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Rehel S, Duivon M, Doidy F, Champetier P, Clochon P, Grellard JM, Segura-Djezzar C, Geffrelot J, Emile G, Allouache D, Levy C, Viader F, Eustache F, Joly F, Giffard B, Perrier J. Sleep oscillations related to memory consolidation during aromatases inhibitors for breast cancer. Sleep Med 2024; 121:210-218. [PMID: 39004011 DOI: 10.1016/j.sleep.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/16/2024]
Abstract
Aromatase inhibitors (AIs) are associated with sleep difficulties in breast cancer (BC) patients. Sleep is known to favor memory consolidation through the occurrence of specific oscillations, i.e., slow waves (SW) and sleep spindles, allowing a dialogue between prefrontal cortex and the hippocampus. Interestingly, neuroimaging studies in BC patients have consistently shown structural and functional modifications in these two brain regions. With the aim to evaluate sleep oscillations related to memory consolidation during AIs, we collected polysomnography data in BC patients treated (AI+, n = 17) or not (AI-, n = 17) with AIs compared to healthy controls (HC, n = 21). None of the patients had received chemotherapy and radiotherapy was finished since at least 6 months, that limit the confounding effects of other treatments than AIs. Fast and slow spindles were detected during sleep stage 2 at centro-parietal and frontal electrodes respectively. SW were detected at frontal electrodes during stage 3. Here, we show lower frontal SW densities in AI + patients compared to HC. These results concord with previous reports about frontal cortical alterations in cancer following AIs administration. Moreover, AI + patients tended to have lower spindle density at C4 electrode. Regression analyses showed that, in both patient groups, spindle density at C4 electrode explained a large variance of memory performances. Slow spindle characteristics did not differ between groups and sleep oscillations characteristics of AI- patients did not differ significantly from those of both AI + patients and HC. Overall, our results add to the compelling evidence of the systemic effects of AIs previously reported in animals, with deleterious effects on cortical activity during sleep and associated memory consolidation in the current study. There is thus a need to further investigate sleep modifications during AIs administration. Longitudinal studies are needed to confirm these findings and investigation in other cancers on this topic should be conducted.
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Affiliation(s)
- S Rehel
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France.
| | - M Duivon
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France
| | - F Doidy
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France
| | - P Champetier
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France
| | - P Clochon
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France
| | - J M Grellard
- Clinical Research Department, Centre François Baclesse, 3 Avenue Du Général Harris, Caen, France
| | - C Segura-Djezzar
- Institut Normand Du Sein, Centre François Baclesse, Caen, France; Department of Medical Oncology, Centre François Baclesse, 3 Avenue Du Général Harris, Caen, France
| | - J Geffrelot
- Institut Normand Du Sein, Centre François Baclesse, Caen, France; Department of Medical Oncology, Centre François Baclesse, 3 Avenue Du Général Harris, Caen, France
| | - G Emile
- Institut Normand Du Sein, Centre François Baclesse, Caen, France; Department of Medical Oncology, Centre François Baclesse, 3 Avenue Du Général Harris, Caen, France
| | - D Allouache
- Institut Normand Du Sein, Centre François Baclesse, Caen, France; Department of Medical Oncology, Centre François Baclesse, 3 Avenue Du Général Harris, Caen, France
| | - C Levy
- Institut Normand Du Sein, Centre François Baclesse, Caen, France; Department of Medical Oncology, Centre François Baclesse, 3 Avenue Du Général Harris, Caen, France
| | - F Viader
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France
| | - F Eustache
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France
| | - F Joly
- Clinical Research Department, Centre François Baclesse, 3 Avenue Du Général Harris, Caen, France; Institut Normand Du Sein, Centre François Baclesse, Caen, France; Department of Medical Oncology, Centre François Baclesse, 3 Avenue Du Général Harris, Caen, France; INSERM, Normandie Univ, UNICAEN, U1086 ANTICIPE, Caen, France; Cancer and Cognition Platform, Ligue Nationale Contre le Cancer, 14076, Caen, France
| | - B Giffard
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France; Cancer and Cognition Platform, Ligue Nationale Contre le Cancer, 14076, Caen, France
| | - J Perrier
- Normandie Univ, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de La Mémoire Humaine, 14000, Caen, France.
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Bloxham A, Horton CL. Enhancing and advancing the understanding and study of dreaming and memory consolidation: Reflections, challenges, theoretical clarity, and methodological considerations. Conscious Cogn 2024; 123:103719. [PMID: 38941924 DOI: 10.1016/j.concog.2024.103719] [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: 08/06/2023] [Revised: 04/24/2024] [Accepted: 06/12/2024] [Indexed: 06/30/2024]
Abstract
Empirical investigations that search for a link between dreaming and sleep-dependent memory consolidation have focused on testing for an association between dreaming of what was learned, and improved memory performance for learned material. Empirical support for this is mixed, perhaps owing to the inherent challenges presented by the nature of dreams, and methodological inconsistencies. The purpose of this paper is to address critically prevalent assumptions and practices, with the aim of clarifying and enhancing research on this topic, chiefly by providing a theoretical synthesis of existing models and evidence. Also, it recommends the method of Targeted Memory Reactivation (TMR) as a means for investigating if dream content can be linked to specific cued activations. Other recommendations to enhance research practice and enquiry on this subject are also provided, focusing on the HOW and WHY we search for memory sources in dreams, and what purpose (if any) they might serve.
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Affiliation(s)
- Anthony Bloxham
- Nottingham Trent University, Nottingham, NG1 4FQ, United Kingdom.
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3
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Capone C, Paolucci PS. Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences. Sci Rep 2024; 14:14656. [PMID: 38918553 PMCID: PMC11199658 DOI: 10.1038/s41598-024-65631-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: 02/05/2024] [Accepted: 06/21/2024] [Indexed: 06/27/2024] Open
Abstract
Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing the number of necessary interactions with the environment to learn a desirable policy. However, these methods require biological implausible ingredients, such as the detailed storage of older experiences, and long periods of offline learning. The optimal way to learn and exploit world-models is still an open question. Taking inspiration from biology, we suggest that dreaming might be an efficient expedient to use an inner model. We propose a two-module (agent and model) spiking neural network in which "dreaming" (living new experiences in a model-based simulated environment) significantly boosts learning. Importantly, our model does not require the detailed storage of experiences, and learns online the world-model and the policy. Moreover, we stress that our network is composed of spiking neurons, further increasing the biological plausibility and implementability in neuromorphic hardware.
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Affiliation(s)
- Cristiano Capone
- INFN, Sezione di Roma, Rome, RM, 00185, Italy.
- Natl. Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanitá, Rome, 00161, Italy.
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4
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Höhn C, Hahn MA, Gruber G, Pletzer B, Cajochen C, Hoedlmoser K. Effects of evening smartphone use on sleep and declarative memory consolidation in male adolescents and young adults. Brain Commun 2024; 6:fcae173. [PMID: 38846535 PMCID: PMC11154150 DOI: 10.1093/braincomms/fcae173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/08/2024] [Accepted: 05/16/2024] [Indexed: 06/09/2024] Open
Abstract
Exposure to short-wavelength light before bedtime is known to disrupt nocturnal melatonin secretion and can impair subsequent sleep. However, while it has been demonstrated that older adults are less affected by short-wavelength light, there is limited research exploring differences between adolescents and young adults. Furthermore, it remains unclear whether the effects of evening short-wavelength light on sleep architecture extend to sleep-related processes, such as declarative memory consolidation. Here, we recorded polysomnography from 33 male adolescents (15.42 ± 0.97 years) and 35 male young adults (21.51 ± 2.06 years) in a within-subject design during three different nights to investigate the impact of reading for 90 min either on a smartphone with or without a blue-light filter or from a printed book. We measured subjective sleepiness, melatonin secretion, sleep physiology and sleep-dependent memory consolidation. While subjective sleepiness remained unaffected, we observed a significant melatonin attenuation effect in both age groups immediately after reading on the smartphone without a blue-light filter. Interestingly, adolescents fully recovered from the melatonin attenuation in the following 50 min before bedtime, whereas adults still, at bedtime, exhibited significantly reduced melatonin levels. Sleep-dependent memory consolidation and the coupling between sleep spindles and slow oscillations were not affected by short-wavelength light in both age groups. Nevertheless, adults showed a reduction in N3 sleep during the first night quarter. In summary, avoiding smartphone use in the last hour before bedtime is advisable for adolescents and young adults to prevent sleep disturbances. Our research empirically supports general sleep hygiene advice and can inform future recommendations regarding the use of smartphones and other screen-based devices before bedtime.
