Polysomnographic Predictors of Treatment Response to Cognitive Behavioral Therapy for Insomnia in Participants With Co-morbid Insomnia and Sleep Apnea: Secondary Analysis of a Randomized Controlled Trial

The Future of Insomnia Research-There's Still Work to Be Done

Insomnia Disorder (ID) is a highly debilitating disorder affecting up to 10% of the general population. In recent years, the number of studies in this area has increased rapidly, resulting in a wealth of accumulated knowledge. ID is generally regarded as a hyperarousal disorder affecting cognitive, emotional, cortical and physiological domains. Nevertheless, there is still a significant lack of knowledge about the pathophysiology of ID. For example, the existence of insomnia subtypes is discussed, albeit no uniform definition has yet been found. Significant progress has been made in understanding the neurobiology of insomnia, which points to a dysfunction in emotion regulation. However, neuroimaging studies frequently have small sample sizes and allow only for limited causal conclusions. The assessment of sleep has been significantly influenced by the increasing availability of methods for ambulatory sleep measurement. While these methods enable sleep to be measured more cost-effectively than polysomnography, many devices lack sufficient empirical evidence of validity. In terms of insomnia treatment, cognitive behavioural therapy for insomnia (CBT-I) has been shown to be highly effective. However, the underlying mechanisms of CBT-I remain partially unclear, and the optimal sequence for applying the individual components, as well as the effectiveness of CBT-I in cases of comorbidity, remain open questions. Furthermore, many widely applied pharmacological treatment approaches are used off-label with only a limited empirical evidence base. This narrative review aims to summarise the current state of research on ID and attempts to outline a selection of the important future challenges in insomnia research.

Tripolar concentric ring electrodes for capturing localised electroencephalography signals during sleep

By design, tripolar concentric ring electrodes (TCRE) provide more focal brain activity signals than conventional electroencephalography (EEG) electrodes placed further apart. This study compared spectral characteristics and rates of data loss to noisy epochs with TCRE versus conventional EEG signals recorded during sleep. A total of 20 healthy sleepers (12 females; mean [standard deviation] age 27.8 [9.6] years) underwent a 9-h sleep study. Participants were set up for polysomnography recording with TCRE to assess brain activity from 18 sites and conventional electrodes for EEG, eyes, and muscle movement. A fast Fourier transform using multitaper-based estimation was applied in 5-s epochs to scored sleep. Odds ratios with Bonferroni-adjusted 95% confidence intervals were calculated to determine the proportional differences in the number of noisy epochs between electrode types. Relative power was compared in frequency bands throughout sleep. Linear mixed models showed significant main effects of signal type (p < 0.001) and sleep stage (p < 0.001) on relative spectral power in each power band, with lower relative spectral power across all stages in TCRE versus EEG in alpha, beta, sigma, and theta activity, and greater delta power in all stages. Scalp topography plots showed distinct beta activation in the right parietal lobe with TCRE versus EEG. EEG showed higher rates of noisy epochs compared to TCRE (1.3% versus 0.8%, p < 0.001). TCRE signals showed marked differences in brain activity compared to EEG, consistent with more focal measurements and region-specific differences during sleep. TCRE may be useful for evaluating regional differences in brain activity with reduced muscle artefact compared to conventional EEG.

Clinical Characteristics and Treatment Efficacy for Co-Morbid Insomnia and Sleep Apnea (COMISA): Evidence from Qualitative and Quantitative Analysis

OBJECTIVES

A multitude of physical and mental challenges are being faced in the population with Co-morbid Insomnia and Sleep Apnea (COMISA). Unfortunately, research about clinical characteristics and management of COMISA based on quantitative evidence is lacking.

METHOD

Standard procedures for literature retrieval, selection and quality assessment, data extraction, analysis, and interpretation were conducted step by step. For studying the sleep characteristics, common complications and widely recognized treatment options for COMISA, Weighted Mean Difference (WMD) and Odds Ratio (OR) were applied to assess the mean and risk differences between compared groups. Outcomes included sleep health parameters and secondary impairments in physical and mental well-being.

RESULTS

COMISA showed worse sleep quality than OSA only by PSQI (WMD = 3.38 point) and heavier sleep fragmentation (WMD = 11.46 min) than insomnia only. Besides, COMISA patients showed a higher risk for depression (OR [95%CI] = 5.03[2.31, 10.93]) and PTSD (OR [95%CI] = 3.96[1.85, 8.46]) in comparison with OSA alone. Compared to insomnia alone, COMISA patients suffered from more than two times higher risk of cardiovascular diseases, hypertension, and diabetes. In treating COMISA patients, combining CBTI with PAP treatment can enhance the improvement of insomnia severity (ISI, WMD [95%CI] =-3.26[-4.51, -2.00] point) and sleep efficiency (WMD [95%CI] = 6.39[1.97, 10.81] %) compared to PAP alone.

CONCLUSIONS

Impaired sleep domains in COMISA cover sleep quality and sleep structure. Also, COMISA has a higher risk for cardiometabolic diseases and mental disorders. Combining CBTI with PAP can be a recommended treatment to relieve sleep impairments for COMISA.

