Lella Eufemia, Amoroso Nicola, Diacono Domenico, Lombardi Angela, Maggipinto Tommaso, Monaco Alfonso, Bellotti Roberto, Tangaro Sabina
Dipartimento Interateneo di Fisica, Università degli Studi di Bari, 70125 Bari, Italy.
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy.
Entropy (Basel). 2019 May 6;21(5):475. doi: 10.3390/e21050475.
In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer's disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 × 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity.
在本文中，我们研究了阿尔茨海默病（AD）导致的大脑皮层下网络的连通性改变。大多数情况下，文献研究的是AD在全脑水平或皮层水平的连通性异常，而很少有研究聚焦于仅由皮层下区域组成的子网络，特别是使用扩散加权成像（DWI）数据的研究。在这项工作中，我们检查了一个混合队列，包括来自阿尔茨海默病神经影像倡议（ADNI）数据集的46名健康对照（HC）和40名AD患者。我们通过使用先进的纤维束成像算法重建大脑连接组，并提出一种基于图连通性的方法，以增强皮层下脑区在鉴别AD方面的信息含量。我们开发了一个分类框架，仅使用一个12×12的皮层下特征矩阵，在AD与HC的二元鉴别中，受试者工作特征（ROC）曲线下面积达到了77%。我们发现了一些有趣的与AD相关的连通性模式，突出表明皮层下区域倾向于通过皮层区域增加其连通性，以补偿由于AD导致的它们之间物理连通性的降低。这项研究还表明，AD连通性改变主要涉及皮层下和皮层区域之间的相互连通性，而非皮层下内部的连通性。