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Affiliation(s)
- Christopher Höhn
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, Paris Lodron University of Salzburg, 5020 Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), Paris Lodron University of Salzburg, 5020 Salzburg, Austria
| | - Michael A Hahn
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany
| | - Georg Gruber
- The Siesta Group Schlafanalyse GmbH, 1210 Vienna, Austria
| | - Belinda Pletzer
- Centre for Cognitive Neuroscience Salzburg (CCNS), Paris Lodron University of Salzburg, 5020 Salzburg, Austria
| | - Christian Cajochen
- Centre for Chronobiology, Psychiatric Hospital of the University of Basel, 4002 Basel, Switzerland
- Research Cluster Molecular and Cognitive Neuroscience (MCN), University of Basel, 4055 Basel, Switzerland
| | - Kerstin Hoedlmoser
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, Paris Lodron University of Salzburg, 5020 Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), Paris Lodron University of Salzburg, 5020 Salzburg, Austria
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Mondino A, González J, Li D, Mateos D, Osorio L, Cavelli M, Castro-Nin JP, Serantes D, Costa A, Vanini G, Mashour GA, Torterolo P. Urethane anaesthesia exhibits neurophysiological correlates of unconsciousness and is distinct from sleep. Eur J Neurosci 2024; 59:483-501. [PMID: 35545450 DOI: 10.1111/ejn.15690] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 04/13/2022] [Accepted: 05/06/2022] [Indexed: 11/27/2022]
Abstract
Urethane is a general anaesthetic widely used in animal research. The state of urethane anaesthesia is unique because it alternates between macroscopically distinct electrographic states: a slow-wave state that resembles non-rapid eye movement (NREM) sleep and an activated state with features of both REM sleep and wakefulness. Although it is assumed that urethane produces unconsciousness, this has been questioned because of states of cortical activation during drug exposure. Furthermore, the similarities and differences between urethane anaesthesia and physiological sleep are still unclear. In this study, we recorded the electroencephalogram (EEG) and electromyogram in chronically prepared rats during natural sleep-wake states and during urethane anaesthesia. We subsequently analysed the power, coherence, directed connectivity and complexity of brain oscillations and found that EEG under urethane anaesthesia has clear signatures of unconsciousness, with similarities to other general anaesthetics. In addition, the EEG profile under urethane is different in comparison with natural sleep states. These results suggest that consciousness is disrupted during urethane. Furthermore, despite similarities that have led others to conclude that urethane is a model of sleep, the electrocortical traits of depressed and activated states during urethane anaesthesia differ from physiological sleep states.
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Affiliation(s)
- Alejandra Mondino
- Department of Physiology, Faculty of Medicine, University of the Republic, Montevideo, Uruguay
| | - Joaquín González
- Department of Physiology, Faculty of Medicine, University of the Republic, Montevideo, Uruguay
| | - Duan Li
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, USA
- Center for Consciousness Science, University of Michigan, Ann Arbor, Michigan, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, USA
| | - Diego Mateos
- Institute of Applied Mathematics of the Coast-CONICET-UNL, CCT CONICET, Santa Fe, Argentina
- Faculty of Science and Technology, Autonomous University of Entre Ríos, Parana, Argentina
| | - Lucía Osorio
- Department of Physiology, Faculty of Medicine, University of the Republic, Montevideo, Uruguay
| | - Matías Cavelli
- Department of Physiology, Faculty of Medicine, University of the Republic, Montevideo, Uruguay
- Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, USA
| | - Juan Pedro Castro-Nin
- Department of Physiology, Faculty of Medicine, University of the Republic, Montevideo, Uruguay
| | - Diego Serantes
- Department of Physiology, Faculty of Medicine, University of the Republic, Montevideo, Uruguay
| | - Alicia Costa
- Department of Physiology, Faculty of Medicine, University of the Republic, Montevideo, Uruguay
| | - Giancarlo Vanini
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, USA
- Center for Consciousness Science, University of Michigan, Ann Arbor, Michigan, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, USA
| | - George A Mashour
- Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan, USA
- Center for Consciousness Science, University of Michigan, Ann Arbor, Michigan, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, USA
| | - Pablo Torterolo
- Department of Physiology, Faculty of Medicine, University of the Republic, Montevideo, Uruguay
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Capone C, Lupo C, Muratore P, Paolucci PS. Beyond spiking networks: The computational advantages of dendritic amplification and input segregation. Proc Natl Acad Sci U S A 2023; 120:e2220743120. [PMID: 38019856 PMCID: PMC10710097 DOI: 10.1073/pnas.2220743120] [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: 12/07/2022] [Accepted: 10/11/2023] [Indexed: 12/01/2023] Open
Abstract
The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons and cannot achieve state-of-the-art performance in machine learning. Recent works have proposed that input segregation (neurons receive sensory information and higher-order feedback in segregated compartments), and nonlinear dendritic computation would support error backpropagation in biological neurons. However, these approaches require propagating errors with a fine spatiotemporal structure to all the neurons, which is unlikely to be feasible in a biological network. To relax this assumption, we suggest that bursts and dendritic input segregation provide a natural support for target-based learning, which propagates targets rather than errors. A coincidence mechanism between the basal and the apical compartments allows for generating high-frequency bursts of spikes. This architecture supports a burst-dependent learning rule, based on the comparison between the target bursting activity triggered by the teaching signal and the one caused by the recurrent connections, providing support for target-based learning. We show that this framework can be used to efficiently solve spatiotemporal tasks, such as context-dependent store and recall of three-dimensional trajectories, and navigation tasks. Finally, we suggest that this neuronal architecture naturally allows for orchestrating "hierarchical imitation learning", enabling the decomposition of challenging long-horizon decision-making tasks into simpler subtasks. We show a possible implementation of this in a two-level network, where the high network produces the contextual signal for the low network.
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Affiliation(s)
- Cristiano Capone
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome00185, Italy
| | - Cosimo Lupo
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome00185, Italy
| | - Paolo Muratore
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Visual Neuroscience Lab, Trieste34136, Italy
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7
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Yoshida K, Toyoizumi T. Computational role of sleep in memory reorganization. Curr Opin Neurobiol 2023; 83:102799. [PMID: 37844426 DOI: 10.1016/j.conb.2023.102799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 09/07/2023] [Accepted: 09/21/2023] [Indexed: 10/18/2023]
Abstract
Sleep is considered to play an essential role in memory reorganization. Despite its importance, classical theoretical models did not focus on some sleep characteristics. Here, we review recent theoretical approaches investigating their roles in learning and discuss the possibility that non-rapid eye movement (NREM) sleep selectively consolidates memory, and rapid eye movement (REM) sleep reorganizes the representations of memories. We first review the possibility that slow waves during NREM sleep contribute to memory selection by using sequential firing patterns and the existence of up and down states. Second, we discuss the role of dreaming during REM sleep in developing neuronal representations. We finally discuss how to develop these points further, emphasizing the connections to experimental neuroscience and machine learning.
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Affiliation(s)
- Kensuke Yoshida
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
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8
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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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9
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Parker JL, Appleton SL, Adams RJ, Melaku YA, D'Rozario AL, Wittert GA, Martin SA, Catcheside PG, Lechat B, Teare AJ, Toson B, Vakulin A. The association between sleep spindles and cognitive function in middle-aged and older men from a community-based cohort study. Sleep Health 2023; 9:774-785. [PMID: 37268483 DOI: 10.1016/j.sleh.2023.03.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 06/04/2023]
Abstract
OBJECTIVES Previous studies examining associations between sleep spindles and cognitive function attempted to account for obstructive sleep apnea without consideration for potential moderating effects. To elucidate associations between sleep spindles, cognitive function, and obstructive sleep apnea, this study of community-dwelling men examined cross-sectional associations between sleep spindle metrics and daytime cognitive function outcomes following adjustment for obstructive sleep apnea and potential obstructive sleep apnea moderating effects. METHODS Florey Adelaide Male Ageing Study participants (n = 477, 41-87 years) reporting no previous obstructive sleep apnea diagnosis underwent home-based polysomnography (2010-2011). Cognitive testing (2007-2010) included the inspection time task (processing speed), trail-making tests A (TMT-A) (visual attention) and B (trail-making test-B) (executive function), and Fuld object memory evaluation (episodic memory). Frontal spindle metrics (F4-M1) included occurrence (count), average frequency (Hz), amplitude (µV), and overall (11-16 Hz), slow (11-13 Hz), and fast (13-16 Hz) spindle density (number/minute during N2 and N3 sleep). RESULTS In fully adjusted linear regression models, lower N2 sleep spindle occurrence was associated with longer inspection times (milliseconds) (B = -0.43, 95% confidence interval [-0.74, -0.12], p = .006), whereas higher N3 sleep fast spindle density was associated with worse TMT-B performance (seconds) (B = 18.4, 95% confidence interval [1.62, 35.2], p = .032). Effect moderator analysis revealed that in men with severe obstructive sleep apnea (apnea-hypopnea index ≥30/hour), slower N2 sleep spindle frequency was associated with worse TMT-A performance (χ2 = 12.5, p = .006). CONCLUSIONS Specific sleep spindle metrics were associated with cognitive function, and obstructive sleep apnea severity moderated these associations. These observations support the utility of sleep spindles as useful cognitive function markers in obstructive sleep apnea, which warrants further longitudinal investigation.
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Affiliation(s)
- Jesse L Parker
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.
| | - Sarah L Appleton
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.
| | - Robert J Adams
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia; Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, South Australia, Australia.
| | - Yohannes Adama Melaku
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.
| | - Angela L D'Rozario
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia; The University of Sydney, Faculty of Science, School of Psychology, Sydney, New South Wales, Australia.
| | - Gary A Wittert
- Australian Institute of Family Studies, Melbourne, Victoria, Australia; Freemasons Centre for Male Health and Wellbeing, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
| | - Sean A Martin
- Australian Institute of Family Studies, Melbourne, Victoria, Australia; Freemasons Centre for Male Health and Wellbeing, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
| | - Peter G Catcheside
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.
| | - Bastien Lechat
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.
| | - Alison J Teare
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.
| | - Barbara Toson
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.
| | - Andrew Vakulin
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia; CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia.
