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Titolo:
Generation, description and storage of dendritic morphology data
Autore:
Ascoli, GA; Krichmar, JL; Nasuto, SJ; Senft, SL;
Indirizzi:
George Mason Univ, Krasnow Inst Adv Study, Fairfax, VA 22030 USA George Mason Univ Fairfax VA USA 22030 t Adv Study, Fairfax, VA 22030 USA George Mason Univ, Dept Psychol, Fairfax, VA 22030 USA George Mason Univ Fairfax VA USA 22030 ept Psychol, Fairfax, VA 22030 USA Inst Neurosci, San Diego, CA 92121 USA Inst Neurosci San Diego CA USA 92121 st Neurosci, San Diego, CA 92121 USA
Titolo Testata:
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES B-BIOLOGICAL SCIENCES
fascicolo: 1412, volume: 356, anno: 2001,
pagine: 1131 - 1145
SICI:
0962-8436(20010829)356:1412<1131:GDASOD>2.0.ZU;2-S
Fonte:
ISI
Lingua:
ENG
Soggetto:
CEREBELLAR PURKINJE-CELLS; HUMAN CEREBRAL-CORTEX; PARSIMONIOUS DESCRIPTION; CONFOCAL MICROSCOPY; ELECTRON-MICROSCOPY; HIPPOCAMPAL-NEURONS; PYRAMIDAL CELLS; VISUAL-CORTEX; LIVING CELLS; RAT;
Keywords:
ARBORVITAE; computational neuroanatomy; dendritic morphology; L-NEURON; virtual neurons;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Life Sciences
Citazioni:
72
Recensione:
Indirizzi per estratti:
Indirizzo: Ascoli, GA George Mason Univ, Krasnow Inst Adv Study, MS2A1-4400 Univ Dr, Fairfax, VA22030 USA George Mason Univ MS2A1-4400 Univ Dr Fairfax VA USA 22030 0 USA
Citazione:
G.A. Ascoli et al., "Generation, description and storage of dendritic morphology data", PHI T ROY B, 356(1412), 2001, pp. 1131-1145

Abstract

It is generally assumed that the variability of neuronal morphology has animportant effect on both the connectivity and the activity of 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 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, each represented by diameter, spatial coordinates and the connectivity to other cylinders in the tree. This 'Cartesian' description constitutes a completely accurate mapping of dendritic morphology but it bears little intuitive information for the neuroscientist. In contrast, a classical neuroanatomical analysis characterizes neuronal dendrites 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 'fundamental', measured parameters. This description is as intuitive as a classical neuroanatomical analysis (parameters have an intuitive interpretation), and as complete as a Cartesian file (the algorithms generate and display complete neurons). The advantages of the algorithmic description of neuronal structure are immense. If an algorithm can measure the values of a handful of parameters from an experimental database and generate virtual neurons whose anatomy is statistically indistinguishable from that of their real counterparts, a great deal of data compression and amplification can be achieved. Data compression results from the quantitative and complete description of thousands of neurons with a handful ofstatistical distributions of parameters. Data amplification is possible because, from a set of experimental neurons, many more virtual analogues can be generated. This approach could allow one, in principle, to create and store a neuroanatomical database containing data for an entire human brain ina personal computer. We are using two programs, L-NEURON and ARBORVITAE, to investigate systematically the potential of several different algorithms for the generation of virtual neurons. Using these programs, we have generated anatomically plausible virtual neurons for several morphological classes, including guinea pig cerebellar Purkinje cells and cat spinal cord motorneurons. These virtual neurons are stored in an online electronic archive of dendritic morphology. This process highlights the potential and the limitations of the 'computational neuroanatomy' strategy for neuroscience databases.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 25/01/20 alle ore 15:52:25