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Lambert PM, Salvatore SV, Lu X, Shu HJ, Benz A, Rensing N, Yuede CM, Wong M, Zorumski CF, Mennerick S. A role for δ subunit-containing GABA A receptors on parvalbumin positive neurons in maintaining electrocortical signatures of sleep states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.25.586604. [PMID: 38585911 PMCID: PMC10996536 DOI: 10.1101/2024.03.25.586604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
GABA A receptors containing δ subunits have been shown to mediate tonic/slow inhibition in the CNS. These receptors are typically found extrasynaptically and are activated by relatively low levels of ambient GABA in the extracellular space. In the mouse neocortex, δ subunits are expressed on the surface of some pyramidal cells as well as on parvalbumin positive (PV+) interneurons. An important function of PV+ interneurons is the organization of coordinated network activity that can be measured by EEG; however, it remains unclear what role tonic/slow inhibitory control of PV+ neurons may play in shaping oscillatory activity. After confirming a loss of functional δ mediated tonic currents in PV cells in cortical slices from mice lacking Gabrd in PV+ neurons (PV δcKO), we performed EEG recordings to survey network activity across wake and sleep states. PV δcKO mice showed altered spectral content of EEG during NREM and REM sleep that was a result of increased oscillatory activity in NREM and the emergence of transient high amplitude bursts of theta frequency activity during REM. Viral reintroduction of Gabrd to PV+ interneurons in PV δcKO mice rescued REM EEG phenotypes, supporting an important role for δ subunit mediated inhibition of PV+ interneurons for maintaining normal REM cortical oscillations. Significance statement The impact on cortical EEG of inhibition on PV+ neurons was studied by deleting a GABA A receptor subunit selectively from these neurons. We discovered unexpected changes at low frequencies during sleep that were rescued by viral reintroduction.
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Adra N, Dümmer LW, Paixao L, Tesh RA, Sun H, Ganglberger W, Westmeijer M, Da Silva Cardoso M, Kumar A, Ye E, Henry J, Cash SS, Kitchener E, Leveroni CL, Au R, Rosand J, Salinas J, Lam AD, Thomas RJ, Westover MB. Decoding information about cognitive health from the brainwaves of sleep. Sci Rep 2023; 13:11448. [PMID: 37454163 PMCID: PMC10349883 DOI: 10.1038/s41598-023-37128-7] [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: 08/30/2022] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
Sleep electroencephalogram (EEG) signals likely encode brain health information that may identify individuals at high risk for age-related brain diseases. Here, we evaluate the correlation of a previously proposed brain age biomarker, the "brain age index" (BAI), with cognitive test scores and use machine learning to develop and validate a series of new sleep EEG-based indices, termed "sleep cognitive indices" (SCIs), that are directly optimized to correlate with specific cognitive scores. Three overarching cognitive processes were examined: total, fluid (a measure of cognitive processes involved in reasoning-based problem solving and susceptible to aging and neuropathology), and crystallized cognition (a measure of cognitive processes involved in applying acquired knowledge toward problem-solving). We show that SCI decoded information about total cognition (Pearson's r = 0.37) and fluid cognition (Pearson's r = 0.56), while BAI correlated only with crystallized cognition (Pearson's r = - 0.25). Overall, these sleep EEG-derived biomarkers may provide accessible and clinically meaningful indicators of neurocognitive health.
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Affiliation(s)
- Noor Adra
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Lisa W Dümmer
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- University of Groningen, Groningen, The Netherlands
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Sleep and Health Zurich, University of Zurich, Zurich, Switzerland
| | - Mike Westmeijer
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Utrecht University, Utrecht, The Netherlands
| | - Madalena Da Silva Cardoso
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Anagha Kumar
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Jonathan Henry
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Erin Kitchener
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | | | - Rhoda Au
- Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Joel Salinas
- New York University Grossman School of Medicine, New York, NY, USA
| | - Alice D Lam
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Robert J Thomas
- Division of Pulmonary, Critical Care, and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA.
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA.
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA.
