Date of Award

Spring 1-1-2013

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Integrative Physiology

First Advisor

Kenneth P. Wright Jr.

Second Advisor

Roger M. Enoka

Third Advisor

Monique K. LeBourgeois

Fourth Advisor

Francois G. Meyer

Fifth Advisor

Douglas R. Seals

Abstract

Sleep is altered by the aging process. Not only are the prevalence of sleep disorders and sleep complaints increased with age, notably, the sleep of healthy individuals also undergoes significant changes into older adulthood. The most evident changes are increased awakenings from sleep and a lighter sleep phenotype that shows reduced deep sleep stages. These changes are thought to be related to altered brain mechanisms that reduce homeostatic sleep drive and circadian clock outputs with older age. Though much is known about the neurophysiology of sleep and wakefulness states, less is known about the transitions between these states and how aging may affect them. Sleep that is disturbed by frequent awakenings is associated with negative health and functioning outcomes and, if sustained, can increase the risk for developing sleep, medical, or psychiatric disorders. Therefore, understanding the neurophysiological changes during transitions into and out of sleep has clinical importance for young and older adults.

The standard tool for measuring sleep and wakefulness neurophysiology is electroencephalography (EEG), and quantitative analysis of EEG (QEEG) signals can reveal important properties of brain states. However, transitions between wakefulness and sleep states occur on the order of seconds to minutes and are therefore dynamic in nature. Yet, standard QEEG techniques, like fast Fourier transform (FFT), are limited by their assumptions about signal properties and their resulting frequency and temporal resolutions. Thus, a novel signal analysis technique like Empirical Mode Decomposition (EMD), which is not limited by assumptions about the signal nor frequency or temporal resolutions, is well-suited for measurement of complex signals like the EEG that shows dynamic changes at transitions between wakefulness and sleep states. Further, sleep medication use is highest among older adults yet little is also known about their effects on QEEG measures in older adults. Therefore, the aims of this dissertation were to characterize age-related changes to sleep EEG among groups of healthy young and older adults 1) during wakefulness-to-sleep transitions with EMD techniques, 2) immediately preceding sleep-to-wakefulness transitions with EMD techniques, and 3) with a recommended dose of the most commonly prescribed sleep medication zolpidem.

We found that young and older adults overall show similar patterns for EEG changes during transitional states, both while falling asleep and immediately preceding awakenings from sleep. However, there were some differences between age groups for particular brain regions and frequency ranges. Transitions were also shown to be periods of dynamic change in EEG activity. Additionally, we found age-related differences for most sleep parameters and that zolpidem does not significantly alter sleep patterns for young or older adults in the first ~2 hours of the night. However, zolpidem significantly reduced QEEG activity in theta and alpha frequencies for older, but not young, adults.

These findings suggest that EEG activity patterns between sleep and wakefulness states are largely preserved with age but show some differences in magnitude, frequency, and brain region and are thus affected by the aging process. For the first time, young and older adults are shown to exhibit different EEG patterns with zolpidem, suggesting that this common sleep medication has age-dependent effects on the brain during sleep. Lastly, we conclude that the novel signal analysis technique EMD was effective at quantifying EEG activity during transitional states and may be a useful tool in future QEEG analyses.

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