A directional-linear Bayesian network and its application for clustering and simulation of neural somas

Luengo Sánchez, Sergio and Larrañaga Múgica, Pedro María and Bielza Lozoya, María Concepción (2019). A directional-linear Bayesian network and its application for clustering and simulation of neural somas. "IEEE Access", v. 7 ; pp. 69907-69921. ISSN 2169-3536. https://doi.org/10.1109/ACCESS.2019.2918494.

Description

Title: A directional-linear Bayesian network and its application for clustering and simulation of neural somas
Author/s:
  • Luengo Sánchez, Sergio
  • Larrañaga Múgica, Pedro María
  • Bielza Lozoya, María Concepción
Item Type: Article
Título de Revista/Publicación: IEEE Access
Date: 2019
ISSN: 2169-3536
Volume: 7
Subjects:
Freetext Keywords: Clustering morphology soma; Directional-linear data; Extended Mardia-sutton mixturemodel; Structural Expectation-Maximization algorithm
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Neural somas perform most of the metabolic activities in the neuron and support the chemical process that generates the basic elements of the synapses, and consequently the brain activity. The morphology of the somas is one of the fundamental features for classifying neurons and their functionality. In this paper, we characterize the morphology of the 39 three-dimensional reconstructed human pyramidal somas in terms of their multiresolutional Reeb graph representation, from which we extract a set of directional and linear variables to perform model-based clustering. To deal with this dataset, we introduce the novel Extended Mardia-Sutton mixture model whose mixture components are distributed according to a newly proposed multivariate probability density function that is able to capture the directional-linear correlations. We exploit the capabilities of Bayesian networks in combination with the Structural Expectation-Maximization algorithm to learn the nite mixture model that clusters the neural somas by their morphology and the conditional independence constraints between variables. We also derive the Kullback-Leibler divergence of the Extended Mardia-Sutton distribution to be used as a measure of similarity between soma clusters. The proposed nite mixture model discovered three subtypes of human pyramidal somas. We performed Weltch t-tests and Watson-Williams tests, as well as rule-based identication of clusters to characterize each group by its most prominent features. Furthermore, the resulting model allows us to simulate the 3D virtual representations of somas from each cluster, which can be a useful tool for neuroscientists to reason and suggest new hypotheses.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain
Government of SpainTIN2016-79684-PUnspecifiedUniversidad Politécnica de MadridAvances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional GovernmentS2013/ICE-2845CASI-CAM-CMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes
Horizon 2020785907HBP SGA2École Polytechnique Fédérale de LausanneHuman Brain Project Specific Gran Agreement 2

More information

Item ID: 63608
DC Identifier: http://oa.upm.es/63608/
OAI Identifier: oai:oa.upm.es:63608
DOI: 10.1109/ACCESS.2019.2918494
Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8730327
Deposited by: Memoria Investigacion
Deposited on: 05 Nov 2020 12:55
Last Modified: 06 Nov 2020 09:25
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