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Kam K, Vetter K, Tejiram RA, Pettibone WD, Shim K, Audrain M, Yu L, Daehn IS, Ehrlich ME, Varga AW. Effect of Aging and a Dual Orexin Receptor Antagonist on Sleep Architecture and Non-REM Oscillations Including an REM Behavior Disorder Phenotype in the PS19 Mouse Model of Tauopathy. J Neurosci 2023; 43:4738-4749. [PMID: 37230765 PMCID: PMC10286944 DOI: 10.1523/jneurosci.1828-22.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/21/2023] [Accepted: 04/28/2023] [Indexed: 05/27/2023] Open
Abstract
The impact of tau pathology on sleep microarchitecture features, including slow oscillations, spindles, and their coupling, has been understudied, despite the proposed importance of these electrophysiological features toward learning and memory. Dual orexin receptor antagonists (DORAs) are known to promote sleep, but whether and how they affect sleep microarchitecture in the setting of tauopathy is unknown. In the PS19 mouse model of tauopathy MAPT (microtubule-associated protein tau) P301S (both male and female), young PS19 mice 2-3 months old show a sleep electrophysiology signature with markedly reduced spindle duration and power and elevated slow oscillation (SO) density compared with littermate controls, although there is no significant tau hyperphosphorylation, tangle formation, or neurodegeneration at this age. With aging, there is evidence for sleep disruption in PS19 mice, characterized by reduced REM duration, increased non-REM and REM fragmentation, and more frequent brief arousals at the macrolevel and reduced spindle density, SO density, and spindle-SO coupling at the microlevel. In ∼33% of aged PS19 mice, we unexpectedly observed abnormal goal-directed behaviors in REM, including mastication, paw grasp, and forelimb/hindlimb extension, seemingly consistent with REM behavior disorder (RBD). Oral administration of DORA-12 in aged PS19 mice increased non-REM and REM duration, albeit with shorter bout lengths, and increased spindle density, spindle duration, and SO density without change to spindle-SO coupling, power in either the SO or spindle bands, or the arousal index. We observed a significant effect of DORA-12 on objective measures of RBD, thereby encouraging future exploration of DORA effects on sleep-mediated cognition and RBD treatment.SIGNIFICANCE STATEMENT The specific effect of tauopathy on sleep macroarchitecture and microarchitecture throughout aging remains unknown. Our key findings include the following: (1) the identification of a sleep EEG signature constituting an early biomarker of impending tauopathy; (2) sleep physiology deteriorates with aging that are also markers of off-line cognitive processing; (3) the novel observation that dream enactment behaviors reminiscent of RBD occur, likely the first such observation in a tauopathy model; and (4) a dual orexin receptor antagonist is capable of restoring several of the sleep macroarchitecture and microarchitecture abnormalities.
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Affiliation(s)
- Korey Kam
- Catherine and Henry J. Gaisman Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Kenny Vetter
- Catherine and Henry J. Gaisman Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Rachel A Tejiram
- Catherine and Henry J. Gaisman Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Ward D Pettibone
- Catherine and Henry J. Gaisman Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Kaitlyn Shim
- Catherine and Henry J. Gaisman Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Mickael Audrain
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Liping Yu
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Ilse S Daehn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Michelle E Ehrlich
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Andrew W Varga
- Catherine and Henry J. Gaisman Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029
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Miyamoto D. Neural circuit plasticity for complex non-declarative sensorimotor memory consolidation during sleep. Neurosci Res 2022; 189:37-43. [PMID: 36584925 DOI: 10.1016/j.neures.2022.12.020] [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: 08/01/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022]
Abstract
Evidence is accumulating that the brain actively consolidates long-term memory during sleep. Motor skill memory is a form of non-declarative procedural memory and can be coordinated with multi-sensory processing such as visual, tactile, and, auditory. Conversely, perception is affected by body movement signal from motor brain regions. Although both cortical and subcortical brain regions are involved in memory consolidation, cerebral cortex activity can be recorded and manipulated noninvasively or minimally invasively in humans and animals. NREM sleep, which is important for non-declarative memory consolidation, is characterized by slow and spindle waves representing thalamo-cortical population activity. In animals, electrophysiological recording, optical imaging, and manipulation approaches have revealed multi-scale cortical dynamics across learning and sleep. In the sleeping cortex, neural activity is affected by prior learning and neural circuits are continually reorganized. Here I outline how sensorimotor coordination is formed through awake learning and subsequent sleep.
