Slow-oscillation activity is reduced and high frequency activity is elevated in older adults with insomnia

Abstract

High-frequency electroencephalographic activity (> 16 Hz activity) is often elevated during nonrapid eye movement sleep among individuals with insomnia, in line with the hyperarousal theory of insomnia. Evidence regarding sleep depth marked by slow-wave activity (< 4 Hz) is more mixed. Distinguishing subcomponents of slow-wave activity (slow-oscillation [< 1 Hz] or delta activity [1-4 Hz)]) may be critical in understanding these discrepancies, given that these oscillations have different neural generators and are functionally distinct. Here we tested the effects of insomnia diagnosis and insomnia treatment on nonrapid eye movement electroencephalography in older adults, distinguishing slow-oscillation and delta power. Methods: In 93 older adults with insomnia and 71 good sleeper control participants (mean ages 68 years), effects of insomnia and cognitive behavioral therapy for insomnia (insomnia group only) on electroencephalographic spectral power were analyzed. Main effects and interactions with nonrapid eye movement period were assessed for the following frequency bands: slow-oscillation (0.5-1 Hz), delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), sigma (12-16 Hz), and beta (16-32 Hz). Results: Slow-oscillation absolute and relative power were lower in the insomnia group compared with controls. There were no group differences in delta power. Insomnia was also associated with elevated 4-32 Hz absolute and relative power. After cognitive behavioral therapy for insomnia, absolute sigma and beta activity decreased. Conclusions: Deficits in slow-wave activity in insomnia are specific to the slow-oscillation. Elevated high frequency activity is reduced for sigma and beta power following cognitive behavioral therapy for insomnia . These findings inform the pathophysiology of insomnia, including the mechanisms underlying cognitive behavioral therapy for insomnia in older adults.

Effectiveness of Digital Cognitive Behavioral Therapy for Insomnia in Young People: Preliminary Findings from Systematic Review and Meta-Analysis

“…One study investigated the changes in sleep patterns and sleep-related cortical activities before and after in-person CBT-i treatment in patients with insomnia. No significant improvement of polysomnography-measured sleep structures was reported in regards to the increase of deep sleep (stage 3) and reduction of shallow sleep (stage 1) among patients [ 56 , 57 ]. However, significant changes in electroencephalogram during non-rapid-eye-movement (NREM) sleep were observed [ 56 , 57 , 58 ], suggesting that CBT-i altered the microstructure but not the macrostructure of sleep.…”

EEG Power Spectral Analysis of Abnormal Cortical Activations During REM/NREM Sleep in Obstructive Sleep Apnea

“…(1) delta activities (1-4 Hz) is generally considered to be an indicator of sleep depth and homeostasis during NREM sleep (16,17); (2) theta activities (4-8 Hz) in the frontal region serve as a marker of emotional memory consolidation (18,19); (3) alpha activities (8-12 Hz) are suggested to be a cortical signature of visual perception and mental imagery (20), and a reflection of neuronal activities, with low alpha levels indicating a state of excitation and vice versa (21,22); (4) sigma activities (12-15 Hz) during N2 sleep serve as a proxy for the sleep spindle and is shown to facilitate learning and memory consolidation (23) and protect sleep from external disturbances (24,25); and (5) high-frequency activities in the beta (15-30 Hz) and gamma (30-40 Hz) ranges are associated with arousals (24,26,27). As such, PSA enables a closer examination of the relative changes in prominence of each frequency band in OSA patients, to reveal the functional significance that underlies the change in EEG patterns.…”

Sleep‐related hypermotor epilepsy and non‐rapid eye movement parasomnias: Differences in the periodic and aperiodic component of the electroencephalographic power spectra

“…We extracted from the signal epochs the features characterizing: (i) the periodic component, namely the relative power of the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), sigma (12–16 Hz), beta (16–32 Hz) and gamma (32–45 Hz) frequency bands (Hogan et al., 2020 ); and (ii) the parameters of the aperiodic component, namely the slope and the offset. The relative power for each of the six frequency bands was computed within MATLAB (The MathWorks, Natick, Massachusetts, USA, version R2020) as the ratio between the absolute band‐specific power and absolute total power (between 1 and 45 Hz) using the power spectral density estimated using Welch's method, using Hamming windowing, set to obtain a 50% overlap, and with a number of FFT set to the next power of two greater than the length of each section.…”

“…We extracted from the signal epochs the features characterizing: (i) the periodic component, namely the relative power of the delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), sigma (12-16 Hz), beta (16-32 Hz) and gamma (32-45 Hz) frequency bands (Hogan et al, 2020); and (ii) the parameters of the aperiodic component, namely the slope and the offset.…”