title: Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty.
abstract: Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.
authors.affiliation: Computational Intelligence Group, Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid Madrid, Spain.
authors.forename: Bojan
authors.initials: B
authors.lastname: Mihaljević
authors.affiliation: Computational Intelligence Group, Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid Madrid, Spain.
authors.forename: Concha
authors.initials: C
authors.lastname: Bielza
authors.affiliation: Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid Madrid, Spain ; Instituto Cajal, Consejo Superior de Investigaciones Científicas Madrid, Spain.
authors.forename: Ruth
authors.initials: R
authors.lastname: Benavides-Piccione
authors.affiliation: Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid Madrid, Spain ; Instituto Cajal, Consejo Superior de Investigaciones Científicas Madrid, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: DeFelipe
authors.affiliation: Computational Intelligence Group, Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid Madrid, Spain.
authors.forename: Pedro
authors.initials: P
authors.lastname: Larrañaga
keywords: consensus; distance-weighted k nearest neighbors; multiple annotators; neuronal morphology; probabilistic labels