Ferenc Gombos, Róbert Bódizs, Ilona Kovács
Abstract:
Sleep EEG alterations are emerging features of several developmental disabilities, but detailed quantitative EEG data on the sleep phenotype of patients with Williams syndrome (WS) is still lacking. Based on laboratory (Study I) and home sleep records (Study II) here we report WS-related features of the patterns of antero-posterior 8-16 Hz non-rapid-eye-movement (NREM) sleep EEG power distributions. WS participants were characterized by region-independent decreases in 10.50-12.50 Hz and central increases in 14.75-15.75 Hz EEG power. Region-independent decreases and increases in z-scores of the spectra were observed in the 10.25-12.25 Hz and 14-16 Hz ranges, respectively. Parietal fast sigma peaks and the antero-posterior shifts in power distributions were of higher frequencies in WS (0.5-1 Hz difference). Altogether these data suggest a decrease in alpha/low sigma power, as well as a redistribution of NREM sleep 8-16 Hz EEG power toward the higher frequencies and/or a higher frequency of NREM sleep thalamocortical oscillations in WS.Introduction:
Evidence supports the robustness and stability of individual differences in non-rapid eye movement (NREM) sleep EEG spectra with a special emphasis on the 8–16 Hz range corresponding to alpha and sigma activity (De Gennaro et al., 2005; De Gennaro et al., 2008; Tan et al., 2000). A substantial genetic influence on spectral composition of NREM sleep in humans, in particular with respect to alpha and sigma frequencies was also reported (Ambrosius et al., 2008). Furthermore, the unique profile of the 8–16 Hz EEG spectra of NREM sleep was considered as the EEG fingerprint of sleep, and found to be the one of the most heritable human traits (heritability of 96%), not influenced by sleep need and intensity (De Gennaro et al., 2008). Given the increasing evidence for the robustness of this trait-like feature of NREM sleep EEG, further studies unravelling the distinct phenotypes peculiar to specific developmental disabilities are of potential interest. No study focusing on developmental disorders characterized the fine structure of the genetically determined 8–16 Hz NREM sleep EEG.Williams syndrome (WS) is a genetically determined developmental disorder linked to a microdeletion in chromosome 7q11.23 and characterized by mild to moderate mental retardation, learning difficulties, high sociability and empathy and a distinctive cognitive-linguistic profile (Järvinen-Pasley et al., 2008; Meyer- Lindenberg et al., 2006). Available data on the sleep of patients with WS suggest decreases in sleep time, sleep efficiency and REM time as well as increased slow-wave sleep (Arens et al., 1998; Goldman et al., 2009; Gombos et al., 2011). Our aim is to unravel the peculiarities of the individual sleep EEG fingerprints of WS participants. Results of these analyses might shed light on both the neural correlates of WS and the genetic factors determining sleep EEG in general.
Based on laboratory (Study I) and home sleep records (Study II) here we report WS-related features of the patterns of antero-posterior 8– 16 Hz NREM sleep EEG power distributions. Our further aim were to test the stability of the WS related sleep EEG pattern described in Study I by the inclusion of participants of the sample in Study II.
Methods:
Study I: Nine WS and 9 age- and gender matched TD control participants were enrolled in the study. Age- and sex-matching of TD participants were performed inlimits of 2 years, but usually less than 1 year difference) and sex. One except in sex-matching was intentionally introduced in the case of a 16 years old dizygotic twin pair discordant for WS and sex, the non-WS 16 year old girl serving as a TD control of her WS brother. Age range of the whole sample was 14–29 years, the mean age being 20.44 years. Five males and 13 females participated in the study. Participants underwent polysomnographic examinations on two consecutive nights during their sleep in the laboratory. Electroencephalograms (EEG) according to the 10–20 system (Jasper, 1958) at 18 recording sites referred to the mathematically linked mastoids, left and right electrooculograms (EOG) with contralateral mastoid reference, respectively, as well as bipolar submental electromyograms (EMG), electrocardiograms (ECG), and accelerometry-based left and right leg movement detections were carried out.
Study II: WS participants examined in Study I were followed up after 1 year. Moreover, the study population was extended by including 11 further WS participants (N = 20, 7 males and 13 females, age range 6–29 years, mean age 19.6±7.066 years) together with 11 TD controls (N = 20, 6 males and 14 females, age range 6–29 years, mean age 19.6±7.007 years). The twin pair discordant for WS and sex examined in Study I was again considered as a pair case control. Home sleep was recorded according to the participants preferred sleeping. We recorded EEG according to the 10–20 system (Jasper, 1958) at 21 recording sites referred to the mathematically linked mastoids. Bipolar EOG, ECG and submental as well as tibialis EMG were also recorded.
Data analysis: Sleep recordings were visually scored according to standard criteria (Rechtschaffen & Kales, 1968) in 20 s epochs. Next the 4 s epochs containing artifacts were manually removed before further automatic analyses. Average power spectral densities were calculated by Fast Fourier Transformation applied to 50% overlapping, Hanning-tapered, artifact-free 4 s epochs of whole night stages 2–4 NREM sleep.
