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

ABSTRACT: 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.

SUBMITTER: Lysen TS

PROVIDER: S-EPMC9285071 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Purpose To describe a fully automated segmentation method that yields object-based morphologic estimates of enlarged perivascular spaces (ePVSs) in clinical-field-strength (3.0-T) magnetic resonance (MR) imaging data. Materials and Methods In this HIPAA-compliant study, MR imaging data were obtained with a 3.0-T MR imager in research participants without dementia (mean age, 85.3 years; range, 70.4-101.2 years) who had given written informed consent. This method is built on (a) relative normalized white matter, ventricular and cortical signal intensities within T1-weighted, fluid-attenuated inversion recovery, T2-weighted, and proton density data and (b) morphologic (width, volume, linearity) characterization of each resultant cluster. Visual rating was performed by three raters, including one neuroradiologist, after established single-section guidelines. Correlations between visual counts and automated counts, as well session-to-session correlation of counts within each participant, were assessed with the Pearson correlation coefficient r. Results There was a significant correlation between counts by visual raters and automated detection of ePVSs in the same section (r = 0.65, P < .001; r = 0.69, P < .001; and r = 0.54, P < .01 for raters 1, 2, and 3, respectively). With regard to visual ratings and whole-brain count consistency, average visual rating scores were highly correlated with automated detection of total burden volume (r = 0.58, P < .01) and total ePVS number (r = 0.76, P < .01). Morphology of clusters across 28 data sets was consistent with published radiographic estimates of ePVS; mean width of clusters segmented was 3.12 mm (range, 1.7-13.5 mm). Conclusion This MR imaging-based method for multimodal autoidentification of perivascular spaces yields individual whole-brain morphologic characterization of ePVS in clinical MR imaging data and is an important tool in the detailed assessment of these features. © RSNA, 2017 Online supplemental material is available for this article.

Project description:BackgroundIn older populations disturbed 24-h activity rhythms, poor sleep, and depressive symptoms are often lingering and co-morbid, making treatment difficult. To improve insights into these commonly co-occurring problems, we assessed the bidirectional association of sleep and 24-h activity rhythms with depressive symptoms in middle-aged and elderly persons.MethodsIn 1734 participants (mean age: 62.3 ± 9.3 years, 55% women) from the prospective Rotterdam Study, 24-h activity rhythms and sleep were estimated with actigraphy (mean duration: 146 ± 19.6 h), sleep quality with the Pittsburgh Sleep Quality Index, and depressive symptoms with the Center for Epidemiological Studies Depression scale. Repeated measures were available for 947 participants (54%) over a median follow-up of 6 years (interquartile range = 5.6-6.3). Linear-mixed models were used to assess temporal associations of 24-h activity rhythms and sleep with depressive symptoms in both directions.ResultsHigh 24-h activity rhythm fragmentation (IV) (B = 1.002, 95% confidence interval (CI) = 0.641-1.363), long time in bed (TIB) (B = 0.111, 95% CI = 0.053-0.169), low sleep efficiency (SE) (B = -0.015, 95% CI = -0.020 to -0.009), long sleep onset latency (SOL) (B = 0.009, 95% CI = 0.006-0.012), and low self-rated sleep quality (B = 0.112, 95% CI = 0.0992-0.124) at baseline were associated with increasing depressive symptoms over time. Conversely, more depressive symptoms at baseline were associated with an increasing 24-h activity rhythm fragmentation (B = 0.002, 95% CI = 0.001-0.003) and TIB (B = 0.009, 95% CI = 0.004-0.015), and a decreasing SE (B = -0.140, 95% CI = -0.196 to -0.084), SOL (B = 0.013, 95% CI = 0.008-0.018), and self-rated sleep quality (B = 0.193, 95% CI = 0.171-0.215) over time.ConclusionThis study demonstrates a bidirectional association of 24-h activity rhythms, actigraphy-estimated sleep, and self-rated sleep quality with depressive symptoms over a time frame of multiple years in middle-aged and elderly persons.

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

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