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Páez A, Gillman SO, Dogaheh SB, Carnes A, Dakterzada F, Barbé F, Dang‐Vu TT, Ripoll GP. Sleep spindles and slow oscillations predict cognition and biomarkers of neurodegeneration in mild to moderate Alzheimer's disease. Alzheimers Dement 2025; 21:e14424. [PMID: 39878233 PMCID: PMC11848347 DOI: 10.1002/alz.14424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Revised: 10/29/2024] [Accepted: 11/01/2024] [Indexed: 01/31/2025]
Abstract
INTRODUCTION Changes in sleep physiology can predate cognitive symptoms by decades in persons with Alzheimer's disease (AD), but it remains unclear which sleep characteristics predict cognitive and neurodegenerative changes after AD onset. METHODS Using data from a prospective cohort of mild to moderate AD (n = 60), we analyzed non-rapid eye movement sleep spindles and slow oscillations (SOs) at baseline and their associations with baseline amyloid beta (Aβ) and tau and with cognition from baseline to 3-year follow-up. RESULTS Higher spindle and SO activity predicted significant changes in Aβ and tau at baseline, lower Alzheimer's Disease Assessment Scale Cognitive Subscale (better cognitive performance) score, and higher Mini-Mental State Examination score from baseline to 36 months. Spindles and SOs mediated the effect of phosphorylated tau 181 (pTau181)/Aβ42 on cognition, while pTau181/aβ42 moderated the effect of spindles and SOs on cognition. DISCUSSION Our findings demonstrate that spindle and SO activity during sleep constitute predictive and non-invasive biomarkers of neurodegeneration and cognition in AD patients. HIGHLIGHTS Sleep spindles predict long-term cognitive performance in AD. Sleep spindle and SOs can be predictive, non-invasive biomarkers for AD. Sleep may be one of the most important modifiable risk factors for AD progression. Sleep microarchitecture is a novel therapeutic target for preserving brain heath. Sleep physiology can provide novel therapeutic targets to slow AD progression.
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Affiliation(s)
- Arsenio Páez
- Sleep, Cognition and Neuroimaging LaboratoryConcordia UniversityMontrealCanada
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM)Montréal (Québec)Canada
- Nuffield Department for Primary Care Health SciencesUniversity of OxfordOxfordUK
| | - Sam O. Gillman
- Sleep, Cognition and Neuroimaging LaboratoryConcordia UniversityMontrealCanada
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM)Montréal (Québec)Canada
| | | | - Anna Carnes
- Unitat de Trastorns CognitiusCognition and Behavior Study GroupHospital Universitari Santa Maria Universitat de LleidaLleidaSpain
| | - Faride Dakterzada
- Unitat de Trastorns CognitiusCognition and Behavior Study GroupHospital Universitari Santa Maria Universitat de LleidaLleidaSpain
| | - Ferran Barbé
- Translational Research in Respiratory Medicine (TRRM)Hospital Universitari Arnau de Vilanova‐Santa MariaBiomedical Research Institute of Lleida (IRBLleida)LleidaSpain
| | - Thien Thanh Dang‐Vu
- Sleep, Cognition and Neuroimaging LaboratoryConcordia UniversityMontrealCanada
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM)Montréal (Québec)Canada
| | - Gerard Piñol Ripoll
- Unitat de Trastorns CognitiusCognition and Behavior Study GroupHospital Universitari Santa Maria Universitat de LleidaLleidaSpain
- Alzheimer's Disease and Other Cognitive Disorders UnitNeurology ServiceHospital Clínic de BarcelonaFundació de Recerca Clínic ‐ Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
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Shao Y, Guo Y, Chen Y, Zou G, Chen J, Gao X, Lu P, Tong Y, Li Y, Yao P, Liu J, Zhou S, Xu J, Gao JH, Zou Q, Sun H. Increased spindle-related brain activation in right middle temporal gyrus during N2 than N3 among healthy sleepers: Initial discovery and independent sample replication. Neuroimage 2025; 305:120976. [PMID: 39681244 DOI: 10.1016/j.neuroimage.2024.120976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2024] [Revised: 12/01/2024] [Accepted: 12/14/2024] [Indexed: 12/18/2024] Open
Abstract
The association between spindle metrics and sleep architecture differs during N2 vs. N3 sleep, the underlying neural mechanism is not clearly illustrated. Here, we tested the discrepancy in spindle-related brain activation between N2 and N3 within healthy college students (dataset 1: n = 27, 59 % females, median age 23 years), using simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI). To assess the replicability of the finding, we repeated the analysis among normal adults (independent dataset 2: n = 30, 50 % females, median age 32 years). The finding from dataset 1 indicated significantly increased blood-oxygen level-dependent signal in the right middle temporal gyrus during N2 compared with N3, which was well replicated in dataset 2. Furthermore, correlation analysis was performed to explore the association between this spindle-related brain activation and N2, N3 sleep duration during EEG-fMRI. We conducted the correlation analysis in N2 and N3, respectively. The negative association between spindle-related brain activation in the right middle temporal gyrus and sleep duration was only observed in N2. Our findings emphasize the unique role of spindle-related brain activation in the right middle temporal gyrus during N2 in shortening N2 sleep duration.
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Affiliation(s)
- Yan Shao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China
| | - Yupeng Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China
| | - Yun Chen
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China
| | - Guangyuan Zou
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, PR China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China
| | - Jie Chen
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China
| | - Xuejiao Gao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China
| | - Panpan Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China
| | - Yujie Tong
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China
| | - Yuezhen Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China; Department of Neuropsychiatry, Behavioral Neurology and Sleep Center, Beijing Tian Tan Hospital, Capital Medical University, Beijing, PR China
| | - Ping Yao
- Mental Health Institute of Inner Mongolia Autonomous Region, The Third Hospital of Inner Mongolia Autonomous Region, Hohhot 010010, PR China
| | - Jiayi Liu
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, PR China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China
| | - Shuqin Zhou
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, PR China
| | - Jing Xu
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, PR China; Laboratory of Applied Brain and Cognitive Sciences, College of International Business, Shanghai International Studies University, Shanghai, PR China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, PR China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China; McGovern Institute for Brain Research, Peking University, Beijing, PR China.
| | - Qihong Zou
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, PR China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China.
| | - Hongqiang Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, PR China.
