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Roshchupkina L, Wens V, Coquelet N, Urbain C, de Tiege X, Peigneux P. Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep. Sci Rep 2024; 14:7531. [PMID: 38553500 PMCID: PMC10980824 DOI: 10.1038/s41598-024-58123-6] [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: 08/09/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.
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Affiliation(s)
- Liliia Roshchupkina
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium.
- UNI - ULB Neuroscience Institute, Brussels, Belgium.
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium.
- Faculté des Sciences Psychologiques et de l'Éducation, Campus du Solbosch - CP 191, Avenue F.D. Roosevelt, 50, 1050, Brussels, Belgium.
| | - Vincent Wens
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Nicolas Coquelet
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Charline Urbain
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
| | - Xavier de Tiege
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
- UNI - ULB Neuroscience Institute, Brussels, Belgium
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Denis D, Cairney SA. Neural reactivation during human sleep. Emerg Top Life Sci 2023; 7:487-498. [PMID: 38054531 PMCID: PMC10754334 DOI: 10.1042/etls20230109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/07/2023]
Abstract
Sleep promotes memory consolidation: the process by which newly acquired memories are stabilised, strengthened, and integrated into long-term storage. Pioneering research in rodents has revealed that memory reactivation in sleep is a primary mechanism underpinning sleep's beneficial effect on memory. In this review, we consider evidence for memory reactivation processes occurring in human sleep. Converging lines of research support the view that memory reactivation occurs during human sleep, and is functionally relevant for consolidation. Electrophysiology studies have shown that memory reactivation is tightly coupled to the cardinal neural oscillations of non-rapid eye movement sleep, namely slow oscillation-spindle events. In addition, functional imaging studies have found that brain regions recruited during learning become reactivated during post-learning sleep. In sum, the current evidence paints a strong case for a mechanistic role of neural reactivation in promoting memory consolidation during human sleep.
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Affiliation(s)
- Dan Denis
- Department of Psychology, University of York, York YO10 5DD, U.K
| | - Scott A. Cairney
- Department of Psychology, University of York, York YO10 5DD, U.K
- York Biomedical Research Institute, University of York, York YO10 5DD, U.K
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Somervail R, Cataldi J, Stephan AM, Siclari F, Iannetti GD. Dusk2Dawn: an EEGLAB plugin for automatic cleaning of whole-night sleep electroencephalogram using Artifact Subspace Reconstruction. Sleep 2023; 46:zsad208. [PMID: 37542730 DOI: 10.1093/sleep/zsad208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 07/20/2023] [Indexed: 08/07/2023] Open
Abstract
Whole-night sleep electroencephalogram (EEG) is plagued by several types of large-amplitude artifacts. Common approaches to remove them are fraught with issues: channel interpolation, rejection of noisy intervals, and independent component analysis are time-consuming, rely on subjective user decisions, and result in signal loss. Artifact Subspace Reconstruction (ASR) is an increasingly popular approach to rapidly and automatically clean wake EEG data. Indeed, ASR adaptively removes large-amplitude artifacts regardless of their scalp topography or consistency throughout the recording. This makes ASR, at least in theory, a highly-promising tool to clean whole-night EEG. However, ASR crucially relies on calibration against a subset of relatively clean "baseline" data. This is problematic when the baseline changes substantially over time, as in whole-night EEG data. Here we tackled this issue and, for the first time, validated ASR for cleaning sleep EEG. We demonstrate that ASR applied out-of-the-box, with the parameters recommended for wake EEG, results in the dramatic removal of slow waves. We also provide an appropriate procedure to use ASR for automatic and rapid cleaning of whole-night sleep EEG data or any long EEG recording. Our procedure is freely available in Dusk2Dawn, an open-source plugin for EEGLAB.
