Wu Jianfeng, Zhu Wenhui, Su Yi, Gui Jie, Lepore Natasha, Reiman Eric M, Caselli Richard J, Thompson Paul M, Chen Kewei, Wang Yalin
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA.
Banner Alzheimer's Institute, Phoenix, USA.
Proc SPIE Int Soc Opt Eng. 2021 Nov;12088. doi: 10.1117/12.2607169. Epub 2021 Dec 10.
Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).
生物标志物辅助诊断及干预阿尔茨海默病（AD）可能是预防取得突破的关键。AD的标志性特征之一是人脑中tau蛋白斑块的积累。然而，目前检测tau蛋白病理的方法要么具有侵入性（腰椎穿刺），要么成本高昂且无法广泛应用（tau蛋白正电子发射断层扫描）。在我们之前的工作中，基于结构磁共振成像的海马多变量形态测量统计（MMS）作为临床前AD的有效神经退行性生物标志物表现出卓越性能，且基于补丁分析的表面相关熵诱导稀疏编码和最大池化（PASCS-MP）具有出色的能力，能够生成具有强大统计能力的低维表示用于脑淀粉样蛋白预测。在这项工作中，我们将此框架与岭回归模型一起应用，分别预测Braak12和Braak34脑区的tau蛋白沉积。我们在来自阿尔茨海默病神经成像倡议（ADNI）的925名受试者上评估我们的框架。每个受试者都有一对大约在同一时间采集的PET图像和MRI扫描图像。实验结果表明，与从其他方法（如海马表面积和体积以及基于球谐函数（SPHARM）的形状形态测量特征）得出的测量值相比，我们的MMS和PASCS-MP生成的表示具有更强的预测能力，并且它们预测的Braak12和Braak34更接近真实值。
