title: Morphological Neuron Classification Using Machine Learning.
abstract: Classification and quantitative characterization of neuronal morphologies from histological neuronal reconstruction is challenging since it is still unclear how to delineate a neuronal cell class and which are the best features to define them by. The morphological neuron characterization represents a primary source to address anatomical comparisons, morphometric analysis of cells, or brain modeling. The objectives of this paper are (i) to develop and integrate a pipeline that goes from morphological feature extraction to classification and (ii) to assess and compare the accuracy of machine learning algorithms to classify neuron morphologies. The algorithms were trained on 430 digitally reconstructed neurons subjectively classified into layers and/or m-types using young and/or adult development state population of the somatosensory cortex in rats. For supervised algorithms, linear discriminant analysis provided better classification results in comparison with others. For unsupervised algorithms, the affinity propagation and the Ward algorithms provided slightly better results.
authors.affiliation: Laboratoire de Recherche en Neurosciences CliniquesSaint-André-de-Sangonis, France; International Business Machines Corporation SystemsParis, France.
authors.forename: Xavier
authors.initials: X
authors.lastname: Vasques
authors.affiliation: International Business Machines Corporation Systems Paris, France.
authors.forename: Laurent
authors.initials: L
authors.lastname: Vanel
authors.affiliation: International Business Machines Corporation Systems Paris, France.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Villette
authors.affiliation: Département de Neurochirurgie, Hôpital Gui de Chauliac, Centre Hospitalier Régional Universitaire de MontpellierMontpellier, France; Université de Montpellier 1Montpellier, France.
authors.forename: Laura
authors.initials: L
authors.lastname: Cif
keywords: classification; machine learning; morphologies; neurons; supervised learning; unsupervised learning