Are age and sex effects on sleep slow waves only a matter of electroencephalogram amplitude?

Abstract

Aging is associated with reduced slow wave (SW) density (number SW/min in nonrapid-eye movement sleep) and amplitude. It has been proposed that an age-related decrease in SW density may be due to a reduction in electroencephalogram (EEG) amplitude instead of a decline in the capacity to generate SW. Here, we propose a data-driven approach to adapt SW amplitude criteria to age and sex. We predicted that the adapted criteria would reduce age and sex differences in SW density and SW characteristics but would not abolish them. A total of 284 healthy younger and older adults participated in one night of sleep EEG recording. We defined age- and sex-adapted SW criteria in a first cohort of younger (n = 97) and older (n = 110) individuals using a signal-to-noise ratio approach. We then used these age- and sex-specific criteria in an independent second cohort (n = 77, 38 younger and 39 older adults) to evaluate age and sex differences on SW density and SW characteristics. After adapting SW amplitude criteria, we showed maintenance of an age-related difference for SW density whereas the sex-related difference vanished. Indeed, older adults produced less SW compared with younger adults. Specifically, the adapted SW amplitude criteria increased the probability of occurrence of low amplitude SW (<80 µV) for older men especially. Our results thereby confirm an age-related decline in SW generation rather than an artifact in the detection amplitude criteria. As for the SW characteristics, the age- and sex-adapted criteria display reproducible effects across the two independent cohorts suggesting a more reliable inventory of the SW.

Keywords: EEG; aging; nonrapid eye movement sleep; sex differences; sleep; slow oscillations; slow waves.

Figure 1.

Joint-probability densities of occurrence of SW for the learning cohort. PtP Amplitude, peak-to-peak amplitude; NegA, negative amplitude; SNR, signal-to-noise ratio. Each graph represents a heat map of the density of SW detected given the NegA (> 5 μV) and PtP amplitude (> 9 μV) on the x- and y-axis, respectively, over all-night NREM sleep (N2–N3 combined). The range displayed is in logarithmic scale (power of 10), from 0 to 10⁷ SW/(μV)². (A) Distribution of detected SW in younger women. (B) Distribution of detected SW in younger men. The upper right quadrant defined by the bold dashed lines in (a) and (b) represents the “signal” of conventional SW detections. From the distributions of the younger cohorts (A and B), the mean of the SNR obtained for the women (SNR = 0.08) and the men (SNR = 0.072) defines the general SNR = 0.076 of the detector. (C–F) The intersection between the SNR curve and the “ratio-r line” defines the adaptive SW amplitude detection criteria for the younger and older women and men, respectively.

Figure 2.

Twenty seconds of sleep EEG (C3) in stage N2 of an older male healthy subject. In blue, the sleep slow waves as detected with the standard SW criteria. In red, the extra SW obtained with the data-driven thresholds adapted for age (older here) and sex (male). The numbers above each detection indicate the PtP amplitude and NegA, respectively.

Figure 3.

SW density for NREM sleep with standard (left side) and data-driven criteria (right side). Only the significant interactions or main effects are presented. Younger subjects (Y) are represented in light grey and older participants (O) are represented in dark grey. Women (W) are in red and Men (M) are in blue. SW density (number of SW/min) is represented on the y-axis. When a similar effect is observed in both the learning (upper row) and testing cohort (lower row), the ANOVA results are shown in bold.

Figure 4.

SW characteristics for NREM sleep SW amplitude with standard (left side) and data-driven criteria (right side). Only the significant interactions or main effects interactions are presented. Younger subjects (Y) are represented in light grey and older participants (O) are represented in dark grey. Women (W) are in red and Men (M) are in blue. SW PtP amplitude (μV) is represented on the y-axis. When a similar effect is observed in both the learning (upper row) and testing cohort (lower row), the ANOVA results are shown in bold.

Figure 5.

Probability of occurrence of sleep SW PtP amplitude in (A) younger and (B) older adults in the testing cohort. The probability distributions were calculated so that the probability of occurrence (on the y-axis) is equal to one. Hence, the area under the curve of the histograms conveniently normalized must be equal to one. Each graph contains the standard SW detection (dashed lines), and the adaptive SW detection that refers to the data-driven SW detection (full lines). The color blue refers to the men, and the color red is for women. Vertical lines indicate each group’s respective PtP amplitude detection criterion. Dashed black lines before the vertical lines represent the “noise” detections. NegA represents negative amplitude of the detected SW.

Figure 6.

SW characteristics for all-night NREM sleep SW slope. Only the significant interaction or main effects are presented. Younger subjects (Y) are represented in light grey and older participants (O) are represented in dark grey. Women (W) are in red and Men (M) are in blue. SW slope (μV/s) is represented on the y-axis. When a similar effect is observed in both the learning (upper row) and testing cohort (lower row), the ANOVA F results are shown in bold.

Figure 7.

Probability density of SW slope in (A) younger and (B) older adults, for women (red) and men (blue). The probability distributions were calculated so that the probability of occurrence (on the y-axis) is equal to one. Hence, the area under the curve of the histograms conveniently normalized must be equal to one. The standard SW detection is displayed in the first row, while the adaptive SW detection is displayed in the second row. For the graphs in the second row, thin lines exhibit the difference between the standard and adaptive distributions, for men and women.