Avants Brian, Anderson Chivon, Grossman Murray, Gee James C
Dept. of Radiology, University of Pennsylvania, Philadelphia, PA 19104-6389, USA.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):303-10. doi: 10.1007/978-3-540-75759-7_37.
We present a unified method, based on symmetric diffeomorphisms, for studying longitudinal neurodegeneration. Our method first uses symmetric diffeomorphic normalization to find a spatiotemporal parameterization of an individual's image time series. The second step involves mapping a representative image or set of images from the time series into an optimal template space. The template mapping is then combined with the intrasubject spatiotemporal map to enable pairwise statistical tests to be performed on a population of normalized time series images. Here, we apply this longitudinal analysis protocol to study the gray matter atrophy patterns induced by frontotemporal dementia (FTD). We sample our normalized spatiotemporal maps at baseline (time zero) and time one year to generate an annualized atrophy map (AAM) that estimates the annual effect of FTD. This spatiotemporal normalization enables us to locate neuroanatomical regions that consistently undergo significant annual gray matter atrophy across the population. We found the majority of annual atrophy to occur in the frontal and temporal lobes in our population of 20 subjects. We also found significant effects in the hippocampus, insula and cingulate gyrus. Our novel results, significant at p < 0.05 after false discovery rate correction, are represented in local template space but also assigned Talairach coordinates and Brodmann and Anatomical Automatic Labeling (AAL) labels. This paper shows the statistical power of symmetric diffeomorphic normalization for performing deformation-based studies of longitudinal atrophy.
我们提出了一种基于对称微分同胚的统一方法，用于研究纵向神经退行性变。我们的方法首先使用对称微分同胚归一化来找到个体图像时间序列的时空参数化。第二步是将时间序列中的代表性图像或一组图像映射到最优模板空间。然后将模板映射与个体内时空图相结合，以便对一组归一化的时间序列图像进行成对统计检验。在此，我们应用这种纵向分析方案来研究额颞叶痴呆（FTD）引起的灰质萎缩模式。我们在基线（时间零）和一年时间对归一化的时空图进行采样，以生成年化萎缩图（AAM），该图估计了FTD的年度效应。这种时空归一化使我们能够定位在整个人口中持续经历显著年度灰质萎缩的神经解剖区域。我们发现，在我们的20名受试者群体中，大部分年度萎缩发生在额叶和颞叶。我们还在海马体、脑岛和扣带回中发现了显著影响。我们的新结果在错误发现率校正后p < 0.05时具有显著性，这些结果在局部模板空间中表示，同时还被赋予了Talairach坐标以及Brodmann和解剖自动标记（AAL）标签。本文展示了对称微分同胚归一化在进行基于变形的纵向萎缩研究中的统计功效。
