Faculty Bibliography

STUDY OBJECTIVES:Phenotyping using polysomnography (PUP) is an algorithmic method to quantify physiologic mechanisms underlying obstructive sleep apnea (OSA): loop gain (LG1), arousal threshold (ArTH), and upper airway collapsibility (Vpassive) and muscular compensation (Vcomp). The consecutive-night test-retest reliability and agreement of PUP-derived estimates are unknown. From a cohort of elderly (age ≥55 years), largely non-sleepy, community-dwelling volunteers who underwent in-lab polysomnography (PSG) on 2 consecutive nights, we determined the test-retest reliability and agreement of PUP-estimated physiologic factors. METHODS:Participants who had an apnea-hypopnea index (AHI3A) of at least 15 events per hour on the first night were included. PUP analyses were performed on each of the two PSGs from each participant. Physiologic factor estimates were derived from NREM sleep and compared across nights using intraclass correlation coefficients for reliability and smallest real differences (SRD) for agreement. RESULTS:Two PSGs from each of 43 participants (86 total) were analyzed. A first-night effect was evident with increased sleep time and stability and decreased OSA severity on the second night. LG1, ArTH, and Vpassive demonstrated good reliability (ICC > 0.80). Vcomp had modest reliability (ICC = 0.67). For all physiologic factors, SRD values were approximately 20% or more of the observed ranges, suggesting limited agreement of longitudinal measurements for a given individual. CONCLUSIONS:For NREM sleep in cognitively normal elderly individuals with OSA, PUP-estimated LG1, ArTH, and Vpassive demonstrated consistent relative ranking of individuals (good reliability) on short-term repeat measurement. For all physiologic factors, longitudinal measurements demonstrated substantial intraindividual variability across nights (limited agreement).

BACKGROUND:Preclinical studies suggest body temperature (Tb) and consequently brain temperature has the potential to bidirectionally interact with tau pathology in Alzheimer's Disease (AD). Tau phosphorylation is substantially increased by small (<1 °C) decreases in temperature within the human physiological range, and thermoregulatory nuclei are affected by tau pathology early in the AD continuum. In this study we evaluated whether Tb (as a proxy for brain temperature) is cross-sectionally associated with clinically utilized markers of tau pathology in cognitively normal older adults. METHODS:Tb was continuously measured with ingestible telemetry sensors for 48-h. This period also included two nights of nocturnal polysomnography to delineate whether Tb during waking vs sleep is differentially associated with tau pathology. Tau phosphorylation was assessed with plasma and cerebrospinal fluid (CSF) tau phosphorylated at threonine 181 (P-tau), sampled the day following Tb measurement. In addition, neurofibrillary tangle (NFT) burden in early Braak stage regions was imaged with PET-MR using the [18F]MK-6240 radiotracer on average one month later. RESULTS:Lower Tb was associated with increased NFT burden, as well as increased plasma and CSF P-tau levels (p < 0.05). NFT burden was associated with lower Tb during waking (p < 0.05) but not during sleep intervals. Plasma and CSF Ptau levels were highly correlated with each other (p < 0.05), and both variables were correlated with tau tangle radiotracer uptake (p < 0.05). CONCLUSIONS:These results, the first available for human, suggest that lower Tb in older adults may be associated with increased soluble and aggregated tau pathology. Our findings add to the substantial preclinical literature associating lower body and brain temperature with tau hyperphosphorylation. CLINICAL TRIAL NUMBER/BACKGROUND:NCT03053908.

