Sleep electroencephalography biomarkers of cognition in obstructive sleep apnea

Abstract

SummaryObstructive sleep apnea has been associated with cognitive impairment and may be linked to disorders of cognitive function. These associations may be a result of intermittent hypoxaemia, sleep fragmentation and changes in sleep microstructure in obstructive sleep apnea. Current clinical metrics of obstructive sleep apnea, such as the apnea–hypopnea index, are poor predictors of cognitive outcomes in obstructive sleep apnea. Sleep microstructure features, which can be identified on sleep electroencephalography of traditional overnight polysomnography, are increasingly being characterized in obstructive sleep apnea and may better predict cognitive outcomes. Here, we summarize the literature on several major sleep electroencephalography features (slow‐wave activity, sleep spindles, K‐complexes, cyclic alternating patterns, rapid eye movement sleep quantitative electroencephalography, odds ratio product) identified in obstructive sleep apnea. We will review the associations between these sleep electroencephalography features and cognition in obstructive sleep apnea, and examine how treatment of obstructive sleep apnea affects these associations. Lastly, evolving technologies in sleep electroencephalography analyses will also be discussed (e.g. high‐density electroencephalography, machine learning) as potential predictors of cognitive function in obstructive sleep apnea.

Medial temporal lobe functional network architecture supports sleep-related emotional memory processing in older adults

“…Sensitivity analyses demonstrated consistency in this result across resolution parameters (γ-values), which are incorporated into the optimization algorithm to tune the number and size of detected modules ( Table S3 ). Although recent work suggests sleep-disordered breathing (SDB) impacts SWS expression, 60 no statistically significant associations between either AHI (p=0.773) or hypoxic burden (i.e., time below 90% oxygen saturation; p=0.142) and NREM SWS were observed in the current study. These data suggest that more modular brain networks, in general, express more NREM SWS, a period of sleep associated with a breakdown of local functional connectivity 71 and enhanced network integration.…”

“…Another limitation is that the participants had varying degrees of SDB. While AHI was adjusted for in all models, SDB impacts on sleep expression, 60 cognitive health, 141 and neurobiology are complex. 142 Additionally, our sample size for probing sleep oscillations in relation to graph theoretical metrics and memory was small.…”

“…For all analyses with Q, sensitivity analyses were performed to ensure that findings were consistent across resolution parameters (see Table S5, Figure S3 ). Given recent research demonstrating that SDB impacts SWS expression, 60 associations between AHI (Pearson’s r) and time spent below 90% oxygen saturation (Kendall’s τ b ) were additionally explored ( Table S4) . Hayes SPSS Process Macro (v4.2) 61 was used to test for an indirect effect of Q on overnight change in negative LDI through NREM SWS expression (model 4), adjusting for the covariates mentioned above.…”

The Contribution of Sleep Texture in the Characterization of Sleep Apnea

“…Sleep recordings may also provide information on the impairment of cognitive processes, a detrimental consequence in patients affected by untreated OSA [ 40 , 41 ]. The extent of cognitive impairment, assessed with the Montreal Cognitive Assessment (MoCA), has been found to be related to the severity of sleep fragmentation as expressed by the CAP A3 rate in patients with sleep apnea [ 42 ].…”

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

“…EEG signals can be used to understand the underlying neural dynamics of cognitive, motor, and pathological phenomena ( Rodriguez-Bermudez and Garcia-Laencina, 2015 ). For example, EEG signals are used in a wide variety of applications such as neuromarketing ( Costa-Feito et al, 2023 ), investigation of sleep architecture ( Gu et al, 2023 ), detection of neurodegenerative conditions such as Alzheimer’s disease ( Modir et al, 2023 ), neurofeedback therapy ( Torres et al, 2023 ), and epileptic seizure detection ( Maher et al, 2023 ). Over time, various linear and non-linear methods have been developed for extracting distinct features from recorded time series signals.…”