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Affiliation(s)
- Richard Somervail
- Neuroscience and Behaviour Laboratory, Italian Institute of Technology (IIT), Rome, Italy
- Department of Neuroscience Physiology and Pharmacology, University College London (UCL), London, UK
| | - Jacinthe Cataldi
- Centre d'Investigation et de Recherche sur le Sommeil, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
| | - Aurélie M Stephan
- Centre d'Investigation et de Recherche sur le Sommeil, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Francesca Siclari
- Centre d'Investigation et de Recherche sur le Sommeil, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
- The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Gian Domenico Iannetti
- Neuroscience and Behaviour Laboratory, Italian Institute of Technology (IIT), Rome, Italy
- Department of Neuroscience Physiology and Pharmacology, University College London (UCL), London, UK
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Ameen MS, Petzka M, Peigneux P, Hoedlmoser K. Post-training sleep modulates motor adaptation and task-related beta oscillations. J Sleep Res 2023:e14082. [PMID: 37950689 DOI: 10.1111/jsr.14082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 11/13/2023]
Abstract
Motor adaptation reflects the ability of the brain's sensorimotor system to flexibly deal with environmental changes to generate effective motor behaviour. Whether sleep contributes to the consolidation of motor adaptation remains controversial. In this study, we investigated the impact of sleep on motor adaptation and its neurophysiological correlates in a novel motor adaptation task that leverages a highly automatised motor skill, that is, typing. We hypothesised that sleep-associated memory consolidation would benefit motor adaptation and induce modulations in task-related beta band (13-30 Hz) activity during adaptation. Healthy young male experts in typing on the regular computer keyboard were trained to type on a vertically mirrored keyboard while brain activity was recorded using electroencephalography. Typing performance was assessed either after a full night of sleep with polysomnography or a similar period of daytime wakefulness. Results showed improved motor adaptation performance after nocturnal sleep but not after daytime wakefulness, and decreased beta power: (a) during mirrored typing as compared with regular typing; and (b) in the post-sleep versus the pre-sleep mirrored typing sessions. Furthermore, the slope of the electroencephalography signal, a measure of aperiodic brain activity, decreased during mirrored as compared with regular typing. Changes in the electroencephalography spectral slope from pre- to post-sleep mirrored typing sessions were correlated with changes in task performance. Finally, increased fast sleep spindle density (13-15 Hz) during the night following motor adaptation training was predictive of successful motor adaptation. These findings suggest that post-training sleep modulates neural activity supporting adaptive motor functions.
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Affiliation(s)
- Mohamed S Ameen
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
| | - Marit Petzka
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany
- Institute of Psychology, University of Hamburg, Hamburg, Germany
| | - Philippe Peigneux
- UR2NF, Neuropsychology and Functional Neuroimaging Research Unit at CRCN-Center for Research in Cognition and Neurosciences, UNI-ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Kerstin Hoedlmoser
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
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Denis D, Baran B, Mylonas D, Spitzer C, Raymond N, Talbot C, Kohnke E, Stickgold R, Keshavan M, Manoach DS. NREM sleep oscillations and their relations with sleep-dependent memory consolidation in early course psychosis and first-degree relatives. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564703. [PMID: 37961668 PMCID: PMC10634996 DOI: 10.1101/2023.10.30.564703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Sleep spindles are believed to mediate sleep-dependent memory consolidation, particularly when coupled to neocortical slow oscillations. Schizophrenia is characterized by a deficit in sleep spindles that correlates with reduced overnight memory consolidation. Here, we examined sleep spindle activity, slow oscillation-spindle coupling, and both motor procedural and verbal declarative memory consolidation in early course, minimally medicated psychosis patients and non-psychotic first-degree relatives. Using a four-night experimental procedure, we observed significant deficits in spindle density and amplitude in patients relative to controls that were driven by individuals with schizophrenia. Schizophrenia patients also showed reduced sleep-dependent consolidation of motor procedural memory, which correlated with spindle density. Contrary to expectations, there were no group differences in the consolidation of declarative memory on a word pairs task. Nor did the relatives of patients differ in spindle activity or memory consolidation compared with controls, however increased consistency in the timing of SO-spindle coupling were seen in both patient and relatives. Our results extend prior work by demonstrating correlated deficits in sleep spindles and sleep-dependent motor procedural memory consolidation in early course, minimally medicated patients with schizophrenia, but not in first-degree relatives. This is consistent with other work in suggesting that impaired sleep-dependent memory consolidation has some specificity for schizophrenia and is a core feature rather than reflecting the effects of medication or chronicity.