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Affiliation(s)
- Daisuke Miyamoto
- Laboratory for Sleeping-Brain Dynamics, Research Center for Idling Brain Science, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan; Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan.
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5
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Avvenuti G, Bernardi G. Local sleep: A new concept in brain plasticity. HANDBOOK OF CLINICAL NEUROLOGY 2022; 184:35-52. [PMID: 35034748 DOI: 10.1016/b978-0-12-819410-2.00003-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Traditionally, sleep and wakefulness have been considered as two global, mutually exclusive states. However, this view has been challenged by the discovery that sleep and wakefulness are actually locally regulated and that islands of these two states may often coexist in the same individual. Importantly, such a local regulation seems to be the key for many essential functions of sleep, including the maintenance of cognitive efficiency and the consolidation of new skills and memories. Indeed, local changes in sleep-related oscillations occur in brain areas that are used and involved in learning during wakefulness. In turn, these changes directly modulate experience-dependent brain adaptations and the consolidation of newly acquired memories. In line with these observations, alterations in the regional balance between wake- and sleep-like activity have been shown to accompany many pathologic conditions, including psychiatric and neurologic disorders. In the last decade, experimental research has started to shed light on the mechanisms involved in the local regulation of sleep and wakefulness. The results of this research have opened new avenues of investigation regarding the function of sleep and have revealed novel potential targets for the treatment of several pathologic conditions.
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Affiliation(s)
- Giulia Avvenuti
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Giulio Bernardi
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy.
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Kam K, Rapoport DM, Parekh A, Ayappa I, Varga AW. WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake. J Neurosci Methods 2021; 360:109224. [PMID: 34052291 DOI: 10.1016/j.jneumeth.2021.109224] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/28/2021] [Accepted: 05/17/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Recent advancement in deep learning provides a pivotal opportunity to potentially supplement or supplant the limiting step of manual sleep scoring. NEW METHOD In this paper, we characterize the WaveSleepNet (WSN), a deep convolutional neural network (CNN) that uses wavelet transformed images of mouse EEG/EMG signals to autoscore sleep and wake. RESULTS WSN achieves an epoch by epoch mean accuracy of 0.86 and mean F1 score of 0.82 compared to manual scoring by a human expert. In mice experiencing mechanically induced sleep fragmentation, an overall epoch by epoch mean accuracy of 0.80 is achieved by WSN and classification of non-REM (NREM) sleep is not compromised, but the high level of sleep fragmentation results in WSN having greater difficulty differentiating REM from NREM sleep. We also find that WSN achieves similar levels of accuracy on an independent dataset of externally acquired EEG/EMG recordings with an overall epoch by epoch accuracy of 0.91. We also compared conventional summary sleep metrics in mice sleeping ad libitum. WSN systematically biases sleep fragmentation metrics of bout number and bout length leading to an overestimated degree of sleep fragmentation. COMPARISON WITH EXISTING METHODS In a cross-validation, WSN has a greater macro and stage-specific accuracy compared to a conventional random forest classifier. Examining the WSN, we find that it automatically learns spectral features consistent with manual scoring criteria that are used to define each class. CONCLUSION These results suggest to us that WSN is capable of learning visually agreeable features and may be useful as a supplement to human manual scoring.
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Affiliation(s)
- Korey Kam
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, USA.
| | - David M Rapoport
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, USA
| | - Ankit Parekh
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, USA
| | - Indu Ayappa
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, USA
| | - Andrew W Varga
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, USA.