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10
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Memon AA, Catiul C, Irwin Z, Pilkington J, Memon RA, Joop A, Wood KH, Cutter G, Miocinovic S, Amara AW. Quantitative sleep electroencephalogram and cognitive performance in Parkinson's disease with and without rapid eye movement sleep behavior disorder. Front Neurol 2023; 14:1223974. [PMID: 37745647 PMCID: PMC10512724 DOI: 10.3389/fneur.2023.1223974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 08/18/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Parkinson's disease (PD) patients with REM sleep behavior disorder (RBD) are at greater risk for cognitive decline and RBD has been associated with alterations in sleep-related EEG oscillations. This study evaluates differences in sleep quantitative EEG (qEEG) and cognition in PD participants with (PD-RBD) and without RBD (PD-no-RBD). Methods In this cross-sectional study, polysomnography (PSG)-derived qEEG and a comprehensive level II neuropsychological assessment were compared between PD-RBD (n = 21) and PD-no-RBD (n = 31). Following artifact rejection, qEEG analysis was performed in the frontal and central leads. Measures included Scalp-slow wave (SW) density, spindle density, morphological properties of SW and sleep spindles, SW-spindle phase-amplitude coupling, and spectral power analysis in NREM and REM. The neurocognitive battery had at least two tests per domain, covering five cognitive domains as recommended by the Movement Disorders Society Task Force for PD-MCI diagnosis. Differences in qEEG features and cognitive performance were compared between the two groups. Stepwise linear regression was performed to evaluate predictors of cognitive performance. Multiple comparisons were corrected using the Benjamini-Hochberg method. Results Spindle density and SW-spindle co-occurrence percent were lower in participants with PD-RBD compared to PD-no-RBD. The PD-RBD group also demonstrated higher theta spectral power during REM. Sleep spindles and years of education, but not RBD, were predictors of cognitive performance. Conclusion PD participants with RBD have alterations in sleep-related qEEG compared to PD participants without RBD. Although PD-RBD participants had worse cognitive performance compared to PD-no-RBD, regression models suggest that lower sleep spindle density, rather than presence of RBD, predicts worse comprehensive cognitive score. Future studies should include longitudinal evaluation to determine whether sleep-related qEEG alterations are associated with more rapid cognitive decline in PD-RBD.
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Affiliation(s)
- Adeel A. Memon
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Neurology, West Virginia University, Morgantown, WV, United States
| | - Corina Catiul
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Zachary Irwin
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jennifer Pilkington
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Raima A. Memon
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Allen Joop
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Kimberly H. Wood
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Psychology, Samford University, Birmingham, AL, United States
| | - Gary Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Amy W. Amara
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Neurology, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States
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11
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Wei Y, Luo M, Mai X, Feng L, Tang T, Yang D, Krishnan GP, Bazhenov M. The role of age-related sleep EEG changes in memory decline: experiments and computational modeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083499 PMCID: PMC11214839 DOI: 10.1109/embc40787.2023.10340681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The slow oscillation (SO) observed during deep sleep is known to facilitate memory consolidation. However, the impact of age-related changes in sleep electroencephalography (EEG) oscillations and memory remains unknown. In this study, we aimed to investigate the contribution of age-related changes in sleep SO and its role in memory decline by combining EEG recordings and computational modeling. Based on the detected SO events, we found that older adults exhibit lower SO density, lower SO frequency, and longer Up and Down state durations during N3 sleep compared to young and middle-aged groups. Using a biophysically detailed thalamocortical network model, we simulated the "aged" brain as a partial loss of synaptic connections between neurons in the cortex. Our simulations showed that the changes in sleep SO properties in the "aged" brain, similar to those observed in older adults, resulting in impaired memory consolidation. Overall, this study provides mechanistic insights into how age-related changes modulate sleep SOs and memory decline.Clinical Relevance- This study contributes towards finding feasible biomarkers and target mechanism for designing therapy in older adults with memory deficits, such as Alzheimer's disease patients.
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12
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Kavi PC. Conscious entry into sleep: Yoga Nidra and accessing subtler states of consciousness. PROGRESS IN BRAIN RESEARCH 2023; 280:43-60. [PMID: 37714572 DOI: 10.1016/bs.pbr.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Human sleep is a dynamic and complex process comprising sleep stages with REM and NREM sleep characteristics that come in cycles. During sleep, there is a loss of responsiveness or a perceptual loss of conscious awareness with increasing thresholds for wakefulness as sleep progresses. There are brief bursts of wakefulness or Wake After Sleep Onset (WASO) throughout a nocturnal sleep. Conscious experience during nocturnal sleep is known to occur during lucid dreaming when one is aware during dreams when the dream is occurring. Most cultures have known lucid dreaming since antiquity. However, conscious experience during dreamless sleep is relatively lesser known. Nevertheless, selected Indo-Tibetan meditation literature has documented it since antiquity. Minimal Phenomenal Experience (MPE) research describes lucid dreamless sleep as its target phenomenology. "Conscious entry into sleep" posits tonic alertness is maintained post sleep onset through the sleep stages for sustained durations of time until an eventual loss of conscious awareness. Entering sleep consciously and being aware during dreamless sleep, including Slow Wave Activity, is plausibly to be in the state of "Yoga Nidra" or Yogic sleep. An attentive sleepful state provides access to subtler states of consciousness and significantly deepens the levels of silence. It is phenomenologically distinct from hypnagogic hallucinations and lucid dreaming. Unfortunately, sleep studies validating this phenomenology are yet to be done. Therefore, an experimental methodology akin to those used in lucid dreaming experiments is described.
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13
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Tadros T, Krishnan GP, Ramyaa R, Bazhenov M. Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks. Nat Commun 2022; 13:7742. [PMID: 36522325 PMCID: PMC9755223 DOI: 10.1038/s41467-022-34938-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an important role in incremental learning by replaying recent and old conflicting memory traces. Here we tested the hypothesis that implementing a sleep-like phase in artificial neural networks can protect old memories during new training and alleviate catastrophic forgetting. Sleep was implemented as off-line training with local unsupervised Hebbian plasticity rules and noisy input. In an incremental learning framework, sleep was able to recover old tasks that were otherwise forgotten. Previously learned memories were replayed spontaneously during sleep, forming unique representations for each class of inputs. Representational sparseness and neuronal activity corresponding to the old tasks increased while new task related activity decreased. The study suggests that spontaneous replay simulating sleep-like dynamics can alleviate catastrophic forgetting in artificial neural networks.
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Affiliation(s)
- Timothy Tadros
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Giri P Krishnan
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ramyaa Ramyaa
- Department of Computer Science, New Mexico Tech, Soccoro, NM, 87801, USA
| | - Maxim Bazhenov
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
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14
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Yoshida K, Toyoizumi T. Information maximization explains state-dependent synaptic plasticity and memory reorganization during non-rapid eye movement sleep. PNAS NEXUS 2022; 2:pgac286. [PMID: 36712943 PMCID: PMC9833047 DOI: 10.1093/pnasnexus/pgac286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Slow waves during the non-rapid eye movement (NREM) sleep reflect the alternating up and down states of cortical neurons; global and local slow waves promote memory consolidation and forgetting, respectively. Furthermore, distinct spike-timing-dependent plasticity (STDP) operates in these up and down states. The contribution of different plasticity rules to neural information coding and memory reorganization remains unknown. Here, we show that optimal synaptic plasticity for information maximization in a cortical neuron model provides a unified explanation for these phenomena. The model indicates that the optimal synaptic plasticity is biased toward depression as the baseline firing rate increases. This property explains the distinct STDP observed in the up and down states. Furthermore, it explains how global and local slow waves predominantly potentiate and depress synapses, respectively, if the background firing rate of excitatory neurons declines with the spatial scale of waves as the model predicts. The model provides a unifying account of the role of NREM sleep, bridging neural information coding, synaptic plasticity, and memory reorganization.
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15
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Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation. PLoS Comput Biol 2022; 18:e1010628. [PMID: 36399437 PMCID: PMC9674146 DOI: 10.1371/journal.pcbi.1010628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/03/2022] [Indexed: 11/19/2022] Open
Abstract
Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of sleep for memory consolidation. Here we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on different tasks. In synaptic weight space, new task training moved the synaptic weight configuration away from the manifold representing old task leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by constraining the network synaptic weight state to the previously learned manifold, while allowing the weight configuration to converge towards the intersection of the manifolds representing old and new tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.
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16
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Singh D, Norman KA, Schapiro AC. A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation. Proc Natl Acad Sci U S A 2022; 119:e2123432119. [PMID: 36279437 PMCID: PMC9636926 DOI: 10.1073/pnas.2123432119] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/11/2022] [Indexed: 08/04/2023] Open
Abstract
How do we build up our knowledge of the world over time? Many theories of memory formation and consolidation have posited that the hippocampus stores new information, then "teaches" this information to the neocortex over time, especially during sleep. But it is unclear, mechanistically, how this actually works-How are these systems able to interact during periods with virtually no environmental input to accomplish useful learning and shifts in representation? We provide a framework for thinking about this question, with neural network model simulations serving as demonstrations. The model is composed of hippocampus and neocortical areas, which replay memories and interact with one another completely autonomously during simulated sleep. Oscillations are leveraged to support error-driven learning that leads to useful changes in memory representation and behavior. The model has a non-rapid eye movement (NREM) sleep stage, where dynamics between the hippocampus and neocortex are tightly coupled, with the hippocampus helping neocortex to reinstate high-fidelity versions of new attractors, and a REM sleep stage, where neocortex is able to more freely explore existing attractors. We find that alternating between NREM and REM sleep stages, which alternately focuses the model's replay on recent and remote information, facilitates graceful continual learning. We thus provide an account of how the hippocampus and neocortex can interact without any external input during sleep to drive useful new cortical learning and to protect old knowledge as new information is integrated.
