The sleep EEG topography in children and adolescents shows sex differences in language areas

Highlights

• SWA is higher in females over cortical areas related to language functions.

• SWA is higher in males over prefrontal areas related to spatial functions.

• These findings may indicate sex-related differences in cortical plasticity.

• HD EEG during sleep may be as a useful method to study brain plasticity.

Abstract

The topographic distribution of slow wave activity (SWA, EEG power between 0.75 and 4.5 Hz) during non-rapid eye movement (NREM) sleep was proposed to mirror cortical maturation with a typical age-related pattern. Here, we examined whether sex differences occur in SWA topography of children and adolescents (22 age-matched subjects, 11 boys, mean age 13.4 years, range: 8.7–19.4, and 11 girls, mean age 13.4 years, range: 9.1–19.0 years). In females, SWA during the first 60 min of NREM sleep was higher over bilateral cortical areas that are related to language functions, while in males SWA was increased over the right prefrontal cortex, a region also involved in spatial abilities. We conclude that cortical areas governing functions in which one sex outperforms the other exhibit increased sleep SWA and, thus, may indicate maturation of sex-specific brain function and higher cortical plasticity during development.

Introduction

Recently, the topographic distribution of slow wave activity (SWA; EEG power between 0.75 and 4.5 Hz) during non-rapid eye movement (NREM) sleep was proposed to parallel cortical maturation from childhood through adolescence (Kurth et al., 2010b). High-density sleep EEG recordings in children and adolescents between 2 and 20 years of age showed that SWA exhibits a regional, age-specific predominance with a developmental shift from occipital to frontal regions reaching frontal derivations only during adolescence. Strikingly, the local SWA maxima paralleled the time course of cortical gray matter (Gogtay et al., 2004, Sowell et al., 2004) and behavioral maturation (Luna and Sweeney, 2004) indicating that SWA may be a marker of brain maturation. Such an interpretation is in line with the increasing number of reports showing a direct relationship between sleep slow waves and plastic cortical processes (Tononi and Cirelli, 2006, Vyazovskiy et al., 2009). More specifically, it has been hypothesized that wakefulness is associated with a net increase in synaptic strength, which is homeostatically rebalanced during sleep. This hypothesis was confirmed in various species examining markers of synaptic strength. For instance, the frequency and amplitude of miniature excitatory post-synaptic currents in cortical slices (Liu et al., 2010), the protein levels of key components of central synapses in Drosophila melanogaster (Bushey et al., 2011), the slope of the local field potential evoked by electrical cortical stimulation in rats (Vyazovskiy et al., 2008) and, finally, the slope of transcranial magnetic stimulation evoked EEG responses in humans (Huber et al., 2013) increased after wakefulness and decreased during sleep.

A close relationship between SWA and cortical synapses has been proposed early on (Feinberg, 1982). Although direct evidence is lacking, recent findings from animal studies or humans using current in vivo measures for cortical structure and activity support the suggestion that synaptic strength is reflected in deep sleep slow waves. Several studies have shown that slow wave characteristics (SWA, topography, slope, amplitude) are closely related to maturational alterations in the cortex (Kurth et al., 2010a, Kurth et al., 2010b, Buchmann et al., 2011). Moreover, slow waves represent synchronized activity among cortical neurons, as shown by multiunit recordings in the rat (Vyazovskiy et al., 2009). Thus, the more neurons show synchronized activity, the larger is the amplitude of slow waves displayed by this network. Increased synchronization is achieved by stronger synaptic connections and/or a denser network (i.e. more connections).

The proposed relationship of SWA and plastic changes in the cortex on the one hand, and the close parallel development of cortical structure and SWA on the other hand might be related via common mechanisms. Thus, the proposed functional relationship between sleep slow waves and synaptic strength possibly explains the parallels between cortical maturation (Shaw et al., 2008) and synaptic characteristics (Huttenlocher and Dabholkar, 1997) and the topography of SWA (Kurth et al., 2010b). In fact, the undisturbed mapping of cortical activity during NREM sleep may serve as a useful tool to uncover structural differences during brain maturation. Because it is generally assumed that behavioral (e.g., language) and structural (e.g., gray matter volume) sex differences (Kimura, 2000, Luders et al., 2009) already exist during childhood (Burman et al., 2008, Plante et al., 2006, Porter et al., 2011), we examined whether sexually dimorphic features are also reflected in the topography of sleep SWA.