Alzheimer's Disease (AD) is characterized by a stereotypical spatial pattern of hippocampus (HP) atrophy over time, but reliable and precise measurement of localized longitudinal change to individual HP in AD have been elusive. We present a method for quantifying subject-specific spatial patterns of longitudinal HP change that aligns serial HP surface pairs together, cuts slices off the ends of the HP that were not shared in the two delineations being aligned, estimates weighted correspondences between baseline and follow-up HP, and finds a concise set of localized spatial change patterns that explains HP changes while down-weighting HP surface points whose estimated changes are biologically implausible. We tested our method on a synthetic HP change dataset as well as a set of 320 real elderly HP measured at 1-year intervals. Our results suggests that the proposed steps reduce the amount of implausible HP changes indicated among individual HP, increase the strength of association between HP change and cognitive function related to AD, and enhance the estimation of reliable spatially-localized HP change patterns.

中文翻译：

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稳健地测量老化海马体的个体局部变化。

阿尔茨海默病 (AD) 的特点是随着时间的推移，海马 (HP) 萎缩的典型空间模式，但对 AD 中个体 HP 的局部纵向变化进行可靠和精确的测量一直难以实现。我们提出了一种量化纵向 HP 变化的特定主题空间模式的方法，该方法将连续的 HP 表面对对齐在一起，切掉在对齐的两个轮廓中不共享的 HP 末端的切片，估计基线和后续之间的加权对应关系。增加 HP，并找到一组简明的局部空间变化模式，可以解释 HP 变化，同时降低 HP 表面点的权重，这些点的估计变化在生物学上是不可信的。我们在合成 HP 变化数据集以及以 1 年为间隔测量的一组 320 名真实老年人 HP 数据集上测试了我们的方法。我们的结果表明，所提出的步骤减少了个体 HP 中表明的难以置信的 HP 变化量，增加了 HP 变化与 AD 相关认知功能之间的关联强度，并增强了对可靠的空间局部 HP 变化模式的估计。