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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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Ability of the models to capture apical and basal bifurcation asymmetry.(A) Apical trees have their bifurcation asymmetry best determined by Radius (RAD = Radius, PD = Path Distance, BO = Branch Order). (B) Basal trees have their bifurcation asymmetry best determined by Path Distance, which wins over the other two models two-thirds of the time. (C) Non-pyramidal trees lie somewhere in the middle, with neither Path Distance nor Radius giving better bifurcation asymmetry results. (D, E) The values of bifurcation asymmetry vary as a function of branch order in representative apical (D) and basal (E) groups (Amaral CA1, N = 23). Path Distance better captures the basal pattern, while the Radius model better captures apical asymmetry.
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pcbi-1000089-g006: Ability of the models to capture apical and basal bifurcation asymmetry.(A) Apical trees have their bifurcation asymmetry best determined by Radius (RAD = Radius, PD = Path Distance, BO = Branch Order). (B) Basal trees have their bifurcation asymmetry best determined by Path Distance, which wins over the other two models two-thirds of the time. (C) Non-pyramidal trees lie somewhere in the middle, with neither Path Distance nor Radius giving better bifurcation asymmetry results. (D, E) The values of bifurcation asymmetry vary as a function of branch order in representative apical (D) and basal (E) groups (Amaral CA1, N = 23). Path Distance better captures the basal pattern, while the Radius model better captures apical asymmetry.

Mentions: The situation is almost reversed if models are evaluated based on another emergent morphometric, namely bifurcation asymmetry instead of the number of bifurcations (Figure 6). Path Distance is the worst model at capturing apical asymmetry (Figure 6A) but the best at capturing basal asymmetry (Figure 6B), both in terms of average distance (top panels) and numbers of groups (bottom). Non-pyramidal cells fall in between apical and basal with both Radius and Path Distance producing the best results more often than Branch Order (Figure 6C). Another example Sholl-like analysis carried out on an single group of pyramidal cells is consistent with the trends observed across the corresponding sets of tree types, and opposite to the patterns observed for number of bifurcations (Figure 6D). In particular, the distribution of apical bifurcation asymmetry values as a function of branch order is better reflected by the Radius model than by the Path Distance model. Figure 6E shows that the converse is true for the basal trees from the same cells.


A comparative computer simulation of dendritic morphology.

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

Ability of the models to capture apical and basal bifurcation asymmetry.(A) Apical trees have their bifurcation asymmetry best determined by Radius (RAD = Radius, PD = Path Distance, BO = Branch Order). (B) Basal trees have their bifurcation asymmetry best determined by Path Distance, which wins over the other two models two-thirds of the time. (C) Non-pyramidal trees lie somewhere in the middle, with neither Path Distance nor Radius giving better bifurcation asymmetry results. (D, E) The values of bifurcation asymmetry vary as a function of branch order in representative apical (D) and basal (E) groups (Amaral CA1, N = 23). Path Distance better captures the basal pattern, while the Radius model better captures apical asymmetry.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000089-g006: Ability of the models to capture apical and basal bifurcation asymmetry.(A) Apical trees have their bifurcation asymmetry best determined by Radius (RAD = Radius, PD = Path Distance, BO = Branch Order). (B) Basal trees have their bifurcation asymmetry best determined by Path Distance, which wins over the other two models two-thirds of the time. (C) Non-pyramidal trees lie somewhere in the middle, with neither Path Distance nor Radius giving better bifurcation asymmetry results. (D, E) The values of bifurcation asymmetry vary as a function of branch order in representative apical (D) and basal (E) groups (Amaral CA1, N = 23). Path Distance better captures the basal pattern, while the Radius model better captures apical asymmetry.
Mentions: The situation is almost reversed if models are evaluated based on another emergent morphometric, namely bifurcation asymmetry instead of the number of bifurcations (Figure 6). Path Distance is the worst model at capturing apical asymmetry (Figure 6A) but the best at capturing basal asymmetry (Figure 6B), both in terms of average distance (top panels) and numbers of groups (bottom). Non-pyramidal cells fall in between apical and basal with both Radius and Path Distance producing the best results more often than Branch Order (Figure 6C). Another example Sholl-like analysis carried out on an single group of pyramidal cells is consistent with the trends observed across the corresponding sets of tree types, and opposite to the patterns observed for number of bifurcations (Figure 6D). In particular, the distribution of apical bifurcation asymmetry values as a function of branch order is better reflected by the Radius model than by the Path Distance model. Figure 6E shows that the converse is true for the basal trees from the same cells.

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