Morphological Neuron Classification Using Machine Learning

TL;DR

This paper develops a machine learning pipeline for classifying neuronal morphologies from histological data, comparing various algorithms to improve accuracy in neuronal cell classification.

Contribution

It introduces an integrated pipeline from feature extraction to classification and evaluates multiple machine learning algorithms for neuronal morphology classification.

Findings

Linear discriminant analysis outperformed other supervised algorithms.

Affinity propagation and Ward algorithms performed best among unsupervised methods.

The pipeline effectively classifies neurons based on morphological features.

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.