The laminar organization of the motor cortex in monodactylous mammals: a comparative assessment based on horse, chimpanzee, and macaque

Andrea De Giorgio, Alberto Granato, Bruno Cozzi, A. Peruffo, S. Montelli, M. Panin, C. Bombardi, A. Grandis, A. Pirone, P. Zambenedetti, L. Corain

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

8 Citazioni (Scopus)

Abstract

The architecture of the neocortex classically consists of six layers, based on cytological criteria and on the layout of intra/interlaminar connections. Yet, the comparison of cortical cytoarchitectonic features across different species proves overwhelmingly difficult, due to the lack of a reliable model to analyze the connection patterns of neuronal ensembles forming the different layers. We first defined a set of suitable morphometric cell features, obtained in digitized Nissl-stained sections of the motor cortex of the horse, chimpanzee, and crab-eating macaque. We then modeled them using a quite general non-parametric data representation model, showing that the assessment of neuronal cell complexity (i.e., how a given cell differs from its neighbors) can be performed using a suitable measure of statistical dispersion such as the mean absolute deviation—mean absolute deviation (MAD). Along with the non-parametric combination and permutation methodology, application of MAD allowed not only to estimate, but also to compare and rank the motor cortical complexity across different species. As to the instances presented in this paper, we show that the pyramidal layers of the motor cortex of the horse are far more irregular than those of primates. This feature could be related to the different organizations of the motor system in monodactylous mammals.
Lingua originaleEnglish
pagine (da-a)1-15
Numero di pagine15
RivistaBrain Structure and Function
DOI
Stato di pubblicazionePubblicato - 2017

Keywords

  • Anatomy
  • Chimpanzee
  • Crab-eating macaque
  • Histology
  • Horse
  • Mean absolute deviation
  • Motor cortex
  • Neuroscience (all)

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