Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/173607
COMPARTIR / EXPORTAR:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

Towards a supervised classification of neocortical interneuron morphologies

AutorMihaljević, Bojan; Larrañaga, Pedro; Benavides-Piccione, Ruth CSIC ORCID ; Hill, Sean; DeFelipe, Javier CSIC ORCID ; Bielza, Concha
Palabras claveFeature selection
Martinotti
Morphometrics
Feature selection
Martinott
Morphometrics
Fecha de publicación17-dic-2018
EditorBioMed Central
CitaciónBMC Bioinformatics 19(1): 511 (2018)
Resumen[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.
Versión del editorhttps://doi.org/10.1186/s12859-018-2470-1
URIhttp://hdl.handle.net/10261/173607
DOI10.1186/s12859-018-2470-1
E-ISSN1471-2105
Aparece en las colecciones: (IC) Artículos




Ficheros en este ítem:
Fichero Descripción Tamaño Formato
12859_2018_Article_2470.pdf2,65 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo

CORE Recommender

PubMed Central
Citations

7
checked on 11-mar-2024

SCOPUSTM   
Citations

13
checked on 15-mar-2024

WEB OF SCIENCETM
Citations

12
checked on 29-feb-2024

Page view(s)

325
checked on 18-mar-2024

Download(s)

201
checked on 18-mar-2024

Google ScholarTM

Check

Altmetric

Altmetric


Artículos relacionados:


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.