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Affiliation(s)
- Dhairyya Singh
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
| | - Kenneth A. Norman
- Department of Psychology, Princeton University, Princeton, NJ 08540
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540
| | - Anna C. Schapiro
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
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17
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Memon AA, Catiul C, Irwin Z, Pilkington J, Memon RA, Joop A, Wood KH, Cutter G, Bamman M, Miocinovic S, Amara AW. Effects of exercise on sleep spindles in Parkinson's disease. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:952289. [PMID: 36188974 PMCID: PMC9397800 DOI: 10.3389/fresc.2022.952289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022]
Abstract
Background In a randomized, controlled trial, we showed that high-intensity rehabilitation, combining resistance training and body-weight interval training, improves sleep efficiency in Parkinson's disease (PD). Quantitative sleep EEG (sleep qEEG) features, including sleep spindles, are altered in aging and in neurodegenerative disease. Objective The objective of this post-hoc analysis was to determine the effects of exercise, in comparison to a sleep hygiene, no-exercise control group, on the quantitative characteristics of sleep spindle morphology in PD. Methods We conducted an exploratory post-hoc analysis of 24 PD participants who were randomized to exercise (supervised 3 times/week for 16 weeks) versus 26 PD participants who were assigned to a sleep hygiene, no-exercise control group. At baseline and post-intervention, all participants completed memory testing and underwent polysomnography (PSG). PSG-derived sleep EEG central leads (C3 and C4) were manually inspected, with rejection of movement and electrical artifacts. Sleep spindle events were detected based on the following parameters: (1) frequency filter = 11–16 Hz, (2) event duration = 0.5–3 s, and (3) amplitude threshold 75% percentile. We then calculated spindle morphological features, including density and amplitude. These characteristics were computed and averaged over non-rapid eye movement (NREM) sleep stages N2 and N3 for the full night and separately for the first and second halves of the recording. Intervention effects on these features were analyzed using general linear models with group x time interaction. Significant interaction effects were evaluated for correlations with changes in performance in the memory domain. Results A significant group x time interaction effect was observed for changes in sleep spindle density due to exercise compared to sleep hygiene control during N2 and N3 during the first half of the night, with a moderate effect size. This change in spindle density was positively correlated with changes in performance on memory testing in the exercise group. Conclusions This study is the first to demonstrate that high-intensity exercise rehabilitation has a potential role in improving sleep spindle density in PD and leading to better cognitive performance in the memory domain. These findings represent a promising advance in the search for non-pharmacological treatments for this common and debilitating non-motor symptom.
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Affiliation(s)
- Adeel Ali Memon
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Corina Catiul
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Zachary Irwin
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jennifer Pilkington
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Raima A. Memon
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Allen Joop
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Kimberly H. Wood
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Psychology, Samford University, Birmingham, AL, United States
| | - Gary Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Marcas Bamman
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- Florida Institute for Human and Machine Cognition, Pensacola, FL, United States
| | | | - Amy W. Amara
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- UAB Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
- Correspondence: Amy W. Amara
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18
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Harrington YA, Parisi JM, Duan D, Rojo-Wissar DM, Holingue C, Spira AP. Sex Hormones, Sleep, and Memory: Interrelationships Across the Adult Female Lifespan. Front Aging Neurosci 2022; 14:800278. [PMID: 35912083 PMCID: PMC9331168 DOI: 10.3389/fnagi.2022.800278] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 06/09/2022] [Indexed: 01/26/2023] Open
Abstract
As the population of older adults grows, so will the prevalence of aging-related conditions, including memory impairments and sleep disturbances, both of which are more common among women. Compared to older men, older women are up to twice as likely to experience sleep disturbances and are at a higher risk of cognitive decline and Alzheimer's disease and related dementias (ADRD). These sex differences may be attributed in part to fluctuations in levels of female sex hormones (i.e., estrogen and progesterone) that occur across the adult female lifespan. Though women tend to experience the most significant sleep and memory problems during the peri-menopausal period, changes in memory and sleep have also been observed across the menstrual cycle and during pregnancy. Here, we review current knowledge on the interrelationships among female sex hormones, sleep, and memory across the female lifespan, propose possible mediating and moderating mechanisms linking these variables and describe implications for ADRD risk in later life.
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Affiliation(s)
- Yasmin A. Harrington
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Jeanine M. Parisi
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Daisy Duan
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Darlynn M. Rojo-Wissar
- The Initiative on Stress, Trauma, and Resilience (STAR), Department of Psychiatry and Human Behavior, Center for Behavioral and Preventive Medicine, The Miriam Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Calliope Holingue
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Adam P. Spira
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United States
- Johns Hopkins Center on Aging and Health, Baltimore, MD, United States
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19
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The effect of interference, offline sleep, and wake on spatial statistical learning. Neurobiol Learn Mem 2022; 193:107650. [DOI: 10.1016/j.nlm.2022.107650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 04/22/2022] [Accepted: 06/03/2022] [Indexed: 11/23/2022]
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20
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Petzka M, Chatburn A, Charest I, Balanos GM, Staresina BP. Sleep spindles track cortical learning patterns for memory consolidation. Curr Biol 2022; 32:2349-2356.e4. [PMID: 35561681 DOI: 10.1016/j.cub.2022.04.045] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/11/2022] [Accepted: 04/14/2022] [Indexed: 10/18/2022]
Abstract
Memory consolidation-the transformation of labile memory traces into stable long-term representations-is facilitated by post-learning sleep. Computational and biophysical models suggest that sleep spindles may play a key mechanistic role for consolidation, igniting structural changes at cortical sites involved in prior learning. Here, we tested the resulting prediction that spindles are most pronounced over learning-related cortical areas and that the extent of this learning-spindle overlap predicts behavioral measures of memory consolidation. Using high-density scalp electroencephalography (EEG) and polysomnography (PSG) in healthy volunteers, we first identified cortical areas engaged during a temporospatial associative memory task (power decreases in the alpha/beta frequency range, 6-20 Hz). Critically, we found that participant-specific topographies (i.e., spatial distributions) of post-learning sleep spindle amplitude correlated with participant-specific learning topographies. Importantly, the extent to which spindles tracked learning patterns further predicted memory consolidation across participants. Our results provide empirical evidence for a role of post-learning sleep spindles in tracking learning networks, thereby facilitating memory consolidation.
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Affiliation(s)
- Marit Petzka
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, UK; Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany
| | - Alex Chatburn
- Cognitive and Systems Neuroscience Research Hub, University of South Australia, Adelaide, SA, Australia
| | - Ian Charest
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - George M Balanos
- School of Sport, Exercise and Rehabilitation, University of Birmingham, Birmingham, UK
| | - 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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Development, validation, and application of a Brazilian sleep myths and truths assessment scale (SLEEP-MTAS). Sleep Med 2022; 90:17-25. [DOI: 10.1016/j.sleep.2021.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 12/15/2021] [Accepted: 12/30/2021] [Indexed: 11/22/2022]
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22
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The Impact of Sleep on Neurocognition and Functioning in Schizophrenia—Is It Time to Wake-Up? JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2022; 7. [PMID: 35224206 PMCID: PMC8880843 DOI: 10.20900/jpbs.20220001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
People with schizophrenia (SZ) display substantial neurocognitive deficits that have been implicated as major contributors to poor daily functioning and disability. Previous reports have identified a number of predictors of poor neurocognition in SZ including demographics, symptoms, and treatment adherence, as well as body mass index, aerobic fitness, and exercise activity. However, the putative impact of sleep has received relatively limited consideration, despite sleep disturbances, which are pervasive in this population, resulting in symptoms that are strikingly similar to the neurocognitive deficits commonly observed in SZ. Here we argue for the consideration of the impact of sleep on neurocognition in people with SZ and propose recommendations for future research to elucidate the links between sleep parameters, neurocognition and daily functioning.
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23
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Vaseghi S, Arjmandi-Rad S, Eskandari M, Ebrahimnejad M, Kholghi G, Zarrindast MR. Modulating role of serotonergic signaling in sleep and memory. Pharmacol Rep 2021; 74:1-26. [PMID: 34743316 DOI: 10.1007/s43440-021-00339-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 01/02/2023]
Abstract
Serotonin is an important neurotransmitter with various receptors and wide-range effects on physiological processes and cognitive functions including sleep, learning, and memory. In this review study, we aimed to discuss the role of serotonergic receptors in modulating sleep-wake cycle, and learning and memory function. Furthermore, we mentioned to sleep deprivation, its effects on memory function, and the potential interaction with serotonin. Although there are thousands of research articles focusing on the relationship between sleep and serotonin; however, the pattern of serotonergic function in sleep deprivation is inconsistent and it seems that serotonin has not a certain role in the effects of sleep deprivation on memory function. Also, we found that the injection type of serotonergic agents (systemic or local), the doses of these drugs (dose-dependent effects), and up- or down-regulation of serotonergic receptors during training with various memory tasks are important issues that can be involved in the effects of serotonergic signaling on sleep-wake cycle, memory function, and sleep deprivation-induced memory impairments. This comprehensive review was conducted in the PubMed, Scopus, and ScienceDirect databases in June and July 2021, by searching keywords sleep, sleep deprivation, memory, and serotonin.
