Cortical thickness analysis in autism with heat kernel smoothing

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A curvature-based approach to estimate local gyrification on the cortical surface (2006, methodology): “T MRI system (General Electric, Waukesha, WI, USA) using a T1-weighted spoiled gradient echo pulse sequence with the following parameters: TR = 24 ms, TE = 8 ms, 30- flip angle, FOV = 250 250 mm2, matrix size = 256 256 124, voxel size = 0.98 0.98 1.5 mm.”
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements (2014, background): “. arge-scale evaluation of ANTs .05.044 such topics of interest as autism (Chung et al., 2005), athletic ability (Wei et al., 2011), male-to-female transsexuality (Luders et al., 2012), obesity (Raji et al., 2010), and Tetris-playing ability in female adolescents (Haier et al., 2009).”
Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data (2006, methodology): “(4) will not hold true for larger bandwidths (Chung et al., 2005).”
Spatial regression models over two-dimensional manifolds (2016, methodology): “The colourmaps are obtained by linear interpolation of the data at xi , using the Matlab code of Chung et al. (2005).”
Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration (2009, background): “Approximating smoothing kernels by iterative smoothing is an active area of research in medical imaging [17], [32].”