Leporé Natasha, Brun Caroline, Chou Yi-Yu, Lee Agatha D, Barysheva Marina, Pennec Xavier, McMahon Katie L, Meredith Matthew, de Zubicaray Greig I, Wright Margaret J, Toga Arthur W, Thompson Paul M
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095, USA.
INRIA Sophia - Asclepios Project, Sophia Antipolis, France.
Proc IEEE Int Symp Biomed Imaging. 2008 May;2008:460-463. doi: 10.1109/ISBI.2008.4541032. Epub 2008 Jun 13.
We study the influence of the choice of template in tensor-based morphometry. Using 3D brain MR images from 10 monozygotic twin pairs, we defined a tensor-based distance in the log-Euclidean framework [1] between each image pair in the study. Relative to this metric, twin pairs were found to be closer to each other on average than random pairings, consistent with evidence that brain structure is under strong genetic control. We also computed the intraclass correlation and associated permutation -value at each voxel for the determinant of the Jacobian matrix of the transformation. The cumulative distribution function (cdf) of the -values was found at each voxel for each of the templates and compared to the null distribution. Surprisingly, there was very little difference between CDFs of statistics computed from analyses using different templates. As the brain with least log-Euclidean deformation cost, the mean template defined here avoids the blurring caused by creating a synthetic image from a population, and when selected from a large population, avoids bias by being geometrically centered, in a metric that is sensitive enough to anatomical similarity that it can even detect genetic affinity among anatomies.
我们研究了基于张量的形态测量中模板选择的影响。使用来自10对同卵双胞胎的3D脑部磁共振图像,我们在对数欧几里得框架[1]中定义了研究中每对图像之间基于张量的距离。相对于此度量,发现双胞胎对平均而言比随机配对彼此更接近,这与脑结构受强大遗传控制的证据一致。我们还计算了变换的雅可比矩阵行列式在每个体素处的组内相关性和相关的置换p值。针对每个模板在每个体素处找到p值的累积分布函数(cdf),并与零分布进行比较。令人惊讶的是,使用不同模板进行分析计算出的统计量的cdf之间差异很小。作为具有最小对数欧几里得变形成本的脑,这里定义的平均模板避免了从总体创建合成图像所导致的模糊,并且当从大量总体中选择时,通过在几何上居中而避免偏差,该度量对解剖相似性足够敏感,甚至可以检测解剖结构之间的遗传亲和力。