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Affiliation(s)
- Salar Vaseghi
- Medicinal Plants Research Center, Institute of Medicinal Plants, ACECR, Karaj, Iran.
| | - Shirin Arjmandi-Rad
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Maliheh Eskandari
- Faculty of Basic Sciences, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahshid Ebrahimnejad
- Department of Physiology, Faculty of Veterinary Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Gita Kholghi
- Department of Psychology, Faculty of Human Sciences, Islamic Azad University, Tonekabon Branch, Tonekabon, Iran
| | - Mohammad-Reza Zarrindast
- Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Hayes TL, Krishnan GP, Bazhenov M, Siegelmann HT, Sejnowski TJ, Kanan C. Replay in Deep Learning: Current Approaches and Missing Biological Elements. Neural Comput 2021; 33:2908-2950. [PMID: 34474476 PMCID: PMC9074752 DOI: 10.1162/neco_a_01433] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 05/28/2021] [Indexed: 11/04/2022]
Abstract
Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated in deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms. In this letter, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks. We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be used to improve artificial neural networks.
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Affiliation(s)
- Tyler L Hayes
- Rochester Institute of Technology, Rochester, NY 14623, U.S.A.
| | - Giri P Krishnan
- University of California at San Diego, La Jolla, CA 92093, U.S.A.
| | - Maxim Bazhenov
- University of California at San Diego, La Jolla, CA 92093, U.S.A.
| | | | - Terrence J Sejnowski
- University of California at San Diego, La Jolla, CA 92093, U.S.A., and Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A.
| | - Christopher Kanan
- Rochester Institute of Technology, Rochester, NY 14623, U.S.A.; Paige, New York, NY 10036, U.S.A.; and Cornell Tech, New York, NY 10044, U.S.A.
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Computational Modeling of Information Propagation during the Sleep–Waking Cycle. BIOLOGY 2021; 10:biology10100945. [PMID: 34681044 PMCID: PMC8533346 DOI: 10.3390/biology10100945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/09/2021] [Accepted: 09/16/2021] [Indexed: 11/16/2022]
Abstract
Simple Summary During the deep phases of sleep we do not normally wake up by a thunder, but we nevertheless notice it when awake. The exact same sound gets to our ears and cortex through the thalamus and still, it triggers two very different responses. There is growing experimental evidence that these two states of the brain—sleep and wakefulness—distribute sensory information in different ways across the cortex. In particular, during sleep, neuronal responses remain local and do not spread out across distant synaptically connected regions. On the contrary, during wakefulness, stimuli are able to elicit a wider spatial response. We have used a computational model of coupled cortical columns to study how these two propagation modes arise. Moreover, the transition from sleep-like to waking-like dynamics occurs in agreement with the synaptic homeostasis hypothesis and only requires the increase of excitatory conductances. We have found that, in order to reproduce the aforementioned observations, this parameter change has to be selectively applied: synaptic conductances between distinct columns have to be potentiated over local ones. Abstract Non-threatening familiar sounds can go unnoticed during sleep despite the fact that they enter our brain by exciting the auditory nerves. Extracellular cortical recordings in the primary auditory cortex of rodents show that an increase in firing rate in response to pure tones during deep phases of sleep is comparable to those evoked during wakefulness. This result challenges the hypothesis that during sleep cortical responses are weakened through thalamic gating. An alternative explanation comes from the observation that the spatiotemporal spread of the evoked activity by transcranial magnetic stimulation in humans is reduced during non-rapid eye movement (NREM) sleep as compared to the wider propagation to other cortical regions during wakefulness. Thus, cortical responses during NREM sleep remain local and the stimulus only reaches nearby neuronal populations. We aim at understanding how this behavior emerges in the brain as it spontaneously shifts between NREM sleep and wakefulness. To do so, we have used a computational neural-mass model to reproduce the dynamics of the sensory auditory cortex and corresponding local field potentials in these two brain states. Following the synaptic homeostasis hypothesis, an increase in a single parameter, namely the excitatory conductance g¯AMPA, allows us to place the model from NREM sleep into wakefulness. In agreement with the experimental results, the endogenous dynamics during NREM sleep produces a comparable, even higher, response to excitatory inputs to the ones during wakefulness. We have extended the model to two bidirectionally connected cortical columns and have quantified the propagation of an excitatory input as a function of their coupling. We have found that the general increase in all conductances of the cortical excitatory synapses that drive the system from NREM sleep to wakefulness does not boost the effective connectivity between cortical columns. Instead, it is the inter-/intra-conductance ratio of cortical excitatory synapses that should raise to facilitate information propagation across the brain.
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Torres FA, Orio P, Escobar MJ. Selection of stimulus parameters for enhancing slow wave sleep events with a neural-field theory thalamocortical model. PLoS Comput Biol 2021; 17:e1008758. [PMID: 34329289 PMCID: PMC8357165 DOI: 10.1371/journal.pcbi.1008758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 08/11/2021] [Accepted: 05/28/2021] [Indexed: 11/30/2022] Open
Abstract
Slow-wave sleep cortical brain activity, conformed by slow-oscillations and sleep spindles, plays a key role in memory consolidation. The increase of the power of the slow-wave events, obtained by auditory sensory stimulation, positively correlates with memory consolidation performance. However, little is known about the experimental protocol maximizing this effect, which could be induced by the power of slow-oscillation, the number of sleep spindles, or the timing of both events' co-occurrence. Using a mean-field model of thalamocortical activity, we studied the effect of several stimulation protocols, varying the pulse shape, duration, amplitude, and frequency, as well as a target-phase using a closed-loop approach. We evaluated the effect of these parameters on slow-oscillations (SO) and sleep-spindles (SP), considering: (i) the power at the frequency bands of interest, (ii) the number of SO and SP, (iii) co-occurrences between SO and SP, and (iv) synchronization of SP with the up-peak of the SO. The first three targets are maximized using a decreasing ramp pulse with a pulse duration of 50 ms. Also, we observed a reduction in the number of SO when increasing the stimulus energy by rising its amplitude. To assess the target-phase parameter, we applied closed-loop stimulation at 0°, 45°, and 90° of the phase of the narrow-band filtered ongoing activity, at 0.85 Hz as central frequency. The 0° stimulation produces better results in the power and number of SO and SP than the rhythmic or random stimulation. On the other hand, stimulating at 45° or 90° change the timing distribution of spindles centers but with fewer co-occurrences than rhythmic and 0° phase. Finally, we propose the application of closed-loop stimulation at the rising zero-cross point using pulses with a decreasing ramp shape and 50 ms of duration for future experimental work.
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Affiliation(s)
- Felipe A. Torres
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile
- Advanced Center for Electrical and Electronic Engineering (AC3E), Valparaíso, Chile
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Advanced Center for Electrical and Electronic Engineering (AC3E), Valparaíso, Chile
| | - María-José Escobar
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile
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Golosio B, De Luca C, Capone C, Pastorelli E, Stegel G, Tiddia G, De Bonis G, Paolucci PS. Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep. PLoS Comput Biol 2021; 17:e1009045. [PMID: 34181642 PMCID: PMC8270441 DOI: 10.1371/journal.pcbi.1009045] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/09/2021] [Accepted: 05/05/2021] [Indexed: 01/19/2023] Open
Abstract
The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories. We created a thalamo-cortical spiking model (ThaCo) with the purpose of demonstrating a link among two phenomena that we believe to be essential for the brain capability of efficient incremental learning from few examples in noisy environments. Grounded in two experimental observations—the first about the effects of deep-sleep on pre- and post-sleep firing rate distributions, the second about the combination of perceptual and contextual information in pyramidal neurons—our model joins these two ingredients. ThaCo alternates phases of incremental learning, classification and deep-sleep. Memories of handwritten digit examples are learned through thalamo-cortical and cortico-cortical plastic synapses. In absence of noise, the combination of contextual information with perception enables fast incremental learning. Deep-sleep becomes crucial when noisy inputs are considered. We observed in ThaCo both homeostatic and associative processes: deep-sleep fights noise in perceptual and internal knowledge and it supports the categorical association of examples belonging to the same digit class, through reinforcement of class-specific cortico-cortical synapses. The distributions of pre-sleep and post-sleep firing rates during classification change in a manner similar to those of experimental observation. These changes promote energetic efficiency during recall of memories, better representation of individual memories and categories and higher classification performances.
