Patterns of Intrahemispheric EEG Asymmetry in Insomnia Sufferers: An Exploratory Study

Sleep apnea evokes right hemisphere dominance and psychological disorders: An exploratory study

BACKGROUND

Obstructive sleep apnea (OSA) is considered one of the major causes of sleep disorders and psychological disorders in individuals. Brain asymmetry (BA) demonstrates individual hemispheric activity and psychological disorders. This study aimed to explore the characteristics of BA and psychology in OSA.

METHODS

Enrolment of patients for sleep assessment at the Sleep Medicine Center. Clinical characteristics, handedness, and psychological scales were prospectively collected from subjects. Subsequently, EEG power in alpha, beta, and theta bilaterally was calculated for the rest and sleep phases.

RESULTS

A total of 152 OSA and 21 non-OSA subjects were included in the study. In the frontal, central and occipital regions, OSA exhibited increased interhemispheric asymmetry with increasing apnea-hypopnea index (AHI) during rest and sleep. Simultaneously, the results showed that greater activity in the right hemisphere was positively associated with anxiety and extraversion, while inversely with positive and lie scale. In addition, the results show that OSA contributes to abnormal BA fluctuations during sleep.

CONCLUSIONS

Our results suggest that sleep disorders associated with apnea-hypopnea and arousal may contribute to increased BA during sleep. Such changes may persist into wakefulness with psychological traits.

Evaluation of wakefulness electroencephalogram in OSA patients

OBJECTIVE

Obstructive sleep apnea (OSA) has been associated with an elevated cardiovascular risk, increased daytime sleepiness, cognitive decline, and slower electroencephalographic activity (EEG). This study assesses EEG patterns during wakefulness in OSA patients compared to those without sleep-disordered breathing.

MATERIALS AND METHODS

This retrospective study analyzed 30 OSA patients with an Apnea/Hypopnea Index (AHI) of 15 or higher, as well as 29 individuals without sleep-disordered breathing (AHI of 5 or lower) who underwent hospital polysomnography and met all inclusion criteria. Sociodemographic and EEG data were obtained from the sleep laboratory database. Blinded EEG analysis was conducted by two observers, assessing activity in the frontal, central, and occipital regions.

RESULTS

No significant differences were observed in EEG activity between OSA and non-OSA patients. However, a weak correlation was found between decreased C3 EEG frequency and higher AHI (p = 0.033), as well as increased total sleep time and higher O2 EEG frequency (p = 0.038). Lower amplitudes in C3 (p = 0.043) and O1 (p = 0.031) were correlated with reduced average oxygen saturation.

CONCLUSION

Our findings suggest that OSA-related hypoxemia may impact neuronal activity, highlighting the need to address this sleep-disordered breathing in order to potentially prevent the cognitive decline observed in OSA patients.

Mechanisms of cognitive behavioural therapy for insomnia

Although much is known now about behavioural, cognitive and physiological consequences of insomnia, little is known about changes after cognitive behavioural therapy for insomnia on these particular factors. We here report baseline findings on each of these factors in insomnia, after which we address findings on their changes after cognitive behavioural therapy. Sleep restriction remains the strongest determinant of insomnia treatment success. Cognitive interventions addressing dysfunctional beliefs and attitudes about sleep, sleep-related selective attention, worry and rumination further drive effectiveness of cognitive behavioural therapy for insomnia. Future studies should focus on physiological changes after cognitive behavioural therapy for insomnia, such as changes in hyperarousal and brain activity, as literature on these changes is sparse. We introduce a detailed clinical research agenda on how to address this topic.

Sleep Continuity, Stability and Cyclic Organization Are Impaired in Insomniacs: A Case-Control Study

The possibility of distinguishing insomniacs from good sleepers based on polysomnography (PSG) remains an open question. While these groups show modest differences in traditional PSG parameters, some studies suggest that finer measures may be more useful. Here we assess differences between good sleepers (GS), poor sleepers (PS) and insomniacs (IN) in classical PSG measures as well as in sleep continuity, stability and cyclic organization. PSG-monitored sleep (two nights) of 17 IN (diagnosed through a standard clinical interview; Pittsburgh Sleep Quality Index (PSQI) ≥ 5, Insomnia Severity Index (ISI) > 14) was compared to that of 33 GS (PSQI < 5) and 20 PS (PSQI ≥ 5, ISI ≤ 14). Compared to GS, IN were impaired in sleep macrostructure (sleep latency, sleep efficiency, WASO%) and in continuity, stability and organization, whereas PS only showed disrupted continuity and stability. Spindle parameters were comparable between IN and GS, but the former displayed enhanced power in fast frequency bands. Our findings support the hypothesis of a continuum between individuals with self-reported poor sleep and insomniacs. Further, they add to extant data on impaired sleep continuity, stability and organization in poor sleepers and elderly individuals, underlining the utility of including these measures in standard sleep assessments.

