English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/173607
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Title

Towards a supervised classification of neocortical interneuron morphologies

AuthorsMihaljević, Bojan; Larrañaga, Pedro; Benavides-Piccione, Ruth ; Hill, Sean; DeFelipe, Javier ; Bielza, Concha
KeywordsFeature selection
Martinotti
Morphometrics
Feature selection
Martinott
Morphometrics
Issue Date17-Dec-2018
PublisherBioMed Central
CitationBMC Bioinformatics 19(1): 511 (2018)
Abstract[Background] The challenge of classifying cortical interneurons is yet to be solved. Data-driven classification into established morphological types may provide insight and practical values. [Results] We trained models using 217 high-quality morphologies of rat somatosensory neocortex interneurons reconstructed by a single laboratory and pre-classified into eight types. We quantified 103 axonal and dendritic morphometrics, including novel ones that capture features such as arbor orientation, extent in layer one, and dendritic polarity. We trained a one-versus-rest classifier for each type, combining well-known supervised classification algorithms with feature selection and over- and under-sampling. We accurately classified the nest basket, Martinotti, and basket cell types with the Martinotti model outperforming 39 out of 42 leading neuroscientists. We had moderate accuracy for the double bouquet, small and large basket types, and limited accuracy for the chandelier and bitufted types. We characterized the types with interpretable models or with up to ten morphometrics. [Conclusion] Except for large basket, 50 high-quality reconstructions sufficed to learn an accurate model of a type. Improving these models may require quantifying complex arborization patterns and finding correlates of bouton-related features. Our study brings attention to practical aspects important for neuron classification and is readily reproducible, with all code and data available online.
Publisher version (URL)https://doi.org/10.1186/s12859-018-2470-1
URIhttp://hdl.handle.net/10261/173607
DOI10.1186/s12859-018-2470-1
E-ISSN1471-2105
Appears in Collections:(IC) Artículos
Files in This Item:
File Description SizeFormat 
12859_2018_Article_2470.pdf2,65 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.