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

Frontiers in Aging Neuroscience

Kostas Papagiannopoulos¹  Maria Panagiotou²  Johanna H. Meijer²  Tom Deboer²  Jos H. T. Rohling² 

[1] Department of Digital Security, Radboud University, Nijmegen, Netherlands;Laboratory for Neurophysiology, Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands;

关键词: EEG;    SWA;    spectral analysis;    running wheel;    exercise;    aging;   

DOI  :  10.3389/fnagi.2018.00233

来源: DOAJ

【 摘 要 】

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, (c) long-term voluntary regular age-matched exercise leads to a younger phenotype.

【 授权许可】

Unknown