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Meng Q, Tan X, Jiang C, Xiong Y, Yan B, Zhang J. Tracking Eye Movements During Sleep in Mice. Front Neurosci 2021; 15:616760. [PMID: 33716648 PMCID: PMC7947631 DOI: 10.3389/fnins.2021.616760] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 02/04/2021] [Indexed: 12/02/2022] Open
Abstract
Eye movement is not only for adjusting the visual field and maintaining the stability of visual information on the retina, but also provides an external manifestation of the cognitive status of the brain. Recent studies showed similarity in eye movement patterns between wakefulness and rapid eye movement (REM) sleep, indicating that the brain status of REM sleep likely resembles that of awake status. REM sleep in humans could be divided into phasic REM and tonic REM sleep according to the difference in eye movement frequencies. Mice are the most commonly used animal model for studying neuronal and molecular mechanisms underlying sleep. However, there was a lack of details for eye movement patterns during REM sleep, hence it remains unknown whether REM sleep can be further divided into different stages in mice. Here we developed a device combining electroencephalogram (EEG), electromyogram (EMG) as well as eye movements recording in mice to study the eye movement patterns during sleep. We implanted a magnet beneath the conjunctiva of eye and tracked eye movements using a magnetic sensor. The magnetic signals showed strong correlation with video-oculography in head-fixed mice, indicating that the magnetic signals reflect the direction and magnitude of eye movement. We also found that the magnet implanted beneath the conjunctiva exhibited good biocompatibility. Finally, we examined eye movement in sleep–wake cycle, and discriminated tonic REM and phasic REM according to the frequency of eye movements, finding that compared to tonic REM, phasic REM exhibited higher oscillation power at 0.50 Hz, and lower oscillation power at 1.50–7.25 Hz and 9.50–12.00 Hz. Our device allowed to simultaneously record EEG, EMG, and eye movements during sleep and wakefulness, providing a convenient and high temporal-spatial resolution tool for studying eye movements in sleep and other researches in mice.
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Affiliation(s)
- Qingshuo Meng
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xinrong Tan
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chengyong Jiang
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yanyu Xiong
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Biao Yan
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jiayi Zhang
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Department of Ophthalmology, Zhongshan Hospital, Fudan University, Shanghai, China
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Eckert MJ, Iyer K, Euston DR, Tatsuno M. Reliable induction of sleep spindles with intracranial electrical pulse stimulation. ACTA ACUST UNITED AC 2020; 28:7-11. [PMID: 33323496 PMCID: PMC7747649 DOI: 10.1101/lm.052464.120] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 09/27/2020] [Indexed: 01/19/2023]
Abstract
Neocortical sleep spindles have been shown to occur more frequently following a memory task, suggesting that a method to increase spindle activity could improve memory processing. Stimulation of the neocortex can elicit a slow oscillation (SO) and a spindle, but the feasibility of this method to boost SO and spindles over time has not been tested. In rats with implanted neocortical electrodes, stimulation during slow wave sleep significantly increased SO and spindle rates compared to control rest periods before and after the stimulation session. Coordination between hippocampal sharp-wave ripples and spindles also increased. These effects were reproducible across five consecutive days of testing, demonstrating the viability of this method to increase SO and spindles.
