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A comparative computer simulation of dendritic morphology.

Donohue DE, Ascoli GA - PLoS Comput. Biol. (2008)

Bottom Line: Hybrid models using combinations of the determinants confirmed these trends and allowed a detailed characterization of morphological relations.The differential findings between morphological groups suggest different underlying developmental mechanisms.By comparing the effects of several morphometric determinants on the simulation of different neuronal classes, this approach sheds light on possible growth mechanism variations responsible for the observed neuronal diversity.

View Article: PubMed Central - PubMed

Affiliation: Neuroscience Program and Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, United States of America.

ABSTRACT
Computational modeling of neuronal morphology is a powerful tool for understanding developmental processes and structure-function relationships. We present a multifaceted approach based on stochastic sampling of morphological measures from digital reconstructions of real cells. We examined how dendritic elongation, branching, and taper are controlled by three morphometric determinants: Branch Order, Radius, and Path Distance from the soma. Virtual dendrites were simulated starting from 3,715 neuronal trees reconstructed in 16 different laboratories, including morphological classes as diverse as spinal motoneurons and dentate granule cells. Several emergent morphometrics were used to compare real and virtual trees. Relating model parameters to Branch Order best constrained the number of terminations for most morphological classes, except pyramidal cell apical trees, which were better described by a dependence on Path Distance. In contrast, bifurcation asymmetry was best constrained by Radius for apical, but Path Distance for basal trees. All determinants showed similar performance in capturing total surface area, while surface area asymmetry was best determined by Path Distance. Grouping by other characteristics, such as size, asymmetry, arborizations, or animal species, showed smaller differences than observed between apical and basal, pointing to the biological importance of this separation. Hybrid models using combinations of the determinants confirmed these trends and allowed a detailed characterization of morphological relations. The differential findings between morphological groups suggest different underlying developmental mechanisms. By comparing the effects of several morphometric determinants on the simulation of different neuronal classes, this approach sheds light on possible growth mechanism variations responsible for the observed neuronal diversity.

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Basic parameter distributions.Example basic parameter (daughter diameter ratio) distributed irrespective of fundamental parameters (inset), as used in previous studies, and the same parameter binned by Path Distance (main plot: columns and error bars are means and standard deviations, respectively). Both the main graphs and the inset only include daughter ratio values greater than one. The solid line (secondary axis in the main plot) shows the percentage of unitary values in each bin. The dotted line represents the overall percentage of unitary daughter ratios.
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pcbi-1000089-g003: Basic parameter distributions.Example basic parameter (daughter diameter ratio) distributed irrespective of fundamental parameters (inset), as used in previous studies, and the same parameter binned by Path Distance (main plot: columns and error bars are means and standard deviations, respectively). Both the main graphs and the inset only include daughter ratio values greater than one. The solid line (secondary axis in the main plot) shows the percentage of unitary values in each bin. The dotted line represents the overall percentage of unitary daughter ratios.

Mentions: In earlier efforts (e.g. [16],[25]), basic parameters were assumed to be uniformly distributed throughout the dendritic tree (Figure 3 inset). While some cell types were well captured in this way, others resulted in virtual trees which continued to bifurcate indefinitely. Later studies [22] determined that this was due to basic parameter values being applied in the virtual trees where they did not occur in the real trees. For example, in the apical trees of one group of pyramidal cells (Figure 3), the daughter diameter ratio tends to be larger near the soma than farther distally. Most importantly, the proportion of bifurcations with two equally sized daughters (unitary values of the diameter ratio) is smaller close to the soma, where most of the bifurcations occurred in this case. Without grouping by fundamental determinant, these dependencies are not captured in the virtual trees. Using radius as a fundamental determinant for all basic parameters in CA1 pyramidal cells prevented the explosive virtual growth, but the resulting trees were still excessively varied in size [22]. The model also proved to be very sensitive to radius, a notoriously noise prone measurement in neuronal reconstructions. Here we expand on this work by applying three different fundamental determinants to a wider variety of tree types.


A comparative computer simulation of dendritic morphology.

