Faculty Bibliography

OBJECTIVE:Mindfulness-based interventions (MBI) have been shown to reduce subjective symptoms of insomnia but the effects on objective measures remain unclear. The purpose of this study was to examine sleep EEG microarchitecture patterns from a randomized controlled trial of Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Therapy for Insomnia (MBTI). METHODS:Sleep EEG spectral analysis was conducted on 36 participants with chronic insomnia (>6 months) randomized to 8-week MBSR, MBTI, or self-monitoring control (SM). Overnight polysomnography with 6-channel EEG was conducted at baseline, post-treatment, and 6-month follow-up. Spectral power averaged from channels C3/C4 across NREM epochs (excluding N1) was examined for within-group changes and relationships with self-report measures. RESULTS:Increases in absolute NREM beta (16-25 Hz) power were observed from baseline to post-treatment (p = .02, d = 0.53) and maintained at 6-month follow-up (p = .01, d = 0.57) in the combined MBI groups, and additionally in the gamma (25-40 Hz) range at follow-up for the MBTI group only. No significant changes in these frequency bands were observed for SM. Following mindfulness intervention, NREM beta was positively associated with Five-Facet Mindfulness (FFM) score (rho = 0.37, p = .091) and negatively associated with Insomnia Severity Index (rho = -0.43, p = .047). CONCLUSION/CONCLUSIONS:These results in people with insomnia corroborate prior reports of increased high-frequency sleep EEG power associated with mindfulness training. This change in beta EEG pattern merits further evaluation as a potential marker of the effects of mindfulness meditation on sleep, especially given the paradoxical findings in the context of insomnia. CLINICAL TRIAL REGISTRATION/BACKGROUND:clinicaltrials.gov, NCT00768781.

Introduction: We determined whether Obstructive Sleep Apnea (OSA) and beta-Amyloid Burden (Abeta) act additively or synergistically to promote conversion from cognitive normal (CN) to mild cognitive impairment (MCI) and from MCI to AD.
Method(s): In this longitudinal observational study, we examined CN (n=298) and MCI (n=418) older adults from the ADNI database (adni.loni.usc.edu). OSA was self-reported during a clinical interview. Brain Abeta was assessed using Florbetapir-PET imaging. The primary outcome of the analysis was conversion from CN to MCI (CN participants) and from MCI to AD (MCI participants). Participants were required to have a baseline and at least one follow-up clinical visit that identified their cognitive status. Logistic mixed-effects models with random intercept and slope were used to assess associations between OSA, Abeta, and risk of conversion from CN to MCI, and MCI to AD. All models included age at baseline, sex, APOE4 status, years of education, and their interactions with time.
Result(s): Of the 716 participants, 329 (46%) were women. The overall mean (SD) age was 74.7 (5.0) years, and the overall mean (SD) follow-up time was 5.5 (1.7) years (Range: 2.7 - 10.9 years). In CN participants at baseline, conversion to MCI was associated with both OSA (beta = 0.418; 95% CI, 0.133 to 0.703; P < .001) and higher Abeta-burden (beta = 0.554; 95% CI, 0.215 to 0.892; P < .001). The interaction of OSA and Abeta burden with time was significant (beta = 1.169, 95% CI, 0.776 to 1.562; P < .001), suggesting a synergistic effect. In MCI participants at baseline, conversion to AD was associated with both OSA (beta = 0.637; 95% CI, 0.291 to 0.982; P < .001) and higher Abeta-burden (beta = 1.061; 95% CI, 0.625 to 1.497; P < .001). The interaction of OSA and Abeta burden with time was significant (beta = 1.312, 95% CI, 0.952 to 1.671; P < .001), suggesting a synergistic effect.
Conclusion(s): In both CN and MCI elderly, Abeta modified the risk of progression to AD in OSA participants. OSA patients maybe more physiologically susceptible as Abeta load becomes increasingly abnormal

