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dc.contributor.authorGarcía-Cabezas, Miguel Ángelen_US
dc.contributor.authorHacker, Julia Liaoen_US
dc.contributor.authorZikopoulos, Basilisen_US
dc.coverage.spatialSwitzerlanden_US
dc.date2020-11-05
dc.date.accessioned2021-11-24T19:28:28Z
dc.date.available2021-11-24T19:28:28Z
dc.date.issued2020
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/33364924
dc.identifier.citationM.Á. García-Cabezas, J.L. Hacker, B. Zikopoulos. 2020. "A Protocol for Cortical Type Analysis of the Human Neocortex Applied on Histological Samples, the Atlas of Von Economo and Koskinas, and Magnetic Resonance Imaging.." Front Neuroanat, Volume 14, 576015. https://doi.org/10.3389/fnana.2020.576015
dc.identifier.issn1662-5129
dc.identifier.urihttps://hdl.handle.net/2144/43412
dc.description.abstractThe human cerebral cortex is parcellated in hundreds of areas using neuroanatomy and imaging methods. Alternatively, cortical areas can be classified into few cortical types according to their degree of laminar differentiation. Cortical type analysis is based on the gradual and systematic variation of laminar features observed across the entire cerebral cortex in Nissl stained sections and has profound implications for understanding fundamental aspects of evolution, development, connections, function, and pathology of the cerebral cortex. In this protocol paper, we explain the general principles of cortical type analysis and provide tables with the fundamental features of laminar structure that are studied for this analysis. We apply cortical type analysis to the micrographs of the Atlas of the human cerebral cortex of von Economo and Koskinas and provide tables and maps with the areas of this Atlas and their corresponding cortical type. Finally, we correlate the cortical type maps with the T1w/T2w ratio from widely used reference magnetic resonance imaging scans. The analysis, tables and maps of the human cerebral cortex shown in this protocol paper can be used to predict patterns of connections between areas according to the principles of the Structural Model and determine their level in cortical hierarchies. Cortical types can also predict the spreading of abnormal proteins in neurodegenerative diseases to the level of cortical layers. In summary, cortical type analysis provides a theoretical and practical framework for directed studies of connectivity, synaptic plasticity, and selective vulnerability to neurologic and psychiatric diseases in the human neocortex.en_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofFront Neuroanat
dc.rightsCopyright © 2020 García-Cabezas, Hacker and Zikopoulos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBrodmann areaen_US
dc.subjectCortical areaen_US
dc.subjectCortical layeren_US
dc.subjectStructural modelen_US
dc.subjectCytoarchitectureen_US
dc.subjectNisslen_US
dc.titleA protocol for cortical type analysis of the human neocortex applied on histological samples, the Atlas of Von Economo and Koskinas, and magnetic resonance imagingen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.3389/fnana.2020.576015
pubs.elements-sourcepubmeden_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Health & Rehabilitation Sciences: Sargent Collegeen_US
pubs.organisational-groupBoston University, College of Health & Rehabilitation Sciences: Sargent College, Health Sciencesen_US
pubs.publication-statusPublished onlineen_US
dc.identifier.mycv584040


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Copyright © 2020 García-Cabezas, Hacker and Zikopoulos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as Copyright © 2020 García-Cabezas, Hacker and Zikopoulos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.