Biomarker-assisted preclinical/early detection and intervention in Alzheimer's disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that MRI-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) (Accuracy (ACC)=0.89 (ADNI)) and in cognitively unimpaired (CU) individuals (ACC=0.79 (ADNI) and ACC=0.81 (OASIS)). These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM), and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

中文翻译：

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使用多元形态计量学统计，稀疏编码和肾上腺皮质性预测脑淀粉样蛋白：从ADNI和OASIS数据库中的1,101个人进行验证

在阿尔茨海默氏病（AD）中进行生物标记物辅助的临床前/早期检测和干预可能是治疗突破的关键。AD的症状前特征之一是人脑中β-淀粉样蛋白（Aβ）斑块的积累。但是，当前检测Aβ病理的方法要么是侵入性的（腰椎穿刺），要么成本很高，并且不能广泛使用（淀粉状蛋白PET）。我们先前的研究表明，基于MRI的海马多元形态统计数据（MMS）是临床前AD的有效神经退行性生物标记。在这里，我们尝试使用MRI-MMS得出关于个体受试者水平上脑Aβ负担的推论。由于MMS数据的维数大于样本大小，因此我们提出了一种稀疏编码算法，即基于补丁分析的表面介导的稀疏编码和最大池化（PASCS-MP），生成每个对象海马形态的低维表示。然后，我们将这些个体表示形式和二进制随机森林分类器应用于每个人的大脑Aβ阳性。我们在两个独立的队列中测试了我们的方法，阿尔茨海默氏病神经影像学倡议（ADNI）的841名受试者和影像研究开放获取系列（OASIS）的260名受试者。实验结果表明，我们提出的PASCS-MP方法和MMS可以区分轻度认知障碍（MCI）（准确度（ACC）= 0.89（ADNI））和认知未受损（CU）个体（ACC = 0.79（ADNI） ）和ACC = 0.81（OASIS））。与从传统算法得出的测量结果（包括海马体积和表面积，