Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource

Abstract

Sleep spindles are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep. Implicated in sleep regulation and cognitive functioning, spindles may represent heritable biomarkers of neuropsychiatric disease. Here we characterize spindles in 11,630 individuals aged 4 to 97 years, as a prelude to future genetic studies. Spindle properties are highly reliable but exhibit distinct developmental trajectories. Across the night, we observe complex patterns of age- and frequency-dependent dynamics, including signatures of circadian modulation. We identify previously unappreciated correlates of spindle activity, including confounding by body mass index mediated by cardiac interference in the EEG. After taking account of these confounds, genetic factors significantly contribute to spindle and spectral sleep traits. Finally, we consider topographical differences and critical measurement issues. Taken together, our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioural and health outcomes, including neuropsychiatric disorders.

Identifying sleep spindles with multichannel EEG and classification optimization

“…The FBT model classified 14.25 more spindles on days that subjects performed the high load memory task than on days that subjects performed the low load memory task. In the Purcel et al study [51] , the average spindle density (spindle counts divided by total nap time) was 1.18, while it is 4.43 (SD = 2.62) in the present study by manual annotating and 2.80 (SD = 1.45) by the FBT model, both of which are higher. One reason densities in the present study are higher than in the Purcel et al study may be because we excluded subjects who had fewer than 5 spindles.…”

“…Eq 3.1 computes a lower boundary of the RMS values of interest, and 3.2 computes a higher boundary of the RMS values of interest. Trimmed mean ( μ τ ) and trimmed standard deviation ( σ τ ) takes account 95% of the data to avoid influence of outliers (high amplitude fluctuation) [51] .…”

“…Given that identifying these neural events may provide a powerful tool to study the relationship between sleep and memory, it is critical that we have a standard to define these events [45, 46] . Unfortunately, the definition of spindle features varies across studies.…”

Developmental changes of sleep spindles and their impact on sleep‐dependent memory consolidation and general cognitive abilities: A longitudinal approach

“…While these decreases follow a marginal trend from adulthood to old age, the most drastic changes in sleep architecture occur during adolescence starting with the onset of puberty between 9 and 12 years of age (Crone & Dahl, ; Ohayon et al., ; Tarokh, Saletin, & Carskadon, ). However, changes in sleep during adolescence are not only present at the macroscopic level of sleep architecture but also at the level of cortical oscillations unique to the sleeping brain, namely sleep spindles (Campbell & Feinberg, ; Nicolas, Petit, Rompre, & Montplaisir, ; Purcell et al., ; Scholle, Zwacka, & Scholle, ; Shinomiya, Nagata, Takahashi, & Masumura, ).…”

Nicotine increases sleep spindle activity

“…Figure shows the general properties of detected spindles. Figure (a) shows the distribution of spindle characteristics under placebo (our study), as compared with those reported in a large sample study (Purcell et al., ) and those scored manually by two experts (MASS‐E1 and MASS‐E2) in an open‐access sleep database (O'Reilly, Gosselin, Carrier, & Nielsen, ). Differences in properties between detections are not surprising and are in line with previous observations (O'Reilly & Nielsen, ).…”