Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Mihaljevic, Bojan ORCID:, Benavides Piccione, Ruth, Guerra Velasco, Luis Pelayo, De Felipe Oroquieta, Javier, Larrañaga Múgica, Pedro María ORCID: and Bielza Lozoya, María Concepción ORCID: (2014). Classifying GABAergic interneurons with semi-supervised projected model-based clustering. "Artificial Intelligence in Medicine", v. 65 (n. 1); pp. 49-59. ISSN 0933-3657.


Title: Classifying GABAergic interneurons with semi-supervised projected model-based clustering
Item Type: Article
Título de Revista/Publicación: Artificial Intelligence in Medicine
Date: September 2014
ISSN: 0933-3657
Volume: 65
Freetext Keywords: Semi-supervised projected clustering, Gaussian mixture models, automatic neuron classification, cerebral cortex
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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Objectives: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names.We sought to automatically classify digitally reconstructed interneuronal morphologies according tothis scheme. Simultaneously, we sought to discover possible subtypes of these types that might emergeduring automatic classification (clustering). We also investigated which morphometric properties weremost relevant for this classification.Materials and methods: A set of 118 digitally reconstructed interneuronal morphologies classified into thecommon basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of theworld?s leading neuroscientists, quantified by five simple morphometric properties of the axon and fourof the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. Wethen removed this class information for each type separately, and applied semi-supervised clustering tothose cells (keeping the others? cluster membership fixed), to assess separation from other types and lookfor the formation of new groups (subtypes). We performed this same experiment unlabeling the cells oftwo types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixtureof Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performedthe described experiments on three different subsets of the data, formed according to how many expertsagreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least26 (47 neurons).Results: Interneurons with more reliable type labels were classified more accurately. We classified HTcells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy,respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, andno subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette widthand ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively,confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a singletype also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometricproperties were more relevant that dendritic ones, with the axonal polar histogram length in the [pi, 2pi) angle interval being particularly useful.Conclusions: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heteroge-neous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones fordistinguishing among the CB, HT, LB, and MA interneuron types.

Funding Projects

Government of Spain
Government of Spain
Madrid Regional Government
The Human Brain Project

More information

Item ID: 35618
DC Identifier:
OAI Identifier:
DOI: 10.1016/j.artmed.2014.12.010
Deposited by: Memoria Investigacion
Deposited on: 03 Mar 2016 20:23
Last Modified: 30 Nov 2022 09:00
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