Towards a supervised classification of neocortical interneuron morphologies

Mihaljevic, Bojan and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción and Benavides Piccione, Ruth and Felipe Oroquieta, Javier de and Hill, Sean L. (2018). Towards a supervised classification of neocortical interneuron morphologies. "BMC Bioinformatics", v. 19 ; pp. 1-22. ISSN 1471-2105. https://doi.org/10.1186/s12859-018-2470-1.

Description

Title: Towards a supervised classification of neocortical interneuron morphologies
Author/s:
  • Mihaljevic, Bojan
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
  • Benavides Piccione, Ruth
  • Felipe Oroquieta, Javier de
  • Hill, Sean L.
Item Type: Article
Título de Revista/Publicación: BMC Bioinformatics
Date: December 2018
ISSN: 1471-2105
Volume: 19
Subjects:
Freetext Keywords: Feature selection; Martinotti; Morphometrics
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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 value. 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.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020785907HBP SGA2UnspecifiedHuman Brain Project Specific Grant Agreement 2
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain
Government of SpainTIN2016-79684-PUnspecifiedUnspecifiedUnspecified
Madrid Regional GovernmentS2013/ICE-2845CASI – CAMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes

More information

Item ID: 54567
DC Identifier: http://oa.upm.es/54567/
OAI Identifier: oai:oa.upm.es:54567
DOI: 10.1186/s12859-018-2470-1
Official URL: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2470-1
Deposited by: Memoria Investigacion
Deposited on: 29 Jan 2020 08:50
Last Modified: 29 Jan 2020 08:50
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