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Affiliation(s)
- Bruno Golosio
- Dipartimento di Fisica, Università di Cagliari, Cagliari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Cagliari, Italy
| | - Chiara De Luca
- Ph.D. Program in Behavioural Neuroscience, “Sapienza” Università di Roma, Rome, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
- * E-mail:
| | - Cristiano Capone
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Elena Pastorelli
- Ph.D. Program in Behavioural Neuroscience, “Sapienza” Università di Roma, Rome, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Giovanni Stegel
- Dipartimento di Chimica e Farmacia, Università di Sassari, Sassari, Italy
| | - Gianmarco Tiddia
- Dipartimento di Fisica, Università di Cagliari, Cagliari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Cagliari, Italy
| | - Giulia De Bonis
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
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Yeo V, Phillips NL, Bogdanov S, Brookes N, Epps A, Teng A, Naismith SL, Lah S. The persistence of sleep disturbance and its correlates in children with moderate to severe traumatic brain injury: A longitudinal study. Sleep Med 2021; 81:387-393. [PMID: 33819841 DOI: 10.1016/j.sleep.2021.03.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 03/04/2021] [Accepted: 03/06/2021] [Indexed: 01/24/2023]
Abstract
OBJECTIVES The primary aim was to examine whether sleep disturbances persist in children in the chronic stage of recovery from moderate or severe traumatic brain injury (TBI). The secondary aim was to examine whether memory difficulties and/or other previously identified factors relate to sleep disturbances in children with moderate to severe TBI. METHODS This longitudinal study included 21 children with moderate to severe TBI, 8-18 years old, recruited from an urban tertiary paediatric specialised brain injury rehabilitation unit. Participants were seen 5 years and again 7 years post-injury, on average. Sleep disturbances were assessed with Sleep Disturbance Scale for Children (SDSC). Correlates that were considered included indicators of TBI severity, and questionnaires assessing everyday memory, fatigue, internalizing and externalizing behaviors and pain intensity. RESULTS The SDSC scores of children with moderate to severe TBI indicated greater disturbances in initiating and maintaining sleep, arousal, sleep-wake transition, and excessive somnolence relative to the norms, at follow-up. The mean SDSC scores and the number of participants with subclinical to clinical sleep disturbances on the SDSC remained unchanged from baseline to follow-up. At follow-up, the SDSC initiating and maintaining sleep, and excessive somnolence scales were associated with poorer everyday memory and greater fatigue. CONCLUSIONS Children with moderate to severe TBI experience ongoing sleep disturbances for years post-injury. Greater sleep disturbances are associated with worse functional outcomes. Further research into sleep disturbances and development of treatments is important, as it could improve the outcomes of children with TBI.
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Affiliation(s)
- Vera Yeo
- School of Psychology, The University of Sydney, New South Wales, Australia
| | - Natalie L Phillips
- School of Psychology, The University of Sydney, New South Wales, Australia
| | - Stefan Bogdanov
- School of Psychology, The University of Sydney, New South Wales, Australia
| | - Naomi Brookes
- Rehab2Kids, Rehabilitation Unit, Sydney Children's Hospital, Randwick, New South Wales, Australia
| | - Adrienne Epps
- Rehab2Kids, Rehabilitation Unit, Sydney Children's Hospital, Randwick, New South Wales, Australia
| | - Arthur Teng
- Department of Sleep Medicine, Sydney Children's Hospital, Randwick, New South Wales, Australia; School of Paediatrics and Women's Health, University of New South Wales, Kensington, New South Wales, Australia
| | - Sharon L Naismith
- School of Psychology, The University of Sydney, New South Wales, Australia; Brain and Mind Centre, and Charles Perkins Centre, The University of Sydney, New South Wales, Australia
| | - Suncica Lah
- School of Psychology, The University of Sydney, New South Wales, Australia.
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Lah S, Bodanov S, Brookes N, Epps A, Phillips NL, Teng A, Naismith SL. Children who sustained traumatic brain injury take longer to fall asleep compared to children who sustained orthopedic injuries: actigraphy findings. Brain Inj 2021; 35:682-689. [PMID: 33689527 DOI: 10.1080/02699052.2021.1895314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Objective: Primary: to examine objective sleep outcomes in children who sustained moderate to severe traumatic brain injury (TBI). Secondary: to examine the relation of objective sleep with subjective sleep, fatigue, and injury variables.Setting: A single tertiary brain injury unit.Participants: Children (5-15 years) with moderate to severe TBI (n = 23) or orthopedic injury (OI; n = 13).Design: Cohort study.Measures: Primary: objective sleep measure (actigraphy watch). Secondary: subjective sleep measure (questionnaire), fatigue questionnaire, and injury variables.Results: On actigraphy, children with TBI had longer sleep onset latency compared to children with OI. On the sleep questionnaire, children with TBI obtained higher scores for total sleep disturbance, initiating and maintaining sleep, and excessive somnolence. On the fatigue questionnaire, greater difficulties were found for total, sleep/rest, and cognitive fatigue for the TBI group. In the TBI group, actigraphy data did not correlate with sleep questionnaire, fatigue, or injury variables.Conclusion: Our study showed evidence of objective and subjective sleep disturbance in children with moderate to severe TBI, but these two types of sleep measures were not related. It is possible that distinct mechanisms underpin objective and subjective sleep disturbance, which may require different interventions.
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Affiliation(s)
- Suncica Lah
- School of Psychology, University of Sydney, Sydney, Australia
| | - Stefan Bodanov
- School of Psychology, University of Sydney, Sydney, Australia
| | - Naomi Brookes
- Rehab2Kids Rehabilitation Unit, Sydney Children's Hospital Randwick, Randwick, Australia
| | - Adrienne Epps
- Rehab2Kids Rehabilitation Unit, Sydney Children's Hospital Randwick, Randwick, Australia
| | | | - Arthur Teng
- Department of Sleep Medicine, Sydney Children's Hospital Randwick, Randwick, Australia.,School of Paediatrics and Women's Health, University of New South Wales, Kensington, Australia
| | - Sharon L Naismith
- School of Psychology, University of Sydney, Sydney, Australia.,Brain and Mind Centre, Charles Perkins Centre, University of Sydney, Camperdown, Australia
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Sanda P, Malerba P, Jiang X, Krishnan GP, Gonzalez-Martinez J, Halgren E, Bazhenov M. Bidirectional Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Spiking Activity During NREM Sleep. Cereb Cortex 2021; 31:324-340. [PMID: 32995860 PMCID: PMC8179633 DOI: 10.1093/cercor/bhaa228] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 06/19/2020] [Accepted: 07/16/2020] [Indexed: 01/17/2023] Open
Abstract
The dialogue between cortex and hippocampus is known to be crucial for sleep-dependent memory consolidation. During slow wave sleep, memory replay depends on slow oscillation (SO) and spindles in the (neo)cortex and sharp wave-ripples (SWRs) in the hippocampus. The mechanisms underlying interaction of these rhythms are poorly understood. We examined the interaction between cortical SO and hippocampal SWRs in a model of the hippocampo-cortico-thalamic network and compared the results with human intracranial recordings during sleep. We observed that ripple occurrence peaked following the onset of an Up-state of SO and that cortical input to hippocampus was crucial to maintain this relationship. A small fraction of ripples occurred during the Down-state and controlled initiation of the next Up-state. We observed that the effect of ripple depends on its precise timing, which supports the idea that ripples occurring at different phases of SO might serve different functions, particularly in the context of encoding the new and reactivation of the old memories during memory consolidation. The study revealed complex bidirectional interaction of SWRs and SO in which early hippocampal ripples influence transitions to Up-state, while cortical Up-states control occurrence of the later ripples, which in turn influence transition to Down-state.
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Affiliation(s)
- Pavel Sanda
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Institute of Computer Science of the Czech Academy of Sciences, Prague 18207, Czech Republic
| | - Paola Malerba
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Battelle Center for Mathematical Medicine, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43215, USA
- Department of Pediatrics and Biophysics Graduate Program, Ohio State University, Columbus, OH 43215, USA
| | - Xi Jiang
- Neurosciences Graduate Program, University of California, San Diego, La Jolla 92093, USA
- Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB T1K4G9, Canada
| | - Giri P Krishnan
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Eric Halgren
- Neurosciences Graduate Program, University of California, San Diego, La Jolla 92093, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Maxim Bazhenov
- Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
- Neurosciences Graduate Program, University of California, San Diego, La Jolla 92093, USA
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31
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Altered Coactive Micropattern Connectivity in the Default-Mode Network during the Sleep-Wake Cycle. Neural Plast 2020. [DOI: 10.1155/2020/8876131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The default-mode network (DMN) is believed to be associated with levels of consciousness, but how the functional connectivity (FC) of the DMN changes across different states of consciousness is still unclear. In the current work, we addressed this issue by exploring the coactive micropattern (CAMP) networks of the DMN according to the CAMPs of rat DMN activity during the sleep-wake cycle and tracking their topological alterations among different states of consciousness. Three CAMP networks were observed in DMN activity, and they displayed greater FC and higher efficiency than the original DMN structure in all states of consciousness, implying more efficient information processing in the CAMP networks. Furthermore, no significant differences in FC or network properties were found among the three CAMP networks in the waking state. However, the three networks were distinct in their characteristics in two sleep states, indicating that different CAMP networks played specific roles in distinct sleep states. In addition, we found that the changes in the FC and network properties of the CAMP networks were similar to those in the original DMN structure, suggesting intrinsic effects of various states of consciousness on DMN dynamics. Our findings revealed three underlying CAMP networks within the DMN dynamics and deepened the current knowledge concerning FC alterations in the DMN during conscious changes in the sleep-wake cycle.
