Age-Related Changes in Electroencephalographic Signal Complexity

Abstract

The study of active and healthy aging is a primary focus for social and neuroscientific communities. Here, we move a step forward in assessing electrophysiological neuronal activity changes in the brain with healthy aging. To this end, electroencephalographic (EEG) resting state activity was acquired in 40 healthy subjects (age 16–85). We evaluated Fractal Dimension (FD) according to the Higuchi algorithm, a measure which quantifies the presence of statistical similarity at different scales in temporal fluctuations of EEG signals. Our results showed that FD increases from age twenty to age fifty and then decreases. The curve that best fits the changes in FD values across age over the whole sample is a parabola, with the vertex located around age fifty. Moreover, FD changes are site specific, with interhemispheric FD asymmetry being pronounced in elderly individuals in the frontal and central regions. The present results indicate that fractal dimension well describes the modulations of brain activity with age. Since fractal dimension has been proposed to be related to the complexity of the signal dynamics, our data demonstrate that the complexity of neuronal electric activity changes across the life span of an individual, with a steady increase during young adulthood and a decrease in the elderly population.

Permutation Entropy for the Characterisation of Brain Activity Recorded with Magnetoencephalograms in Healthy Ageing

“…Males have more GM that is made up of active neurons while females have more WM that is responsible for communication in the brain. Therefore, if MEG is associated more with WM activity, this could explain the differences in the observed regression models [24,25]. Moreover, as noted in [59] the neurons in female brains are more tightly packed in the layers of the cortex when compared to male brains, therefore the more complex evolutions of nmPE observed in the central region could be a reflection of these physiological differences.…”

Healthy and Pathological Brain Aging: From the Perspective of Oscillations, Functional Connectivity, and Signal Complexity

“…The signal complexity of EEG, a reflection of the irregularity of the wave shape and dynamics, is reported to be influenced by healthy aging and associated with brain disorders such as AD [73, 74]. Studies using fractal dimension demonstrated that resting-state EEG signal complexity increases from youth to maturity, followed by a reduction in healthy elderly.…”

“…Studies using fractal dimension demonstrated that resting-state EEG signal complexity increases from youth to maturity, followed by a reduction in healthy elderly. These studies also provided evidence indicating that, in AD, the signal complexity is further reduced as cognitive function declines [73, 74]. …”

BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

“…Several studies have addressed the variability in the spectral amplitudes of different frequency bands using variance (Hawkes and Prescott, 1973;Oken and Chiappa, 1988), coefficient of variation (Burgess and Gruzelier, 1993;Maltez et al, 2004), and complexity (Fernández et al, 2012;Sleimen-Malkoun et al, 2015). For instance, reductions of the complexity in rsEEG signal have been found not only in healthy aging (Yang et al, 2013;Zappasodi et al, 2015) but also in age-related pathologies such as mild cognitive impairment (McBride et al, 2014) and…”