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Affiliation(s)
- Michael J Eckert
- Department of Neuroscience, University of Lethbridge, Lethbridge, Alberta T1K 3M4, Canada
| | - Kartik Iyer
- Department of Neuroscience, University of Lethbridge, Lethbridge, Alberta T1K 3M4, Canada
| | - David R Euston
- Department of Neuroscience, University of Lethbridge, Lethbridge, Alberta T1K 3M4, Canada
| | - Masami Tatsuno
- Department of Neuroscience, University of Lethbridge, Lethbridge, Alberta T1K 3M4, Canada
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Mullins AE, Kam K, Parekh A, Bubu OM, Osorio RS, Varga AW. Obstructive Sleep Apnea and Its Treatment in Aging: Effects on Alzheimer's disease Biomarkers, Cognition, Brain Structure and Neurophysiology. Neurobiol Dis 2020; 145:105054. [PMID: 32860945 PMCID: PMC7572873 DOI: 10.1016/j.nbd.2020.105054] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 08/13/2020] [Accepted: 08/18/2020] [Indexed: 02/08/2023] Open
Abstract
Here we review the impact of obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease (AD) pathogenesis, neuroanatomy, cognition and neurophysiology, and present the research investigating the effects of continuous positive airway pressure (CPAP) therapy. OSA is associated with an increase in AD markers amyloid-β and tau measured in cerebrospinal fluid (CSF), by Positron Emission Tomography (PET) and in blood serum. There is some evidence suggesting CPAP therapy normalizes AD biomarkers in CSF but since mechanisms for amyloid-β and tau production/clearance in humans are not completely understood, these findings remain preliminary. Deficits in the cognitive domains of attention, vigilance, memory and executive functioning are observed in OSA patients with the magnitude of impairment appearing stronger in younger people from clinical settings than in older community samples. Cognition improves with varying degrees after CPAP use, with the greatest effect seen for attention in middle age adults with more severe OSA and sleepiness. Paradigms in which encoding and retrieval of information are separated by periods of sleep with or without OSA have been done only rarely, but perhaps offer a better chance to understand cognitive effects of OSA than isolated daytime testing. In cognitively normal individuals, changes in EEG microstructure during sleep, particularly slow oscillations and spindles, are associated with biomarkers of AD, and measures of cognition and memory. Similar changes in EEG activity are reported in AD and OSA, such as "EEG slowing" during wake and REM sleep, and a degradation of NREM EEG microstructure. There is evidence that CPAP therapy partially reverses these changes but large longitudinal studies demonstrating this are lacking. A diagnostic definition of OSA relying solely on the Apnea Hypopnea Index (AHI) does not assist in understanding the high degree of inter-individual variation in daytime impairments related to OSA or response to CPAP therapy. We conclude by discussing conceptual challenges to a clinical trial of OSA treatment for AD prevention, including inclusion criteria for age, OSA severity, and associated symptoms, the need for a potentially long trial, defining relevant primary outcomes, and which treatments to target to optimize treatment adherence.
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Affiliation(s)
- Anna E Mullins
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Korey Kam
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ankit Parekh
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Omonigho M Bubu
- Center for Brain Health, Department of Psychiatry, NYU Langone Medical Center, New York, NY 10016, USA
| | - Ricardo S Osorio
- Center for Brain Health, Department of Psychiatry, NYU Langone Medical Center, New York, NY 10016, USA
| | - Andrew W Varga
- Mount Sinai Integrative Sleep Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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10
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Ballesteros JJ, Briscoe JB, Ishizawa Y. Neural signatures of α2-Adrenergic agonist-induced unconsciousness and awakening by antagonist. eLife 2020; 9:57670. [PMID: 32857037 PMCID: PMC7455241 DOI: 10.7554/elife.57670] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 08/09/2020] [Indexed: 12/29/2022] Open
Abstract
How the brain dynamics change during anesthetic-induced altered states of consciousness is not completely understood. The α2-adrenergic agonists are unique. They generate unconsciousness selectively through α2-adrenergic receptors and related circuits. We studied intracortical neuronal dynamics during transitions of loss of consciousness (LOC) with the α2-adrenergic agonist dexmedetomidine and return of consciousness (ROC) in a functionally interconnecting somatosensory and ventral premotor network in non-human primates. LOC, ROC and full task performance recovery were all associated with distinct neural changes. The early recovery demonstrated characteristic intermediate dynamics distinguished by sustained high spindle activities. Awakening by the α2-adrenergic antagonist completely eliminated this intermediate state and instantaneously restored awake dynamics and the top task performance while the anesthetic was still being infused. The results suggest that instantaneous functional recovery is possible following anesthetic-induced unconsciousness and the intermediate recovery state is not a necessary path for the brain recovery.
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Affiliation(s)
- Jesus Javier Ballesteros
- Department of Anesthesia, Critical Care & Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Jessica Blair Briscoe
- Department of Anesthesia, Critical Care & Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Yumiko Ishizawa
- Department of Anesthesia, Critical Care & Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, United States
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