Donohue DE, Ascoli GA - PLoS Comput. Biol. (2008)

Basic parameter distributions.Example basic parameter (daughter diameter ratio) distributed irrespective of fundamental parameters (inset), as used in previous studies, and the same parameter binned by Path Distance (main plot: columns and error bars are means and standard deviations, respectively). Both the main graphs and the inset only include daughter ratio values greater than one. The solid line (secondary axis in the main plot) shows the percentage of unitary values in each bin. The dotted line represents the overall percentage of unitary daughter ratios.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2376061&req=5

pcbi-1000089-g003: Basic parameter distributions.Example basic parameter (daughter diameter ratio) distributed irrespective of fundamental parameters (inset), as used in previous studies, and the same parameter binned by Path Distance (main plot: columns and error bars are means and standard deviations, respectively). Both the main graphs and the inset only include daughter ratio values greater than one. The solid line (secondary axis in the main plot) shows the percentage of unitary values in each bin. The dotted line represents the overall percentage of unitary daughter ratios.
Mentions: In earlier efforts (e.g. [16],[25]), basic parameters were assumed to be uniformly distributed throughout the dendritic tree (Figure 3 inset). While some cell types were well captured in this way, others resulted in virtual trees which continued to bifurcate indefinitely. Later studies [22] determined that this was due to basic parameter values being applied in the virtual trees where they did not occur in the real trees. For example, in the apical trees of one group of pyramidal cells (Figure 3), the daughter diameter ratio tends to be larger near the soma than farther distally. Most importantly, the proportion of bifurcations with two equally sized daughters (unitary values of the diameter ratio) is smaller close to the soma, where most of the bifurcations occurred in this case. Without grouping by fundamental determinant, these dependencies are not captured in the virtual trees. Using radius as a fundamental determinant for all basic parameters in CA1 pyramidal cells prevented the explosive virtual growth, but the resulting trees were still excessively varied in size [22]. The model also proved to be very sensitive to radius, a notoriously noise prone measurement in neuronal reconstructions. Here we expand on this work by applying three different fundamental determinants to a wider variety of tree types.

Bottom Line: Hybrid models using combinations of the determinants confirmed these trends and allowed a detailed characterization of morphological relations.The differential findings between morphological groups suggest different underlying developmental mechanisms.By comparing the effects of several morphometric determinants on the simulation of different neuronal classes, this approach sheds light on possible growth mechanism variations responsible for the observed neuronal diversity.

View Article: PubMed Central - PubMed

Affiliation: Neuroscience Program and Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, United States of America.

ABSTRACT
Computational modeling of neuronal morphology is a powerful tool for understanding developmental processes and structure-function relationships. We present a multifaceted approach based on stochastic sampling of morphological measures from digital reconstructions of real cells. We examined how dendritic elongation, branching, and taper are controlled by three morphometric determinants: Branch Order, Radius, and Path Distance from the soma. Virtual dendrites were simulated starting from 3,715 neuronal trees reconstructed in 16 different laboratories, including morphological classes as diverse as spinal motoneurons and dentate granule cells. Several emergent morphometrics were used to compare real and virtual trees. Relating model parameters to Branch Order best constrained the number of terminations for most morphological classes, except pyramidal cell apical trees, which were better described by a dependence on Path Distance. In contrast, bifurcation asymmetry was best constrained by Radius for apical, but Path Distance for basal trees. All determinants showed similar performance in capturing total surface area, while surface area asymmetry was best determined by Path Distance. Grouping by other characteristics, such as size, asymmetry, arborizations, or animal species, showed smaller differences than observed between apical and basal, pointing to the biological importance of this separation. Hybrid models using combinations of the determinants confirmed these trends and allowed a detailed characterization of morphological relations. The differential findings between morphological groups suggest different underlying developmental mechanisms. By comparing the effects of several morphometric determinants on the simulation of different neuronal classes, this approach sheds light on possible growth mechanism variations responsible for the observed neuronal diversity.

Show MeSH
Related in: MedlinePlus