Sources of cortical rhythms in adults during physiological aging: A multicentric EEG study
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Abstract
This electroencephalographic (EEG) study tested whether cortical EEG rhythms (especially delta and alpha) show a progressive increasing or decreasing trend across physiological aging. To this aim, we analyzed the type of correlation (linear and nonlinear) between cortical EEG rhythms and age. Resting eyes-closed EEG data were recorded in 108 young (Nyoung; age range: 18-50 years, mean age 27.3+/-7.3 SD) and 107 elderly (Nold; age range: 51-85 years, mean age 67.3+/-9.2 SD) subjects. The EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). EEG cortical sources were estimated by low-resolution brain electromagnetic tomography (LORETA). Statistical results showed that delta sources in the occipital area had significantly less magnitude in Nold compared to Nyoung subjects. Similarly, alpha 1 and alpha 2 sources in the parietal, occipital, temporal, and limbic areas had significantly less magnitude in Nold compared to Nyoung subjects. These nine EEG sources were given as input for evaluating the type (linear, exponential, logarithmic, and power) of correlation with age. When subjects were considered as a single group there was a significant linear correlation of age with the magnitude of delta sources in the occipital area and of alpha 1 sources in occipital and limbic areas. The same was true for alpha 2 sources in the parietal, occipital, temporal, and limbic areas. In general, the EEG sources showing significant linear correlation with age also supported a nonlinear correlation with age. These results suggest that the occipital delta and posterior cortical alpha rhythms decrease in magnitude during physiological aging with both linear and nonlinear trends. In conclusion, this new methodological approach holds promise for the prediction of dementia in mild cognitive impairment by regional source rather than surface EEG data and by both linear and nonlinear predictors.
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