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

Taras Kotyk, Nadiya Tokaruk, Viktoria Bedej, Mariia Hryshchuk, Oksana Popadynets, Yaroslav Kolinko and João Manuel R. S. Tavares
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Taras Kotyk: Ivano-Frankivsk National Medical University of the Ministry of Health of Ukraine, Ukraine
Nadiya Tokaruk: Ivano-Frankivsk National Medical University of the Ministry of Health of Ukraine, Ukraine
Viktoria Bedej: Ivano-Frankivsk National Medical University of the Ministry of Health of Ukraine, Ukraine
Mariia Hryshchuk: Ivano-Frankivsk National Medical University of the Ministry of Health of Ukraine, Ukraine
Oksana Popadynets: Ivano-Frankivsk National Medical University of the Ministry of Health of Ukraine, Ukraine
Yaroslav Kolinko: Charles University, Czech Republic
João Manuel R. S. Tavares: Universidade do Porto, Portugal

International Journal of Ambient Computing and Intelligence (IJACI), 2021, vol. 12, issue 2, 73-91

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jaci00:v:12:y:2021:i:2:p:73-91

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