Morphological Neuron Classification Based on Dendritic Tree Hierarchy

Neuroinformatics
Evelyn Perez CervantesLuciano da Fontoura Costa

Abstract

The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.

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Citations

Jan 9, 2019·Frontiers in Neuroanatomy·Jose Juan Aliaga MaraverLuis Pastor

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Methods Mentioned

BETA
electron microscopy
PCA

Key Resources (RRID) Mentioned

SCR_003566
SCR_002145

Software Mentioned

Org
NeuroMorpho
LIBSVM

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