Beta-to-Theta Entropy Ratio of EEG in Aging, Frontotemporal Dementia, and Alzheimer's Dementia

Abstract

BackgroundAging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales. ObjectiveOur hypothesis posits that the MDE-derived beta-to-theta entropy ratio, compared to its PSD-based counterpart, reveals more pronounced differences between healthy aging and youth as well as between different dementia subtypes. MethodsScalp EEG recordings were obtained from two datasets: 1) Aging dataset: 133 healthy young and 65 healthy older adult individuals; and 2) Dementia dataset: 29 age-matched healthy controls (HC), 23 FTD, and 36 AD participants. The beta-to-theta ratios based on MDE vs. PSD were analyzed for both datasets. Finally, the relationships between cognitive performance and the beta-to-theta ratios were explored in HC, FTD, and AD. ResultsIn the Aging dataset, the beta-to-theta entropy ratio effectively distinguishes between healthy young and older adults, with older adults showing significantly higher values. In the Dementia dataset, this ratio outperformed the beta-to-theta PSD approach in distinguishing between HC, FTD, and AD. For FTD vs. AD, combining the MMSE with the entropy ratio significantly improved area-under-curve (AUC) values. The AD participants had a significantly lower beta-to-theta entropy ratio than FTD, especially in the temporal region, unlike its corresponding PSD-based ratio. The beta-to-theta entropy ratio correlated significantly with cognitive performance, offering better accuracy in distinguishing FTD from AD than traditional power ratio measures and cognitive testing alone. ConclusionOur study introduces the novel concept of beta-to-theta entropy ratio based on MDE for EEG analysis and demonstrates its potential in aging and cognitive impairment. Moreover, the beta-to-theta entropy ratio, combined with cognitive assessment, offers promising accuracy in distinguishing FTD from AD, surpassing traditional power ratio measures and cognitive testing alone. This research contributes new insights into the development of sensitive biomarkers for the early detection and differentiation of such disorders.