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. 2018 Aug 7;10:233.
doi: 10.3389/fnagi.2018.00233. eCollection 2018.

How Old Is Your Brain? Slow-Wave Activity in Non-rapid-eye-movement Sleep as a Marker of Brain Rejuvenation After Long-Term Exercise in Mice

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How Old Is Your Brain? Slow-Wave Activity in Non-rapid-eye-movement Sleep as a Marker of Brain Rejuvenation After Long-Term Exercise in Mice

Maria Panagiotou et al. Front Aging Neurosci. .
Free PMC article

Abstract

Physical activity is beneficial for health. It has been shown to improve brain functioning and cognition, reduce severity of mood disorders, as well as facilitate healthy sleep and healthy aging. Sleep has been studied in healthy aged mice and absolute slow-wave-activity levels (SWA, electroencephalogram power between 0.75 and 4.0 Hz) in non-rapid-eye-movement sleep (NREM) were elevated, suggesting changes in brain connectivity. To investigate whether physical activity can diminish this aging-induced effect, mice of three age groups were provided with a running wheel (RW) for 1-3 months (6-months-old, n = 9; 18-months-old, n = 9; 24-months-old, n = 8) and were compared with control sedentary mice (n = 11, n = 8 and n = 9 respectively). Two weeks before the sleep-wake recordings the running wheels were removed. The electroencephalogram (EEG) and electromyogram were continuously recorded during undisturbed 24 h baseline (BL) and a sleep-deprivation was conducted during the first 6 h of the second day. Increased waking and decreased NREM sleep was found in the young RW mice, compared to young controls. These effects were not evident in the 18 and 24 months old mice. Unlike sleep architecture, we found that SWA was altered throughout the whole age spectrum. Notably, SWA was increased with aging and attenuated with exercise, exhibiting the lowest levels in the young RW mice. To utilize the cross-age revealing features of SWA, we applied machine learning techniques and found that characteristic information regarding age and exercise was enclosed in SWA. In addition, with cluster analysis, we could classify and accurately distinguish the different groups based solely on their SWA. Therefore, our study comprises a three-fold contribution: (a) effects of exercise on sleep are sustained following 2 weeks after removal of the wheel, (b) we show that EEG SWA can be used as a physiological marker of brain age in the mouse,

Keywords: EEG; SWA; aging; exercise; running wheel; spectral analysis.

Figures

FIGURE 1
FIGURE 1
(A) Schematic overview of the experimental design. Mice were provided with a running wheel in their cage for long-term daily voluntary use of approximately 1 month (young group) and 3 (18 and 24 months old groups) prior to the sleep recordings. (RW: running wheel; Sleep rec: sleep recordings). (B) Representative double-plotted actograms of 28 days running wheel activity from a 6-month, an 18-month and a 24-month old mouse. Young mice showed an increased average daily running wheel activity as compared to aged mice (6 months old: 27746 ± 1974 counts/24 h, 18 months old: 5190 ± 2521 counts/24 h, 24-months old: 6458 ± 1884 counts/24 h) (post hoc t-tests with Bonferroni multiple comparisons correction, p < 0.05 after significant ANOVA, main effect ‘exercise’).
FIGURE 2
FIGURE 2
Time course of vigilance states, for 24-h baseline (BL), 6-h sleep deprivation (SD, hatched bar) and 18-h recovery for 6, 18, and 24 months old control and running-wheel (RW) mice (left, middle, and right, respectively). Curves connect 2-h mean values (±SEM) of Waking, NREM and REM sleep. The black and white bars above each graph indicate the light-dark cycle. Asterisks at the top of each graph represent significant differences between control and wheel access groups across the 48-h period and bars at the bottom of each graph significant differences between recovery and BL day for each age and condition (post hoc unpaired and paired t-tests with Bonferroni multiple comparisons correction, p < 0.05 after significant ANOVAs, main effects ‘treatment,’ ‘time of day,’ ‘day’).
FIGURE 3
FIGURE 3
Absolute electroencephalographic (EEG) power density in Waking, NREM and REM sleep for 6, 18, and 24 months old control and running-wheel (RW) mice (left, middle, and right, respectively). Asterisks indicate significant differences between the control and wheel access groups (post hoc unpaired t-tests with Bonferroni multiple comparisons correction, p < 0.05 after significant ANOVA, main effects ‘treatment,’ ‘frequency bin’).
FIGURE 4
FIGURE 4
Time course of absolute electroencephalographic (EEG) power for the slow-wave activity range (SWA, 0.5–4 Hz) in non-rapid-eye movement (NREM) sleep for 24-h baseline (BL), 6-h sleep deprivation (SD, hatched bar) and 18-h recovery for control and running-wheel (RW) mice (left), 24-h baseline (BL) EEG SWA data in NREM sleep (middle) and 12-h BL EEG SWA in REM sleep (right). EEG SWA in NREM increased as a function of aging, and decreased with the use of a running wheel. Bars at the bottom of each graph significant differences between recovery and BL day for each age and condition (post hoc Bonferroni multiple comparisons correction t-tests, p < 0.05 after significant ANOVA, main effects ‘group,’ ‘treatment,’ ‘time of day,’ ‘day’).
FIGURE 5
FIGURE 5
x-,y-,z- coordinates of the optimal number of clusters after PCA dimensionality reduction. Scatter plots represent the six experimental groups together with the central points (centroids) (see text for more details).
FIGURE 6
FIGURE 6
Suggested relative brain age of mice represented as the distances between the clusters’ centroids of young running-wheel (RW) mice (set as 0, YoungRW) and the other groups. The one-dimension (1-D) values are computed using the Euclidean distances from Table 4 based on the 3-D values reported in Table 3 for young, 18 and 24 months old control and 18 and 24 months RW mice. Hidden brain age is revealed in 18 and 24 months old mice, which is attenuated by 30 and 20% respectively, depicting a rejuvenating path following long-term physical activity.

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