Effects of Aging on Cortical Neural Dynamics and Local Sleep Homeostasis in Mice

Abstract

Healthy aging is associated with marked effects on sleep, including its daily amount and architecture, as well as the specific EEG oscillations. Neither the neurophysiological underpinnings nor the biological significance of these changes are understood, and crucially the question remains whether aging is associated with reduced sleep need or a diminished capacity to generate sufficient sleep. Here we tested the hypothesis that aging may affect local cortical networks, disrupting the capacity to generate and sustain sleep oscillations, and with it the local homeostatic response to sleep loss. We performed chronic recordings of cortical neural activity and local field potentials from the motor cortex in young and older male C57BL/6J mice, during spontaneous waking and sleep, as well as during sleep after sleep deprivation. In older animals, we observed an increase in the incidence of non-rapid eye movement sleep local field potential slow waves and their associated neuronal silent (OFF) periods, whereas the overall pattern of state-dependent cortical neuronal firing was generally similar between ages. Furthermore, we observed that the response to sleep deprivation at the level of local cortical network activity was not affected by aging. Our data thus suggest that the local cortical neural dynamics and local sleep homeostatic mechanisms, at least in the motor cortex, are not impaired during healthy senescence in mice. This indicates that powerful protective or compensatory mechanisms may exist to maintain neuronal function stable across the life span, counteracting global changes in sleep amount and architecture.SIGNIFICANCE STATEMENT The biological significance of age-dependent changes in sleep is unknown but may reflect either a diminished sleep need or a reduced capacity to generate deep sleep stages. As aging has been linked to profound disruptions in cortical sleep oscillations and because sleep need is reflected in specific patterns of cortical activity, we performed chronic electrophysiological recordings of cortical neural activity during waking, sleep, and after sleep deprivation from young and older mice. We found that all main hallmarks of cortical activity during spontaneous sleep and recovery sleep after sleep deprivation were largely intact in older mice, suggesting that the well-described age-related changes in global sleep are unlikely to arise from a disruption of local network dynamics within the neocortex.

Keywords: aging; mice; neocortex; sleep.

Figure 1.

Algorithm for OFF period detection. A, Average LFP slow waves calculated and plotted as a function of OFF period duration. All OFF periods were subdivided into 100 1% percentiles, and the corresponding average LFP signal aligned to the onset of an OFF period was calculated. Inset, The effect of lengthening the minimal duration of OFF periods on their average duration. Short OFF periods were not associated with noticeable changes in the LFP, whereas the highest-amplitude slow waves corresponded to the longest OFF periods. Horizontal line indicates the amplitude threshold used to define OFF periods in B. B, The relationship between the duration of OFF periods and the amplitude of corresponding average LFP slow waves in one individual animal. OFF periods were defined as periods of generalized network silence, which were associated with a slow wave at least 50% the amplitude of the largest slow waves (corresponding to the longest 1% of OFF periods). The corresponding thresholds are depicted as horizontal (slow wave amplitude) and vertical (minimal OFF period duration) lines. C, The average LFP slow wave and corresponding profile of MUA centered on the midpoint of the OFF periods defined based on the above criteria.

Figure 2.

The global alterations of sleep with aging in mice. A, Hypnograms of individual representative animals from each age group (EA, LA, OA). The 24 h profile of EEG SWA (EEG power between 0.5 and 4.0 Hz, represented as percentage of 24 h mean) recorded from the frontal cortex. Green represents Wake. Blue represents NREM sleep. Red represents REM sleep. Top, Bar represents the 12 h light and 12 h dark periods. B, Time course of waking, NREM, and REM sleep during 24 h baseline day, shown in 2 h intervals. The amount of each vigilance state is represented as percentage of the total recording time. Data are mean ± SEM. EA, n = 10; LA, n = 11; OA, n = 10. Significant differences between ages are as follows: blue represents EA versus LA; cyan represents LA versus OA; purple represents EA versus OA. C, The relationship between age and body weight across and within age groups. Filled symbols represent individual animals. Straight lines indicate linear regression lines separately for the three age groups. D, The relationship of age (left) and body weight (right) with the amount of total sleep shown as percentage of recording time over 24 h. C, D, Data are mean ± SEM. EA, n = 10; LA, n = 11; OA, n = 10. R and p values correspond to Pearson's product moment correlation. Welch F test (Games–Howell post hoc) was used to compare age groups.

