Electroencephalographic complexity and cognitive decline: Insights from approximate entropy across aging and dementia

Physiological aging involves gradual modifications of brain activity patterns sometimes leading to pathological outcomes. Understanding changes in brain signal complexity from healthy aging to mild cognitive impairment (MCI) and Alzheimer's disease (AD) may provide insights into cognitive decline and support early diagnosis.

Electroencephalography (EEG) data were collected from 269 participants divided into three groups: healthy elderly (HE, n = 109), MCI (n = 75), and AD (n = 85). Approximate entropy (ApEn) was computed for the total EEG spectrum and frequency bands. A random forest classifier with a one-versus-one strategy and recursive feature elimination (RFE) was used for group discrimination. Additionally, group comparisons and correlations with neuropsychological scores were performed to further investigate the functional relevance of these entropy changes by assessing their association with cognitive and functional performance.

Significant group differences in total ApEn were found, particularly in central brain regions, with progressive decreases from HE to MCI and AD. Frequency-specific patterns emerged: (1) delta ApEn was increased in those with AD across the scalp; (2) alpha 1 ApEn showed frontal decreases and occipital increases in the AD group, with an intermediate pattern in MCI; (3) gamma ApEn was markedly reduced in those with AD compared to both HE and MCI groups, with central and frontal localization, and extended occipital differences in those with MCI versus AD. The classifier achieved 66.55% accuracy, with sensitivities of 78.90% (HE), 49.33% (MCI), and 65.88% (AD). Positive correlations were found between ApEn and cognitive scores in both the total EEG spectrum and gamma band; conversely, delta and alpha 1 ApEn showed negative correlations with cognitive and functional scores.

The distinct entropy patterns observed across frequency bands and scalp/brain regions may help characterize electrophysiological changes associated with cognitive decline and AD.