Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18-72) and one magnetoencephalography (n = 31, ages 20-75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence.

https://www.zora.uzh.ch/id/eprint/154653/1/ZORA_NL_154653.pdf

Spatiotemporal Dependency of Age-Related Changes in Brain Signal Variability

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.

/pdf/age-related-changes-in-electroencephalographic-signal-4t2qcmqa4w.pdf

Age-Related Changes in Electroencephalographic Signal Complexity.

While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/f frequency scaling as well as measures of complexity from nonlinear dynamics. Specifically, we tested whether analyses that characterize time-delayed embeddings, correlation dimension (CD), phase-space geometric analysis, and multiscale entropy (MSE) capture loss-of-consciousness changes in EEG activity. We performed these analyses on EEG activity collected from a traditionally hard-to-monitor patient population: geriatric patients on beta-adrenergic blockade who were anesthetized using a combination of fentanyl and propofol. We compared these analyses to traditional frequency-derived measures to test how well they discriminated EEG states before and after loss of response to verbal stimuli. We found spectral changes similar to those reported previously during loss of response. We also found significant changes in 1/f frequency scaling. Additionally, we found that our phase-space geometric characterization of time-delayed embeddings showed significant differences before and after loss of response, as did measures of MSE. Our results suggest that our new spectral and complexity measures are capable of capturing subtle differences in EEG activity with anesthesia administration-differences which future work may reveal to improve geriatric patient monitoring.

/pdf/do-complexity-measures-of-frontal-eeg-distinguish-loss-of-4h3kemrvf8.pdf

Do Complexity Measures of Frontal EEG Distinguish Loss of Consciousness in Geriatric Patients Under Anesthesia

Charting moment-to-moment brain signal variability from early to late childhood.

The loss of complexity in ageing hypothesis (LOCH) has found support from EEG studies, most of which adopted signal-domain complexity measures. The present study adopted the largest Lyapunov exponent (LLE) to measure complexity from a nonlinear dynamical systems perspective. A total of 144 participants were included and divided into young, young-old and old-old groups. Both sensor-space and source-space results showed significantly lower LLE for older than younger adults. The age-related differences were region-dependent, being most prominent in the frontal region, followed by bilateral temporal regions. The occipital region showed non-significant differences. Significant reduction of LLE in the posterior cingulate was also observed by virtue of the source-space analysis. We also evaluated the relationships between LLE and other complexity measures. The most intriguing result was the negative correlation between LLE and Lempel-Ziv complexity (LZC). The age-related decrease in LLE indicated a higher regularity in dynamics, while the higher LZC indicated a higher randomness in the signal domain. The new findings support the LOCH by demonstrating the simultaneous increase in regularity and randomness.

Regularity and randomness in ageing: Differences in resting-state EEG complexity measured by largest Lyapunov exponent

Eighteen older adults and 18 younger adults were compared on two quantitative measures describing changes over time in the spatial distribution of running EEG. EEG was collected from 128 electrodes under resting eyes-open and eyes-closed conditions and during performance of a 13 minute sustained attention task. One EEG measure, the recrudescence rate, represented the number of changes in the location of the highest squared voltage per second. A second EEG measure consisted of the algorithmic complexity of changes in the location of the highest squared voltage over time. Regardless of the task condition, older adults had significantly higher scores than younger adults on both the recrudescence rate and the measure of algorithmic complexity. The implications of the results for neurologically-based theories of performance declines in older adults are discussed.

Age differences in dynamic measures of EEG.

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Age differences in dynamic measures of EEG.