Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG were shown to predict AD with high accuracy, e.g. signal complexity and synchronisation. However, the dynamics of how the brain transitions between stable states have not been properly studied in the case of AD and EEG data. Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. Energy landscape assigns energy value to each possible state, i.e. pattern of activations across brain regions. The energy is inversely proportional to the probability of occurrence. By studying the features of energy landscapes of 20 AD patients and 20 healthy age-matched counterparts, significant differences were found. The dynamics of AD patients' brain networks were shown to be more constrained - with more local minima, less variation in basin size, and smaller basins. We show that energy landscapes can predict AD with high accuracy, performing significantly better than baseline models.
翻译:阿尔茨海默氏性阿尔茨海默氏病(AD)是最常见的神经退化疾病之一,全世界约有5 000万病人。因此,迫切需要使用无障碍和非侵入性的方法来诊断和定性AD。电脑物理学(EEG)符合这些标准,在研究AD时经常使用。从EEG得出的一些特征显示,AD的预测具有很高的准确性,例如信号复杂性和同步性。然而,在AD和EEEG数据方面,没有适当研究稳定国家之间的大脑转变动态。能源景观分析是用来量化这些动态的一种方法。这项工作为AD和EEG提供了这种方法的首次应用。能源景观将能源价值分配给每一个可能的州,即各个脑区域的活动模式。能量与发生频率的概率成反比反比。通过研究20个AD病人和20个健康年龄相配对的同体的能源景观特征,发现了显著的差异。AD脑网络的动态显示,受限制程度更强,其地方迷你,比EDB的精确度更小的模型更小。