Due in part to overall improvements in health, the population of elderly individuals is increasing rapidly. Similarly, obstructive sleep apnoea (OSA) is both gaining increased recognition and also increasing due to the worldwide obesity epidemic. The overlap of OSA and ageing is large, but there is strong plausibility for causation in both directions: OSA is associated with pathological processes that may accelerate ageing and ageing-related processes; ageing may cause physical and neurological changes that predispose to obstructive (and central) apnoea. In addition, the common symptoms (e.g. excessive daytime sleepiness, and defects in memory and cognition), possible physiological consequences of OSA (e.g. accelerated cardiovascular and cerebrovascular atherosclerosis), and changes in metabolic and inflammatory markers overlap with the symptoms and associated conditions seen in ageing. There is also the possibility of synergy in the effects of these symptoms and conditions on quality of life, as well as a need to separate treatable consequences of OSA from age-related complaints. Taken together, the aforementioned considerations make it essential to review the interaction of OSA and ageing, both proven and suspected. The present review examines some aspects of what is known and points to the need for further investigation of the relationships, given the large number of potentially affected subjects.

STUDY OBJECTIVES/OBJECTIVE:Determine if changes in K-complexes associated with sustained inspiratory airflow limitation (SIFL) during N2 sleep are associated with next-day vigilance and objective sleepiness. METHODS:Data from thirty subjects with moderate-to-severe OSA who completed three in-lab polysomnograms: diagnostic, on therapeutic continuous positive airway pressure (CPAP), and on suboptimal CPAP (4cmH20 below optimal titrated CPAP level) were analyzed. Four 20-min psychomotor vigilance tests (PVT) were performed after each PSG, every two hours. Changes in proportion of spontaneous K-complexes and spectral characteristics surrounding K-complexes were evaluated for K-complexes associated with both delta (∆SWAK), alpha (∆αK) frequencies. RESULTS:Suboptimal CPAP induced SIFL (14.7(20.9) vs. 2.9(9.2); %total sleep time, p<0.001) with a small increase in apnea hypopnea index (AHI3A: 6.5(7.7) vs. 1.9(2.3); p<0.01) versus optimal CPAP. K-complex density (num./min of stage N2) was higher on suboptimal CPAP (0.97±0.7 vs. 0.65±0.5, #/min, mean±SD, p<0.01) above and beyond the effect of age, sex, AHI3A, and duration of SIFL. A decrease in ∆SWAK with suboptimal CPAP was associated with increased PVT lapses and explained 17% of additional variance in PVT lapses. Within-night during suboptimal CPAP K-complexes appeared to alternate between promoting sleep and as arousal surrogates. EEG changes were not associated with objective sleepiness. CONCLUSIONS:Sustained inspiratory airflow limitation is associated with altered K-complex morphology including increased occurrence of K-complexes with bursts of alpha as arousal surrogates. These findings suggest that sustained inspiratory flow limitation may be associated with non-visible sleep fragmentation and contribute to increased lapses in vigilance.

BACKGROUND:Recent advancement in deep learning provides a pivotal opportunity to potentially supplement or supplant the limiting step of manual sleep scoring. NEW METHOD/UNASSIGNED:In this paper, we characterize the WaveSleepNet (WSN), a deep convolutional neural network (CNN) that uses wavelet transformed images of mouse EEG/EMG signals to autoscore sleep and wake. RESULTS:WSN achieves an epoch by epoch mean accuracy of 0.86 and mean F1 score of 0.82 compared to manual scoring by a human expert. In mice experiencing mechanically induced sleep fragmentation, an overall epoch by epoch mean accuracy of 0.80 is achieved by WSN and classification of non-REM (NREM) sleep is not compromised, but the high level of sleep fragmentation results in WSN having greater difficulty differentiating REM from NREM sleep. We also find that WSN achieves similar levels of accuracy on an independent dataset of externally acquired EEG/EMG recordings with an overall epoch by epoch accuracy of 0.91. We also compared conventional summary sleep metrics in mice sleeping ad libitum. WSN systematically biases sleep fragmentation metrics of bout number and bout length leading to an overestimated degree of sleep fragmentation. COMPARISON WITH EXISTING METHODS/UNASSIGNED:In a cross-validation, WSN has a greater macro and stage-specific accuracy compared to a conventional random forest classifier. Examining the WSN, we find that it automatically learns spectral features consistent with manual scoring criteria that are used to define each class. CONCLUSION/CONCLUSIONS:These results suggest to us that WSN is capable of learning visually agreeable features and may be useful as a supplement to human manual scoring.