The effect of cognitive behavioural therapy for insomnia in people with comorbid insomnia and sleep apnoea: A systematic review and meta-analysis

Comorbid insomnia and sleep apnoea (COMISA) is a highly prevalent and debilitating sleep disorder. Cognitive behavioural therapy for insomnia (CBTi) may be an appropriate treatment for COMISA; however, no previous study has systematically reviewed and meta-analysed literature reporting on the effect of CBTi in people with COMISA. A systematic literature search was conducted across PsychINFO and PubMed (n = 295). In all, 27 full-text records were independently reviewed by at least two authors. Forward- and backward-chain referencing, and hand-searches were used to identify additional studies. Authors of potentially eligible studies were contacted to provide COMISA subgroup data. In total, 21 studies, including 14 independent samples of 1040 participants with COMISA were included. Downs and Black quality assessments were performed. A meta-analysis including nine primary studies measuring the Insomnia Severity Index indicated that CBTi is associated with a large improvement in insomnia severity (Hedges' g = -0.89, 95% confidence interval [CI] -1.35, -0.43). Subgroup meta-analyses indicated that CBTi is effective in samples with untreated obstructive sleep apnoea (OSA) (five studies, Hedges' g = -1.19, 95% CI -1.77, -0.61) and treated OSA (four studies, Hedges' g = -0.55, 95% CI -0.75, -0.35). Publication bias was evaluated by examining the Funnel plot (Egger's regression p = 0.78). Implementation programmes are required to embed COMISA management pathways in sleep clinics worldwide that currently specialise in the management of OSA alone. Future research should investigate and refine CBTi interventions in people with COMISA, including identifying the most effective CBTi components, adaptations, and developing personalised management approaches for this highly prevalent and debilitating condition.

Connectomic disturbances underlying insomnia disorder and predictors of treatment response

Objective

Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy.

Materials and methods

51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up.

Results

Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change.

Conclusion

Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.

A novel EEG marker predicts perceived sleepiness and poor sleep quality

STUDY OBJECTIVES

To determine if a novel EEG-derived continuous index of sleep depth/alertness, the odds ratio product (ORP), predicts self-reported daytime sleepiness and poor sleep quality in two large population-based cohorts.

METHODS

ORP values which range from 0 (deep sleep) to 2.5 (fully alert) were calculated in 3s intervals during awake periods (ORPwake) and NREM sleep (ORPNREM) determined from home sleep studies in the HypnoLaus (N = 2162: 1106 females, 1056 males) and men androgen inflammation lifestyle environment and stress (MAILES) cohorts (N = 754 males). Logistic regression was used to examine associations between ORPwake, ORPNREM, and traditional polysomnography measures (as comparators) with excessive sleepiness (Epworth sleepiness scale >10) and poor sleep quality (Pittsburgh sleep quality index >5) and insomnia symptoms.

RESULTS

High ORPwake was associated with a ~30% increase in poor sleep quality in both HypnoLaus (odds ratio, OR, and 95% CI) 1.28 (1.09, 1.51), and MAILES 1.36 (1.10, 1.68). High ORPwake was also associated with a ~28% decrease in excessive daytime sleepiness in the MAILES dataset. ORPNREM was associated with a ~30% increase in poor sleep quality in HypnoLaus but not in MAILES. No consistent associations across cohorts were detected using traditional polysomnography markers.

CONCLUSIONS

ORP, a novel EEG-derived metric, measured during wake periods predicts poor sleep quality in two independent cohorts. Consistent with insomnia symptomatology of poor perceived sleep in the absence of excessive daytime sleepiness, ORPwake may provide valuable objective mechanistic insight into physiological hyperarousal.

A Novel EEG Derived Measure of Disrupted Delta Wave Activity during Sleep Predicts All-Cause Mortality Risk

RATIONALE

Conventional markers of sleep disturbance, based on manual electroencephalography scoring, may not adequately capture important features of more fundamental electroencephalography-related sleep disturbance.

OBJECTIVES

This study aimed to determine if more comprehensive power-spectral measures of delta wave activity during sleep are stronger independent predictors of mortality than conventional sleep quality and disturbance metrics.

METHODS

Power spectral analysis of the delta frequency band and spectral entropy-based markers to quantify disruption of electroencephalography delta power during sleep were performed to examine potential associations with mortality risk in the Sleep Heart Health Study cohort (N = 5804). Adjusted Cox proportional hazard models were used to determine the association between disrupted delta wave activity at baseline and all-cause mortality over an ~11y follow-up period.

RESULTS

Disrupted delta electroencephalography power during sleep was associated with a 32% increased risk of all-cause mortality compared with no fragmentation (hazard ratios 1.32 [95% confidence interval 1.14, 1.50], after adjusting for total sleep time and other clinical and life-style related covariates including sleep apnea. The association was of similar magnitude to a reduction in total sleep time from 6.5h to 4.25h. Conventional measures of sleep quality, including wake after sleep onset and arousal index were not predictive of all-cause mortality.

CONCLUSIONS

Delta wave activity disruption during sleep is strongly associated with all-cause mortality risk, independent of traditional potential confounders. Future investigation into the potential role of delta sleep disruption on other specific adverse health consequences such as cardiometabolic, mental health and safety outcomes has considerable potential to provide unique neurophysiological insight.