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

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.

Sleep spindle characteristics and sleep architecture are associated with learning of executive functions in school‐age children

“…These associations of sleep spindle features with TOH performance increments (moderate to large effects) suggest that the neural processes during NREM sleep contribute to the consolidation of planning and problem‐solving skills. Our results correspond to previous findings in children, in which enhanced overnight motor skills (Astill et al., ) were related to higher slow spindle density. Moreover, our negative associations for spindle characteristics are in line with studies in children that found relations between spindle frequency and EF or general cognitive ability (Chatburn et al., ; Geiger et al., ; Gruber et al., ).…”

“…First, several studies in children failed to find significant associations of general intelligence with TOH and other executive function task performances (Bull, Espy, & Senn, ). Second, different associations with sleep were found for a procedural learning task performed in the same sample of children as reported here (Astill et al., ). Third, a general factor is unlikely given the finding that individuals differ significantly with respect to the tasks on which performance worsens most with sleep loss (Van Dongen, Baynard, Maislin, & Dinges, ).…”

“…Furthermore, it applies high‐pass filtering (transition band below 1 Hz, –3 dB at 0.3 Hz) and a 50 Hz notch filter (1 Hz bandwidth). The montage of electrodes was validated against the AASM C4‐A1 montage (Van Sweden, Kemp, Kamphuisen, & Van Der Velde, ), and previously applied successfully to obtain sleep variables, including spindle parameters, in adults with and without sleep disorders (Fronczek et al., ; Raymann & Van Someren, ) and in children (Astill et al., ; Piantoni et al., ).…”

“…The automated spindle detection method is universal, i.e. not specific to a montage, and has successfully been applied to the sleep EEG of children assessed with 128 channels (Lustenberger, Wehrle, Tushaus, Achermann, & Huber, ) and with the current montage (Astill et al., ; Piantoni et al., ). Spindle features quantified were duration, maximal amplitude, duration × maximal amplitude, power and density (the number of spindles per 30 s epoch of sleep).…”

“…The 12 Hz cut‐off was determined in a data‐driven way using the frequency distributions of typically slower spindles at F pz and typically faster spindles at C z , as previously reported by Astill et al. ().…”