论文标题
开发单变量神经退行性生物标志物，具有较低和稀疏子空间分解
Developing Univariate Neurodegeneration Biomarkers with Low-Rank and Sparse Subspace Decomposition
论文作者
论文摘要
由于阿尔茨海默氏病（AD）引起的认知能力下降与结构磁共振成像（SMRI）捕获的大脑结构改变密切相关。它支持开发基于SMRI的单变量神经变性生物标志物（UNB）的有效性。但是，现有的UNB工作要么无法建模较大的群体差异，要么没有捕获AD痴呆（添加）诱导的变化。我们提出了一种新型的低级和稀疏子空间分解方法，能够稳定地量化ADD引起的形态变化。具体而言，我们提出了一种数值高效的秩最小化机制来提取组共同的结构并施加正则化约束以编码原始的3D形态计量学连接。此外，我们生成利率区域（ROI），在$Aβ+$ AD的常见子空间和$Aβ-$认知无损（CU）组之间进行群体差异研究。单变量形态计量指数（UMI）是通过这些ROI构建的，它通过汇总了$Aβ+$ AD和$Aβ-$ CU组之间的归一化差异来加权的个体形态特征。我们使用海马表面径向距离特征来计算UMIS并在阿尔茨海默氏病神经影像学计划（ADNI）同时验证我们的工作。 With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25$\%$ reduction in the mean annual change with 80$\%$ power and two-tailed $P=0.05$ are 116, 279 and 387 for the longitudinal $Aβ+$ AD, $Aβ+$ mild cognitive impairment (MCI) and $Aβ+$ CU groups, respectively.此外，对于MCI患者而言，UMIS与转换为AD的危险比（$ 4.3 $，95美元\％$ CI = $ 2.3-8.2 $）在18个月内。我们的实验结果表现优于传统海马体积量度，并表明将UMI作为电势UNC的应用。
Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of $Aβ+$ AD and $Aβ-$ cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between $Aβ+$ AD and $Aβ-$ CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25$\%$ reduction in the mean annual change with 80$\%$ power and two-tailed $P=0.05$ are 116, 279 and 387 for the longitudinal $Aβ+$ AD, $Aβ+$ mild cognitive impairment (MCI) and $Aβ+$ CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD ($4.3$, $95\%$ CI=$2.3-8.2$) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.
