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Resource:Virtual NeuroMorphology Electronic Database

Name: Resource:Virtual NeuroMorphology Electronic Database
Description: It is generally assumed that the variability of neuronal morphology has an important effect on the connectivity and response within the nervous system, but this effect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure-function relationships in the brain. We are developing computational tools to describe, generate, store, and render large sets of three-dimensional neuronal structures in a format that is both compact, quantitative, accurate, and readily accessible to the neuroscientist.

Single-cell neuroanatomy can be characterized quantitatively at several levels. In computer-aided neuronal tracing files, a dendritic tree is described as a series of cylinders ("branches"), each represented by diameter, spatial coordinates (x, y, and z), and the connectivity to other branches in the tree. This "Cartesian" description constitutes a completely accurate mapping of dendritic morphology, but it bears little "intuitive" information for the neuroscientist (e.g. it is difficult to establish the morphological class of a neuron by simply looking at its Cartesian file). In a classical neuroanatomical analysis, in contrast, neuronal dendrites are characterized on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise "blueprint" of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic "generation" of neuronal structures within a certain morphological class based on a set of measured parameters. Given the right algorithm, these "fundamental" parameters describe that morphological class as intuitively as in classical neuroanatomical analysis (because their statistical distributions have an intuitive geometrical meaning), and as completely as in the Cartesian format (because they are sufficient to generate and display complete neurons). Since fundamental parameters measured from experimental data result in statistical distributions, the algorithms that generate "virtual neurons" sample values from these distributions stochastically. As a result, like in nature, no two virtual neurons are identical, even if they belong to a recognizable anatomical class.

Two major types of algorithms have been proposed for the generation and description of dendritic trees. Local algorithms rely entirely on a set of local rules correlating morphological parameters (such as branch diameter and length) to let each branch grow independent of the other dendrites in the tree and independent of its absolute position within the tree. In global algorithms, new dendritic branches are dealt "from outside" to competing groups of growing tips, also depending on their position in the tree (e.g. on their distance from the soma). We are developing two programs, L-Neuron and ArborVitae, which implement several global and local algorithms, to investigate systematically the potential of the "computational neuroanatomy" approach for neuroscience databases. We virtually generated anatomically plausible neurons for several morphological classes, including cerebellar Purkinje cells, hippocampal pyramidal and granule cells, and spinal cord motoneurons.

Traced Neurons:

  • Amaral Cell archive
  • Neuron_Morpho reconstructions
  • Mouse Alpha Motoneurons

Generated Neurons:

  • Motoneurons
  • Purkinje Cells
  • Hippocampal Pyramidal Cells

Hippocampal Axonal Morphology

Experimental files of motoneuron morphology were kindly provided by Dr. Burke's lab at the NIH. The relative data is described and published by Cullheim's et al., 1987 (PMID: 3819010)

Experimental files of Purkinje cell morphology were kindly provided by Dr. Rapp at the Hebrew University. The relative data is described and published by Rapp et al., 1994 (PMID: 8014888).
Other Name(s): LN Database
Abbreviation: L-Neuron Database
Parent Organization: Resource:Computational Neuroanatomy Group
Supporting Agency: Resource:Human Brain Project
Grant: R01-NS39600-01
Resource Type(s): Data set, Image
Resource: Resource
URL: http://krasnow1.gmu.edu/L-Neuron/L-Neuron/database/index.html#Moto
Id: nif-0000-10546
Publication link: http://www.hirn.uni-duesseldorf.de/rk/neurodat.htm
Keywords: Neuron, morphology, computational neuroanatomy, neuroanatomy
Link to OWL / RDF: Download this content as OWL/RDF

Curation status: Curated

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Notes

This page uses this default form:Resource

Old URL: http://krasnow.gmu.edu/cn3/L-Neuron/database/index.html

Contributors

Aarnaud, Ccdbuser, Nifbot2



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Facts about Resource:Virtual NeuroMorphology Electronic DatabaseRDF feed
AbbrevL-Neuron Database  +
CurationStatuscurated  +
DefiningCitationhttp://krasnow1.gmu.edu/L-Neuron/L-Neuron/database/index.html#Moto  +
DefinitionIt is generally assumed that the variabili It is generally assumed that the variability of neuronal morphology has an important effect on the connectivity and response within the nervous system, but this effect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure-function relationships in the brain. We are developing computational tools to describe, generate, store, and render large sets of three-dimensional neuronal structures in a format that is both compact, quantitative, accurate, and readily accessible to the neuroscientist.

