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Statistical analysis and data mining of digital reconstructions of dendritic morphologies

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dc.contributor.author Polavaram, Sridevi
dc.contributor.author Gillette, Todd A.
dc.contributor.author Parekh, Ruchi
dc.contributor.author Ascoli, Giorgio A.
dc.date.accessioned 2015-09-22T14:49:49Z
dc.date.available 2015-09-22T14:49:49Z
dc.date.issued 2014-12-04
dc.identifier.citation Polavaram S, Gillette TA, Parekh R and Ascoli GA (2014) Statistical analysis and data mining of digital reconstructions of dendritic morphologies. Front. Neuroanat. 8:138 en_US
dc.identifier.uri https://hdl.handle.net/1920/9894
dc.description.abstract Neuronal morphology is diverse among animal species, developmental stages, brain regions, and cell types. The geometry of individual neurons also varies substantially even within the same cell class. Moreover, specific histological, imaging, and reconstruction methodologies can differentially affect morphometric measures. The quantitative characterization of neuronal arbors is necessary for in-depth understanding of the structure-function relationship in nervous systems. The large collection of community-contributed digitally reconstructed neurons available at NeuroMorpho.Org constitutes a “big data” research opportunity for neuroscience discovery beyond the approaches typically pursued in single laboratories. To illustrate these potential and related challenges, we present a database-wide statistical analysis of dendritic arbors enabling the quantification of major morphological similarities and differences across broadly adopted metadata categories. Furthermore, we adopt a complementary unsupervised approach based on clustering and dimensionality reduction to identify the main morphological parameters leading to the most statistically informative structural classification. We find that specific combinations of measures related to branching density, overall size, tortuosity, bifurcation angles, arbor flatness, and topological asymmetry can capture anatomically and functionally relevant features of dendritic trees. The reported results only represent a small fraction of the relationships available for data exploration and hypothesis testing enabled by sharing of digital morphological reconstructions.
dc.description.sponsorship Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund en_US
dc.language.iso en_US en_US
dc.publisher Frontiers Media en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.subject L-Measure (RRID:nif-0000-00003) en_US
dc.subject NeuroMorpho.Org (RRID:nif-0000-00006) en_US
dc.subject neuroinformatics en_US
dc.subject dendritic topology en_US
dc.subject cluster analysis en_US
dc.subject cellular neuroanatomy en_US
dc.title Statistical analysis and data mining of digital reconstructions of dendritic morphologies en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.3389/fnana.2014.00138


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