Introduction: We determined whether the co-occurrence of OSA and hypertension interact synergistically to promote beta-Amyloid burden and cognitive decline in clinically normal older adults Methods: Prospective longitudinal study utilizing NYU cohort of community-dwelling cognitively-normal elderly, with baseline and at least one follow-up of CSF-Abeta42 (measured using ELISA), and neuropsychological visits. OSA was defined using AHI4%. Hypertension diagnosis was according to AHA-guidelines. Cognitive variables assessed included Logic-2, Animal-Fluency [AF], Vegetable-Fluency [VF]), Boston-Naming-Test [BNT], Digit-Symbol-Substitution-Test [DSST], Trails Making Test-A and B [TMT-A and B]). Linear mixed-effects models with random intercept and slope were used to assess associations between OSA, hypertension, and longitudinal changes in CSF-Abeta and cognition, controlling for age-at-baseline, sex, APOE4-status, years-of-education, and their interactions with time.
Result(s): Of the 98 participants, 63 (64.3%) were women. The mean (SD) age was 69.6 (7.3) years and follow-up time was 2.46 (0.64) years. OSA and hypertension were each associated with faster rate-of-change in CSF-Abeta42 (beta = -3.11; 95%CI, -3.71, -2.51; and beta= -2.82, 95% CI -3.29, -2.35, P < .01 for both respectively). The interaction of OSA and hypertension with time was significant (beta= -1.28, 95% CI -1.78 to -0.78, P < .01) suggesting a synergistic effect. No significant associations were seen between annual-changes in CSF-Abeta42 and cognitive-decline. However, faster decline in VF, and DSST were associated with OSA (beta = -0.054; 95%CI, -0.094, -0.013; P = .02; beta = -0.058; 95%CI, -0.084, -0.033; P < .05 for both respectively), and with hypertension (beta = -0.048; 95%CI, -0.079, -0.017; P = .04; beta = -0.078; 95%CI, -0.098, -0.057; P = .002; respectively). The interaction of OSA and hypertension with time was significant for both VF and DSST (beta = -0.033, 95%CI, -0.048, -0.018; P < .001 and beta = -0.040, 95%CI, -0.064, -0.016; P < .001, respectively), suggesting a synergistic effect.
Conclusion(s): In cognitive-normal elderly OSA individuals, vascular risk may complement AD-biomarkers in assessing risk of prospective cognitive-decline in preclinical AD

Introduction: Using the National Health and Aging Trends Study (NHATS), we examined use of sleeping medication, difficulty falling asleep, and trouble falling back asleep among individuals with and without cognitive impairment.
Method(s): Binomial logistic regression examined sleep medication use and insomnia symptoms (difficulty falling asleep or falling back asleep after awakening) by cognitive impairment (no dementia and possible or probable dementia). Sleep-related variables were collected on frequency scales ranging from 1 (every day) to 5 (never). Of the sample, 71.1% were White (n=3,369), 20.7% were Black (n=982), 5.0% were Hispanic (n=235), and 2.4% other (n=113); 60.4% were female (n=2,662) and 39.6% were male (n=1,875).
Result(s): Respondents were classified as having no dementia (63.7%), possible dementia (8.5%), or probable dementia (12.9%). Of the sample, 10.7% reported medication use every night, 2.5% 5-6 nights/week, 5.7% 2-4 nights/week, 6.6% once/week and 59.4% reported no use. Of the respondents, 8.3% reported difficulty sleeping every night, 8.0% reported 5-6 nights/week, 21.4% reported 2-4 nights/week, 22.9% reported rarely, and 23.5% reported never experiencing difficulty sleeping. Regarding difficulty falling back asleep, 4.9% reported difficulty every night, 7.4% reported 5-6 nights/week, 26.0% reported 2-4 nights/week, 20.4% reported rarely, and 24.3% reported never. Compared to individuals who reported never using sleep medications, those reporting nightly use were significantly more likely to be cognitively impaired (OR=1.44,95%CI: 1.14-1.82). Compared to individuals reporting never having difficulty falling asleep, those reporting difficulty falling asleep nightly were not more likely to have cognitive impairment (OR=0.74 95%CI: 0.67 to 1.19). Compared to individuals reporting never having difficulty falling back asleep after awakening, those frequently reporting difficulty falling back asleep were less likely to be cognitively impaired (OR=0.44,95%CI:0.22 to 0.64).
Conclusion(s): Cognitive impairment was positively associated with sleep medication use in adjusted models, but not with trouble falling asleep or difficulty falling back asleep after awakening. Our findings are consistent with the literature on deleterious consequences of sleep medications