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Clark VP, Valverde HP, Briggs MS, Mullins T, Ortiz J, Pirrung CJH, O’Keeffe OS, Hwang M, Crowley S, Šarlija M, Matsangas P. Closed-Loop Auditory Stimulation (CLAS) During Sleep Augments Language and Discovery Learning. Brain Sci 2024; 14:1138. [PMID: 39595901 PMCID: PMC11591805 DOI: 10.3390/brainsci14111138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2024] [Revised: 11/07/2024] [Accepted: 11/12/2024] [Indexed: 11/28/2024] Open
Abstract
Background/Objectives: Slow oscillation (SO) brainwaves observed during sleep have been shown to reflect the process of memory consolidation, that underlies the critical role of sleep in learning, memory, and other cognitive functions. Closed-loop auditory stimulation (CLAS) uses tones presented in phase with SOs to increase their amplitude and number, along with other brainwave signatures related to memory consolidation. Prior studies have found that CLAS maximizes the ability to perform rote memorization tasks, although this remains controversial. The present study examined whether CLAS affects a broader range of learning tasks than has been tested previously, including a rote language learning task requiring basic memorization and also two discovery learning tasks requiring insight, hypothesis testing, and integration of experience, all processes that benefit from memory consolidation. Methods: Twenty-eight healthy participants performed language and discovery learning tasks before sleeping in our laboratory for three continuous nights per week over two weeks, with verum or control CLAS using a prototype NeuroGevity system (NeuroGeneces, Inc., Santa Fe, NM, USA) in a crossed, randomized, double-blind manner. Results: Language learning showed a 35% better word recall (p = 0.048), and discovery learning showed a 26% better performance (p < 0.001) after three continuous nights of CLAS vs. control. EEG measures showed increased SO amplitude and entrainment, SO-spindle coupling, and other features that may underlie the learning benefits of CLAS. Conclusions: Taken together, the present results show that CLAS can alter brain dynamics and enhance learning, especially in complex discovery learning tasks that may benefit more from memory consolidation compared with rote word pair or language learning.
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Affiliation(s)
- Vincent P. Clark
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Hector P. Valverde
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Mason S. Briggs
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Teagan Mullins
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Jacqueline Ortiz
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Christopher J. H. Pirrung
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Olivia S. O’Keeffe
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Madeline Hwang
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Sidney Crowley
- Psychology Clinical Neuroscience Center, Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Marko Šarlija
- Department of Information Sciences, University of Zadar, 23000 Zadar, Croatia;
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Lamsam L, Gu B, Liang M, Sun G, Khan KJ, Sheth KN, Hirsch LJ, Pittenger C, Kaye AP, Krystal JH, Damisah EC. The human claustrum tracks slow waves during sleep. Nat Commun 2024; 15:8964. [PMID: 39419999 PMCID: PMC11487173 DOI: 10.1038/s41467-024-53477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 10/11/2024] [Indexed: 10/19/2024] Open
Abstract
Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an evolutionarily conserved process critical for brain function. Non-human studies suggest that the claustrum, a small subcortical nucleus, coordinates slow waves. We show that, in contrast to neurons from other brain regions, claustrum neurons in the human brain increase their spiking activity and track slow waves during NREM sleep, suggesting that the claustrum plays a role in coordinating human sleep architecture.
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Affiliation(s)
- Layton Lamsam
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Brett Gu
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Mingli Liang
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - George Sun
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Kamren J Khan
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Lawrence J Hirsch
- Department of Neurology, Yale School of Medicine, Yale University, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, Comprehensive Epilepsy Center, Yale University, New Haven, CT, USA
| | - Christopher Pittenger
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA
- Center for Brain and Mind Health, Yale University, New Haven, CT, USA
| | - Alfred P Kaye
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Clinical Neurosciences Division, VA National Center for PTSD, West Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Clinical Neurosciences Division, VA National Center for PTSD, West Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Eyiyemisi C Damisah
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
- Center for Brain and Mind Health, Yale University, New Haven, CT, USA.
- Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, USA.
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5
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Putilov AA, Verevkin EG. Weekday and weekend sleep times across the human lifespan: a model-based simulation. Sleep Breath 2024; 28:2223-2236. [PMID: 39085561 DOI: 10.1007/s11325-024-03124-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 07/07/2024] [Accepted: 07/26/2024] [Indexed: 08/02/2024]
Abstract
PURPOSE The shifts in the opposite directions, toward later and earlier sleep timing, occur during the transition through adolescence and adulthood, respectively. Such a n-shape of age-associated change in sleep timing does not resemble the inverse relationship of sleep duration with ages. Age-associated variation in the parameters of the mechanisms of circadian and homeostatic regulation of sleep would underlie these different shapes of relationship of sleep times with ages. Here, we searched for a parsimonious explanation of these different shapes by simulating sleep times on weekdays and weekends with one of the variants of the two-process model of sleep regulation. METHODS Using mean age of a sample with reported sleep times on weekdays and weekends, the whole set of 1404 such samples was subdivided into 15 age subsets. Simulations of sleep times in these subsets were performed with and without the suggestion of age-associated variation in the circadian phase. RESULTS Simulations showed that the age-associated decay of slow-wave activity can parsimoniously explain not only the parallel decreases in weekend sleep duration and rate of the buildup of sleep pressure during the wake phase of the sleep-wake cycle, but also both the delay and advance of sleep timing during the transition through adolescence and adulthood, respectively. CONCLUSION The almost functional relationships were revealed between the age-related changes in sleep duration, rate of the buildup of sleep pressure, and slow-wave activity that is a good electrophysiological marker of cortical metabolic rate and synaptic density, strength and efficacy.
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Affiliation(s)
- Arcady A Putilov
- Laboratory of Sleep/Wake Neurobiology, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, 117865, Russia.
- , 11, Nipkowstr, 12489, Berlin, Germany.