Figure 3.

The relationship between LFP slow waves and cortical MUA in younger and older mice. A, LFP (top) and MUA (bottom) traces from a representative LFP channel in representative animals from each age group. B, Average LFP slow wave (top) and corresponding average MUA triggered by slow waves (plots below) in the three age groups. In all three ages, the positive LFP wave is associated with a clear-cut suppression of neuronal spiking. C, Frontal EEG (left) and LFP (right) spectral power density during NREM sleep. Data are mean ± SEM. Bottom, Triangles represent frequency bins where EEG spectra differed significantly between the age groups (p < 0.05, unpaired t test on log-transformed values; top: EA vs LA; bottom: EA vs OA). D, Average LFP slow wave triggered by the onset of generalized neuronal silence (an OFF period) across all recorded neurons. Despite the average duration of OFF periods being similar between ages, the amplitude of the resulting slow wave was higher in EA animals, compared with LA and OA mice. E, The effect of aging on the amplitude of the average LFP slow wave triggered by population OFF periods (as shown in D). EA, n = 10; LA, n = 11; OA, n = 10. A one-way ANOVA (Tukey post hoc test) was used to compare age groups. F, The effect of aging on the incidence of slow waves and OFF periods during baseline NREM sleep. EA, n = 10; LA, n = 11; OA, n = 10. For slow wave incidence, a Welch F test (Games–Howell post hoc) was used to compare age groups. For OFF period incidence, a one-way ANOVA (Tukey post hoc) was used to compare age groups.

Figure 4.

Effects of SD on cortical slow waves and OFF periods in older mice. A, Representative hypnograms of individual animals from each age group (EA, LA, OA). The 12 h profile of EEG SWA (EEG power between 0.5 and 4.0 Hz, represented as percentage of baseline 24 h mean), recorded in the frontal cortex, is color-coded according to the vigilance state: green represents Wake; blue represents NREM sleep; red represents REM sleep. SD was performed for 6 h from light onset. After SD, a robust increase in EEG SWA is evident in all 3 animals, which is followed by a progressive decline during the subsequent recovery period. B, Time course of EEG (top) and LFP (bottom) SWA during 6 h period after 6 h SD. Data are mean ± SEM. EA, n = 7; LA, n = 5 or 6; OA, n = 9. The time course of the decline in SWA was not significantly different between age groups for either the frontal EEG of LFP (repeated-measures ANOVA). *Significant difference between EA and OA mice in the first hour after SD (p = 0.01, one-way ANOVA with Tukey post hoc test). C, The effect of SD on the incidence of LFP slow waves for a 12 h baseline period and the 6 h period following SD. Values are shown in 1 h intervals. Data are mean ± SEM. EA n = 7; LA n = 7; OA n = 10. Repeated-measures ANOVAs used to identify age differences during baseline and recovery after SD. One-way ANOVAs used to identify age differences in the initial rebound after SD. D, E, The same analyses were performed as in C, but for OFF period incidence and duration.

Figure 5.

Aging and the vigilance state dependence of cortical neuronal firing. A, The distribution of the firing rates of all putative individual neurons across all vigilance states, plotted as a proportion of the total number of neurons. On average, 17.4 ± 2.9, 15.6 ± 1.7, and 16.7 ± 2.5 putative single neurons per mouse contributed to the analysis for EA, LA, and OA mice, respectively. Inset, The proportion of neurons that fired at slow rates (0–3 Hz), which was significantly different between age groups (one-way ANOVA, Tukey post hoc test). B, Distribution of firing rates across 4 s epochs expressed as a percentage of the total number of epochs, for three representative individual putative single units. Green represents Wake; blue represents NREM sleep; red represents REM sleep. Subplots represent the corresponding average spike waveform (± SD) and autocorrelogram. Individual neurons are highly variable with regards to their vigilance-state specific firing. C, The predominant firing rates for all putative neurons were extracted from the distribution histograms (representative examples shown in B), sorted by their peak firing rate, and plotted in ascending order for each vigilance state separately. Neurons did not fall into distinct categories based on their firing rates but rather formed a continuum in which all possible peak firing rates could be observed. D, The proportion of neurons discharging at a specific frequency is shown separately for Wake, NREM sleep, and REM sleep. E, Mean firing rate distribution widths for Wake, NREM sleep, and REM sleep are shown for the three age groups as follows: EA, n = 10; LA, n = 11; OA, n = 10. No statistical differences between age groups were identified (one-way ANOVA).

