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Analyzing the neocortical fine-structure

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Kruggel,  F.
Department Cognitive Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Wiggins,  Christopher J.
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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von Cramon,  D. Yves
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Kruggel, F., Brückner, M. K., Arendt, T., Wiggins, C. J., & von Cramon, D. Y. (2003). Analyzing the neocortical fine-structure. Medical Image Analysis, 7(3), 251-264. doi:10.1016/S1361-8415(03)00006-9.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-9C60-3
Abstract
Cytoarchitectonic fields of the human neocortex are defined by characteristic variations in the composition of a general six-layer structure. It is commonly accepted that these fields correspond to functionally homogeneous entities. Diligent techniques were developed to characterize cytoarchitectonic fields by staining sections of post-mortem brains and subsequent statistical evaluation. Fields were found to show a considerable interindividual variability in extent and relation to macroscopic anatomical landmarks. With upcoming new high-resolution magnetic resonance imaging (MRI) protocols, it appears worthwhile to examine the feasibility of characterizing the neocortical fine-structure from anatomical MRI scans, thus, defining neocortical fields by in vivo techniques. A fixated brain hemisphere was scanned at a resolution of approximately 0.3 mm. After correcting for intensity inhomogeneities in the dataset, the cortex boundaries (the white/grey matter and grey matter/background interfaces) were determined as a triangular mesh. Radial intensity profiles following the shortest path through the cortex were computed and characterized by a sparse set of features. A statistical similarity measure between features of different regions was defined, and served to define the extent of Brodmann’s Areas 4, 17, 44 and 45 in this dataset.