A cross-platform freeware tool for digital reconstruction of neuronal arborizations from image stacks

Digital reconstruction of neuronal arborizations is an important step in the quantitative investigation of cellular neuroanatomy. In this process, neurites imaged by microscopy are semi-manually traced through the use of specialized computer software and represented as binary trees of branching cylinders (or truncated cones). Such form of the reconstruction files is efficient and parsimonious, and allows extensive morphometric analysis as well as the implementation of biophysical models of electrophysiology. Here, we describe Neuron_Morpho, a plugin for the popular Java application ImageJ that mediates the digital reconstruction of neurons from image stacks. Both the executable and code of Neuron_Morpho are freely distributed (www.maths.soton.ac.uk/staff/D’Alessandro/morpho or www.krasnow.gmu.edu/L-Neuron), and are compatible with all major computer platforms (including Windows, Mac, and Linux). We tested Neuron_Morpho by reconstructing two neurons from each of the two preparations representing different brain areas (hippocampus and cerebellum), neuritic type (pyramidal cell dendrites and olivar axonal projection terminals), and labeling method (rapid Golgi impregnation and anterograde dextran amine), and quantitatively comparing the resulting morphologies to those of the same cells reconstructed with the standard commercial system, Neurolucida. None of the numerous morphometric measures that were analyzed displayed any significant or systematic difference between the two reconstructing systems.

[1]  Jonathan Knudsen,et al.  Learning Java , 2000 .

[2]  Michael L. Hines,et al.  Semi-automated population of an online database of neuronal models (ModelDB) with citation information, using PubMed for validation , 2007, Neuroinformatics.

[3]  Douglas B. Ehlenberger,et al.  Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images. , 2003, Methods.

[4]  G A Ascoli,et al.  Generation, description and storage of dendritic morphology data. , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[5]  T. Freund,et al.  Total number and distribution of inhibitory and excitatory synapses on hippocampal CA1 pyramidal cells , 2001, Neuroscience.

[6]  Giorgio A. Ascoli,et al.  Algorithmic Extraction of Morphological Statistics from Electronic Archives of Neuroanatomy , 2001, IWANN.

[7]  Joseph J. Capowski,et al.  Computer Techniques in Neuroanatomy , 1989, Springer US.

[8]  D. Jaeger.  Accurate Reconstruction of Neuronal Morphology , 2000 .

[9]  Michael Scholz,et al.  New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks , 2004, NeuroImage.

[10]  W. Levy,et al.  A quantitative anatomical study of the granule cell dendritic fields of the rat dentate gyrus using a novel probabilistic method , 1982, The Journal of comparative neurology.

[11]  J. Morrison,et al.  Quantitative analysis of the dendritic morphology of corticocortical projection neurons in the macaque monkey association cortex , 2002, Neuroscience.

[12]  T. Freund,et al.  Total Number and Ratio of Excitatory and Inhibitory Synapses Converging onto Single Interneurons of Different Types in the CA1 Area of the Rat Hippocampus , 1999, The Journal of Neuroscience.

[13]  E M Glaser,et al.  Neuron imaging with Neurolucida--a PC-based system for image combining microscopy. , 1990, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[14]  Michael L. Hines,et al.  ModelDB - Making models publicly accessible to support computational neuroscience , 2003, Neuroinformatics.

[15]  Ginette Horcholle-Bossavit,et al.  Neuronal morphology data bases: morphological noise and assesment of data quality , 2002, Network.

[16]  G. Ascoli,et al.  Quantitative morphometry of hippocampal pyramidal cells: Differences between anatomical classes and reconstructing laboratories , 2004, The Journal of comparative neurology.

[17]  Erik De Schutter,et al.  Effects of variability in anatomical reconstruction techniques on models of synaptic integration by dendrites: a comparison of three internet archives , 2004, The European journal of neuroscience.

[18]  T J Holmes,et al.  Blind deconvolution of 3D transmitted light brightfield micrographs , 2000, Journal of microscopy.

[19]  Mark Farrant,et al.  Maturation of EPSCs and Intrinsic Membrane Properties Enhances Precision at a Cerebellar Synapse , 2003, The Journal of Neuroscience.

[20]  Badrinath Roysam,et al.  Automated Three-Dimensional Tracing of Neurons in Confocal and Brightfield Images , 2003, Microscopy and Microanalysis.

[21]  James M. Bower,et al.  The book of GENESIS - exploring realistic neural models with the GEneral NEural models SImulation system , 1995 .

[22]  R. C Cannon,et al.  An on-line archive of reconstructed hippocampal neurons , 1998, Journal of Neuroscience Methods.

[23]  Giorgio A. Ascoli,et al.  Web-Based Neuronal Archives , 2003 .

[24]  Y Shinoda,et al.  Morphology of single olivocerebellar axons labeled with biotinylated dextran amine in the rat , 1999, The Journal of comparative neurology.

[25]  M L Hines,et al.  Neuron: A Tool for Neuroscientists , 2001, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.