Obstructive sleep apnea: Brain hemodynamics, structure, and function

Abnormal prefrontal functional network in adult obstructive sleep apnea: A resting-state fNIRS study

To assess prefrontal brain network abnormality in adults with obstructive sleep apnea (OSA), resting-state functional near infrared spectroscopy (rs-fNIRS) was used to evaluate 52 subjects, including 27 with OSA and 25 healthy controls (HC). The study found that patients with OSA had a decreased connection edge number, particularly in the connection between the right medial frontal cortex (MFG-R) and other right-hemisphere regions. Graph-based analysis also revealed that patients with OSA had a lower global efficiency, local efficiency, and clustering coefficient than the HC group. Additionally, the study found a significant positive correlation between the Montreal Cognitive Assessment (MoCA) score and both the connection edge number and the graph-based indicators in patients with OSA. These preliminary results suggest that prefrontal rs-fNIRS could be a useful tool for objectively and quantitatively assessing cognitive function impairment in patients with OSA.

Cumulative brain desaturation: Time to consider brain derived parameters to measure daytime sleepiness in obstructive sleep apnea

OBJECTIVE

/Background: The change in cerebral hemodynamics induced by sleep apneas and hypopneas may contribute to the daytime sleepiness in patients with obstructive sleep apnea (OSA). However, previous studies failed to discovery their relationship. We propose and test a new parameter, the cumulative brain oxygen desaturation, which may contribute to OSA patient's daytime sleepiness.

PATIENTS/METHODS

22 patients with severe OSA (apnea-hypopnea index (AHI) at diagnosis [mean ± standard deviation, std.]: 52.1 ± 21.6/h, median: 45.1/h, interquartile range: 34.4-60.2/h) were monitored by polysomnography during routine continuous positive airway pressure titration. The reductions of brain tissue oxygen saturation (StO2) in all respiratory events at baseline sleep were measured by frequency-domain near-infrared spectroscopy (NIRS). The cumulative brain desaturation was calculated as AHI times the mean StO2 desaturation (i.e., AHI×ΔStO2‾). Similarly, cumulative peripheral desaturation was also calculated, i.e., AHI×ΔSpO2‾ where ΔSpO2‾ was the mean reduction of peripheral arterial oxygen saturation (SpO2). The correlations between Epworth sleepiness scale (ESS) and AHI, ΔStO2‾, AHI×ΔStO2‾, and AHI×ΔSpO2‾ were tested, respectively. Linear regression was applied to predict ESS using AHI×ΔStO2‾ and AHI×ΔSpO2‾, with age and BMI as covariates.

RESULTS

ESS significantly correlates to the cumulative brain desaturation (Pearson's correlation coefficient: 0.68, p = 0.00056), not the other parameters. Regression analysis only finds significant association between ESS and the cumulative cerebral desaturation (p = 0.00195) but not the cumulative peripheral desaturation (p = 0.71).

CONCLUSIONS

The cumulative brain oxygen desaturation, which comprehensively combines total sleep time, the frequency of apnea and hypopnea events, and the severity of cerebral oxygen desaturation, is a new indicator for daytime sleepiness in severe OSA.

Extraction of cardiac-related signals from a suprasternal pressure sensor during sleep

Objective.The accurate detection of respiratory effort during polysomnography is a critical element in the diagnosis of sleep-disordered breathing conditions such as sleep apnea. Unfortunately, the sensors currently used to estimate respiratory effort are either indirect and ignore upper airway dynamics or are too obtrusive for patients. One promising alternative is the suprasternal notch pressure (SSP) sensor: a small element placed on the skin in the notch above the sternum within an airtight capsule that detects pressure swings in the trachea. Besides providing information on respiratory effort, the sensor is sensitive to small cardiac oscillations caused by pressure perturbations in the carotid arteries or the trachea. While current clinical research considers these as redundant noise, they may contain physiologically relevant information.Approach.We propose a method to separate the signal generated by cardiac activity from the one caused by breathing activity. Using only information available from the SSP sensor, we estimate the heart rate and track its variations, then use a set of tuned filters to process the original signal in the frequency domain and reconstruct the cardiac signal. We also include an overview of the technical and physiological factors that may affect the quality of heart rate estimation. The output of our method is then used as a reference to remove the cardiac signal from the original SSP pressure signal, to also optimize the assessment of respiratory activity. We provide a qualitative comparison against methods based on filters with fixed frequency cutoffs.Main results.In comparison with electrocardiography (ECG)-derived heart rate, we achieve an agreement error of 0.06 ± 5.09 bpm, with minimal bias drift across the measurement range, and only 6.36% of the estimates larger than 10 bpm.Significance.Together with qualitative improvements in the characterization of respiratory effort, this opens the development of novel portable clinical devices for the detection and assessment of sleep disordered breathing.

