Bayesian network classifiers for categorizing cortical gABAergic interneurons

Mihaljevic, Bojan and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María and Benavides Piccione, Ruth and Felipe Oroquieta, Javier de (2015). Bayesian network classifiers for categorizing cortical gABAergic interneurons. "Neuroinformatics", v. 13 (n. 2); pp. 193-208. ISSN 1539-2791.


Title: Bayesian network classifiers for categorizing cortical gABAergic interneurons
  • Mihaljevic, Bojan
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
  • Benavides Piccione, Ruth
  • Felipe Oroquieta, Javier de
Item Type: Article
Título de Revista/Publicación: Neuroinformatics
Date: April 2015
ISSN: 1539-2791
Volume: 13
Freetext Keywords: Keywords : Neuronal classification · Morphological features · Label reliability · Multiple annotators · Weightednaive Bayes
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 An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1–F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts’ assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell’s label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1–F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52 % accuracy, and single out the number of branches at 180 µm from the soma, the convex hull 2D area, and axonal features F1–F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.

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Item ID: 41018
DC Identifier:
OAI Identifier:
DOI: 10.1007/s12021-014-9254-1
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 27 Apr 2017 18:03
Last Modified: 27 Apr 2017 21:53
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