Figure 6.

Intraepisodic dynamics of cortical firing at the transition to NREM sleep. A, Representative example of a cortical LFP recording at the transition from waking to NREM sleep. B, The effect of aging on the total amount of NREM sleep during 24 h. EA, n = 10; LA, n = 11; OA, n = 10. One-way ANOVA (Tukey post hoc test) was used to compare age groups. C, The effect of aging on the total number of NREM sleep episodes during 24 h. EA, n = 10; LA, n = 11; OA, n = 10. One-way ANOVA (Tukey post hoc test) was used to compare age groups. D, The time course of relative LFP slow wave incidence during the first 2 min after the onset of NREM sleep episodes. Values are percentage of the first 12 s. Data are mean ± SEM. Line indicates the second minute of NREM sleep, which undergoes further analysis in G. E, The same analysis as in D, but for the incidence of OFF periods. F, The dynamics of firing rates during the first 2 min after the onset of an NREM sleep episode are shown for all individual putative single units across all animals. The neurons are sorted as a function of the relative firing rates attained during the second minute after the episode onset. G, Slow wave incidence during the second minute of NREM sleep episode shown as percentage of the corresponding value during the first 12 s after the onset of NREM sleep episode. EA, n = 9; LA, n = 11; OA, n = 10. A nonparametric Kruskal–Wallis test with Mann–Whitney post hoc test (exact, two-tailed) was used to test for significant differences between age groups. Post hoc tests for EA versus OA and LA versus OA gave p values of 0.033 and 0.037, respectively; this was not significant after correcting for multiple testing (critical value p = 0.0167). H, Distribution of all putative neurons as a function of the change in their firing frequency within NREM sleep episodes. I, The proportion of neurons, which show at least a 30% increase in their rate of discharge during the second minute after NREM sleep onset relative to the first 12 s after the initiation of corresponding NREM sleep episodes. A nonparametric Kruskal–Wallis test with Mann–Whitney post hoc test (exact, two-tailed) was used to test for significant differences between age groups. EA versus OA: U = 4, z = −3.348, p < 0.0001. Post hoc testing for EA versus LA gave a p value of 0.031; however, this was not significant after correcting for multiple testing (critical value p = 0.0167).

Figure 7.

The effects of aging on the neuronal dynamics at the transition from NREM to REM sleep. A, Representative example of cortical LFP at the transition from NREM sleep to REM sleep. On average, 51.9 ± 3.7, 45.8 ± 2.0, and 45.3 ± 2.5 N-R transitions contributed to the analysis below for EA, LA, and OA animals, respectively. B, Distribution of firing rates across 4 s epochs in NREM sleep (blue) and REM sleep (red) shown for three representative individual putative single units. Each subplot represents the average spike waveform (± SD) and the autocorrelogram. Individual neurons show great diversity in their state dependent firing within sleep. C, The dynamics in firing rates during the last minute of NREM sleep and the first minute of subsequent REM sleep are shown for all individual putative single units across all animals. The neurons are sorted as a function of their relative firing rates attained during REM sleep. D, The proportion of putative single neurons, discharging, on average, at a higher rate during REM sleep compared with NREM sleep, expressed as a percentage of all neurons in the three age groups: EA, n = 10; LA, n = 11; OA, n = 10. One-way ANOVA did not identify any significant differences between age groups.