| | - Evgeniy G Verevkin
- Laboratory of Sleep/Wake Neurobiology, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, 117865, Russia
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Poirson B, Vandel P, Bourdin H, Galli S. Age-related changes in sleep spindle characteristics in individuals over 75 years of age: a retrospective and comparative study. BMC Geriatr 2024; 24:778. [PMID: 39304816 DOI: 10.1186/s12877-024-05364-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Accepted: 09/06/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND Sleep and its architecture are affected and changing through the whole lifespan. We know main modifications of the macro-architecture with a shorter sleep, occurring earlier and being more fragmented. We have been studying sleep micro-architecture through its pathological modification in sleep, psychiatric or neurocognitive disorders whereas we are still unable to say if the sleep micro-architecture of an old and very old person is rather normal, under physiological changes, or a concern for a future disorder to appear. We wanted to evaluate age-related changes in sleep spindle characteristics in individuals over 75 years of age compared with younger individuals. METHODS This was an exploratory study based on retrospective and comparative laboratory-based polysomnography data registered in the normal care routine for people over 75 years of age compared to people aged 65-74 years. We were studying their sleep spindle characteristics (localization, density, frequency, amplitude, and duration) in the N2 and N3 sleep stages. ANOVA and ANCOVA using age, sex and OSA were applied. RESULTS We included 36 participants aged > 75 years and 57 participants aged between 65 and 74 years. An OSA diagnosis was most common in both groups. Older adults receive more medication to modify their sleep. Spindle localization becomes more central after 75 years of age. Changes in the other sleep spindle characteristics between the N2 and N3 sleep stages and between the slow and fast spindles were conformed to literature data, but age was a relevant modifier only for density and duration. CONCLUSION We observed the same sleep spindle characteristics in both age groups except for localization. We built our study on a short sample, and participants were not free of all sleep disorders. We could establish normative values through further studies with larger samples of people without any sleep disorders to understand the modifications in normal aging and pathological conditions and to reveal the predictive biomarker function of sleep spindles.
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Affiliation(s)
- Bastien Poirson
- CHU de Besançon, Service de Gériatrie, Besançon, F-25000, France.
- Université de Franche-Comté, UMR INSERM 1322 LINC, Besançon, F-25000, France.
| | - Pierre Vandel
- Université de Franche-Comté, UMR INSERM 1322 LINC, Besançon, F-25000, France
- Service of Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, 1008, Switzerland
| | - Hubert Bourdin
- CHU de Besançon, Unité d'Explorations du Sommeil et de la Vigilance, Besançon, F-25000, France
- Université de Franche-Comté, UMR INSERM 1322 LINC, Besançon, F-25000, France
| | - Silvio Galli
- CHU de Besançon, Unité d'Explorations du Sommeil et de la Vigilance, Besançon, F-25000, France
- Université de Franche-Comté, UMR INSERM 1322 LINC, Besançon, F-25000, France
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Ng T, Noh E, Spencer RMC. Does slow oscillation-spindle coupling contribute to sleep-dependent memory consolidation? A Bayesian meta-analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.28.610060. [PMID: 39257832 PMCID: PMC11383665 DOI: 10.1101/2024.08.28.610060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
The active system consolidation theory suggests that information transfer between the hippocampus and cortex during sleep underlies memory consolidation. Neural oscillations during sleep, including the temporal coupling between slow oscillations (SO) and sleep spindles (SP), may play a mechanistic role in memory consolidation. However, differences in analytical approaches and the presence of physiological and behavioral moderators have led to inconsistent conclusions. This meta-analysis, comprising 23 studies and 297 effect sizes, focused on four standard phase-amplitude coupling measures including coupling phase, strength, percentage, and SP amplitude, and their relationship with memory retention. We developed a standardized approach to incorporate non-normal circular-linear correlations. We found strong evidence supporting that precise and strong SO-fast SP coupling in the frontal lobe predicts memory consolidation. The strength of this association is mediated by memory type, aging, and dynamic spatio-temporal features, including SP frequency and cortical topography. In conclusion, SO-SP coupling should be considered as a general physiological mechanism for memory consolidation.
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Affiliation(s)
- Thea Ng
- Neuroscience & Behavior Program, Mount Holyoke College
- Department of Mathematics & Statistics, Mount Holyoke College
| | - Eunsol Noh
- Neuroscience & Behavior Program, University of Massachusetts, Amherst
| | - Rebecca M. C. Spencer
- Neuroscience & Behavior Program, University of Massachusetts, Amherst
- Department of Psychological & Brain Sciences, University of Massachusetts, Amherst
- Institute of Applied Life Sciences, University of Massachusetts, Amherst
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Mushtaq M, Marshall L, ul Haq R, Martinetz T. Possible mechanisms to improve sleep spindles via closed loop stimulation during slow wave sleep: A computational study. PLoS One 2024; 19:e0306218. [PMID: 38924001 PMCID: PMC11207127 DOI: 10.1371/journal.pone.0306218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Sleep spindles are one of the prominent EEG oscillatory rhythms of non-rapid eye movement sleep. In the memory consolidation, these oscillations have an important role in the processes of long-term potentiation and synaptic plasticity. Moreover, the activity (spindle density and/or sigma power) of spindles has a linear association with learning performance in different paradigms. According to the experimental observations, the sleep spindle activity can be improved by closed loop acoustic stimulations (CLAS) which eventually improve memory performance. To examine the effects of CLAS on spindles, we propose a biophysical thalamocortical model for slow oscillations (SOs) and sleep spindles. In addition, closed loop stimulation protocols are applied on a thalamic network. Our model results show that the power of spindles is increased when stimulation cues are applied at the commencing of an SO Down-to-Up-state transition, but that activity gradually decreases when cues are applied with an increased time delay from this SO phase. Conversely, stimulation is not effective when cues are applied during the transition of an Up-to-Down-state. Furthermore, our model suggests that a strong inhibitory input from the reticular (RE) layer to the thalamocortical (TC) layer in the thalamic network shifts leads to an emergence of spindle activity at the Up-to-Down-state transition (rather than at Down-to-Up-state transition), and the spindle frequency is also reduced (8-11 Hz) by thalamic inhibition.
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Affiliation(s)
| | - Lisa Marshall
- Institute of Experimental and Clinical Pharmacology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, Lübeck, Germany
- University Clinic Hospital Schleswig Holstein, Lübeck, Germany
| | - Rizwan ul Haq
- Department of Pharmacy, Abbottabad University of Science and Technology, Abbottabad, Pakistan
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, Lübeck, Germany
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9
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Zhang B, Lu S, Guo H, Xu J, Xiao Z, Tang J. Relationship between ODI and sleep structure of obstructive sleep apnea and cardiac remodeling. Sleep Breath 2024; 28:173-181. [PMID: 37453997 DOI: 10.1007/s11325-023-02872-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 04/27/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE The purpose of the study was to evaluate the quantitative relationship between Oxygen Desaturation Index (ODI) and sleep structure of obstructive sleep apnea (OSA) and cardiac remodeling. METHODS In this study, patients were enrolled from January 2015 to October 2022, and were divided into 3 groups according to AHI: patients with AHI < 15, patients with 15 ≤ AHI < 30, and 260 patients with AHI ≥ 30. Stratified linear regression was used to analyze independent risk factors for cardiac remodeling in OSA. RESULTS A total of 479 patients were enrolled. We found that compared with AHI < 15 group (n = 120), the group with AHI > 30 (n = 260) had increased left atrial anteroposterior diameter, left ventricular end-diastolic internal diameter, left ventricular posterior wall thickness, right ventricular anteroposterior diameter, and interventricular septal thickness (P < 0.05). The group with 15 ≤ AHI ≤ 30 (n = 99) had increased left atrial anteroposterior diameter (P < 0.05). Multivariate linear regression revealed that N2 sleep was an independent risk factor for left ventricular posterior wall thickness, with positive correlation (p < 0.05). N3 sleep was an independent risk factor for transverse right atrial diameter and right ventricular anteroposterior diameter, with negative correlation (P < 0.05). ODI was an independent risk factor for interventricular septal thickness, with positive correlation (P < 0.05). The arousal index was an independent risk factor for increased left atrial anteroposterior diameter, with positive correlation (P < 0.05). CONCLUSIONS Increased ODI is an independent risk factor for interventricular septal thickness, while decreased slow wave sleep is an independent risk factor for right heart remodeling in OSA.