Introduction: Obstructive sleep apnea (OSA) is a common sleep disorder associated with inconsistent cognitive consequences. Spatial disorientation increases with age and is an early sign of cognitive dysfunction in Alzheimer disease (AD). Sleep and related EEG oscillations, slow wave activity (SWA) and slow oscillations (SOs), are important for processing spatial memories, however it is not known if OSA-related sleep disruption effects spatial navigational memory processing in older adults.
Method(s): 42 older (age=66.5+/-7.9 years, 54.8% female) cognitively normal adults were recruited from the community. Participants performed timed trials on a 3D spatial maze navigational task and psychomotor vigilance test (PVT), before and after polysomnography (PSG). Maze completion time, PVT, sleep EEG macro and microstructure measures were compared between participants with and without OSA (AHI4%>=5.0/hour). Associations between sleep EEG microstructure (relative SWA (0.5-4Hz) & SOs (<1Hz) spectral power) and maze completion times were explored separately according to OSA diagnosis.
Result(s): Median AHI4% was 0.5/hour in those without OSA(n=30) and 10.7/hour in OSA(n=12). N1 sleep was significantly increased and N2 significantly decreased with OSA. No significant group differences in SWS, REM sleep or PVT performance were observed. There were no significant groups differences in pre-sleep maze completion time, whereas post-sleep maze performance was significantly different. On average participants without OSA continued to improve maze completion time across 3 morning trials whereas participants with OSA performed best on the first morning trial and performed worse on average with each subsequent trial (significant interaction between OSA group and morning trial number, p=0.016, Two Way Repeated Measures ANOVA). There were no significant differences in EEG microstructure observed between groups but in OSA, post-sleep maze performance showed a significant negative association with <1Hz spectral power at frontal (-0.78, p=0.007), central (-0.8, p=0.005) and occipital EEG (-0.71, p=0.02) during SWS.
Conclusion(s): Cognitively normal older adults with mild OSA demonstrated significantly worse morning spatial navigation performance compared to individuals without OSA after equivalent evening encoding. The associations between greater SOs and worse morning maze performance in OSA require replication

