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      Sleep spindle and slow wave frequency reflect motor skill performance in primary school-age children

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          Abstract

          Background and Aim: The role of sleep in the enhancement of motor skills has been studied extensively in adults. We aimed to determine involvement of sleep and characteristics of spindles and slow waves in a motor skill in children.

          Hypothesis: We hypothesized sleep-dependence of skill enhancement and an association of interindividual differences in skill and sleep characteristics.

          Methods: 30 children (19 females, 10.7 ± 0.8 years of age; mean ± SD) performed finger sequence tapping tasks in a repeated-measures design spanning 4 days including 1 polysomnography (PSG) night. Initial and delayed performance were assessed over 12 h of wake; 12 h with sleep; and 24 h with wake and sleep. For the 12 h with sleep, children were assigned to one of three conditions: modulation of slow waves and spindles was attempted using acoustic perturbation, and compared to yoked and no-sound control conditions.

          Analyses: Mixed effect regression models evaluated the association of sleep, its macrostructure and spindles and slow wave parameters with initial and delayed speed and accuracy.

          Results and Conclusions: Children enhance their accuracy only over an interval with sleep. Unlike previously reported in adults, children enhance their speed independent of sleep, a capacity that may to be lost in adulthood. Individual differences in the dominant frequency of spindles and slow waves were predictive for performance: children performed better if they had less slow spindles, more fast spindles and faster slow waves. On the other hand, overnight enhancement of accuracy was most pronounced in children with more slow spindles and slower slow waves, i.e., the ones with an initial lower performance. Associations of spindle and slow wave characteristics with initial performance may confound interpretation of their involvement in overnight enhancement. Slower frequencies of characteristic sleep events may mark slower learning and immaturity of networks involved in motor skills.

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          Most cited references56

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          Functional MRI evidence for adult motor cortex plasticity during motor skill learning.

          Performance of complex motor tasks, such as rapid sequences of finger movements, can be improved in terms of speed and accuracy over several weeks by daily practice sessions. This improvement does not generalize to a matched sequence of identical component movements, nor to the contralateral hand. Here we report a study of the neural changes underlying this learning using functional magnetic resonance imaging (MRI) of local blood oxygenation level-dependent (BOLD) signals evoked in primary motor cortex (M1). Before training, a comparable extent of M1 was activated by both sequences. However, two ordering effects were observed: repeating a sequence within a brief time window initially resulted in a smaller area of activation (habituation), but later in larger area of activation (enhancement), suggesting a switch in M1 processing mode within the first session (fast learning). By week 4 of training, concurrent with asymptotic performance, the extent of cortex activated by the practised sequence enlarged compared with the unpractised sequence, irrespective of order (slow learning). These changes persisted for several months. The results suggest a slowly evolving, long-term, experience-dependent reorganization of the adult M1, which may underlie the acquisition and retention of the motor skill.
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            Sleep-dependent learning and memory consolidation.

            While the functions of sleep remain largely unknown, one of the most exciting and contentious hypotheses is that sleep contributes importantly to memory. A large number of studies offer a substantive body of evidence supporting this role of sleep in what is becoming known as sleep-dependent memory processing. This review will provide evidence of sleep-dependent memory consolidation and sleep-dependent brain plasticity and is divided into five sections: (1) an overview of sleep stages, memory categories, and the distinct stages of memory development; (2) a review of the specific relationships between sleep and memory, both in humans and animals; (3) a survey of evidence describing sleep-dependent brain plasticity, including human brain imaging studies as well as animal studies of cellular neurophysiology and molecular biology. We close (4) with a consideration of unanswered questions as well as existing arguments against the role of sleep in learning and memory and (5) a concluding summary.
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              The role of sleep in learning and memory.

              P Maquet (2001)
              Sleep has been implicated in the plastic cerebral changes that underlie learning and memory. Indications that sleep participates in the consolidation of fresh memory traces come from a wide range of experimental observations. At the network level, reactivations during sleep of neuronal assemblies recently challenged by new environmental circumstances have been reported in different experimental designs. These neuronal assemblies are proposed to be involved in the processing of memory traces during sleep. However, despite this rapidly growing body of experimental data, evidence for the influence of sleep discharge patterns on memory traces remains fragmentary. The underlying role of sleep in learning and memory has yet to be precisely characterized.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                11 November 2014
                2014
                : 8
                : 910
                Affiliations
                [1] 1Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences Amsterdam, Netherlands
                [2] 2Department of Clinical Neurophysiology, Amsterdam Sleep Centre, Slotervaartziekenhuis Amsterdam, Netherlands
                [3] 3Department of Neurology, Massachusetts General Hospital Boston, MA, USA
                [4] 4Sleepvision, Berg en Dal Netherlands
                [5] 5Department of Technology and Software Development, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences Amsterdam, Netherlands
                [6] 6Sleep and Neuroimaging Laboratory, Department of Psychology, University of California Berkeley, CA, USA
                [7] 7Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School Boston, MA, USA
                [8] 8Department of Emotion and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences Amsterdam, Netherlands
                [9] 9Department of Anatomy and Neurosciences, VU University and Medical Center Amsterdam, Netherlands
                [10] 10Departments of Integrative Neurophysiology and Medical Psychology, Center for Neurogenomics and Cognitive Research (CNCR), Neuroscience Campus Amsterdam, VU University and Medical Center Amsterdam, Netherlands
                Author notes

                Edited by: Simon C. Warby, Stanford University, USA

                Reviewed by: Julie Carrier, Université de Montréal, Canada; Reut Gruber, McGill University, Canada

                *Correspondence: Eus J. W. Van Someren, Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, Netherlands e-mail: e.van.someren@ 123456nin.knaw.nl

                This article was submitted to the journal Frontiers in Human Neuroscience.

                Article
                10.3389/fnhum.2014.00910
                4227520
                25426055
                0716eaec-a274-4b86-831a-4067cf45423b
                Copyright © 2014 Astill, Piantoni, Raymann, Vis, Coppens, Walker, Stickgold, Van Der Werf and Van Someren.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 July 2014
                : 23 October 2014
                Page count
                Figures: 5, Tables: 1, Equations: 3, References: 74, Pages: 13, Words: 11236
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                children,learning,motor skill,memory,sleep,spindles,slow waves,frequency
                Neurosciences
                children, learning, motor skill, memory, sleep, spindles, slow waves, frequency

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