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...ng Introduction The cerebral cortex is a highly convoluted sheet of gray matter with varying thickness. The pattern of cortical thickness across the cortex varies with different clinical populations (=-=Chung et al., 2003-=-; Kabani et al., 2000). Cortical thickness can be used as an anatomical index to quantify local cortical shape differences. We measure cortical thickness using the inner and outer boundaries of the gr...

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...s do trees, pillows, and so on. A natural research goal is therefore to extend ICP to accommodate local object transformations. Following recent work primarily found in the medical imaging literature =-=[5; 16; 6; 3]-=-, we proposes an approach suited for scan registration and 3D modeling of nonrigid objects that can efficiently deal with hundreds of thouFigure 1: The essential idea: Rather assuming that the relatio...

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...ng the linked distance between the white and pial surfaces, tlink (MacDonald et al., 2000; Lerch and Evans, 2005). The thickness map is blurred using a 30 mm surface based diffusion smoothing kernel (=-=Chung et al., 2003-=-). These methods have been validated using both manual measurements (Kabani et al., 2001) and a population simulation (Lerch and Evans, 2005), and used in an Alzheimer’s Disease population study (Lerc...

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... analyses will detect signals from foci that display variability in their precise cortical location across subjects. Spatial smoothing also ensures that the data more closely approximate a continuous field of random values, a necessary assumption of the random field theory used for multiple comparisons correction (Worsley et al., 1992, 1996, 1999; Petersson et al., 1999b; Andrade et al., 2001). The vertices of a cortical surface mesh are not, however, arranged on a grid with regular spacing; instead distances and angles between neighboring vertices are slightly variable (Fischl et al., 1999a; Chung et al., 2003). Because of this, direct application of a Gaussian blurring kernel – as is done with 3D data sets – is computationally intensive for surface-based data. A computationally efficient method is to iteratively perform nearestneighbor averaging; where each vertex's value is averaged with those of its neighbors. Slightly more complicated iterative smoothing algorithms have also been developed, which rely on a heat diffusion model and involve unequally weighting the contribution from each neighbor in the post-iteration value of a given vertex (Andrade et al., 2001; Chung et al., 2003, 2005). For exa...

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...onic (SPHARM), tensor-based morphometry. I. INTRODUCTION I N many previous cortical morphometric studies, cortical thickness has been mainly used to quantify cortical shape variations in a population =-=[13]-=-, [21], [27], [33]–[35]. The cortical thickness measures the amount of gray matter along the normal direction on a cortical surface. However, the gray matter growth can be characterized by both the no...

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...e regularized with this technique as well (see [24] and paper by Faugeras et al. in this issue). To make the presentation easier, we discuss the regularization via the Laplace-Beltrami flow (see also =-=[13, 14, 15]-=-), while of course other PDEs or variational formulations could be used as well. We assume as before that the two dimensional surface S of interest (here the cortex) is given in implicit form, as the ...

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...rtical regions can only be measured relative to that local orientation of the cortical surface [6]. It is likely that different clinical population will show different brain surface shape differences =-=[4, 5, 14, 24, 25]-=-. By computing how surface metrics such as the cortical thickness, curvature and local surface area differ among different groups, brain shape differences can be quantified locally. The first obstacle...

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...oothing method, it is useful to know the full-width at the half-maximum (FWHM) of the underlying kernel. The computed FWHM is later used in the random field based multiple comparison corrections [9], =-=[10]-=-, [52], [53]. Computing the FWHM of the heat kernel used in WFS is not trivial since there is no known close form expression for FWHM as a function of bandwidth . Therefore, FWHM is computed numerical...