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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 number of bifurcations.(A) Apical trees have their number of bifurcations best captured by Path Distance (RAD = Radius, PD = Path Distance, BO = Branch Order). (B) Basal and (C) non-pyramidal trees have their bifurcation numbers best determined by Branch Order. This may point to different underlying developmental mechanisms between apical and other tree types. (D, E) Sholl-like plots showing bifurcation number as a function of branch order for sample apical (D) and basal (E) groups of cortical pyramidal cells (Markram layer 4, N = 24). Path Distance better captures apical bifurcations while Branch Order better captures basal arbors.
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pcbi-1000089-g005: Ability of the models to capture apical and basal number of bifurcations.(A) Apical trees have their number of bifurcations best captured by Path Distance (RAD = Radius, PD = Path Distance, BO = Branch Order). (B) Basal and (C) non-pyramidal trees have their bifurcation numbers best determined by Branch Order. This may point to different underlying developmental mechanisms between apical and other tree types. (D, E) Sholl-like plots showing bifurcation number as a function of branch order for sample apical (D) and basal (E) groups of cortical pyramidal cells (Markram layer 4, N = 24). Path Distance better captures apical bifurcations while Branch Order better captures basal arbors.

Mentions: These trends were generally robust throughout individual tree groups. However, a finer analysis organized by morphological classes revealed additional insights. The tree groups were first divided into apical (n = 18), basal (n = 18), and non-pyramidal (n = 32). The Branch Order model was significantly better than either Radius or Path Distance at determining the number of bifurcations in both basal and non-pyramidal tree types (Figure 5B and 5C). In particular, Branch Order “won” more than three quarters of the basal groups. This was definitely not the case for apical trees, where over half of the 18 groups had their number of bifurcations best determined by Path Distance (Figure 5A). Figure 5D shows a more detailed analysis for a representative apical tree group. In this example, Path Distance better captures not only the mean, but also the pattern of bifurcations as a function of branch order (“Sholl-like” plots). In contrast, when looking at basal trees from the same cells (Figure 5E) the Branch Order model provides a much better match to the real data.


A comparative computer simulation of dendritic morphology.

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

Ability of the models to capture apical and basal number of bifurcations.(A) Apical trees have their number of bifurcations best captured by Path Distance (RAD = Radius, PD = Path Distance, BO = Branch Order). (B) Basal and (C) non-pyramidal trees have their bifurcation numbers best determined by Branch Order. This may point to different underlying developmental mechanisms between apical and other tree types. (D, E) Sholl-like plots showing bifurcation number as a function of branch order for sample apical (D) and basal (E) groups of cortical pyramidal cells (Markram layer 4, N = 24). Path Distance better captures apical bifurcations while Branch Order better captures basal arbors.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000089-g005: Ability of the models to capture apical and basal number of bifurcations.(A) Apical trees have their number of bifurcations best captured by Path Distance (RAD = Radius, PD = Path Distance, BO = Branch Order). (B) Basal and (C) non-pyramidal trees have their bifurcation numbers best determined by Branch Order. This may point to different underlying developmental mechanisms between apical and other tree types. (D, E) Sholl-like plots showing bifurcation number as a function of branch order for sample apical (D) and basal (E) groups of cortical pyramidal cells (Markram layer 4, N = 24). Path Distance better captures apical bifurcations while Branch Order better captures basal arbors.
Mentions: These trends were generally robust throughout individual tree groups. However, a finer analysis organized by morphological classes revealed additional insights. The tree groups were first divided into apical (n = 18), basal (n = 18), and non-pyramidal (n = 32). The Branch Order model was significantly better than either Radius or Path Distance at determining the number of bifurcations in both basal and non-pyramidal tree types (Figure 5B and 5C). In particular, Branch Order “won” more than three quarters of the basal groups. This was definitely not the case for apical trees, where over half of the 18 groups had their number of bifurcations best determined by Path Distance (Figure 5A). Figure 5D shows a more detailed analysis for a representative apical tree group. In this example, Path Distance better captures not only the mean, but also the pattern of bifurcations as a function of branch order (“Sholl-like” plots). In contrast, when looking at basal trees from the same cells (Figure 5E) the Branch Order model provides a much better match to the real data.

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