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Affiliation(s)
- Dan Denis
- Department of Psychology, University of York, York, UK
| | - Bengi Baran
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Dimitrios Mylonas
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | | | | | - Christine Talbot
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Erin Kohnke
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Robert Stickgold
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dara S Manoach
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
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Baxter BS, Mylonas D, Kwok KS, Talbot CE, Patel R, Zhu L, Vangel M, Stickgold R, Manoach DS. The effects of closed-loop auditory stimulation on sleep oscillatory dynamics in relation to motor procedural memory consolidation. Sleep 2023; 46:zsad206. [PMID: 37531587 PMCID: PMC11009689 DOI: 10.1093/sleep/zsad206] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/13/2023] [Indexed: 08/04/2023] Open
Abstract
STUDY OBJECTIVES Healthy aging and many disorders show reduced sleep-dependent memory consolidation and corresponding alterations in non-rapid eye movement sleep oscillations. Yet sleep physiology remains a relatively neglected target for improving memory. We evaluated the effects of closed-loop auditory stimulation during sleep (CLASS) on slow oscillations (SOs), sleep spindles, and their coupling, all in relation to motor procedural memory consolidation. METHODS Twenty healthy young adults had two afternoon naps: one with auditory stimulation during SO upstates and another with no stimulation. Twelve returned for a third nap with stimulation at variable times in relation to SO upstates. In all sessions, participants trained on the motor sequence task prior to napping and were tested afterward. RESULTS Relative to epochs with no stimulation, upstate stimuli disrupted sleep and evoked SOs, spindles, and SO-coupled spindles. Stimuli that successfully evoked oscillations were delivered closer to the peak of the SO upstate and when spindle power was lower than stimuli that failed to evoke oscillations. Across conditions, participants showed similar significant post-nap performance improvement that correlated with the density of SO-coupled spindles. CONCLUSIONS Despite its strong effects on sleep physiology, CLASS failed to enhance motor procedural memory. Our findings suggest methods to overcome this failure, including better sound calibration to preserve sleep continuity and the use of real-time predictive algorithms to more precisely target SO upstates and to avoid disrupting endogenous SO-coupled spindles and their mnemonic function. They motivate continued development of CLASS as an intervention to manipulate sleep oscillatory dynamics and improve memory.
