Abstract
This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process.
Highlights
The human brain is a highly complex self-organizing system
The advantage of the entropy measure consists in the integration of the complete spatiotemporal alterations due to Alzheimer’s disease (AD). We show that this new framework allows conducting a refined brain network analysis, which highly contributes to a better understanding of the evolution of AD from subjective cognitive impairment (SCI) to dementia through the mild cognitive impairment (MCI) stage
The present study on rsEEG investigated brain network analysis over different stages of cognitive decline from SCI to AD passing through MCI
Summary
The human brain is a highly complex self-organizing system. Its functioning relies on the collective dynamics of millions of neurons interconnected through a sophisticated network of synapses that are well organized in their structure and connectivity. Synaptic dysfunction has received significant attention, since there has been evidence that the loss of neuronal synapses occurs in the early stage of neurodegenerative diseases (NDD) [1]. Recent research suggested that synapses are sites of aberrant protein misfolding in NDD [2]. Alzheimer’s disease (AD) is the most prevalent NDD, which accounts for 50% to 70%. It is a chronic and insidious disease that produces a progressive cognitive decline. There is a growing interest in earlier stages due to the lack of curative treatments
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