Electrical Activity During Slow-Wave Sleep and the Relationship With Enlarged Perivascular Spaces in Arteriosclerotic Cerebral Small Vessel Disease

Lighter sleep is associated with higher enlarged perivascular spaces burden in middle-aged and elderly individuals

BACKGROUND

While healthy sleep is suggested to promote glymphatic clearance in the brain, poorer sleep may be associated with higher enlarged perivascular spaces (ePVS) burden, potentially representing impaired perivascular drainage. This study aims to evaluate the association between ePVS burden and polysomnographic sleep characteristics in a large community-based sample.

METHODS

552 dementia and stroke-free Framingham Heart Study participants (age: 58.6 ± 8.9 years; 50.4% men) underwent a full-night in-home polysomnography. Three years later on average, participants underwent a brain MRI. ePVS were rated in the basal ganglia and centrum semiovale, and dichotomized as low burden (<20 counts, grades 1 and 2) or high burden (>20 counts, grades 3 and 4). Logistic regression analyses relating sleep variables to subsequent ePVS burden were used, adjusted for age, sex, time interval between polysomnography and MRI, ApoE ε4 allele carrier status, hypertension, and smoking.

RESULTS

Longer N1 sleep and shorter N3 sleep duration were associated with higher ePVS burden in the centrum semiovale. When stratifying these associations by subpopulations, longer N1 sleep duration with ePVS burden was observed especially in older individuals and hypertensive participants. Associations between ePVS burden and other sleep characteristics such as total sleep time and REM sleep duration varied according to ApoE ε4 allele carrier status.

CONCLUSIONS

Lighter sleep, as characterized by longer N1 sleep and shorter slow-wave sleep, is associated with higher ePVS burden. These findings suggest that sleep architecture may be involved in glymphatic clearance and cerebral small vessel disease, which could be an important biological link between sleep and dementia risk.

Intelligent Algorithm-Based Quantitative Electroencephalography in Evaluating Cerebral Small Vessel Disease Complicated by Cognitive Impairment

To analyze the application value of artificial intelligence model based on Visual Geometry Group- (VGG-) 16 combined with quantitative electroencephalography (QEEG) in cerebral small vessel disease (CSVD) with cognitive impairment, 72 patients with CSVD complicated by cognitive impairment were selected as the research subjects. As per Diagnostic and Statistical Manual (5th Edition), they were divided into the vascular dementia (VD) group of 34 cases and vascular cognitive impairment with no dementia (VCIND) group of 38 cases. The two groups were analyzed for the clinical information, neuropsychological test results, and monitoring results of QEEG based on intelligent algorithms for more than 2 hours. The accuracy rate of VGG was 84.27% and Kappa value was 0.7, while that of modified VGG (nVGG) was 88.76% and Kappa value was 0.78. The improved VGG algorithm obviously had higher accuracy. The test results found that the QEEG identified 8 normal, 19 mild, 10 moderate, and 0 severe cases in the VCIND group, while in the VD group, the corresponding numbers were 4, 13, 11, and 7; in the VCIND group, 7 cases had the normal QEEG, 11 cases had background changes, 9 cases had abnormal waves, and 11 cases had in both background changes and abnormal waves, and in the VD group, the corresponding numbers were 5, 2, 5, and 22, respectively; in the VCIND group, QEEG of 18 patients had no abnormal waves, QEEG of 11 patients had a few abnormal waves, and QEEG of 9 patients had many abnormal waves, and QEEG of 0 people had a large number of abnormal waves, and in the VD group, the corresponding numbers were 7, 6, 12, and 9. The above data were statistically different between the two groups (P < 0.05). Hence, QEEG based on intelligent algorithms can make a good assessment of CSVD with cognitive impairment, which had good clinical application value.

Sleep and perivascular spaces in the middle-aged and elderly population

Sleep has been hypothesised to facilitate waste clearance from the brain. We aimed to determine whether sleep is associated with perivascular spaces on brain magnetic resonance imaging (MRI), a potential marker of impaired brain waste clearance, in a population-based cohort of middle-aged and elderly people. In 559 participants (mean [SD] age 62 [6] years, 52% women) from the population-based Rotterdam Study, we measured total sleep time, sleep onset latency, wake after sleep onset and sleep efficiency with actigraphy and polysomnography. Perivascular space load was determined with brain MRI in four regions (centrum semiovale, basal ganglia, hippocampus, and midbrain) via a validated machine learning algorithm using T2-weighted MR images. Associations between sleep characteristics and perivascular space load were analysed with zero-inflated negative binomial regression models adjusted for various confounders. We found that higher actigraphy-estimated sleep efficiency was associated with a higher perivascular space load in the centrum semiovale (odds ratio 1.10, 95% confidence interval 1.04-1.16, p = 0.0008). No other actigraphic or polysomnographic sleep characteristics were associated with perivascular space load in other brain regions. We conclude that, contrary to our hypothesis, associations of sleep with perivascular space load in this middle-aged and elderly population remained limited to an association of a high actigraphy-estimated sleep efficiency with a higher perivascular space load in the centrum semiovale.