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Affiliation(s)
- Baokun Zhang
- Department of Neurology, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, NO. 16766 Jingshi Road, Jinan, Shandong, 250012, People's Republic of China
| | - Shanshan Lu
- Department of Neurology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Neuroimmunology, Jinan, People's Republic of China
| | - Huiying Guo
- Department of Neurology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Neuroimmunology, Jinan, People's Republic of China
| | - Juanjuan Xu
- Department of Neurology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Neuroimmunology, Jinan, People's Republic of China
| | - Zhang Xiao
- Department of Neurology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Neuroimmunology, Jinan, People's Republic of China
| | - Jiyou Tang
- Department of Neurology, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, NO. 16766 Jingshi Road, Jinan, Shandong, 250012, People's Republic of China.
- Department of Neurology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Neuroimmunology, Jinan, People's Republic of China.
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10
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Lamsam L, Liang M, Gu B, Sun G, Hirsch LJ, Pittenger C, Kaye AP, Krystal JH, Damisah EC. The human claustrum tracks slow waves during sleep. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577851. [PMID: 38352615 PMCID: PMC10862750 DOI: 10.1101/2024.01.29.577851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2024]
Abstract
Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an evolutionarily conserved process critical for brain function. Non-human studies posit that the claustrum, a small subcortical nucleus, coordinates slow waves. We recorded claustrum neurons in humans during sleep. In contrast to neurons from other brain regions, claustrum neurons increased their activity and tracked slow waves during NREM sleep suggesting that the claustrum plays a role in human sleep architecture.
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11
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Pulver RL, Kronberg E, Medenblik LM, Kheyfets VO, Ramos AR, Holtzman DM, Morris JC, Toedebusch CD, Sillau SH, Bettcher BM, Lucey BP, McConnell BV. Mapping sleep's oscillatory events as a biomarker of Alzheimer's disease. Alzheimers Dement 2024; 20:301-315. [PMID: 37610059 PMCID: PMC10840635 DOI: 10.1002/alz.13420] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/05/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023]
Abstract
INTRODUCTION Memory-associated neural circuits produce oscillatory events including theta bursts (TBs), sleep spindles (SPs), and slow waves (SWs) in sleep electroencephalography (EEG). Changes in the "coupling" of these events may indicate early Alzheimer's disease (AD) pathogenesis. METHODS We analyzed 205 aging adults using single-channel sleep EEG, cerebrospinal fluid (CSF) AD biomarkers, and Clinical Dementia Rating® (CDR®) scale. We mapped SW-TB and SW-SP neural circuit coupling precision to amyloid positivity, cognitive impairment, and CSF AD biomarkers. RESULTS Cognitive impairment correlated with lower TB spectral power in SW-TB coupling. Cognitively unimpaired, amyloid positive individuals demonstrated lower precision in SW-TB and SW-SP coupling compared to amyloid negative individuals. Significant biomarker correlations were found in oscillatory event coupling with CSF Aβ42 /Aβ40 , phosphorylated- tau181 , and total-tau. DISCUSSION Sleep-dependent memory processing integrity in neural circuits can be measured for both SW-TB and SW-SP coupling. This breakdown associates with amyloid positivity, increased AD pathology, and cognitive impairment. HIGHLIGHTS At-home sleep EEG is a potential biomarker of neural circuits linked to memory. Circuit precision is associated with amyloid positivity in asymptomatic aging adults. Levels of CSF amyloid and tau also correlate with circuit precision in sleep EEG. Theta burst EEG power is decreased in very early mild cognitive impairment. This technique may enable inexpensive wearable EEGs for monitoring brain health.
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Affiliation(s)
- Rachelle L. Pulver
- Department of NeurologyUniversity of Colorado School of MedicineAuroraColoradoUSA
- University of Colorado Alzheimer's and Cognition CenterUniversity of Colorado School of MedicineAuroraColoradoUSA
| | - Eugene Kronberg
- Department of NeurologyUniversity of Colorado School of MedicineAuroraColoradoUSA
| | - Lindsey M. Medenblik
- Department of NeurologyUniversity of Colorado School of MedicineAuroraColoradoUSA
- University of Colorado Alzheimer's and Cognition CenterUniversity of Colorado School of MedicineAuroraColoradoUSA
| | - Vitaly O. Kheyfets
- Department of Pediatric Critical Care MedicineUniversity of Colorado School of MedicineAuroraColoradoUSA
| | - Alberto R. Ramos
- Department of NeurologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
| | - David M. Holtzman
- Department of NeurologyWashington University School of MedicineSt LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
- Hope Center for Neurological DisordersWashington University School of MedicineSt LouisMissouriUSA
| | - John C. Morris
- Department of NeurologyWashington University School of MedicineSt LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
- Hope Center for Neurological DisordersWashington University School of MedicineSt LouisMissouriUSA
| | | | - Stefan H Sillau
- Department of NeurologyUniversity of Colorado School of MedicineAuroraColoradoUSA
- University of Colorado Alzheimer's and Cognition CenterUniversity of Colorado School of MedicineAuroraColoradoUSA
| | - Brianne M. Bettcher
- Department of NeurologyUniversity of Colorado School of MedicineAuroraColoradoUSA
- University of Colorado Alzheimer's and Cognition CenterUniversity of Colorado School of MedicineAuroraColoradoUSA
| | - Brendan P. Lucey
- Department of NeurologyWashington University School of MedicineSt LouisMissouriUSA
- Knight Alzheimer Disease Research CenterWashington University School of MedicineSt LouisMissouriUSA
- Hope Center for Neurological DisordersWashington University School of MedicineSt LouisMissouriUSA
| | - Brice V. McConnell
- Department of NeurologyUniversity of Colorado School of MedicineAuroraColoradoUSA
- University of Colorado Alzheimer's and Cognition CenterUniversity of Colorado School of MedicineAuroraColoradoUSA
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12
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Schreiner T, Petzka M, Staudigl T, Staresina BP. Respiration modulates sleep oscillations and memory reactivation in humans. Nat Commun 2023; 14:8351. [PMID: 38110418 PMCID: PMC10728072 DOI: 10.1038/s41467-023-43450-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 11/09/2023] [Indexed: 12/20/2023] Open
Abstract
The beneficial effect of sleep on memory consolidation relies on the precise interplay of slow oscillations and spindles. However, whether these rhythms are orchestrated by an underlying pacemaker has remained elusive. Here, we tested the relationship between respiration, which has been shown to impact brain rhythms and cognition during wake, sleep-related oscillations and memory reactivation in humans. We re-analysed an existing dataset, where scalp electroencephalography and respiration were recorded throughout an experiment in which participants (N = 20) acquired associative memories before taking a nap. Our results reveal that respiration modulates the emergence of sleep oscillations. Specifically, slow oscillations, spindles as well as their interplay (i.e., slow-oscillation_spindle complexes) systematically increase towards inhalation peaks. Moreover, the strength of respiration - slow-oscillation_spindle coupling is linked to the extent of memory reactivation (i.e., classifier evidence in favour of the previously learned stimulus category) during slow-oscillation_spindles. Our results identify a clear association between respiration and memory consolidation in humans and highlight the role of brain-body interactions during sleep.
