Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
J Neural Eng. 2023 May 31;20(3). doi: 10.1088/1741-2552/acd41c.
Brain connectivity networks are usually characterized in terms of properties coming from the complex network theory. Using new measures to summarize the attributes of functional connectivity networks can be an important step for their better understanding and characterization, as well as to comprehend the alterations associated with neuropsychiatric and neurodegenerative disorders. In this context, the main objective of this study was to introduce a novel methodology to evaluate network robustness, which was subsequently applied to characterize the brain activity in the Alzheimer's disease (AD) continuum.Functional connectivity networks were built using 478 electroencephalographic and magnetoencephalographic resting-state recordings from three different databases. These functional connectivity networks computed in the conventional frequency bands were modified simulating an iterative attack procedure using six different strategies. The network changes caused by these attacks were evaluated by means of Spearman's correlation. The obtained results at the conventional frequency bands were aggregated in a correlation surface, which was characterized in terms of four gradient distribution properties: mean, variance, skewness, and kurtosis.The new proposed methodology was able to consistently quantify network robustness. Our results showed statistically significant differences in the inherent ability of the network to deal with attacks (i.e. differences in network robustness) between controls, mild cognitive impairment subjects, and AD patients for the three different databases. In addition, we found a significant correlation between mini-mental state examination scores and the changes in network robustness.To the best of our knowledge, this is the first study which assesses the robustness of the functional connectivity network in the AD continuum. Our findings consistently evidence the loss of network robustness as the AD progresses for the three databases. Furthermore, the changes in this complex network property may be related with the progressive deterioration in brain functioning due to AD.
脑连接网络通常以复杂网络理论的属性为特征。使用新的指标来总结功能连接网络的属性，对于更好地理解和描述这些网络，以及理解与神经精神和神经退行性疾病相关的改变，是一个重要的步骤。在这种情况下，本研究的主要目的是引入一种新的方法来评估网络鲁棒性，然后将其应用于描述阿尔茨海默病（AD）连续体中的大脑活动。使用来自三个不同数据库的 478 个脑电图和脑磁图静息状态记录构建了功能连接网络。这些在常规频带中计算的功能连接网络通过使用六种不同策略模拟迭代攻击过程进行了修改。通过 Spearman 相关性评估这些攻击引起的网络变化。在常规频带中获得的结果被汇总在相关表面中，该表面根据四个梯度分布特性进行了描述：均值、方差、偏度和峰度。所提出的新方法能够一致地量化网络鲁棒性。我们的结果表明，在三个不同的数据库中，对照组、轻度认知障碍患者和 AD 患者的网络固有能力（即网络鲁棒性的差异）存在统计学上的显著差异。此外，我们还发现简易精神状态检查评分与网络鲁棒性变化之间存在显著相关性。据我们所知，这是首次在 AD 连续体中评估功能连接网络鲁棒性的研究。我们的发现一致表明，随着 AD 的进展，三个数据库的网络鲁棒性都会降低。此外，这种复杂网络属性的变化可能与 AD 导致的大脑功能进行性恶化有关。
