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A software tool for the analysis of neuronal morphology data.

Ledderose J, Sención L, Salgado H, Arias-Carrión O, Treviño M - Int Arch Med (2014)

Bottom Line: However, such 'Cartesian' descriptions bear little intuitive information for neuroscientists.Here, we developed a simple prototype of a MATLAB-based software tool to quantitatively describe the 3D neuronal structures from public repositories.Using these morphological distributions, our algorithm can generate a set of virtual neurons readily usable for network simulations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Neurociencias, Universidad de Guadalajara, Guadalajara, México. mariomtv@hotmail.com.

ABSTRACT
Anatomy plays a fundamental role in supporting and shaping nervous system activity. The remarkable progress of computer processing power within the last two decades has enabled the generation of electronic databases of complete three-dimensional (3D) dendritic and axonal morphology for neuroanatomical studies. Several laboratories are freely posting their reconstructions online after result publication v.gr. NeuroMorpho.Org (Nat Rev Neurosci7:318-324, 2006). These neuroanatomical archives represent a crucial resource to explore the relationship between structure and function in the brain (Front Neurosci6:49, 2012). However, such 'Cartesian' descriptions bear little intuitive information for neuroscientists. Here, we developed a simple prototype of a MATLAB-based software tool to quantitatively describe the 3D neuronal structures from public repositories. The program imports neuronal reconstructions and quantifies statistical distributions of basic morphological parameters such as branch length, tortuosity, branch's genealogy and bifurcation angles. Using these morphological distributions, our algorithm can generate a set of virtual neurons readily usable for network simulations.

No MeSH data available.


Frequency Distributions of Morphological Parameters. Three groups of dendrites from granule cells (A), and from apical (B) and basal (C) dendrites of CA1 pyramidal cells were used to compute relevant morphological parameters such as number of branches, number of bifurcations, bifurcation angle, radius and number of branches with a specific branch length. Bin selection was made independently for each group according to Sturge’s rule. Bar-plots represent mean ± standard error of the mean. Calibration bars 100 μm.
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Figure 4: Frequency Distributions of Morphological Parameters. Three groups of dendrites from granule cells (A), and from apical (B) and basal (C) dendrites of CA1 pyramidal cells were used to compute relevant morphological parameters such as number of branches, number of bifurcations, bifurcation angle, radius and number of branches with a specific branch length. Bin selection was made independently for each group according to Sturge’s rule. Bar-plots represent mean ± standard error of the mean. Calibration bars 100 μm.

Mentions: All basic parameters were measured from digital files of traced neurons. Raw data for each parameter were extracted in the form of simple arrays, grouped for each cell class and characterized with histograms representing frequency distributions. Group results for three groups of granule cells (4A), apical (4B), and basilar (4C) dendritic trees of CA1 pyramidal cells are shown in Figure 4. Bin selection was made independently for each group according to Sturge’s rule. However, selecting the same bin width for each histogram distribution allowed us to detect correlations between measurements for the same trees (such as depth×length and Euclidean distance×path distance; data not shown) as well as differences between morphological basic parameters of different dendritic trees (Figure 5). For example, apical CA1 dendritic trees project at longer distances than basilar dendritic trees. This feature is reflected in the frequency distribution of the number of branches located at different 'Euclidean' path lengths (with the starting point at the soma), and also when comparing the number of branches at different paths from the soma (Figure 5). Notably, branches from both dendritic trees show similar lengths, which indicates that both dendritic trees are constructed upon equally long branches (Figure 5). In other words, a longer dendritic tree (in spatial terms) is a consequence of the number of total branches employed to build the tree.


A software tool for the analysis of neuronal morphology data.

Ledderose J, Sención L, Salgado H, Arias-Carrión O, Treviño M - Int Arch Med (2014)

Frequency Distributions of Morphological Parameters. Three groups of dendrites from granule cells (A), and from apical (B) and basal (C) dendrites of CA1 pyramidal cells were used to compute relevant morphological parameters such as number of branches, number of bifurcations, bifurcation angle, radius and number of branches with a specific branch length. Bin selection was made independently for each group according to Sturge’s rule. Bar-plots represent mean ± standard error of the mean. Calibration bars 100 μm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3928584&req=5

Figure 4: Frequency Distributions of Morphological Parameters. Three groups of dendrites from granule cells (A), and from apical (B) and basal (C) dendrites of CA1 pyramidal cells were used to compute relevant morphological parameters such as number of branches, number of bifurcations, bifurcation angle, radius and number of branches with a specific branch length. Bin selection was made independently for each group according to Sturge’s rule. Bar-plots represent mean ± standard error of the mean. Calibration bars 100 μm.
Mentions: All basic parameters were measured from digital files of traced neurons. Raw data for each parameter were extracted in the form of simple arrays, grouped for each cell class and characterized with histograms representing frequency distributions. Group results for three groups of granule cells (4A), apical (4B), and basilar (4C) dendritic trees of CA1 pyramidal cells are shown in Figure 4. Bin selection was made independently for each group according to Sturge’s rule. However, selecting the same bin width for each histogram distribution allowed us to detect correlations between measurements for the same trees (such as depth×length and Euclidean distance×path distance; data not shown) as well as differences between morphological basic parameters of different dendritic trees (Figure 5). For example, apical CA1 dendritic trees project at longer distances than basilar dendritic trees. This feature is reflected in the frequency distribution of the number of branches located at different 'Euclidean' path lengths (with the starting point at the soma), and also when comparing the number of branches at different paths from the soma (Figure 5). Notably, branches from both dendritic trees show similar lengths, which indicates that both dendritic trees are constructed upon equally long branches (Figure 5). In other words, a longer dendritic tree (in spatial terms) is a consequence of the number of total branches employed to build the tree.

Bottom Line: However, such 'Cartesian' descriptions bear little intuitive information for neuroscientists.Here, we developed a simple prototype of a MATLAB-based software tool to quantitatively describe the 3D neuronal structures from public repositories.Using these morphological distributions, our algorithm can generate a set of virtual neurons readily usable for network simulations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Neurociencias, Universidad de Guadalajara, Guadalajara, México. mariomtv@hotmail.com.

ABSTRACT
Anatomy plays a fundamental role in supporting and shaping nervous system activity. The remarkable progress of computer processing power within the last two decades has enabled the generation of electronic databases of complete three-dimensional (3D) dendritic and axonal morphology for neuroanatomical studies. Several laboratories are freely posting their reconstructions online after result publication v.gr. NeuroMorpho.Org (Nat Rev Neurosci7:318-324, 2006). These neuroanatomical archives represent a crucial resource to explore the relationship between structure and function in the brain (Front Neurosci6:49, 2012). However, such 'Cartesian' descriptions bear little intuitive information for neuroscientists. Here, we developed a simple prototype of a MATLAB-based software tool to quantitatively describe the 3D neuronal structures from public repositories. The program imports neuronal reconstructions and quantifies statistical distributions of basic morphological parameters such as branch length, tortuosity, branch's genealogy and bifurcation angles. Using these morphological distributions, our algorithm can generate a set of virtual neurons readily usable for network simulations.

No MeSH data available.