Changes in cognitive performance due to neurodegenerative diseases such as Alzheimer's disease (AD) are closely correlated to the brain structure alteration. A univariate and personalized neurodegenerative biomarker with strong statistical power based on magnetic resonance imaging (MRI) will benefit clinical diagnosis and prognosis of neurodegenerative diseases. However, few biomarkers of this type have been developed, especially those that are robust to image noise and applicable to clinical analyses. In this paper, we introduce a variational framework to compute optimal transportation (OT) on brain structural MRI volumes and develop a univariate neuroimaging index based on OT to quantify neurodegenerative alterations. Specifically, we compute the OT from each image to a template and measure the Wasserstein distance between them. The obtained Wasserstein distance, Wasserstein Index (WI) for short to specify the distance to a template, is concise, informative and robust to random noise. Comparing to the popular linear programming-based OT computation method, our framework makes use of Newton's method, which makes it possible to compute WI in large-scale datasets. Experimental results, on 314 subjects (140 Aβ + AD and 174 Aβ- normal controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset, provide preliminary evidence that the proposed WI is correlated with a clinical cognitive measure (the Mini-Mental State Examination (MMSE) score), and it is able to identify group difference and achieve a good classification accuracy, outperforming two other popular univariate indices including hippocampal volume and entorhinal cortex thickness. The current pilot work suggests the application of WI as a potential univariate neurodegenerative biomarker.

中文翻译：

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使用体积最佳运输计算单变量神经退行性生物标志物：一项试点研究。


阿尔茨海默病（AD）等神经退行性疾病导致的认知能力变化与大脑结构改变密切相关。基于磁共振成像（MRI）的具有强大统计能力的单变量、个性化神经退行性生物标志物将有利于神经退行性疾病的临床诊断和预后。然而，这种类型的生物标志物很少被开发出来，特别是那些对图像噪声具有鲁棒性并适用于临床分析的生物标志物。在本文中，我们引入了一种变分框架来计算大脑结构 MRI 体积的最佳传输 (OT)，并开发基于 OT 的单变量神经影像指数来量化神经退行性改变。具体来说，我们计算每个图像到模板的 OT，并测量它们之间的 Wasserstein 距离。获得的 Wasserstein 距离（简称 WI）用于指定到模板的距离，简洁、信息丰富且对随机噪声具有鲁棒性。与流行的基于线性规划的OT计算方法相比，我们的框架利用牛顿法，这使得在大规模数据集中计算WI成为可能。来自阿尔茨海默病神经影像计划 (ADNI) 基线数据集的 314 名受试者（140 名 Aβ + AD 和 174 名 Aβ- 正常对照）的实验结果提供了初步证据，表明所提出的 WI 与临床认知测量（迷你精神状态）相关检查（MMSE）评分），能够识别群体差异并取得良好的分类准确性，优于其他两个流行的单变量指标，包括海马体积和内嗅皮层厚度。目前的试点工作表明 WI 作为潜在的单变量神经退行性生物标志物的应用。