Multi-Step Clustering Approach of Myelinated Nerve Fibers in Experimental Neuromorphology

Abstract

One of the unresolved issues in experimental neuromorphology is searching for a solution for myelinated nerve fibers clustering on set of morphometric parameters. Therefore, in this article, a new approach for cluster analysis of myelinated fibers is proposed based on their morpho-functional features. The proposed clustering approach was developed in R software environment and uses model-based clustering, which is performed in few steps with increasing number of morphometric parameters on each next step. Applying the proposed clustering solution shown high similarity of identified groups' morphometric parameters with respective physiological types of myelinated A-fibers. This fact, in addition to the algorithm implementation simplicity, facilitates its use on identifying clusters of myelinated fibers that represent different myelinated fibers subpopulation in experimental neuromorphological research with high level of reliability.