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Affiliation(s)
- Thomas Schreiner
- Department of Psychology, Ludwig-Maximilians-Universität München, München, Germany.
| | - Marit Petzka
- Max Planck Institute for Human Development, Berlin, Germany
- Institute of Psychology, University of Hamburg, Hamburg, Germany
| | - Tobias Staudigl
- Department of Psychology, Ludwig-Maximilians-Universität München, München, Germany
| | - Bernhard P Staresina
- Department of Experimental Psychology, University of Oxford, Oxford, UK.
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK.
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13
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Dehnavi F, Koo-Poeggel PC, Ghorbani M, Marshall L. Memory ability and retention performance relate differentially to sleep depth and spindle type. iScience 2023; 26:108154. [PMID: 37876817 PMCID: PMC10590735 DOI: 10.1016/j.isci.2023.108154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/09/2023] [Accepted: 10/03/2023] [Indexed: 10/26/2023] Open
Abstract
Temporal interactions between non-rapid eye movement (NREM) sleep rhythms especially the coupling between cortical slow oscillations (SO, ∼1 Hz) and thalamic spindles (∼12 Hz) have been proposed to contribute to multi-regional interactions crucial for memory processing and cognitive ability. We investigated relationships between NREM sleep depth, sleep spindles and SO-spindle coupling regarding memory ability and memory consolidation in healthy humans. Findings underscore the functional relevance of spindle dynamics (slow versus fast), SO-phase, and most importantly NREM sleep depth for cognitive processing. Cross-frequency coupling analyses demonstrated stronger precise temporal coordination of slow spindles to SO down-state in N2 for subjects with higher general memory ability. A GLM model underscored this relationship, and furthermore that fast spindle properties were predictive of overnight memory consolidation. Our results suggest cognitive fingerprints dependent on conjoint fine-tuned SO-spindle temporal coupling, spindle properties, and brain sleep state.
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Affiliation(s)
- Fereshteh Dehnavi
- Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
- Center for International Scientific Studies & Collaborations (CISSC), Shahid Azodi Street, Karim-Khane Zand Boulevard, Tehran 15875-7788, Iran
| | - Ping Chai Koo-Poeggel
- Institute of Experimental and Clinical Pharmacology and Toxicology, University of Luebeck, Ratzeburger Allee 160, Bldg. 66, 23562 Luebeck, Germany
- Center for Brain, Behavior and Metabolism, University of Luebeck, 23562 Luebeck, Germany
| | - Maryam Ghorbani
- Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
- Rayan Center for Neuroscience and Behavior, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
- Center for International Scientific Studies & Collaborations (CISSC), Shahid Azodi Street, Karim-Khane Zand Boulevard, Tehran 15875-7788, Iran
| | - Lisa Marshall
- Institute of Experimental and Clinical Pharmacology and Toxicology, University of Luebeck, Ratzeburger Allee 160, Bldg. 66, 23562 Luebeck, Germany
- Center for Brain, Behavior and Metabolism, University of Luebeck, 23562 Luebeck, Germany
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14
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Chappel-Farley MG, Adams JN, Betzel RF, Janecek JC, Sattari NS, Berisha DE, Meza NJ, Niknazar H, Kim S, Dave A, Chen IY, Lui KK, Neikrug AB, Benca RM, Yassa MA, Mander BA. Medial temporal lobe functional network architecture supports sleep-related emotional memory processing in older adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564260. [PMID: 37961192 PMCID: PMC10634911 DOI: 10.1101/2023.10.27.564260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memory consolidation occurs via reactivation of a hippocampal index during non-rapid eye movement slow-wave sleep (NREM SWS) which binds attributes of an experience existing within cortical modules. For memories containing emotional content, hippocampal-amygdala dynamics facilitate consolidation over a sleep bout. This study tested if modularity and centrality-graph theoretical measures that index the level of segregation/integration in a system and the relative import of its nodes-map onto central tenets of memory consolidation theory and sleep-related processing. Findings indicate that greater network integration is tied to overnight emotional memory retention via NREM SWS expression. Greater hippocampal and amygdala influence over network organization supports emotional memory retention, and hippocampal or amygdala control over information flow are differentially associated with distinct stages of memory processing. These centrality measures are also tied to the local expression and coupling of key sleep oscillations tied to sleep-dependent memory consolidation. These findings suggest that measures of intrinsic network connectivity may predict the capacity of brain functional networks to acquire, consolidate, and retrieve emotional memories.