Insomnia subtypes characterised by objective sleep duration and NREM spectral power and the effect of acute sleep restriction: an exploratory analysis

Insomnia disorder (ID) is a heterogeneous disorder with proposed subtypes based on objective sleep duration. We speculated that insomnia subtyping with additional power spectral analysis and measurement of response to acute sleep restriction may be informative in overall assessment of ID. To explore alternative classifications of ID subtypes, insomnia patients (n = 99) underwent two consecutive overnight sleep studies: (i) habitual sleep opportunity (polysomnography, PSG) and, (ii) two hours less sleep opportunity (electroencephalography, EEG), with the first night compared to healthy controls (n = 25). ID subtypes were derived from data-driven classification of PSG, EEG spectral power and interhemispheric EEG asymmetry index. Three insomnia subtypes with different sleep duration and NREM spectral power were identified. One subtype (n = 26) had shorter sleep duration and lower NREM delta power than healthy controls (short-sleep delta-deficient; SSDD), the second subtype (n = 51) had normal sleep duration but lower NREM delta power than healthy controls (normal-sleep delta-deficient; NSDD) and a third subtype showed (n = 22) no difference in sleep duration or delta power from healthy controls (normal neurophysiological sleep; NNS). Acute sleep restriction improved multiple objective sleep measures across all insomnia subtypes including increased delta power in SSDD and NSDD, and improvements in subjective sleep quality for SSDD (p = 0.03), with a trend observed for NSDD (p = 0.057). These exploratory results suggest evidence of novel neurophysiological insomnia subtypes that may inform sleep state misperception in ID and with further research, may provide pathways for personalised care.

Exposure to relaxing words during sleep promotes slow-wave sleep and subjective sleep quality

Our thoughts alter our sleep, but the underlying mechanisms are still unknown. We propose that mental processes are active to a greater or lesser extent during sleep and that this degree of activation affects our sleep depth. We examined this notion by activating the concept of "relaxation" during sleep using relaxation-related words in 50 healthy participants. In support of our hypothesis, playing relaxing words during non-rapid eye movement sleep extended the time spent in slow-wave sleep, increased power in the slow-wave activity band after the word cue, and abolished an asymmetrical sleep depth during the word presentation period. In addition, participants reported a higher sleep quality and elevated subjective alertness. Our results support the notion that the activation of mental concepts during sleep can influence sleep depth. They provide a basis for interventions using targeted activations to promote sleep depth and sleep quality to foster well-being and health.

Single-channel EEG based insomnia detection with domain adaptation

Insomnia is one of the most common sleep disorders which can dramatically impair life quality and negatively affect an individual's physical and mental health. Recently, various deep learning based methods have been proposed for automatic and objective insomnia detection, owing to the great success of deep learning techniques. However, due to the scarcity of public insomnia data, a deep learning model trained on a dataset with a small number of insomnia subjects may compromise the generalization capacity of the model and eventually limit the performance of insomnia detection. Meanwhile, there have been a number of public EEG datasets collected from a large number of healthy subjects for various sleep research tasks such as sleep staging. Therefore, to utilize such abundant EEG datasets for addressing the data scarcity issue in insomnia detection, in this paper we propose a domain adaptation based model to better extract insomnia related features of the target domain by leveraging stage annotations from the source domain. For each domain, two pairs of common encoder and private encoder are firstly trained to extract sleep related features and sleep irrelevant features, respectively. In order to further discriminate source domain and target domain, a domain classifier is introduced. Then, the common encoder of the target domain will be used together with the Long Short Term Memory (LSTM) network for insomnia detection. To the best of our knowledge, this is the first deep learning based domain adaptation model using single channel raw EEG signals to detect insomnia at subject level. We use the Montreal Archive of Sleep Studies (MASS) dataset which contains only healthy subjects as source domain and two datasets which contain both healthy and insomnia subjects as target domain to validate our model's generalizability. Experimental results on the two target domain datasets (a public one and an in-house one) demonstrate that our model generalizes well on two target domain datasets with different sampling rates. In particular, our proposed method is able to improve insomnia detection performance from 50.0% to 90.9% and 66.7%-79.2% in terms of accuracy on the two target domain datasets, respectively.

Cortical Thinning and Abnormal Structural Covariance Network After Three Hours Sleep Restriction

Sleep loss leads to serious health problems, impaired attention, and emotional processing. It has been suggested that the abnormal neurobehavioral performance after sleep deprivation was involved in dysfunction of specific functional connectivity between brain areas. However, to the best of our knowledge, there was no study investigating the structural connectivity mechanisms underlying the dysfunction at network level. Surface morphological analysis and graph theoretical analysis were employed to investigate changes in cortical thickness following 3 h sleep restriction, and test whether the topological properties of structural covariance network was affected by sleep restriction. We found that sleep restriction significantly decreased cortical thickness in the right parieto-occipital cortex (Brodmann area 19). In addition, graph theoretical analysis revealed significantly enhanced global properties of structural covariance network including clustering coefficient and local efficiency, and increased nodal properties of the left insula cortex including nodal efficiency and betweenness, after 3 h sleep restriction. These results provided insights into understanding structural mechanisms of dysfunction of large-scale functional networks after sleep restriction.