Both the raw values and the z-scores of 8–16 Hz spectra were used in the statistical analyses. z-transformation was introduced in order to follow previous approaches in the field (De Gennaro et al., 2005) as well as to control for potentially simultaneous differences in general EEG amplitude and delta power, the latter being supported by our own results (Gombos et al., 2011) and indirectly by the reports of an increased SWS in WS (Gombos et al., 2011; Mason et al., 2008). The z-transformation results in series with a mean of 0 and standard deviation of 1, varying between 8 and 16 Hz, and reflecting the individual- and derivation-specific shapes of the spectra.
In order to test the differences in the distribution of NREM sleep 8–16 Hz EEG power along the antero-posterior cortical axis sagittally and parasagittally derived z-transformed power values were averaged [Fp = mean (Fp1, Fp2), F = mean (F3, F4, Fz), C = mean (C3, C4, Cz), P = mean (P3, P4), O = mean (O1, O2)]. Positive outputs expressed power values with relative anterior, while negative ones with relative posterior dominance at the given frequency bin and region. Statistical analyses included group comparisons by one-way analyses of variance (ANOVA). These analyses were completed with the test of the stability of NREM sleep 8– 16 Hz EEG profiles, by calculating matrix correlations within and between-participants.
Results:
Study I: The comparisons of raw EEG power values by one-way ANOVA revealed WS-specific ranges of decreased power between the frequency limits of 9–13Hz in almost all derivations. WS-dependent increases in power were observed at the vertex, at recording sites Cz (14.75–15.75 Hz) and C4 (15.25 Hz). Similar differences were reflected by the comparisons of z-scores, revealing WS-specific decreases and increases in lower (9.25–13 Hz) and higher (13.75–16 Hz) sigma ranges, respectively.
The antero-posterior distribution of z-transformed power is characterized by a predominance of lower frequency scores at the anterior and a predominance of the higher frequency scores at the posterior sites. These tendencies covered the frontopolar-to-parietal regions in TD participants and the frontopolar-to-central regions in WS participants This is supported by the visually observable differences in the peak frequencies of the group means of series Fp, F, C, P, and O (WS>TD), as well as by the cross-correlation functions of group means in series Fp–F, F–C and P–O, which reached their maxima at 1 Hz delay, suggesting a higher frequency of WS sleep spindling.
Study II: WS participants had higher sleep latency, more wake time after sleep onset, lower sleep efficiency, higher NREM and slow wave sleep percent and lower REM sleep percent. Spectral analysis revealed significant alterations in band power values of WS participants: frontopolar increases in delta power as well as more or less global decreases in alpha, sigma, beta and gamma power. The z-scores of the 8–16 Hz NREM sleep EEG spectra were significantly different among the groups, revealing WS-specific decreases and increases in lower (8–11.75 Hz) and upper (13–16 Hz) sigma ranges, respectively.
The antero-posterior distribution of the z-scores of 8–16 Hz NREM sleep EEG spectra is characterized by a predominance of lower frequency values at the anterior and a predominance of higher frequency values at the posterior sites. These tendencies covered the frontopolar-to-parietal regions in both WS and TD participants. The parieto-occipital z-score distribution was characterized by a predominance of higher sigma scores in the parietal as compared to the occipital derivations in both groups. The significant group differences in the antero-posterior z-score distributions are suggestive for higher anterior-to-posterior differences at lower and higher frequencies in Fp–F and P–O series in WS participants in comparison with TD participants. Moreover, the higher frequency at which the antero-posterior shift occurs is evident in several series and supported by the maxima in the cross-correlation functions occurring at 0.5 Hz delay in series Fp-F & F-C.
The reliability of the z-scores of the 8–16 Hz NREM sleep EEG spectra was tested by using matrix correlations. Mean within- and between participant correlations were 0.94 and 0.61, respectively. Thus, 89% and 37% of the variance was explained by within- and between participant similarity, respectively. In other words the individual-specific 8–16 Hz NREM sleep EEG profile explains somewhat more than 50% (89–37) of the variance.
The difference between mean within- and between participant zF correlations was significant (z = 3.13; p = .0009, one-tailed).
Conclusions:
Our results provide evidence for the existence of a distinct phenotype of NREM sleep EEG 8–16 Hz activity found to be characteristic for WS. This phenotype is characterized by an increased frequency of sleep spindle activity and/or a redistribution of alpha/sigma activities toward the higher frequency end of the spectrum together with the characteristic reorganization of the frequency x topography relationship. The 25–28 genes which are deleted in WS are among the candidates of regulators of sleep spindlecorroboration of our results with the compelling evidences suggesting that bursting thalamocortical oscillations are the neurophysiological bases of sleep spindling (Steriade, 2000), these issues point to an accelerated NREM sleep-dependent thalamocortical oscillatory dynamics in WS.
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