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Affiliation(s)
- Miranda G. Chappel-Farley
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Jenna N. Adams
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, University of Indiana Bloomington, Bloomington IN, 47405
| | - John C. Janecek
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Negin S. Sattari
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Destiny E. Berisha
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Novelle J. Meza
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Hamid Niknazar
- Department of Cognitive Sciences, University of California Irvine, Irvine CA, 92697, USA
| | - Soyun Kim
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Abhishek Dave
- Department of Cognitive Sciences, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Ivy Y. Chen
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Kitty K. Lui
- San Diego State University/University of California San Diego, Joint Doctoral Program in Clinical Psychology, San Diego, CA, 92093, USA
| | - Ariel B. Neikrug
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Ruth M. Benca
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, 53706, WI, USA
- Department of Psychiatry and Behavioral Medicine, Wake Forest University, Winston-Salem, NC, 27109, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
| | - Michael A. Yassa
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
- Department of Neurology, University of California Irvine, Irvine CA, 92697, USA
| | - Bryce A. Mander
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Cognitive Sciences, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
- Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine CA, 92697, USA
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15
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Züst MA, Mikutta C, Omlin X, DeStefani T, Wunderlin M, Zeller CJ, Fehér KD, Hertenstein E, Schneider CL, Teunissen CE, Tarokh L, Klöppel S, Feige B, Riemann D, Nissen C. The Hierarchy of Coupled Sleep Oscillations Reverses with Aging in Humans. J Neurosci 2023; 43:6268-6279. [PMID: 37586871 PMCID: PMC10490476 DOI: 10.1523/jneurosci.0586-23.2023] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/11/2023] [Accepted: 07/31/2023] [Indexed: 08/18/2023] Open
Abstract
A well orchestrated coupling hierarchy of slow waves and spindles during slow-wave sleep supports memory consolidation. In old age, the duration of slow-wave sleep and the number of coupling events decrease. The coupling hierarchy deteriorates, predicting memory loss and brain atrophy. Here, we investigate the dynamics of this physiological change in slow wave-spindle coupling in a frontocentral electroencephalography position in a large sample (N = 340; 237 females, 103 males) spanning most of the human life span (age range, 15-83 years). We find that, instead of changing abruptly, spindles gradually shift from being driven by slow waves to driving slow waves with age, reversing the coupling hierarchy typically seen in younger brains. Reversal was stronger the lower the slow-wave frequency, and starts around midlife (age range, ∼40-48 years), with an established reversed hierarchy between 56 and 83 years of age. Notably, coupling strength remains unaffected by age. In older adults, deteriorating slow wave-spindle coupling, measured using the phase slope index (PSI) and the number of coupling events, is associated with blood plasma glial fibrillary acidic protein levels, a marker for astrocyte activation. Data-driven models suggest that decreased sleep time and higher age lead to fewer coupling events, paralleled by increased astrocyte activation. Counterintuitively, astrocyte activation is associated with a backshift of the coupling hierarchy (PSI) toward a "younger" status along with increased coupling occurrence and strength, potentially suggesting compensatory processes. As the changes in coupling hierarchy occur gradually starting at midlife, we suggest there exists a sizable window of opportunity for early interventions to counteract undesirable trajectories associated with neurodegeneration.SIGNIFICANCE STATEMENT Evidence accumulates that sleep disturbances and cognitive decline are bidirectionally and causally linked, forming a vicious cycle. Improving sleep quality could break this cycle. One marker for sleep quality is a clear hierarchical structure of sleep oscillations. Previous studies showed that sleep oscillations decouple in old age. Here, we show that, rather, the hierarchical structure gradually shifts across the human life span and reverses in old age, while coupling strength remains unchanged. This shift is associated with markers for astrocyte activation in old age. The shifting hierarchy resembles brain maturation, plateau, and wear processes. This study furthers our comprehension of this important neurophysiological process and its dynamic evolution across the human life span.
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Affiliation(s)
- Marc Alain Züst
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Christian Mikutta
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
- Private Clinic Meiringen, 3860 Meiringen, Switzerland
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, United Kingdom
| | - Ximena Omlin
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Tatjana DeStefani
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Marina Wunderlin
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Céline Jacqueline Zeller
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Kristoffer Daniel Fehér
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
- Division of Psychiatric Specialties, Geneva University Hospitals (HUG), 1201 Geneva, Switzerland
| | - Elisabeth Hertenstein
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Carlotta L Schneider
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Charlotte Elisabeth Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Leila Tarokh
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
| | - Bernd Feige
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, 79104 Freiburg, Germany
| | - Dieter Riemann
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, 79104 Freiburg, Germany
| | - Christoph Nissen
- University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland
- Division of Psychiatric Specialties, Geneva University Hospitals (HUG), 1201 Geneva, Switzerland
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16
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Rodheim K, Kainec K, Noh E, Jones B, Spencer RMC. Emotional memory consolidation during sleep is associated with slow oscillation-spindle coupling strength in young and older adults. Learn Mem 2023; 30:237-244. [PMID: 37770106 PMCID: PMC10547370 DOI: 10.1101/lm.053685.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023]
Abstract
Emotional memories are processed during sleep; however, the specific mechanisms are unclear. Understanding such mechanisms may provide critical insight into preventing and treating mood disorders. Consolidation of neutral memories is associated with the coupling of NREM sleep slow oscillations (SOs) and sleep spindles (SPs). Whether SO-SP coupling is likewise involved in emotional memory processing is unknown. Furthermore, there is an age-related emotional valence bias such that sleep consolidates and preserves reactivity to negative but not positive emotional memories in young adults and positive but not negative emotional memories in older adults. If SO-SP coupling contributes to the effect of sleep on emotional memory, then it may selectively support negative memory in young adults and positive memory in older adults. To address these questions, we examined whether emotional memory recognition and overnight change in emotional reactivity were associated with the strength of SO-SP coupling in young (n = 22) and older (n = 32) adults. In younger adults, coupling strength predicted negative but not positive emotional memory performance after sleep. In contrast, coupling strength predicted positive but not negative emotional memory performance after sleep in older adults. Coupling strength was not associated with emotional reactivity in either age group. Our findings suggest that SO-SP coupling may play a mechanistic role in sleep-dependent consolidation of emotional memories.
