Topological characterization of neuronal arbor morphology via sequence representation: I - motif analysis

BMC Bioinformatics

Todd A Gillette¹  Giorgio A Ascoli¹ 

[1] Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), George Mason University, Fairfax, VA, USA

关键词: Motif analysis;    Tree topology;    Neuronal morphology;   

Others  :  1230990
DOI  :  10.1186/s12859-015-0604-2

 received in 2015-03-02, accepted in 2015-04-30,  发布年份 2015

【 摘 要 】

Background

The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns.

Results

Using all possible branching topologies for comparison we show that the relative abundance of short patterns of up to three bifurcations, together with overall tree size, effectively capture the local branching patterns of neurons. Dendrites and axons display broadly similar topological motifs (over-represented patterns) and anti-motifs (under-represented patterns), differing most in their proportions of bifurcations with one terminal branch and in select sub-sequences of three bifurcations. In addition, pyramidal apical dendrites reveal a distinct motif profile.

Conclusions

The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.

【 授权许可】

   
2015 Gillette and Ascoli.

【 图 表 】

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