is small compared to the overall neuron size the obtained networks share similarities with the uniform random networks as each neuron connects to a small number of distant neurons. Large neurites with densely packed branches lead to a large effective radius. If this effective radius is large compared to the neuron size, the obtained networks have many local connections. In between these extremes, the networks maximize the variability of connection repertoires. The presented approach connects the properties of neuron morphology with large scale network properties without requiring heavy simulations with many model parameters. The two-steps procedure provides an easier interpretation of the role of each modeled parameter. The model is flexible and each of its components can be further expanded. We identified a range of model parameters that maximizes variability in network connectivity, the property that might affect network capacity to exhibit different dynamical regimes. |
format | Online Article Text |
id | pubmed-4461825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-44618252015-06-25 The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model Aćimović, Jugoslava Mäki-Marttunen, Tuomo Linne, Marja-Leena Front Neuroanat Neuroscience We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses, node degree, and the effective radius, the maximal distance between two neurons expected to form at least one synapse. We related these quantities to the network connectivity described using standard measures from graph theory, such as motif counts, clustering coefficient, minimal path length, and small-world coefficient. These measures are used in a neuroscience context to study phenomena from synaptic connectivity in the small neuronal networks to large scale functional connectivity in the cortex. For these measures we provide analytical solutions that clearly relate different model properties. Neurites that sparsely cover space lead to a small effective radius. If the effective radius is small compared to the overall neuron size the obtained networks share similarities with the uniform random networks as each neuron connects to a small number of distant neurons. Large neurites with densely packed branches lead to a large effective radius. If this effective radius is large compared to the neuron size, the obtained networks have many local connections. In between these extremes, the networks maximize the variability of connection repertoires. The presented approach connects the properties of neuron morphology with large scale network properties without requiring heavy simulations with many model parameters. The two-steps procedure provides an easier interpretation of the role of each modeled parameter. The model is flexible and each of its components can be further expanded. We identified a range of model parameters that maximizes variability in network connectivity, the property that might affect network capacity to exhibit different dynamical regimes. Frontiers Media S.A. 2015-06-10 /pmc/articles/PMC4461825/ /pubmed/26113811 http://dx.doi.org/10.3389/fnana.2015.00076 Text en Copyright © 2015 Aćimović, Mäki-Marttunen and Linne. 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) or licensor 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 Aćimović, Jugoslava Mäki-Marttunen, Tuomo Linne, Marja-Leena The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model |
title | The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model |
title_full | The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model |
title_fullStr | The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model |
title_full_unstemmed | The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model |
title_short | The effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model |
title_sort | effects of neuron morphology on graph theoretic measures of network connectivity: the analysis of a two-level statistical model |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4461825/ https://www.ncbi.nlm.nih.gov/pubmed/26113811 http://dx.doi.org/10.3389/fnana.2015.00076 |
work_keys_str_mv | AT acimovicjugoslava theeffectsofneuronmorphologyongraphtheoreticmeasuresofnetworkconnectivitytheanalysisofatwolevelstatisticalmodel AT makimarttunentuomo theeffectsofneuronmorphologyongraphtheoreticmeasuresofnetworkconnectivitytheanalysisofatwolevelstatisticalmodel AT linnemarjaleena theeffectsofneuronmorphologyongraphtheoreticmeasuresofnetworkconnectivitytheanalysisofatwolevelstatisticalmodel AT acimovicjugoslava effectsofneuronmorphologyongraphtheoreticmeasuresofnetworkconnectivitytheanalysisofatwolevelstatisticalmodel AT makimarttunentuomo effectsofneuronmorphologyongraphtheoreticmeasuresofnetworkconnectivitytheanalysisofatwolevelstatisticalmodel AT linnemarjaleena effectsofneuronmorphologyongraphtheoreticmeasuresofnetworkconnectivitytheanalysisofatwolevelstatisticalmodel |