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Affiliation(s)
- Katrina Rodheim
- Neuroscience and Behavior Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
| | - Kyle Kainec
- Neuroscience and Behavior Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
| | - Eunsol Noh
- Neuroscience and Behavior Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
| | - Bethany Jones
- Neuroscience and Behavior Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
| | - Rebecca M C Spencer
- Neuroscience and Behavior Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
- Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
- Developmental Sciences Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA
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17
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Pulver RL, Kronberg E, Medenblik LM, Kheyfets VO, Ramos AR, Holtzman DM, Morris JC, Toedebusch CD, Sillau SH, Bettcher BM, Lucey BP, McConnell BV. Mapping Sleep's Oscillatory Events as a Biomarker of Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528725. [PMID: 36824720 PMCID: PMC9949053 DOI: 10.1101/2023.02.15.528725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Objective Memory-associated neural circuits produce oscillatory events within single-channel sleep electroencephalography (EEG), including theta bursts (TBs), sleep spindles (SPs) and multiple subtypes of slow waves (SWs). Changes in the temporal "coupling" of these events are proposed to serve as a biomarker for early stages of Alzheimer's disease (AD) pathogenesis. Methods We analyzed data from 205 aging adults, including single-channel sleep EEG, cerebrospinal fluid (CSF) AD-associated biomarkers, and Clinical Dementia Rating® (CDR®) scale. Individual SW events were sorted into high and low transition frequencies (TF) subtypes. We utilized time-frequency spectrogram locations within sleep EEG to "map" the precision of SW-TB and SW-SP neural circuit coupling in relation to amyloid positivity (by CSF Aβ 42 /Aβ 40 threshold), cognitive impairment (by CDR), and CSF levels of AD-associated biomarkers. Results Cognitive impairment was associated with lower TB spectral power in both high and low TF SW-TB coupling (p<0.001, p=0.001). Cognitively unimpaired, amyloid positive aging adults demonstrated lower precision of the neural circuits propagating high TF SW-TB (p<0.05) and low TF SW-SP (p<0.005) event coupling, compared to cognitively unimpaired amyloid negative individuals. Biomarker correlations were significant for high TF SW-TB coupling with CSF Aβ 42 /Aβ 40 (p=0.005), phosphorylated-tau 181 (p<0.005), and total-tau (p<0.05). Low TF SW-SP coupling was also correlated with CSF Aβ 42 /Aβ 40 (p<0.01). Interpretation Loss of integrity in neural circuits underlying sleep-dependent memory processing can be measured for both SW-TB and SW-SP coupling in spectral time-frequency space. Breakdown of sleep's memory circuit integrity is associated with amyloid positivity, higher levels of AD-associated pathology, and cognitive impairment.
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18
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Tabuena DR, Huynh R, Metcalf J, Richner T, Stroh A, Brunton BW, Moody WJ, Easton CR. Large-scale waves of activity in the neonatal mouse brain in vivo occur almost exclusively during sleep cycles. Dev Neurobiol 2022; 82:596-612. [PMID: 36250606 PMCID: PMC10166374 DOI: 10.1002/dneu.22901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/09/2022] [Accepted: 10/06/2022] [Indexed: 01/30/2023]
Abstract
Spontaneous electrical activity plays major roles in the development of cortical circuitry. This activity can occur highly localized regions or can propagate over the entire cortex. Both types of activity coexist during early development. To investigate how different forms of spontaneous activity might be temporally segregated, we used wide-field trans-cranial calcium imaging over an entire hemisphere in P1-P8 mouse pups. We found that spontaneous waves of activity that propagate to cover the majority of the cortex (large-scale waves; LSWs) are generated at the end of the first postnatal week, along with several other forms of more localized activity. We further found that LSWs are segregated into sleep cycles. In contrast, cortical activity during wake states is more spatially restricted and the few large-scale forms of activity that occur during wake can be distinguished from LSWs in sleep based on their initiation in the motor cortex and their correlation with body movements. This change in functional cortical circuitry to a state that is permissive for large-scale activity may temporally segregate different forms of activity during critical stages when activity-dependent circuit development occurs over many spatial scales. Our data also suggest that LSWs in early development may be a functional precursor to slow sleep waves in the adult, which play critical roles in memory consolidation and synaptic rescaling.
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Affiliation(s)
- Dennis R Tabuena
- Department of Biology, University of Washington, Seattle, Washington, USA.,Graduate Program in Neuroscience, University of Washington, Seattle, Washington, USA
| | - Randy Huynh
- Department of Biology, University of Washington, Seattle, Washington, USA
| | - Jenna Metcalf
- Department of Biology, University of Washington, Seattle, Washington, USA
| | - Thomas Richner
- Institute for Neuroengineering, University of Washington, Seattle, Washington, USA
| | - Albrecht Stroh
- Institute of Pathophysiology, University Medical Center Mainz, Mainz, Germany.,Leibniz Institute for Resilience Research, University Medical Center Mainz, Mainz, Germany
| | - Bingni W Brunton
- Department of Biology, University of Washington, Seattle, Washington, USA.,Institute for Neuroengineering, University of Washington, Seattle, Washington, USA
| | - William J Moody
- Department of Biology, University of Washington, Seattle, Washington, USA.,Institute for Neuroengineering, University of Washington, Seattle, Washington, USA
| | - Curtis R Easton
- Department of Biology, University of Washington, Seattle, Washington, USA
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Stokes PA, Rath P, Possidente T, He M, Purcell S, Manoach DS, Stickgold R, Prerau MJ. Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification. Sleep 2022; 46:6701543. [PMID: 36107467 PMCID: PMC9832519 DOI: 10.1093/sleep/zsac223] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/30/2022] [Indexed: 01/19/2023] Open
Abstract
Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability-even within healthy controls-yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient non-rapid eye movement events in low-alpha (7-10 Hz) and theta (4-6 Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.
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Affiliation(s)
- Patrick A Stokes
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Preetish Rath
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Department of Computer Science, Tufts University, Medford, MA, USA
| | - Thomas Possidente
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Mingjian He
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Shaun Purcell
- Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, USA
| | - Dara S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael J Prerau
- Corresponding author. Michael J. Prerau, Brigham and Women's Hospital, Division of Sleep and Circadian Disorders, 221 Longwood Avenue, Boston, MA, 02115, USA.
