Alzheimer's disease (AD) is the main cause of dementia in the elderly. To date, it remains largely unknown whether and how dynamic characteristics of the functional networks differ from cognitively normal (CN) to AD. Here, we propose an AD dynamic network complexity intelligent detecting algorithm based on visibility graph. The focal regions that caused the dynamic abnormality of the connection mode were intelligently detected by creating a dynamic complexity network on the basis of the dynamic functional network. The results showed that the brain areas with different dynamic complexity gradually shifted from the frontal lobe to the temporal lobe and the occipital lobe. This was significantly related to the disorder of clinical patients from mood to memory and language. The increased dynamic complexity illustrates the compensatory effect of the brain area of AD lesions. In addition, the small-world topological properties of the dynamic complexity network have significant differences from CN to AD. To the best of our knowledge, this is the first time that such a concept is proposed. Our method of intelligently detecting the complexity of AD dynamic network provides new insights for understanding the internal dynamic mechanism of AD brain.

中文翻译：

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从 CN 到 AD 的动态功能脑网络复杂度智能算法

阿尔茨海默病（AD）是老年人痴呆的主要原因。迄今为止，功能网络的动态特征是否以及如何从认知正常 (CN) 到 AD 仍然很大程度上未知。在此，我们提出了一种基于可见性图的AD动态网络复杂度智能检测算法。通过在动态功能网络的基础上创建动态复杂网络，智能检测导致连接方式动态异常的焦点区域。结果表明，具有不同动态复杂性的大脑区域逐渐从额叶转移到颞叶和枕叶。这与临床患者从情绪到记忆和语言的障碍显着相关。动态复杂性的增加说明了 AD 病变脑区的代偿作用。此外，动态复杂性网络的小世界拓扑性质在 CN 和 AD 之间存在显着差异。据我们所知，这是第一次提出这样的概念。我们的智能检测AD动态网络复杂性的方法为理解AD大脑的内部动态机制提供了新的见解。