Sources of cortical rhythms in adults during physiological aging: a multicentric EEG study

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.

Figure 1

Grand average of LORETA solutions (i.e., normalized relative current density at the cortical voxels) modeling the distributed EEG sources for delta, theta, alpha 1, alpha 2, beta 1, and beta 2 bands in Nyoung and Nold groups. Left side of the maps (top view) corresponds to the left hemisphere. LORETA, low‐resolution brain electromagnetic tomography. Color scale: all power estimates were scaled based on the averaged maximum value (i.e., alpha 1 power value of occipital region in Nyoung). The maximal value of power is reported under each column. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

Figure 2

Regional LORETA solutions (mean across subjects) relative to a statistical ANOVA interaction among factors Group (Nyoung, Nold), Band (delta, theta, alpha 1, alpha 2, beta 1, beta 2), and ROI (central, frontal, parietal, occipital, temporal, limbic). This ANOVA design used the normalized relative current density values of the LORETA solutions at the ROI level as a dependent variable. Regional LORETA solutions modeled EEG relative power spectra as revealed by “virtual” intracranial macroelectrodes “placed” on the macrocortical regions of interest. Rectangles indicate the cortical regions and frequency bands in which LORETA solutions presented statistically significant differences between Nyoung and Nold groups (P < 0.05, planned Tukey post‐hoc testing). See Subjects and Methods for further details. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

Figure 3

Scatterplots between individual regional LORETA solutions and age in all Nyoung and Nold subjects considered as a single group. These solutions refer to the EEG sources showing a statistically significant difference in magnitude between Nold and Nyoung groups, namely, occipital delta source as well as occipital, and limbic alpha 1 source as well as parietal, occipital, temporal, and limbic alpha 2 sources. Results of the linear correlation (Pearson test, Bonferroni‐corrected at P < 0.05) between individual regional LORETA solutions and age are reported in the diagrams. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]