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Schmid SR, Nissen C, Riemann D, Spiegelhalder K, Frase L. Auditorische Stimulation während des Schlafs. SOMNOLOGIE 2020. [DOI: 10.1007/s11818-020-00255-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
ZusammenfassungDie Insomnie, d. h. eine Ein- und/oder Durchschlafstörung, die sich negativ auf die Leistungsfähigkeit und Tagesbefindlichkeit auswirkt, ist eine der häufigsten Erkrankungen in der Allgemeinbevölkerung. Sie wird derzeit meistens pharmakologisch und/oder psychotherapeutisch behandelt, wobei die pharmakologische Behandlung mit Benzodiazepin-Rezeptor-Agonisten zu Abhängigkeit führen kann und die Verfügbarkeit von für die Insomnie-Therapie ausgebildeten Psychotherapeuten momentan nicht in ausreichendem Maße gegeben ist. Durch innovative Behandlungsmethoden könnte hier eine Versorgungslücke effektiv geschlossen werden. Hierzu zählt die auditorische Stimulation, welche vorhandene Sinneskanäle nutzt, um den Schlaf zu beeinflussen. Bisher wurde die auditorische Stimulation vor allem zur Untersuchung von Prozessen der Gedächtniskonsolidierung bei gesunden Probanden angewendet, wobei erfolgreich eine Erhöhung langsamer Oszillationen erreicht wurde, welche vor allem während des Tiefschlafs auftreten. Erste Befunde und sekundäre Outcome-Parameter liefern Hinweise, dass die Potenzierung langsamer Oszillationen durch auditorische Stimulation den Schlaf vertiefen kann, jedoch wurde hierzu bislang keine Studie mit Insomniepatienten durchgeführt. Weitere Forschung bezüglich des Einflusses der Potenzierung langsamer Oszillationen auf die Linderung von Ein- und Durchschlafproblemen bei vorliegender nichtorganischer Insomnie erscheint daher geboten zu sein, um der hohen Beschwerdelast dieser Patientengruppe entgegenzuwirken.
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González OC, Sokolov Y, Krishnan GP, Delanois JE, Bazhenov M. Can sleep protect memories from catastrophic forgetting? eLife 2020; 9:e51005. [PMID: 32748786 PMCID: PMC7440920 DOI: 10.7554/elife.51005] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 07/19/2020] [Indexed: 11/13/2022] Open
Abstract
Continual learning remains an unsolved problem in artificial neural networks. The brain has evolved mechanisms to prevent catastrophic forgetting of old knowledge during new training. Building upon data suggesting the importance of sleep in learning and memory, we tested a hypothesis that sleep protects old memories from being forgotten after new learning. In the thalamocortical model, training a new memory interfered with previously learned old memories leading to degradation and forgetting of the old memory traces. Simulating sleep after new learning reversed the damage and enhanced old and new memories. We found that when a new memory competed for previously allocated neuronal/synaptic resources, sleep replay changed the synaptic footprint of the old memory to allow overlapping neuronal populations to store multiple memories. Our study predicts that memory storage is dynamic, and sleep enables continual learning by combining consolidation of new memory traces with reconsolidation of old memory traces to minimize interference.
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Affiliation(s)
- Oscar C González
- Department of Medicine, University of California, San DiegoLa JollaUnited States
| | - Yury Sokolov
- Department of Medicine, University of California, San DiegoLa JollaUnited States
| | - Giri P Krishnan
- Department of Medicine, University of California, San DiegoLa JollaUnited States
| | - Jean Erik Delanois
- Department of Medicine, University of California, San DiegoLa JollaUnited States
- Department of Computer Science and Engineering, University of California, San DiegoLa JollaUnited States
| | - Maxim Bazhenov
- Department of Medicine, University of California, San DiegoLa JollaUnited States
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Wei Y, Krishnan GP, Marshall L, Martinetz T, Bazhenov M. Stimulation Augments Spike Sequence Replay and Memory Consolidation during Slow-Wave Sleep. J Neurosci 2020; 40:811-824. [PMID: 31792151 PMCID: PMC6975295 DOI: 10.1523/jneurosci.1427-19.2019] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 10/14/2019] [Accepted: 11/03/2019] [Indexed: 11/21/2022] Open
Abstract
Newly acquired memory traces are spontaneously reactivated during slow-wave sleep (SWS), leading to the consolidation of recent memories. Empirical studies found that sensory stimulation during SWS can selectively enhance memory consolidation with the effect depending on the phase of stimulation. In this new study, we aimed to understand the mechanisms behind the role of sensory stimulation on memory consolidation using computational models implementing effects of neuromodulators to simulate transitions between awake and SWS sleep, and synaptic plasticity to allow the change of synaptic connections due to the training in awake or replay during sleep. We found that when closed-loop stimulation was applied during the Down states of sleep slow oscillation, particularly right before the transition from Down to Up state, it significantly affected the spatiotemporal pattern of the slow waves and maximized memory replay. In contrast, when the stimulation was presented during the Up states, it did not have a significant impact on the slow waves or memory performance after sleep. For multiple memories trained in awake, presenting stimulation cues associated with specific memory trace could selectively augment replay and enhance consolidation of that memory and interfere with consolidation of the others (particularly weak) memories. Our study proposes a synaptic-level mechanism of how memory consolidation is affected by sensory stimulation during sleep.SIGNIFICANCE STATEMENT Stimulation, such as training-associated cues or auditory stimulation, during sleep can augment consolidation of the newly encoded memories. In this study, we used a computational model of the thalamocortical system to describe the mechanisms behind the role of stimulation in memory consolidation during slow-wave sleep. Our study suggests that stimulation preferentially strengthens memory traces when delivered at a specific phase of the slow oscillation, just before the Down to Up state transition when it makes the largest impact on the spatiotemporal pattern of sleep slow waves. In the presence of multiple memories, presenting sensory cues during sleep could selectively strengthen selected memories. Our study proposes a synaptic-level mechanism of how memory consolidation is affected by sensory stimulation during sleep.
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Affiliation(s)
- Yina Wei
- Department of Medicine, University of California, San Diego, La Jolla California 92093
| | - Giri P Krishnan
- Department of Medicine, University of California, San Diego, La Jolla California 92093
| | - Lisa Marshall
- Institute for Experimental and Clinical Pharmacology and Toxicology
- Center for Brain, Behavior and Metabolism, and
| | - Thomas Martinetz
- Institute for Neuro- and Bioinformatics, University of Lübeck, 23562 Lübeck, Germany
| | - Maxim Bazhenov
- Department of Medicine, University of California, San Diego, La Jolla California 92093,
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Dash MB. Infraslow coordination of slow wave activity through altered neuronal synchrony. Sleep 2019; 42:5540154. [PMID: 31353415 DOI: 10.1093/sleep/zsz170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 04/29/2019] [Indexed: 11/14/2022] Open
Abstract
Slow wave activity (SWA; the EEG power between 0.5 and 4 Hz during non-rapid eye movement sleep [NREM]) is the best electrophysiological marker of sleep need; SWA dissipates across the night and increases following sleep deprivation. In addition to these well-documented homeostatic SWA trends, SWA exhibits extensive variability across shorter timescales (seconds to minutes) and between local cortical regions. The physiological underpinnings of SWA variability, however, remain poorly characterized. In male Sprague-Dawley rats, we observed that SWA exhibits pronounced infraslow fluctuations (~40- to 120-s periods) that are coordinated across disparate cortical locations. Peaks in SWA across infraslow cycles were associated with increased slope, amplitude, and duration of individual slow waves and a reduction in the total number of waves and proportion of multipeak waves. Using a freely available data set comprised of extracellular unit recordings during consolidated NREM episodes in male Long-Evans rats, we further show that infraslow SWA does not appear to arise as a consequence of firing rate modulation of putative excitatory or inhibitory neurons. Instead, infraslow SWA was associated with alterations in neuronal synchrony surrounding "On"/"Off" periods and changes in the number and duration of "Off" periods. Collectively, these data provide a mechanism by which SWA can be coordinated across disparate cortical locations and thereby connect local and global expression of this patterned neuronal activity. In doing so, infraslow SWA may contribute to the regulation of cortical circuits during sleep and thereby play a critical role in sleep function.
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Affiliation(s)
- Michael B Dash
- Department of Psychology, Middlebury College, Middlebury, VT
- Program in Neuroscience, Middlebury College, Middlebury, VT
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Knowland VCP, Fletcher F, Henderson LM, Walker S, Norbury CF, Gaskell MG. Sleep Promotes Phonological Learning in Children Across Language and Autism Spectra. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2019; 62:4235-4255. [PMID: 31770054 DOI: 10.1044/2019_jslhr-s-19-0098] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Purpose Establishing stable and flexible phonological representations is a key component of language development and one which is thought to vary across children with neurodevelopmental disorders affecting language acquisition. Sleep is understood to support the learning and generalization of new phonological mappings in adults, but this remains to be examined in children. This study therefore explored the time course of phonological learning in childhood and how it varies by structural language and autism symptomatology. Method Seventy-seven 7- to 13-year-old children, 30 with high autism symptomatology, were included in the study; structural language ability varied across the sample. Children learned new phonological mappings based on synthesized speech tokens in the morning; performance was then charted via repetition (without feedback) over 24 hr and followed up 4 weeks later. On the night following learning, children's sleep was monitored with polysomnography. Results A period of sleep but not wake was associated with improvement on the phonological learning task in childhood. Sleep was associated with improved performance for both trained items and novel items. Structural language ability predicted overall task performance, though language ability did not predict degree of change from one session to the next. By contrast, autism symptomatology did not explain task performance. With respect to sleep architecture, rapid eye movement features were associated with greater phonological generalization. Conclusions Children's sleep was associated with improvement in performance on both trained and novel items. Phonological generalization was associated with brain activity during rapid eye movement sleep. This study furthers our understanding of individual differences in the acquisition of new phonological mappings and the role of sleep in this process over childhood. Supplemental Material https://doi.org/10.23641/asha.11126732.
