Scene: Supplementary Figure 2B

study:
Folding-compensated cortical thickness

SCENE FILE:
Demirci_2025_MRcorrThick_figures

SCENE:
Supplementary Figure 2B

DESCRIPTION:
Vertex-wise (normalized with the standard deviation of the corresponding curvature feature) regression coefficient maps displayed on the inflated surface of two individuals of the HCP-YA dataset (A: case 100408, B: case 100307). Red and blue represent positive and negative modeled effect of a curvature feature on thickness, respectively. Cortical thickness is more strongly associated with principal curvatures and curvedness and less strongly with Gaussian curvature, as Gaussian curvature fails to disambiguate between a cup and a cap morphology. Linear regression coefficients show the strongest effects, while quadratic terms contribute moderately and cubic terms minimally. Accordingly, we included terms up to the second order in our polynomial model, as cubic coefficients account for only negligible effects.

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