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Affiliation(s)
- Bryan S Baxter
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Dimitrios Mylonas
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Kristi S Kwok
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christine E Talbot
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rudra Patel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lin Zhu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mark Vangel
- Department of Biostatistics, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Dara S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
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Mylonas D, Sjøgård M, Shi Z, Baxter B, Hämäläinen M, Manoach DS, Khan S. A Novel Approach to Estimating the Cortical Sources of Sleep Spindles Using Simultaneous EEG/MEG. Front Neurol 2022; 13:871166. [PMID: 35785365 PMCID: PMC9243385 DOI: 10.3389/fneur.2022.871166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/18/2022] [Indexed: 11/15/2022] Open
Abstract
Sleep spindles, defining oscillations of stage II non-rapid eye movement sleep (N2), mediate sleep-dependent memory consolidation. Spindles are disrupted in several neurodevelopmental, neuropsychiatric, and neurodegenerative disorders characterized by cognitive impairment. Increasing spindles can improve memory suggesting spindles as a promising physiological target for the development of cognitive enhancing therapies. This effort would benefit from more comprehensive and spatially precise methods to characterize spindles. Spindles, as detected with electroencephalography (EEG), are often widespread across electrodes. Available evidence, however, suggests that they act locally to enhance cortical plasticity in the service of memory consolidation. Here, we present a novel method to enhance the spatial specificity of cortical source estimates of spindles using combined EEG and magnetoencephalography (MEG) data constrained to the cortex based on structural MRI. To illustrate this method, we used simultaneous EEG and MEG recordings from 25 healthy adults during a daytime nap. We first validated source space spindle detection using only EEG data by demonstrating strong temporal correspondence with sensor space EEG spindle detection (gold standard). We then demonstrated that spindle source estimates using EEG alone, MEG alone and combined EEG/MEG are stable across nap sessions. EEG detected more source space spindles than MEG and each modality detected non-overlapping spindles that had distinct cortical source distributions. Source space EEG was more sensitive to spindles in medial frontal and lateral prefrontal cortex, while MEG was more sensitive to spindles in somatosensory and motor cortices. By combining EEG and MEG data this method leverages the differential spatial sensitivities of the two modalities to obtain a more comprehensive and spatially specific source estimation of spindles than possible with either modality alone.
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Affiliation(s)
- Dimitrios Mylonas
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- *Correspondence: Dimitrios Mylonas
| | - Martin Sjøgård
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Zhaoyue Shi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Carle Illinois Advanced Imaging Center, Carle Foundation Hospital, Urbana, IL, United States
| | - Bryan Baxter
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Dara S. Manoach
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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Solano A, Riquelme LA, Perez-Chada D, Della-Maggiore V. Visuomotor Adaptation Modulates the Clustering of Sleep Spindles Into Trains. Front Neurosci 2022; 16:803387. [PMID: 35368282 PMCID: PMC8966394 DOI: 10.3389/fnins.2022.803387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/21/2022] [Indexed: 11/26/2022] Open
Abstract
Sleep spindles are thought to promote memory consolidation. Recently, we have shown that visuomotor adaptation (VMA) learning increases the density of spindles and promotes the coupling between spindles and slow oscillations, locally, with the level of spindle-SO synchrony predicting overnight memory retention. Yet, growing evidence suggests that the rhythmicity in spindle occurrence may also influence the stabilization of declarative and procedural memories. Here, we examined if VMA learning promotes the temporal organization of sleep spindles into trains. We found that VMA increased the proportion of spindles and spindle-SO couplings in trains. In agreement with our previous work, this modulation was observed over the contralateral hemisphere to the trained hand, and predicted overnight memory retention. Interestingly, spindles grouped in a cluster showed greater amplitude and duration than isolated spindles. The fact that these features increased as a function of train length, provides evidence supporting a biological advantage of this temporal arrangement. Our work opens the possibility that the periodicity of NREM oscillations may be relevant in the stabilization of procedural memories.
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Affiliation(s)
- Agustín Solano
- IFIBIO Houssay, Department of Physiology, School of Medicine, University of Buenos Aires, Buenos Aires, Argentina
| | - Luis A. Riquelme
- IFIBIO Houssay, Department of Physiology, School of Medicine, University of Buenos Aires, Buenos Aires, Argentina
| | - Daniel Perez-Chada
- Department of Internal Medicine, Pulmonary and Sleep Medicine Service, Austral University Hospital, Buenos Aires, Argentina
| | - Valeria Della-Maggiore
- IFIBIO Houssay, Department of Physiology, School of Medicine, University of Buenos Aires, Buenos Aires, Argentina
- *Correspondence: Valeria Della-Maggiore,
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Using EEG to study sensorimotor adaptation. Neurosci Biobehav Rev 2022; 134:104520. [PMID: 35016897 DOI: 10.1016/j.neubiorev.2021.104520] [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: 08/10/2021] [Revised: 12/10/2021] [Accepted: 12/30/2021] [Indexed: 11/23/2022]
Abstract
Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.
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