Single-cell neuroanatomy can be characterized quantitatively at several levels. In computer-aided neuronal tracing files, a dendritic tree is described as a series of cylinders ("branches"), each represented by diameter, spatial coordinates (x, y, and z), and the connectivity to other branches in the tree. This "Cartesian" description constitutes a completely accurate mapping of dendritic morphology, but it bears little "intuitive" information for the neuroscientist (e.g. it is difficult to establish the morphological class of a neuron by simply looking at its Cartesian file). In a classical neuroanatomical analysis, in contrast, neuronal dendrites are characterized on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise "blueprint" of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic "generation" of neuronal structures within a certain morphological class based on a set of measured parameters. Given the right algorithm, these "fundamental" parameters describe that morphological class as intuitively as in classical neuroanatomical analysis (because their statistical distributions have an intuitive geometrical meaning), and as completely as in the Cartesian format (because they are sufficient to generate and display complete neurons). Since fundamental parameters measured from experimental data result in statistical distributions, the algorithms that generate "virtual neurons" sample values from these distributions stochastically. As a result, like in nature, no two virtual neurons are identical, even if they belong to a recognizable anatomical class.

Two major types of algorithms have been proposed for the generation and description of dendritic trees. Local algorithms rely entirely on a set of local rules correlating morphological parameters (such as branch diameter and length) to let each branch grow independent of the other dendrites in the tree and independent of its absolute position within the tree. In global algorithms, new dendritic branches are dealt "from outside" to competing groups of growing tips, also depending on their position in the tree (e.g. on their distance from the soma). We are developing two programs, L-Neuron and ArborVitae, which implement several global and local algorithms, to investigate systematically the potential of the "computational neuroanatomy" approach for neuroscience databases. We virtually generated anatomically plausible neurons for several morphological classes, including cerebellar Purkinje cells, hippocampal pyramidal and granule cells, and spinal cord motoneurons.

Traced Neurons:

  • Amaral Cell archive
  • Neuron_Morpho reconstructions
  • Mouse Alpha Motoneurons

Generated Neurons:

  • Motoneurons
  • Purkinje Cells
  • Hippocampal Pyramidal Cells

Hippocampal Axonal Morphology

Experimental files of motoneuron morphology were kindly provided by Dr. Burke's lab at the NIH. The relative data is described and published by Cullheim's et al., 1987 (PMID: 3819010)

Experimental files of Purkinje cell morphology were kindly provided by Dr. Rapp at the Hebrew University. The relative data is described and published by Rapp et al., 1994 (PMID: 8014888).
shed by Rapp et al., 1994 (PMID: 8014888).
ExampleImageVirtual NeuroMorphology Electronic Database.PNG  +
GrantCategory:R01-NS39600-01   +
Has default formThis property is a special property in this wiki.Resource  +
Has roleData set  +, and Image  +
Idnif-0000-10546  +
Is part ofResource:Computational Neuroanatomy Group  +
KeywordsNeuron  +, Morphology  +, Computational neuroanatomy  +, and Neuroanatomy  +
LabelResource:Virtual NeuroMorphology Electronic Database  +
ModifiedDate8 August 2012  +
Page has default formThis property is a special property in this wiki.Resource  +
PublicationLinkhttp://www.hirn.uni-duesseldorf.de/rk/neurodat.htm  +
SuperCategoryResource  +
Supporting AgencyResource:Human Brain Project  +
SynonymLN Database  +