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A stereological approach to human cortical architecture: Identification and delineation of cortical areas

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Geyer,  Stefan
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Schleicher, A., Amunts, K., Geyer, S., Kowalski, T., Schormann, T., Palomero-Gallagher, N., et al. (2000). A stereological approach to human cortical architecture: Identification and delineation of cortical areas. Journal of Chemical Neuroanatomy, 20(1), 31-47. doi:10.1016/S0891-0618(00)00076-4.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-618D-1
Abstract
Stereology offers a variety of procedures to analyze quantitatively the regional and laminar organization in cytoarchitectonically defined areas of the human cerebral cortex. Conventional anatomical atlases are of little help in localizing specific cortical areas, since most of them are based on a single brain and use highly observer-dependent criteria for the delineation of cortical areas. In consequence, numerous cortical maps exist which greatly differ with respect to number, position, size and extent of cortical areas. We describe a novel algorithm-based procedure for the delineation of cortical areas, which exploits the automated estimation of volume densities of cortical cell bodies. Spatial sampling of the laminar pattern is performed with density profiles, followed by multivariate analysis of the profiles‘ shape, which locates the cytoarchitectonic borders between neighboring cortical areas at sites where the laminar pattern changes significantly. The borders are then mapped to a human brain atlas system comprising tools for three dimensional reconstruction, visualization and morphometric analysis. A sample of brains with labeled cortical areas is warped into the reference brain of the atlas system in order to generate a population map of the cortical areas, which describes the intersubject variability in spatial conformation of cortical areas. These population maps provide a novel tool for the interpretation of images obtained with functional imaging techniques.