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Donnelly NA, Bartsch U, Moulding HA, Eaton C, Marston H, Hall JH, Hall J, Owen MJ, van den Bree MBM, Jones MW. Sleep EEG in young people with 22q11.2 deletion syndrome: A cross-sectional study of slow-waves, spindles and correlations with memory and neurodevelopmental symptoms. eLife 2022; 11:e75482. [PMID: 36039635 PMCID: PMC9477499 DOI: 10.7554/elife.75482] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 08/12/2022] [Indexed: 11/20/2022] Open
Abstract
Background Young people living with 22q11.2 Deletion Syndrome (22q11.2DS) are at increased risk of schizophrenia, intellectual disability, attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In common with these conditions, 22q11.2DS is also associated with sleep problems. We investigated whether abnormal sleep or sleep-dependent network activity in 22q11.2DS reflects convergent, early signatures of neural circuit disruption also evident in associated neurodevelopmental conditions. Methods In a cross-sectional design, we recorded high-density sleep EEG in young people (6-20 years) with 22q11.2DS (n=28) and their unaffected siblings (n=17), quantifying associations between sleep architecture, EEG oscillations (spindles and slow waves) and psychiatric symptoms. We also measured performance on a memory task before and after sleep. Results 22q11.2DS was associated with significant alterations in sleep architecture, including a greater proportion of N3 sleep and lower proportions of N1 and REM sleep than in siblings. During sleep, deletion carriers showed broadband increases in EEG power with increased slow-wave and spindle amplitudes, increased spindle frequency and density, and stronger coupling between spindles and slow-waves. Spindle and slow-wave amplitudes correlated positively with overnight memory in controls, but negatively in 22q11.2DS. Mediation analyses indicated that genotype effects on anxiety, ADHD and ASD were partially mediated by sleep EEG measures. Conclusions This study provides a detailed description of sleep neurophysiology in 22q11.2DS, highlighting alterations in EEG signatures of sleep which have been previously linked to neurodevelopment, some of which were associated with psychiatric symptoms. Sleep EEG features may therefore reflect delayed or compromised neurodevelopmental processes in 22q11.2DS, which could inform our understanding of the neurobiology of this condition and be biomarkers for neuropsychiatric disorders. Funding This research was funded by a Lilly Innovation Fellowship Award (UB), the National Institute of Mental Health (NIMH 5UO1MH101724; MvdB), a Wellcome Trust Institutional Strategic Support Fund (ISSF) award (MvdB), the Waterloo Foundation (918-1234; MvdB), the Baily Thomas Charitable Fund (2315/1; MvdB), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment (IMAGINE) (MR/L011166/1; JH, MvdB and MO), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment 2 (IMAGINE-2) (MR/T033045/1; MvdB, JH and MO); Wellcome Trust Strategic Award 'Defining Endophenotypes From Integrated Neurosciences' Wellcome Trust (100202/Z/12/Z MO, JH). NAD was supported by a National Institute for Health Research Academic Clinical Fellowship in Mental Health and MWJ by a Wellcome Trust Senior Research Fellowship in Basic Biomedical Science (202810/Z/16/Z). CE and HAM were supported by Medical Research Council Doctoral Training Grants (C.B.E. 1644194, H.A.M MR/K501347/1). HMM and UB were employed by Eli Lilly & Co during the study; HMM is currently an employee of Boehringer Ingelheim Pharma GmbH & Co KG. The views and opinions expressed are those of the author(s), and not necessarily those of the NHS, the NIHR or the Department of Health funders.
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Affiliation(s)
- Nicholas A Donnelly
- Centre for Academic Mental Health, University of Bristol, Bristol, United Kingdom
- Avon and Wiltshire Partnership NHS Mental Health Trust, Avon, United Kingdom
| | - Ullrich Bartsch
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, United Kingdom
- Translational Neuroscience, Eli Lilly, Windlesham, United States
| | - Hayley A Moulding
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Christopher Eaton
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Hugh Marston
- Translational Neuroscience, Eli Lilly, Windlesham, United States
| | - Jessica H Hall
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Jeremy Hall
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Michael J Owen
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Marianne B M van den Bree
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
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McConnell BV, Kronberg E, Medenblik LM, Kheyfets VO, Ramos AR, Sillau SH, Pulver RL, Bettcher BM. The Rise and Fall of Slow Wave Tides: Vacillations in Coupled Slow Wave/Spindle Pairing Shift the Composition of Slow Wave Activity in Accordance With Depth of Sleep. Front Neurosci 2022; 16:915934. [PMID: 35812239 PMCID: PMC9260314 DOI: 10.3389/fnins.2022.915934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/03/2022] [Indexed: 11/21/2022] Open
Abstract
Slow wave activity (SWA) during sleep is associated with synaptic regulation and memory processing functions. Each cycle of non-rapid-eye-movement (NREM) sleep demonstrates a waxing and waning amount of SWA during the transitions between stages N2 and N3 sleep, and the deeper N3 sleep is associated with an increased density of SWA. Further, SWA is an amalgam of different types of slow waves, each identifiable by their temporal coupling to spindle subtypes with distinct physiological features. The objectives of this study were to better understand the neurobiological properties that distinguish different slow wave and spindle subtypes, and to examine the composition of SWA across cycles of NREM sleep. We further sought to explore changes in the composition of NREM cycles that occur among aging adults. To address these goals, we analyzed subsets of data from two well-characterized cohorts of healthy adults: (1) The DREAMS Subjects Database (n = 20), and (2) The Cleveland Family Study (n = 60). Our analyses indicate that slow wave/spindle coupled events can be characterized as frontal vs. central in their relative distribution between electroencephalography (EEG) channels. The frontal predominant slow waves are identifiable by their coupling to late-fast spindles and occur more frequently during stage N3 sleep. Conversely, the central-associated slow waves are identified by coupling to early-fast spindles and favor occurrence during stage N2 sleep. Together, both types of slow wave/spindle coupled events form the composite of SWA, and their relative contribution to the SWA rises and falls across cycles of NREM sleep in accordance with depth of sleep. Exploratory analyses indicated that older adults produce a different composition of SWA, with a shift toward the N3, frontal subtype, which becomes increasingly predominant during cycles of NREM sleep. Overall, these data demonstrate that subtypes of slow wave/spindle events have distinct cortical propagation patterns and differ in their distribution across lighter vs. deeper NREM sleep. Future efforts to understand how slow wave sleep and slow wave/spindle coupling impact memory performance and neurological disease may benefit from examining the composition of SWA to avoid potential confounds that may occur when comparing dissimilar neurophysiological events.
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Affiliation(s)
- Brice V. McConnell
- Department of Neurology, University of Colorado, Denver, Denver, CO, United States
- *Correspondence: Brice V. McConnell,
| | - Eugene Kronberg
- Department of Neurology, University of Colorado, Denver, Denver, CO, United States
| | - Lindsey M. Medenblik
- Department of Neurology, University of Colorado, Denver, Denver, CO, United States
| | - Vitaly O. Kheyfets
- Pediatric Critical Care Medicine, University of Colorado, Denver, Denver, CO, United States
| | - Alberto R. Ramos
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Stefan H. Sillau
- Department of Neurology, University of Colorado, Denver, Denver, CO, United States
| | - Rachelle L. Pulver
- Department of Neurology, University of Colorado, Denver, Denver, CO, United States
| | - Brianne M. Bettcher
- Department of Neurology, University of Colorado, Denver, Denver, CO, United States
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