Limits...
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.

Show MeSH

Related in: MedlinePlus

Relative magnitude of apical-basal divide.(A) Number of bifurcations is better captured by both Radius and Branch Order in basal than in apical trees. (B) Conversely, bifurcation asymmetry is better captured by both models for apical trees. In either case, non-pyramidal trees tend to lie in between apical and basal trees. (C) The relative ability of the individual models to differentiate apical from basal trees is greater than for other tree divisions. The Performance Ratio is the absolute value of the log of the ratio between the two tree types of the mean differences between real and virtual trees. Number of bifurcations is shown as positive bars (black), bifurcation asymmetry as negative bars (gray). With models based on Branch Order and Radius, the apical-basal divide shows the largest performance ratios for both bifurcation number and asymmetry. The numbers above the Radius columns represent the count of tree groups for the corresponding divisions.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2376061&req=5

pcbi-1000089-g007: Relative magnitude of apical-basal divide.(A) Number of bifurcations is better captured by both Radius and Branch Order in basal than in apical trees. (B) Conversely, bifurcation asymmetry is better captured by both models for apical trees. In either case, non-pyramidal trees tend to lie in between apical and basal trees. (C) The relative ability of the individual models to differentiate apical from basal trees is greater than for other tree divisions. The Performance Ratio is the absolute value of the log of the ratio between the two tree types of the mean differences between real and virtual trees. Number of bifurcations is shown as positive bars (black), bifurcation asymmetry as negative bars (gray). With models based on Branch Order and Radius, the apical-basal divide shows the largest performance ratios for both bifurcation number and asymmetry. The numbers above the Radius columns represent the count of tree groups for the corresponding divisions.

Mentions: While Figures 5 and 6 show that the interaction between fundamental determinants and emergent morphometrics is different for apical and basal trees, it is important to notice that the overall quality of the simulations is different as well, as becomes apparent when the units are on the same scale (Figure 7). Both Branch Order and Radius are better able to capture the number of bifurcations in basal than in apical arbors (Figure 7A), but the inverse relation holds for bifurcation asymmetry (Figure 7B). In both cases, non-pyramidal cells fall in between. This differential performance can be quantified for a given fundamental determinant and emergent morphometric as the ratio of the larger over the smaller of the mean differences between real and virtual trees for the two arbor types. In particular, we formalize the performance ratio as the absolute value of the logarithm of this value (this definition yields a positive value that is independent of the numerator vs. denominator). This value is larger for Branch Order and number of bifurcations and smaller for Radius and asymmetry, i.e. the contrast between apical and basal trees is greatest when testing the Branch Order model for number of bifurcations.


A comparative computer simulation of dendritic morphology.

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

Relative magnitude of apical-basal divide.(A) Number of bifurcations is better captured by both Radius and Branch Order in basal than in apical trees. (B) Conversely, bifurcation asymmetry is better captured by both models for apical trees. In either case, non-pyramidal trees tend to lie in between apical and basal trees. (C) The relative ability of the individual models to differentiate apical from basal trees is greater than for other tree divisions. The Performance Ratio is the absolute value of the log of the ratio between the two tree types of the mean differences between real and virtual trees. Number of bifurcations is shown as positive bars (black), bifurcation asymmetry as negative bars (gray). With models based on Branch Order and Radius, the apical-basal divide shows the largest performance ratios for both bifurcation number and asymmetry. The numbers above the Radius columns represent the count of tree groups for the corresponding divisions.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000089-g007: Relative magnitude of apical-basal divide.(A) Number of bifurcations is better captured by both Radius and Branch Order in basal than in apical trees. (B) Conversely, bifurcation asymmetry is better captured by both models for apical trees. In either case, non-pyramidal trees tend to lie in between apical and basal trees. (C) The relative ability of the individual models to differentiate apical from basal trees is greater than for other tree divisions. The Performance Ratio is the absolute value of the log of the ratio between the two tree types of the mean differences between real and virtual trees. Number of bifurcations is shown as positive bars (black), bifurcation asymmetry as negative bars (gray). With models based on Branch Order and Radius, the apical-basal divide shows the largest performance ratios for both bifurcation number and asymmetry. The numbers above the Radius columns represent the count of tree groups for the corresponding divisions.
Mentions: While Figures 5 and 6 show that the interaction between fundamental determinants and emergent morphometrics is different for apical and basal trees, it is important to notice that the overall quality of the simulations is different as well, as becomes apparent when the units are on the same scale (Figure 7). Both Branch Order and Radius are better able to capture the number of bifurcations in basal than in apical arbors (Figure 7A), but the inverse relation holds for bifurcation asymmetry (Figure 7B). In both cases, non-pyramidal cells fall in between. This differential performance can be quantified for a given fundamental determinant and emergent morphometric as the ratio of the larger over the smaller of the mean differences between real and virtual trees for the two arbor types. In particular, we formalize the performance ratio as the absolute value of the logarithm of this value (this definition yields a positive value that is independent of the numerator vs. denominator). This value is larger for Branch Order and number of bifurcations and smaller for Radius and asymmetry, i.e. the contrast between apical and basal trees is greatest when testing the Branch Order model for number of bifurcations.

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