Age-related complexity of the resting state MEG signals: a multiscale entropy analysis

Abstract

The effects of aging on the brain can be studied by examining the changes in complexity of brain signals and fluid cognitive abilities. This paper is a relatively large-scale study in which the complexity of the resting-state MEG (rsMEG) signal was investigated in 602 healthy participants (298 females and 304 males) aged 18 to 87. In order to quantify the brain signals' complexity, the multiscale entropy is applied. This study investigates the relationship between age and fluid intelligence with brain complexity and the variations of the complexity asymmetry between the left-right brain hemispheres across the life span. In the analysis of the brain signals, the gender difference was considered. The results showed that the complexity of rsMEG decreases across the lifespan. However, the complexity difference between the left-right brain hemispheres positively correlates with age. Furthermore, the results demonstrated that fluid intelligence and age have a positive correlation. Finally, the frequency analysis revealed a significant increase in the relative power of low and high gamma rhythms in females compared to males in all age groups.

Temporal Analysis of Infectious Diseases: A Case Study on COVID-19

“…Schneckenreither et al [18] proposed an effective aggregation dispersion index using approximate entropy for disease research. Rodriguez et al [19] used deep neural networks and approximate entropy as a tool for early diagnosis of Chagas disease and cardiac damage [20]. Regarding DTW, Dallas et al [21] hypothesized that geographically proximate locations may have similar infectious disease dynamics.…”