Intelligent algorithm for dynamic functional brain network complexity from CN to AD

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.

Automated summary

A new intelligent detecting algorithm for the complexity of Alzheimer's disease (AD) dynamic network was proposed based on visibility graph, revealing a shift of dynamic complexity in brain regions from frontal to temporal and occipital lobes, which correlated significantly with clinical symptoms. The small-world topological properties of the dynamic complexity network also exhibited significant differences between cognitively normal (CN) individuals and AD patients.