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Cytoarchitecture and myeloarchitecture of the entorhinal cortex of the common marmoset monkey (Callithrix jacchus).

Paulo L A de Góis MoraisRuthnaldo R M de LimaJorge Alexander Ríos-FlórezTúlio F V MeloFrancisco ClascáJeferson de Souza CavalcanteFausto P GuzenJosé R L P CavalcantiExpedito S Nascimento Junior
Published in: The Journal of comparative neurology (2019)
The entorhinal cortex (EC) is associated with impaired cognitive function such as in the case of Alzheimer's disease, Parkinson's disease and Huntington's disease. The present study provides a detailed analysis of the cytoarchitectural and myeloarchitectural organization of the EC in the common marmoset Callithrix jacchus. Data were collected using Nissl and fiber stained preparations, supplemented with acetylcholinesterase and parvalbumin immunohistochemistry. The EC layers and subfields in the marmoset seem to be architectonically similar to those that have been proposed in nonhuman primates and humans to date; however, slight differences could be revealed using the present techniques. Throughout its rostrocaudal length, the entorhinal cortex presents a clear six-layered pattern. The entorhinal cortex is divided into six fields, named mainly in accordance to their rostrocaudal and mediolateral positions. At rostral levels, the neurons tend to be organized in patches that are surrounded by large, thick, radially oriented bundles of fibers, and the deep layers are poorly developed. At caudal levels, the divisions are more laminated in appearance. AChE staining at the borders of adjacent fields are consistent with the changes in layering revealed in Nissl-stained sections, of which the lateral regions of the EC display denser AChE staining than that of the medial banks. PV immunoreactivity was found in the labeled somata, dendrites, and axons in all layers and subdivisions. Additionally, we distinguished three subtypes of PV-immunoreactive neurons: multipolar, bipolar and spherical-shaped neurons, based on the shape of the somata and the disposition of the dendrites.
Keyphrases
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  • machine learning
  • deep learning