Multi-aspect testing and ranking inference to quantify dimorphism in the cytoarchitecture of cerebellum of male, female
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ORIGINAL ARTICLE
Multi‑aspect testing and ranking inference to quantify dimorphism in the cytoarchitecture of cerebellum of male, female and intersex individuals: a model applied to bovine brains L. Corain1 · E. Grisan2,3 · J.‑M. Graïc4 · R. Carvajal‑Schiaffino5 · B. Cozzi4 · A. Peruffo4 Received: 30 March 2020 / Accepted: 8 September 2020 © The Author(s) 2020
Abstract The dimorphism among male, female and freemartin intersex bovines, focusing on the vermal lobules VIII and IX, was analyzed using a novel data analytics approach to quantify morphometric differences in the cytoarchitecture of digitalized sections of the cerebellum. This methodology consists of multivariate and multi-aspect testing for cytoarchitecture-ranking, based on neuronal cell complexity among populations defined by factors, such as sex, age or pathology. In this context, we computed a set of shape descriptors of the neural cell morphology, categorized them into three domains named size, regularity and density, respectively. The output and results of our methodology are multivariate in nature, allowing an in-depth analysis of the cytoarchitectonic organization and morphology of cells. Interestingly, the Purkinje neurons and the underlying granule cells revealed the same morphological pattern: female possessed larger, denser and more irregular neurons than males. In the Freemartin, Purkinje neurons showed an intermediate setting between males and females, while the granule cells were the largest, most regular and dense. This methodology could be a powerful instrument to carry out morphometric analysis providing robust bases for objective tissue screening, especially in the field of neurodegenerative pathologies. Keywords Brain dimorphism · Cerebellum · Cytoarchitecture morphometrics · Image analysis · Multi-aspect analysis in neuroanatomy
Introduction Morphometric data analytics L. Corain and E. Grisan have contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00429-020-02147-x) contains supplementary material, which is available to authorized users. * J.‑M. Graïc [email protected] 1
Department of Management and Engineering, University of Padova, 36100 Vicenza, VI, Italy
2
Department of Information Engineering, University of Padova, 35131 Padua, PD, Italy
3
School of Engineering, London South Bank University, London SE1 0AA, UK
4
Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell’Università 16, 35020 Legnaro, PD, Italy
5
Department of Mathematics and Computer Science, University of Santiago de Chile, Santiago, Chile
The concept of morphometrics was introduced in the early 1900s, but it was not until the 1980s that researchers started to use tools for the morphological analysis of cells and the identification of phenotypes. Since then, morphometric descriptors and tools were employed for the quantitative analysis of cell structure, and relevant geometrical features of the cell were, thus,
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