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Affiliation(s)
| | - Fay Fletcher
- Department of Psychology, University of York, United Kingdom
| | | | - Sarah Walker
- Department of Psychology, University of York, United Kingdom
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Halonen R, Kuula L, Lahti J, Makkonen T, Räikkönen K, Pesonen AK. BDNF Val66Met polymorphism moderates the association between sleep spindles and overnight visual recognition. Behav Brain Res 2019; 375:112157. [DOI: 10.1016/j.bbr.2019.112157] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 08/16/2019] [Accepted: 08/17/2019] [Indexed: 10/26/2022]
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Jiang X, Gonzalez-Martinez J, Cash SS, Chauvel P, Gale J, Halgren E. Improved identification and differentiation from epileptiform activity of human hippocampal sharp wave ripples during NREM sleep. Hippocampus 2019; 30:610-622. [PMID: 31763750 DOI: 10.1002/hipo.23183] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/09/2019] [Accepted: 11/07/2019] [Indexed: 01/26/2023]
Abstract
In rodents, pyramidal cell firing patterns from waking may be replayed in nonrapid eye movement sleep (NREM) sleep during hippocampal sharp wave ripples (HC-SWR). In humans, HC-SWR have only been recorded with electrodes implanted to localize epileptogenicity. Here, we characterize human HC-SWR with rigorous rejection of epileptiform activity, requiring multiple oscillations and coordinated sharp waves. We demonstrated typical SWR in those rare HC recordings which lack interictal epileptiform spikes (IIS) and with no or minimal seizure involvement. These HC-SWR have a similar rate (~12 min-1 on average, variable across NREM stages and anterior/posterior HC) and apparent intra-HC topography (ripple maximum in putative stratum pyramidale, slow wave in radiatum) as rodents, though with lower frequency (~85 Hz compared to ~140 Hz in rodents). Similar SWR are found in HC with IIS, but no significant seizure involvement. These SWR were modulated by behavior, being largely absent (<2 min-1 ) except during NREM sleep in both Stage 2 (~9 min-1 ) and Stage 3 (~15 min-1 ), distinguishing them from IIS. This study quantifies the basic characteristics of a strictly selected sample of SWR recorded in relatively healthy human hippocampi.
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Affiliation(s)
- Xi Jiang
- Department of Neurosciences, University of California at San Diego, La Jolla, California
| | | | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - John Gale
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Eric Halgren
- Department of Neurosciences, University of California at San Diego, La Jolla, California.,Department of Radiology, University of California at San Diego, La Jolla, California
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Klinzing JG, Niethard N, Born J. Mechanisms of systems memory consolidation during sleep. Nat Neurosci 2019; 22:1598-1610. [PMID: 31451802 DOI: 10.1038/s41593-019-0467-3] [Citation(s) in RCA: 452] [Impact Index Per Article: 90.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 07/12/2019] [Indexed: 02/06/2023]
Abstract
Long-term memory formation is a major function of sleep. Based on evidence from neurophysiological and behavioral studies mainly in humans and rodents, we consider the formation of long-term memory during sleep as an active systems consolidation process that is embedded in a process of global synaptic downscaling. Repeated neuronal replay of representations originating from the hippocampus during slow-wave sleep leads to a gradual transformation and integration of representations in neocortical networks. We highlight three features of this process: (i) hippocampal replay that, by capturing episodic memory aspects, drives consolidation of both hippocampus-dependent and non-hippocampus-dependent memory; (ii) brain oscillations hallmarking slow-wave and rapid-eye movement sleep that provide mechanisms for regulating both information flow across distant brain networks and local synaptic plasticity; and (iii) qualitative transformations of memories during systems consolidation resulting in abstracted, gist-like representations.
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Affiliation(s)
- Jens G Klinzing
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
| | - Niels Niethard
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Jan Born
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany. .,Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
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Capone C, Pastorelli E, Golosio B, Paolucci PS. Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model. Sci Rep 2019; 9:8990. [PMID: 31222151 PMCID: PMC6586839 DOI: 10.1038/s41598-019-45525-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/03/2019] [Indexed: 01/19/2023] Open
Abstract
The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems.
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Affiliation(s)
| | - Elena Pastorelli
- INFN Sezione di Roma, Rome, Italy.,PhD Program in Behavioural Neuroscience, "Sapienza" University of Rome, Rome, Italy
| | - Bruno Golosio
- Dipartimento di Fisica, Università di Cagliari, Cagliari, Italy.,INFN Sezione di Cagliari, Cagliari, Italy
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Kulkarni PM, Xiao Z, Robinson EJ, Jami AS, Zhang J, Zhou H, Henin SE, Liu AA, Osorio RS, Wang J, Chen Z. A deep learning approach for real-time detection of sleep spindles. J Neural Eng 2019; 16:036004. [PMID: 30790769 PMCID: PMC6527330 DOI: 10.1088/1741-2552/ab0933] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. APPROACH Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. MAIN RESULTS Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~two-three spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. SIGNIFICANCE SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments.
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Affiliation(s)
- Prathamesh M Kulkarni
- Department of Psychiatry, School of Medicine, New York University, New York, NY 10016, United States of America
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Langille JJ. Remembering to Forget: A Dual Role for Sleep Oscillations in Memory Consolidation and Forgetting. Front Cell Neurosci 2019; 13:71. [PMID: 30930746 PMCID: PMC6425990 DOI: 10.3389/fncel.2019.00071] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 02/13/2019] [Indexed: 12/20/2022] Open
Abstract
It has been known since the time of patient H. M. and Karl Lashley's equipotentiality studies that the hippocampus and cortex serve mnestic functions. Current memory models maintain that these two brain structures accomplish unique, but interactive, memory functions. Specifically, most modeling suggests that memories are rapidly acquired during waking experience by the hippocampus, before being later consolidated into the cortex for long-term storage. Sleep has been shown to be critical for the transfer and consolidation of memories in the cortex. Like memory consolidation, a role for sleep in adaptive forgetting has both historical precedent, as Francis Crick suggested in 1983 that sleep was for "reverse-learning," and recent empirical support. In this article I review the evidence indicating that the same brain activity involved in sleep replay associated memory consolidation is responsible for sleep-dependent forgetting. In reviewing the literature, it became clear that both a cellular mechanism for systems consolidation and an agreed upon general, as well as cellular, mechanism for sleep-dependent forgetting is seldom discussed or is lacking. I advocate here for a candidate cellular systems consolidation mechanism wherein changes in calcium kinetics and the activation of consolidative signaling cascades arise from the triple phase locking of non-rapid eye movement sleep (NREMS) slow oscillation, sleep spindle and sharp-wave ripple rhythms. I go on to speculatively consider several sleep stage specific forgetting mechanisms and conclude by discussing a notional function of NREM-rapid eye movement sleep (REMS) cycling. The discussed model argues that the cyclical organization of sleep functions to first lay down and edit and then stabilize and integrate engrams. All things considered, it is increasingly clear that hallmark sleep stage rhythms, including several NREMS oscillations and the REMS hippocampal theta rhythm, serve the dual function of enabling simultaneous memory consolidation and adaptive forgetting. Specifically, the same sleep rhythms that consolidate new memories, in the cortex and hippocampus, simultaneously organize the adaptive forgetting of older memories in these brain regions.
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Affiliation(s)
- Jesse J Langille
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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Abstract
Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms—the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.
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Affiliation(s)
- Svetlana Postnova
- School of Physics, University of Sydney, Sydney 2006, NSW, Australia;
- Center of Excellence for Integrative Brain Function, University of Sydney, Sydney 2006, NSW, Australia
- Charles Perkins Center, University of Sydney, Sydney 2006, NSW, Australia
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Kuula L, Tamminen J, Makkonen T, Merikanto I, Räikkönen K, Pesonen AK. Higher sleep spindle activity is associated with fewer false memories in adolescent girls. Neurobiol Learn Mem 2018; 157:96-105. [PMID: 30553019 DOI: 10.1016/j.nlm.2018.12.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 11/14/2018] [Accepted: 12/12/2018] [Indexed: 11/15/2022]
Abstract
BACKGROUND Sleep facilitates the extraction of semantic regularities amongst newly encoded memories, which may also lead to increased false memories. We investigated sleep stage proportions and sleep spindles in the recollection of adolescents' false memories, and their potential sex-specific differences. METHODS 196 adolescents (mean age 16.9 y; SD = 0.1, 61% girls) underwent the Deese, Roediger & McDermott (DRM) false memory procedure and overnight polysomnography, with free recall the following morning. Sleep was scored manually into stages 1, 2, 3 and REM. Stage 2 sleep spindle frequency, density, and peak amplitude were used as measures of spindle activity for slow (10-13 Hz) and fast (13-16 Hz) ranges. RESULTS In girls, a lower number of critical lures was associated with higher spindle frequency (p ≤ 0.01), density (p ≤ 0.01), and amplitude (p = 0.03). Additionally, girls' longer sleep duration was associated with more intrusion words (p = 0.03), but not with critical lures. These associations survived adjustment for age, pubertal status, and intelligence. No significant results emerged in boys. CONCLUSIONS In adolescent girls, higher spindle activity was associated with fewer critical lures being falsely recalled in the DRM paradigm. Unlike studies using adult participants, we did not observe any association between slow-wave sleep and false memory recollection.
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Affiliation(s)
- Liisa Kuula
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Jakke Tamminen
- Department of Psychology, Royal Holloway, University of London, United Kingdom
| | - Tommi Makkonen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ilona Merikanto
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anu-Katriina Pesonen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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