Development of Sleep Spindles in Young Children and Adolescents

Shinomiya, Shigeko; Nagata, Kazuyoshi; Takahashi, Kazumi; Masumura, Toshiaki

doi:10.1177/155005949903000203

Cited by 95 publications

(97 citation statements)

References 14 publications

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“…Slow spindles dominate over frontal EEG derivations and are thought to involve the superior frontal gyrus, while fast spindles show up stronger in central and parietal EEG derivations and are thought to involve the precuneus, hippocampus, medial frontal cortex, and sensorimotor areas (Schabus et al, 2007; Dehghani et al, 2011). Relevant to the present study, the topographic representation of sleep spindles change with age (Tanguay et al, 1975; Shinomiya et al, 1999). Frontal spindles are more prominent in younger children while older children show more centroparietal spindles (Shinomiya et al, 1999).…”

Section: Introductionmentioning

confidence: 51%

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

Astill

Piantoni

Raymann

et al. 2014

Front. Hum. Neurosci.

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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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Section: Introductionmentioning

confidence: 51%

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

Astill

Piantoni

Raymann

et al. 2014

Front. Hum. Neurosci.

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“…However, assessing automated spindles detection, specifically in childhood, seems important as spindles show modification in amplitude, frequency, length, density, interspindle interval and topological distribution from infancy to adolescence (Nagata et al, 1996;Shinomiya et al, 1999;Scholle et al, 2007); therefore, automated algorithms should be able to adapt to those variations. Published spindle detection algorithms in children and in infants are based on empirical-mode decomposition and Hilbert-Huang transform (Causa et al, 2010), expert procedure and fuzzy logic (Held et al, 2004(Held et al, , 2006, peak identification , merge neural gas model (Estévez et al, 2007), and neuro fuzzy approach .…”

Section: Introductionmentioning

confidence: 99%

Sleep spindle detection through amplitude–frequency normal modelling

Nonclercq

Urbain

Verheulpen

et al. 2013

Journal of Neuroscience Methods

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h i g h l i g h t sAn automated model-based spindle detection algorithm is proposed. It models the amplitude-frequency spindle distribution with a bivariate normal distribution. It automatically adapts to each individual subject and derivation. It was tested in seven healthy children and six adult patients suffering from different pathologies, and performs similarly or better than sleep experts. Normal modelling enhances spindle detection quality compared to fixed amplitude and frequency thresholds. t r a c tManual scoring of sleep spindles can be very time-consuming, and achieving accurate manual scoring on a long-term recording requires high and sustained levels of vigilance, which makes it a highly demanding task with the associated risk of decreased diagnosis accuracy. Although automatic spindle detection would be attractive, most available algorithms are sensitive to variations in spindle amplitude and frequency that occur between both subjects and derivations, reducing their effectiveness.We propose here an algorithm that models the amplitude-frequency spindle distribution with a bivariate normal distribution (one normal model per derivation). Subsequently, spindles are detected when their amplitude-frequency characteristics are included within a given tolerance interval of the corresponding model. As a consequence, spindle detection is not directly based on amplitude and frequency thresholds, but instead on a spindle distribution model that is automatically adapted to each individual subject and derivation.The algorithm was first assessed against the scoring of one sleep scoring expert on EEG samples from seven healthy children. Afterward, a second study compared performance of two additional experts versus the algorithm on a dataset of six EEG samples from adult patients suffering from different pathologies, to submit the method to more challenging and clinically realistic conditions. Smaller and shorter spindles were more difficult to evaluate, as false positives and false negatives showed lower amplitude and smaller length than true positives. In both studies, normal modelling enhanced performance compared to fixed amplitude and frequency thresholds. Normal modelling is therefore attractive, as it enhances spindle detection quality.

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“…It was shown that different types of spindles appear to reflect neuronal networks that react differentially to age-related changes, developmental maturation changes, circadian factors, pharmacological agents and homeostatic changes (Dijk and Czeisler, 1995;Landolt et al, 1996;Shinomiya et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Cortical locations of maximal spindle activity: magnetoencephalography (MEG) study

Gumenyuk

Roth

Moran

et al. 2009

Journal of Sleep Research

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SUMMAR Y The aim of this study was to determine the main cortical regions related to maximal spindle activity of sleep stage 2 in healthy individual subjects during a brief morning nap using magnetoencephalography (MEG). Eight volunteers (mean age: 26.1 ± 8.7, six women) all right handed, free of any medical psychiatric or sleep disorders were studied. Whole-head 148-channel MEG and a conventional polysomnography montage (EEG; C3, C4, O1 and O2 scalp electrodes and EOG, EMG and ECG electrodes) were used for data collection. Sleep MEG ⁄ EEG spindles were visually identified during 15 min of stage 2 sleep for each participant. The distribution of brain activity corresponding to each spindle was calculated using a combination of independent component analysis and a current source density technique superimposed upon individual MRIs. The absolute maximum of spindle activation was localized to frontal, temporal and parietal lobes. However, the most common cortical regions for maximal source spindle activity were precentral and ⁄ or postcentral areas across all individuals. The present study suggests that maximal spindle activity localized to these two regions may represent a single event for two types of spindle frequency: slow (at 12 Hz) and fast (at 14 Hz) within global thalamocortical coherence.k e y w o r d s magnetoencephalography, MR-FOCUSS, sleep spindle, source localization

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Development of Sleep Spindles in Young Children and Adolescents

Cited by 95 publications

References 14 publications

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

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

Sleep spindle detection through amplitude–frequency normal modelling

Cortical locations of maximal spindle activity: magnetoencephalography (MEG) study

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