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      High Resolution Topography of Age-Related Changes in Non-Rapid Eye Movement Sleep Electroencephalography

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          Abstract

          Sleeping brain activity reflects brain anatomy and physiology. The aim of this study was to use high density (256 channel) electroencephalography (EEG) during sleep to characterize topographic changes in sleep EEG power across normal aging, with high spatial resolution. Sleep was evaluated in 92 healthy adults aged 18–65 years old using full polysomnography and high density EEG. After artifact removal, spectral power density was calculated for standard frequency bands for all channels, averaged across the NREM periods of the first 3 sleep cycles. To quantify topographic changes with age, maps were generated of the Pearson’s coefficient of the correlation between power and age at each electrode. Significant correlations were determined by statistical non-parametric mapping. Absolute slow wave power declined significantly with increasing age across the entire scalp, whereas declines in theta and sigma power were significant only in frontal regions. Power in fast spindle frequencies declined significantly with increasing age frontally, whereas absolute power of slow spindle frequencies showed no significant change with age. When EEG power was normalized across the scalp, a left centro-parietal region showed significantly less age-related decline in power than the rest of the scalp. This partial preservation was particularly significant in the slow wave and sigma bands. The effect of age on sleep EEG varies substantially by region and frequency band. This non-uniformity should inform the design of future investigations of aging and sleep. This study provides normative data on the effect of age on sleep EEG topography, and provides a basis from which to explore the mechanisms of normal aging as well as neurodegenerative disorders for which age is a risk factor.

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          Cortical firing and sleep homeostasis.

          The need to sleep grows with the duration of wakefulness and dissipates with time spent asleep, a process called sleep homeostasis. What are the consequences of staying awake on brain cells, and why is sleep needed? Surprisingly, we do not know whether the firing of cortical neurons is affected by how long an animal has been awake or asleep. Here, we found that after sustained wakefulness cortical neurons fire at higher frequencies in all behavioral states. During early NREM sleep after sustained wakefulness, periods of population activity (ON) are short, frequent, and associated with synchronous firing, while periods of neuronal silence are long and frequent. After sustained sleep, firing rates and synchrony decrease, while the duration of ON periods increases. Changes in firing patterns in NREM sleep correlate with changes in slow-wave activity, a marker of sleep homeostasis. Thus, the systematic increase of firing during wakefulness is counterbalanced by staying asleep.
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            Sleep, memory, and plasticity.

            Although the functions of sleep remain largely unknown, one of the most exciting hypotheses is that sleep contributes importantly to processes of memory and brain plasticity. Over the past decade, a large body of work, spanning most of the neurosciences, has provided a substantive body of evidence supporting this role of sleep in what is becoming known as sleep-dependent memory processing. We review these findings, focusing specifically on the role of sleep in (a) memory encoding, (b) memory consolidation, (c) brain plasticity, and (d) memory reconsolidation; we finish with a summary of the field and its potential future directions.
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              Trajectories of brain aging in middle-aged and older adults: regional and individual differences.

              The human brain changes with age. However, the rate and the trajectories of change vary among the brain regions and among individuals, and the reasons for these differences are unclear. In a sample of healthy middle-aged and older adults, we examined mean volume change and individual differences in the rate of change in 12 regional brain volumes over approximately 30 months. In addition to the baseline assessment, there were two follow-ups, 15 months apart. We observed significant average shrinkage of the hippocampus, entorhinal cortex, orbital-frontal cortex, and cerebellum in each of the intervals. Shrinkage of the hippocampus accelerated with time, whereas shrinkage of the caudate nucleus, prefrontal subcortical white matter, and corpus callosum emerged only at the second follow-up. Throughout both assessment intervals, the mean volumes of the lateral prefrontal and primary visual cortices, putamen, and pons did not change. Significant individual differences in shrinkage rates were observed in the lateral prefrontal cortex, the cerebellum, and all the white matter regions throughout the study, whereas additional regions (medial-temporal structures, the insula, and the basal ganglia) showed significant individual variation in change during the second follow-up. No individual variability was noted in the change of orbital frontal and visual cortices. In two white matter regions, we were able to identify factors associated with individual differences in brain shrinkage. In corpus callosum, shrinkage rate was greater in persons with hypertension, and in the pons, women and carriers of the ApoEepsilon4 allele exhibited declines not noted in the whole sample. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                22 February 2016
                2016
                : 11
                : 2
                : e0149770
                Affiliations
                [1 ]Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, United States of America
                [2 ]Wisconsin Center for Sleep Medicine and Research, University of Wisconsin, Madison, Wisconsin, United States of America
                [3 ]Neuroscience Training Program, University of Wisconsin, Madison, Wisconsin, United States of America
                [4 ]Department of Psychology, University of Wisconsin, Madison, Wisconsin, United States of America
                [5 ]Center for Investigating Healthy Minds, University of Wisconsin, Madison, Wisconsin, United States of America
                Hôpital du Sacré-Coeur de Montréal, CANADA
                Author notes

                Competing Interests: Giulio Tononi has consulted for Philips Respironics and has been involved in a research study in humans supported by Philips Respironics. Giulio Tononi is also a consultant for the Allen Institute for Brain Research. Ruth M. Benca has served as a consultant to Merck and Jazz and received grant support from Merck. The article submitted is not related to any of these relationships. The remaining authors have reported that no competing interests exist. This did not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: KES BAR GT RJD RMB. Performed the experiments: KES BAR RFS. Analyzed the data: KES BAR RFS. Contributed reagents/materials/analysis tools: KES BAR. Wrote the paper: KES BAR RMB.

                Article
                PONE-D-15-36444
                10.1371/journal.pone.0149770
                4764685
                26901503
                7cc647ed-b2d8-4096-a95a-bfb881579b5d
                © 2016 Sprecher et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 August 2015
                : 20 January 2016
                Page count
                Figures: 4, Tables: 1, Pages: 16
                Funding
                This work was supported by The National Center for Complementary and Alternative Medicine (NCAAM) grant P01AT004952 to GT and RJD, National Institute on Mental Health (NIMH) grant 1 P20 MH077967 to RB and GT, National Research Service Award (NRSA) T32 GM007507 to KS, and the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Due to restrictions related to protecting patient privacy, access to the data is restricted by the UW-Madison IRB. Requests for a de-identified, minimal data set can be made to Kate Sprecher ( ksprecher@ 123456wisc.edu ).

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