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. 2014 Aug;24(8):2219-28.
doi: 10.1093/cercor/bht082. Epub 2013 Mar 29.

Differential Tangential Expansion as a Mechanism for Cortical Gyrification

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Free PMC article

Differential Tangential Expansion as a Mechanism for Cortical Gyrification

Lisa Ronan et al. Cereb Cortex. .
Free PMC article

Abstract

Gyrification, the developmental buckling of the cortex, is not a random process-the forces that mediate expansion do so in such a way as to generate consistent patterns of folds across individuals and even species. Although the origin of these forces is unknown, some theories have suggested that they may be related to external cortical factors such as axonal tension. Here, we investigate an alternative hypothesis, namely, whether the differential tangential expansion of the cortex alone can account for the degree and pattern-specificity of gyrification. Using intrinsic curvature as a measure of differential expansion, we initially explored whether this parameter and the local gyrification index (used to quantify the degree of gyrification) varied in a regional-specific pattern across the cortical surface in a manner that was replicable across independent datasets of neurotypicals. Having confirmed this consistency, we further demonstrated that within each dataset, the degree of intrinsic curvature of the cortex was predictive of the degree of cortical folding at a global and regional level. We conclude that differential expansion is a plausible primary mechanism for gyrification, and propose that this perspective offers a compelling mechanistic account of the co-localization of cytoarchitecture and cortical folds.

Keywords: cortical development; differential expansion; gyrification; intrinsic curvature.

Figures

Figure 1.
Figure 1.
The mean and intrinsic curvature of the cerebral cortex quantified at a millimeter scale. Mean curvature reflects the extrinsic folds of sulci and gyri; however, the intrinsic curvature is of a much higher spatial frequency.
Figure 2.
Figure 2.
Illustration of Caret-derived intrinsic curvature which is calculated per vertex on the FreeSurfer-derived surface reconstruction. In the vertex illustrated, the associated surface normals are drawn. For the calculation of curvature at this vertex, the surface normal is taken as an average of these surrounding surface normals.
Figure 3.
Figure 3.
(a) The natural variation of lGI was used to delineate regions of “high” lGI (>3.5) and “low” lGI (<3.5). Line plots of mean and standard error indication that for each dataset, the intrinsic curvature demonstrated a similar variation between regions of high and low lGI. (b) Line plots of mean and standard error for. lGI and intrinsic curvature across 6 cortical lobes indicate a consistent pattern across datasets for each parameter.
Figure 4.
Figure 4.
Scatter plots of intrinsic curvature and lGI values per hemisphere for each independent dataset.
Figure 5.
Figure 5.
Scatter plots of intrinsic curvature and lGI values per hemisphere per region (high lGI vs. low lGI) for each independent dataset.
Figure 6.
Figure 6.
Scatter plots of intrinsic curvature and lGI values per hemisphere per lobe (cingulate, frontal, insula, occipital, parietal, temporal) for each independent dataset.

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