https://surfer.nmr.mgh.harvard.edu/ftp/articles/desikan06-parcellation.pdf

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

https://surfer.nmr.mgh.harvard.edu/ftp/articles/dale99-recon1.pdf

Cortical surface-based analysis. I. Segmentation and surface reconstruction

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

https://surfer.nmr.mgh.harvard.edu/ftp/articles/fischl99b-recon2.pdf

Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System

The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the cortical surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the cortical surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D cortical sheet. In order to address the need for a more natural surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain.

/pdf/high-resolution-intersubject-averaging-and-a-coordinate-4nq72z0n9o.pdf

High-resolution intersubject averaging and a coordinate system for the cortical surface.

The degree of cortical folding found in adult human brains has been analyzed using a gyrification index (GI). This parameter permits the description of a mean value for the whole brain, but also a local specific analysis of different brain regions. Correlation analyses of the GI with age, body weight, body length, brain weight and volume of the prosencephalon and the cortex show no significant results. GI values do not differ significantly between male and female brains, right and left hemispheres or right and left sides of the superior temporal plane. The GI shows maximal values over the prefrontal and the parieto-temporo-occipital association cortex. A comparison between the rostro-caudal GI patterns of human brains and those of prosimians and Old World monkeys shows the largest difference over the prefrontal cortex. The mean GI increases from prosimians to human brains with the highest values for non-human primates being in the pongid group.

The human pattern of gyrification in the cerebral cortex

The degree of cortical folding in primates has been analyzed using a gyrification index (GI). Correlation analyses of the GI with body weight, brain weight and neopallial volume show that the human data fit the general trend of the nonhuman anthropoids. Bigger primate brains exhibit a higher degree of fissurization, but a taxonomic difference that is independent of brain weight between prosimians and anthropoids has also been observed. In these regressions, anthropoids differed from prosimians by having a larger increase in gyrification for every unit increase in body or brain weight or neopallial volume. A stepwise regression also shows a prosimian-anthropoid difference. The best predictor for convolutedness in anthropoids is neocortical volume, while in prosimians it is brain weight. The GI in catarrhines is correlated with total sulcal length but not number of sulci. This result suggests paleontological studies of total sulcal length can give direct information on the evolution of cortical folding in primates.

Gyrification in the cerebral cortex of primates.

Total surface of temporoparietal intrasylvian cortex: diverging left-right asymmetries.

Cytoarchitectonic investigations are based on the analysis of structural inhomogeneities in the neuronal composition of nervous tissue. Boundaries of brain regions are established at locations where local structural properties, such as density, shape, orientation or arrangement of the nerve cells, change. A quantitative approach requires a complete scanning of histological sections and the measurement of at least one of these properties using an automatic device. In contrast to recently developed stereological methods which result in unbiased estimates of parameters and which are based on efficient sampling procedures, measurements with automatic devices are biased due to section thickness and problems in the segmentation of nerve cell bodies. Automatic measurements are necessary, however, if a complete scanning of histological sections is required in order to localize areal or laminar boundaries. In this approach, the grey level index (GLI) is measured with a TV-based image analysing system from routine histological sections. Using Nissl-staining, this parameter is a biased estimate of the local volume density of Nissl-positive structures (cell bodies). The histological section is digitized into a GLI image by a scanning procedure. The GLI image is processed by image enhancement procedures in order to visualize the laminar pattern. Areal boundaries are localized at positions where this laminar pattern changes. GLI statistics of single brain regions can easily be evaluated by delineating these regions with a cursor in the GLI images of the sections. Information from a series of sections is compiled by specific application programs.

Karl Zilles

Papers

The human pattern of gyrification in the cerebral cortex

Gyrification in the cerebral cortex of primates.

Total surface of temporoparietal intrasylvian cortex: diverging left-right asymmetries.

A quantitative approach to cytoarchitectonics: analysis of structural inhomogeneities in nervous tissue using an image analyser.

Cortical folding, the lunate sulcus and the evolution of the human brain