The effects of neuron morphology on graph theoretic measures of network connectivity: The analysis of a two-level statistical model

    Research output: Contribution to journalArticleScientificpeer-review

    9 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number76
    JournalFrontiers in Neuroanatomy
    Volume9
    Issue numberJune
    DOIs
    Publication statusPublished - 10 Jun 2015
    Publication typeA1 Journal article-refereed

    Keywords

    • Graph theory
    • Motifs
    • Network connectivity
    • Neurite density field
    • Neuron morphology
    • Theoretical model

    Publication forum classification

    • Publication forum level 0

    ASJC Scopus subject areas

    • Anatomy
    • Neuroscience (miscellaneous)
    • Cellular and Molecular Neuroscience

    Fingerprint

    Dive into the research topics of 'The effects of neuron morphology on graph theoretic measures of network connectivity: The analysis of a two-level statistical model'. Together they form a unique fingerprint.

    Cite this