The aging slow wave: a shifting amalgam of distinct slow wave and spindle coupling subtypes define slow wave sleep across the human lifespan

Abstract

Study objectives: Slow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan.

Methods: Coupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles.

Results: Two different subtypes of spindles were identified during the upstates of (distinct) slow waves: an "early-fast" spindle, more common in stage N2 sleep, and a "late-fast" spindle, more common in stage N3. We further found stages N2 and N3 sleep contain a mixture of discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails with increasing age.

Conclusions: Distinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.

Keywords: EEG; biomarker; coupling; memory; sleep spindle; slow wave.

Figure 1.

Subject selection flow chart diagram details exclusion criteria.

Figure 2.

Time-frequency window (TF-window) selection. (A) Four TF-windows were identified a priori (using a separate cohort) as peri-slow wave coupled events. (B) The average of wavelets across all age groups demonstrates differences in relative intensities between stages N2 and N3 wavelets.

Figure 3.

Changes in spindle coupling during childhood and adolescence. (A) Sleep stage N2-specific time-frequency wavelet plots visually illustrate a prominent early-fast spindle signal within the slow wave upstate across multiple age groups. (B) Sleep stage N3-specific time-frequency wavelet plots visually illustrate an increasing intensity of late-fast spindles within the slow wave upstate as age progresses from young children to young adults. Also notable is a relatively intense early-fast spindle signal among subjects aged 6–10 during N3 sleep. (C) Statistical modeling with 95% confidence interval estimation for the percentage of early-fast spindle TF-windows (as a percent of the measured upstate spindle power) through childhood to young adulthood (ages 6–20).

Figure 4.

Changes in spindle coupling during young adulthood through late age. (A) Sleep stage N2-specific time-frequency wavelet plots visually illustrate the relative amount of each peri-slow wave event across multiple age groups. (B) Sleep stage N3-specific time-frequency wavelet plots visually illustrate the relative amount of each peri-slow wave event across multiple age groups. (C) Statistical modeling with 95% confidence interval estimation for the percentage of early-fast spindle TF-windows (as a percent of the measured upstate spindle power) through childhood to young adulthood (ages 21–88).

Figure 5.

Two methods are used to cross-validate separation of distinct slow wave and spindle coupling pairs (Data averaged from 10 subjects, ages 20–21 years old). (A) The average EEG time-series signal of early-fast spindles that were detected and used for subsequent analysis. The average spindle composite is centered on t = 0 for illustrative purposes. (B) The average EEG time-series signal of late-fast spindles that were detected and used for subsequent analysis. The average spindle composite is centered on t = 0 for illustrative purposes. (C) The average EEG time-series signal of slow wave events that were detected and used for subsequent analysis. The average slow wave composite is centered on t = 0 for illustrative purposes. (D) The relative spatial location of early-fast and late-fast spindles with regard to slow waves is illustrated (upper; not to scale). The combined N2 and N3 average composite of slow wave time-frequency wavelets maps are graphed with TF-windows referencing the early-fast and late-fast spindle locations. (E and F) Slow waves coupled to early-fast and late-fast spindles were enriched by their relatively high power within the respective TF-windows. Note that upper illustrations of spindle and slow wave shapes are not to scale. (G and H) Early-fast and late-fast spindle events were first identified independently from EEG time-series data (as graphed in A and B), then used to identify their respective coupled slow wave events by matching spindles to slow waves that occur −0.5 s to 1.5 s from the trough of each slow wave. TF-windows are depicted for reference of early-fast and late-fast spindle locations. Note that upper illustrations of spindle and slow wave shapes are not to scale.

Figure 6.

Sleep stage specific slow wave sorting (Data averaged from 10 subjects, ages 20–21 years old). (A) An illustration of coupled spindles depicts mixed slow waves on the left, versus slow waves separated by their coupling to spindles in the middle and right panels. Note that spindle and slow wave shapes are not drawn to scale. (B–D) Slow waves were identified by their coupling to early-fast or late-fast spindle subtypes by TF-window and separated into two distinct slow wave populations from stage N2 sleep. (E–G) Slow waves were identified by their coupling to early-fast or late-fast spindle subtypes by TF-window and separated into two distinct slow wave populations from stage N3 sleep.