Introduction: Assessment of habitual sleep duration and obstructive sleep apnea (OSA) severity and their relationships with subjective sleepiness and vigilance in cognitively normal older subjects is limited.
Method(s): Data are from subjects participating in an ongoing longitudinal study of sleep and Alzheimer's disease biomarkers in cognitively normal elderly subjects (CDR=0, MMSE>=24). Demographic data, comorbidities, medications and Apolipoprotein E4 (ApoE4) genotype were collected. Habitual nocturnal sleep duration was measured by 7-day actigraphy. OSA was evaluated from in-laboratory nocturnal polysomnography (NPSG) and/or 2-night home-sleep test (HST). Excessive daytime sleepiness (EDS) was determined from Epworth Sleep Scale (ESS), and vigilance by 20-min psychomotor vigilance test (PVT). OSA was defined by Apnea hypopnea Index 4 (AHI4)>=5 and/or respiratory disturbance index (RDI)>=15.
Result(s): Among 267 subjects (age 68.4+/-8.1 years, BMI 26.3+/-5 kg/ m2, 36.4% male), 185 underwent HST alone, 11 NPSG alone, and 71 both HST and NPSG. 58.7% of subjects had OSA. Of these, 67.3% had AHI4<15/hr and 32.7% had AHI4>=15/hr. Sleep duration was 7+/-1.1 hours. Median ESS was 5 (IQR 5), with 16.4% subjects having ESS>=10. Median PVT lapses was 3.2 (IQR 2.7). ESS and PVT showed no relationship (rho=0.093, p-value 0.14). There was a significant inverse correlation between actigraphy sleep duration and ESS (rho=-0.348, p-value<0.01), but not lapses. AHI4 (rho=0.188, p-value<0.01) and RDI (rho=0.166, p-value 0.01) from HST were correlated with ESS but not PVT. Sleep duration explained 12% of variance in ESS even after adjusting for AHI4. In 82 subjects with NPSG, we found no correlation between ESS or PVT and in-lab total sleep time, sleep stages or OSA severity. No differences in sleepiness were seen in ApoE4 carriers compared to others.
Conclusion(s): Our data confirm that OSA is highly prevalent in cognitively normal elderly subjects. We found limited subjective sleepiness, even in those with OSA. Typical sleep duration measured in the home was the main predictor of sleepiness. To date, conventional NPSG metrics do not explain the lack of EDS in OSA in this group

BACKGROUND:, P-tau, and T-tau with sleep spindle density and other biophysical properties of sleep spindles in a sample of cognitively normal elderly individuals. METHODS:, P-tau and T-tau. Seven days of actigraphy were collected to assess habitual total sleep time. RESULTS:, P-tau and T-tau. From the three, CSF T-tau was the most significantly associated with spindle density, after adjusting for age, sex and ApoE4. Spindle duration, count and fast spindle density were also negatively correlated with T-tau levels. Sleep duration and other measures of sleep quality were not correlated with spindle characteristics and did not modify the associations between sleep spindle characteristics and the CSF biomarkers of AD. CONCLUSIONS:Reduced spindles during N2 sleep may represent an early dysfunction related to tau, possibly reflecting axonal damage or altered neuronal tau secretion, rendering it a potentially novel biomarker for early neuronal dysfunction. Given their putative role in memory consolidation and neuroplasticity, sleep spindles may represent a mechanism by which tau impairs memory consolidation, as well as a possible target for therapeutic interventions in cognitive decline.

OBJECTIVE:Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. APPROACH/METHODS:Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. MAIN RESULTS/RESULTS:Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~2-3 spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. SIGNIFICANCE/CONCLUSIONS:SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments. &#13.

Although there is an increasing agreement that hypertension is associated with cerebrovascular compromise, relationships between blood pressure (BP) and cerebral blood flow are not fully understood. It is not known what BP level, and consequently what therapeutic goal, is optimal for brain perfusion. Moreover, there is limited data on how BP affects hippocampal perfusion, a structure critically involved in memory. We conducted a cross-sectional (n=445) and longitudinal (n=185) study of adults and elderly without dementia or clinically apparent stroke, who underwent clinical examination and brain perfusion assessment (age 69.2±7.5 years, 62% women, 45% hypertensive). Linear models were used to test baseline BP-blood flow relationship and to examine how changes in BP influence changes in perfusion. In the entire group, systolic BP (SBP) was negatively related to cortical (β=-0.13, P=0.005) and hippocampal blood flow (β=-0.12, P=0.01). Notably, this negative relationship was apparent already in subjects without hypertension. Hypertensive subjects showed a quadratic relationship between SBP and hippocampal blood flow (β=-1.55, P=0.03): Perfusion was the highest in subjects with mid-range SBP around 125 mm Hg. Longitudinally, in hypertensive subjects perfusion increased with increased SBP at low baseline SBP but increased with decreased SBP at high baseline SBP. Cortical and hippocampal perfusion decrease with increasing SBP across the entire BP spectrum. However, in hypertension, there seems to be a window of mid-range SBP which maximizes perfusion.