Literature DB >> 17917126

Successes and rewards in sharing digital reconstructions of neuronal morphology.

Giorgio A Ascoli1.   

Abstract

The computer-assisted three-dimensional reconstruction of neuronal morphology is becoming an increasingly popular technique to quantify the arborization patterns of dendrites and axons. The resulting digital files are suitable for comprehensive morphometric analyses as well as for building anatomically realistic compartmental models of membrane biophysics and neuronal electrophysiology. The digital tracings acquired in a lab for a specific purpose can be often re-used by a different research group to address a completely unrelated scientific question, if the original investigators are willing to share the data. Since reconstructing neuronal morphology is a labor-intensive process, data sharing and re-analysis is particularly advantageous for the neuroscience and biomedical communities. Here we present numerous cases of "success stories" in which digital reconstructions of neuronal morphology were shared and re-used, leading to additional, independent discoveries and publications, and thus amplifying the impact of the "source" study for which the data set was first collected. In particular, we overview four main applications of this kind of data: comparative morphometric analyses, statistical estimation of potential synaptic connectivity, morphologically accurate electrophysiological simulations, and computational models of neuronal shape and development.

Mesh:

Year:  2007        PMID: 17917126     DOI: 10.1007/s12021-007-0010-7

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  58 in total

1.  Geometry and structural plasticity of synaptic connectivity.

Authors:  Armen Stepanyants; Patrick R Hof; Dmitri B Chklovskii
Journal:  Neuron       Date:  2002-04-11       Impact factor: 17.173

Review 2.  Should the neuroscience community make a paradigm shift to sharing primary data?

Authors:  S H Koslow
Journal:  Nat Neurosci       Date:  2000-09       Impact factor: 24.884

3.  Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern.

Authors:  Andreas T Schaefer; Matthew E Larkum; Bert Sakmann; Arnd Roth
Journal:  J Neurophysiol       Date:  2003-02-26       Impact factor: 2.714

4.  Class-specific features of neuronal wiring.

Authors:  Armen Stepanyants; Gábor Tamás; Dmitri B Chklovskii
Journal:  Neuron       Date:  2004-07-22       Impact factor: 17.173

5.  Local diameter fully constrains dendritic size in basal but not apical trees of CA1 pyramidal neurons.

Authors:  Duncan E Donohue; Giorgio A Ascoli
Journal:  J Comput Neurosci       Date:  2005-10       Impact factor: 1.621

6.  A parsimonious description of motoneuron dendritic morphology using computer simulation.

Authors:  R E Burke; W B Marks; B Ulfhake
Journal:  J Neurosci       Date:  1992-06       Impact factor: 6.167

7.  Morphological homeostasis in cortical dendrites.

Authors:  Alexei V Samsonovich; Giorgio A Ascoli
Journal:  Proc Natl Acad Sci U S A       Date:  2006-01-17       Impact factor: 11.205

Review 8.  Neurogeometry and potential synaptic connectivity.

Authors:  Armen Stepanyants; Dmitri B Chklovskii
Journal:  Trends Neurosci       Date:  2005-07       Impact factor: 13.837

9.  An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice.

Authors:  E De Schutter; J M Bower
Journal:  J Neurophysiol       Date:  1994-01       Impact factor: 2.714

10.  Impulse encoding mechanisms of ganglion cells in the tiger salamander retina.

Authors:  J F Fohlmeister; R F Miller
Journal:  J Neurophysiol       Date:  1997-10       Impact factor: 2.714

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  16 in total

1.  The tree-edit-distance, a measure for quantifying neuronal morphology.

Authors:  Holger Heumann; Gabriel Wittum
Journal:  Neuroinformatics       Date:  2009-05-28

2.  Non-parametric algorithmic generation of neuronal morphologies.

Authors:  Benjamin Torben-Nielsen; Stijn Vanderlooy; Eric O Postma
Journal:  Neuroinformatics       Date:  2008-09-17

3.  Models and simulation of 3D neuronal dendritic trees using Bayesian networks.

Authors:  Pedro L López-Cruz; Concha Bielza; Pedro Larrañaga; Ruth Benavides-Piccione; Javier DeFelipe
Journal:  Neuroinformatics       Date:  2011-12

4.  Highlights from the Era of Open Source Web-Based Tools.

Authors:  Kristin R Anderson; Julie A Harris; Lydia Ng; Pjotr Prins; Sara Memar; Bengt Ljungquist; Daniel Fürth; Robert W Williams; Giorgio A Ascoli; Dani Dumitriu
Journal:  J Neurosci       Date:  2021-01-20       Impact factor: 6.167

5.  Turning the Tide of Data Sharing.

Authors:  Giorgio A Ascoli
Journal:  Neuroinformatics       Date:  2019-10

Review 6.  Neuronal morphology goes digital: a research hub for cellular and system neuroscience.

Authors:  Ruchi Parekh; Giorgio A Ascoli
Journal:  Neuron       Date:  2013-03-20       Impact factor: 17.173

7.  Detection of the optimal neuron traces in confocal microscopy images.

Authors:  Zlatko Vasilkoski; Armen Stepanyants
Journal:  J Neurosci Methods       Date:  2008-11-19       Impact factor: 2.390

8.  NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF.

Authors:  Maryam Halavi; Sridevi Polavaram; Duncan E Donohue; Gail Hamilton; Jeffrey Hoyt; Kenneth P Smith; Giorgio A Ascoli
Journal:  Neuroinformatics       Date:  2008-10-24

Review 9.  Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology.

Authors:  Ruchi Parekh; Giorgio A Ascoli
Journal:  Neuroscientist       Date:  2014-06-27       Impact factor: 7.519

10.  Communication and wiring in the cortical connectome.

Authors:  Julian M L Budd; Zoltán F Kisvárday
Journal:  Front Neuroanat       Date:  2012-10-16       Impact factor: 3.856

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