Qualitative and quantitative analysis of cortical type gradients in the human prefrontal cortex
Hacker, Julia Liao
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The cerebral cortex, the outer part of the brain that has expanded in humans, has layers whose differentiation varies within gradients. Along those gradients, we can define cortical types which range in number of layers and degrees of laminar differentiation. From least to most elaborate types there is an increase in the presence of granular layer IV, a shift in relative prominence of deep (layers V–VI) to superficial layers (layers II–III), and shift in location of large pyramidal neurons from deep (layers V–VI) to superficial layers (layers II–III), an increase in differentiation of deep layers (layers V–VI), and an increase in a defined boundary between layers I–II. According to this criteria, the following cortical types were defined: agranular, dysgranular, eulaminate I, and eulaminate II. In addition, primary areas in the cerebral cortex show distinct cortical features and are named koniocortices. Prior studies have shown that cortical types are related to epigenetics, synaptic plasticity, connections, pathologies, and evolution. Therefore, an algorithm to determine cortical type across areas in the human cortex will be a useful tool for the study of normal and pathological cortical networks. The Nissl stain, a standard histological staining method, was used in this study to observe differences in cortical type characteristics across the cerebral cortex. Qualitative analysis was performed on several cortical regions of an established neuroanatomical atlas, the prefrontal cortex of a post-mortem human, and the cerebral cortex of a rhesus macaque. Five laminar features were identified and used to group cortical regions into types, with less than 5% of disagreement amongst at least three experienced neuroanatomists. From these cortical type characteristics, an algorithm was created that can be used to systematically to determine cortical type throughout the cerebral cortex of humans and rhesus macaques. Additionally, quantitative analyses were performed in order to see if this cortical type classification could be an automated practice, that can be performed by individuals who are not experienced neuroanatomists. These quantitative measurements showed varying ability to classify cortical types; therefore, further studies will need to be performed in order to find the optimal quantitative measures of cortical type. A NMDS study was performed to summarize results of the various quantitative measurements, which showed an undisputable gradual trend of cortical types throughout the prefrontal cortex of the human brain. Overall, this study provides a cortical type classification algorithm that reliably and reproducibly identifies different cortical types in the cerebral cortex of human and rhesus macaque brains.