The treatment of sleep dysfunction to improve cognitive function: A meta-analysis of randomized controlled trials

OBJECTIVE

This meta-analysis of randomized controlled trials (RCTs) evaluates if treating sleep disturbances improves cognitive function over at least 12 weeks.

METHODS

Multiple data sources were searched until November 1, 2021. RCTs were included if they examined the effect of an intervention (behavioral or medical) on sleep and cognition in an adult sample with sleep disturbances and had an intervention duration and follow-up of at least 12 weeks. Two independent reviewers located 3784 studies; 16 satisfied the inclusion criteria. Primary outcomes included the broad cognitive domains of visual processing, short-term memory, long-term storage and retrieval, processing speed, and reaction time.

RESULTS

Most trials were conducted in participants with obstructive sleep apnea (OSA; N = 13); the most studied intervention was continuous positive airway pressure (CPAP; N = 10). All RCTs were 12 months in duration or less. The estimates of mean pooled effects were not indicative of significant treatment effect for any primary outcome. Although the interventions reduced daytime sleepiness (Hedge's g, 0.51; 95% confidence interval, 0.29-0.74; p < 0.01), this did not lead to cognitive enhancement.

CONCLUSIONS

Overall, there was insufficient evidence to suggest that treating sleep dysfunction can improve cognition. Further studies with longer follow-up duration and supporting biomarkers are needed.

A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment

Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed system is dedicated to mapping the pathogenic characteristics of OSA to dynamic changes in blood oxygen concentration and to constructing an automatic approach for assessing OSA. An algorithm including feature extraction, feature selection, and classification is proposed to signals. Permutation entropy(PE), for quantitative measuring the complexity of time series, is firstly involved to characterize the features of the physiological signals. Subsequently, the principal component analysis (PCA) for feature dimensionality reduction and support vector machine (SVM) algorithm for OSA classification are applied. The proposed method has been validated on a dataset that collected by the wearable system. It includes 40 subjects and composes of normal, and various severity cessation of breathing (e.g., mild, moderate, and severe). Experimental results exhibit that the proposed system can effectively distinguish OSA and non-OSA subjects, with an accuracy of 91.89%. The proposed system is expected to pave the novel perspective for OSA assessment in terms of cerebral hemodynamics.

Preliminary study on brain resting-state networks and cognitive impairments of patients with obstructive sleep apnea-hypopnea syndrome

BACKGROUND

To investigate functional changes in brain resting-state networks (RSNs) in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) and their correlations with sleep breathing disorders and neurocognitive performance.

METHODS

In this study, 18 OSAHS patients and 18 matched healthy controls underwent neurocognitive assessment and magnetic resonance imaging (MRI). Group-level independent component analysis (ICA) and statistical analyses were used to explore between-group differences in RSNs and the relationship between functional changes in RSNs, sleep breathing disorders and neurocognitive performance.

RESULTS

The OSAHS patients performed worse on neuropsychological tests than the healthy controls. Eight RSNs were identified, and between-group analyses showed that OSAHS patients displayed significantly decreased functional connectivity in the bilateral posterior cingulate gyri (PCC) within the default mode network (DMN), the right middle frontal gyrus (MFG) within the dorsal attention network (DAN), and the left superior temporal gyrus (STG) within the ventral attention network (VAN), and increased functional connectivity in the right superior frontal gyrus (SFG) within the salience network (SN). Further correlation analyses revealed that the average ICA z-scores in the bilateral PCC were correlated with sleep breathing disorders.

CONCLUSIONS

Our findings demonstrate that the DMN, SN, DAN, and VAN are impaired during the resting state and are associated with decreased functionally distinct aspects of cognition in patients with OSAHS. Moreover, the intermittent hypoxia and sleep fragmentation caused by OSAHS are likely to be the main influencing factors.