Different maturational changes of fast and slow sleep spindles in the first four years of life

Accepted Manuscript Different Maturational Changes of Fast and Slow Sleep Spindles in The First Four Years of Life Aurora D’Atri, Luana Novelli, Michele Ferrara, Oliviero Bruni, Luigi De Gennaro PII:

S1389-9457(17)31583-6

DOI:

10.1016/j.sleep.2017.11.1138

Reference:

SLEEP 3581

To appear in:

Sleep Medicine

Received Date: 11 September 2017 Revised Date:

14 November 2017

Accepted Date: 28 November 2017

Please cite this article as: D’Atri A, Novelli L, Ferrara M, Bruni O, De Gennaro L, Different Maturational Changes of Fast and Slow Sleep Spindles in The First Four Years of Life, Sleep Medicine (2018), doi: 10.1016/j.sleep.2017.11.1138. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT DIFFERENT MATURATIONAL CHANGES OF FAST AND SLOW SLEEP SPINDLES IN THE FIRST FOUR YEARS OF LIFE

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Aurora D’Atria, Luana Novellia, Michele Ferrarab, Oliviero Brunic, Luigi De Gennaroa

Department of Psychology, University of Rome “Sapienza”, 00185 Rome, Italy

Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy

Department of Developmental and Social Psychology, University of Rome “Sapienza”, 00185

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Rome, Italy

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Abbreviated title: Slow and fast sleep spindles in the first 4 years

Corresponding author: Luigi De Gennaro

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Dept. of Psychology

University of Rome “Sapienza”

00185 Rome (Italy)

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Via dei Marsi, 78

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Tel.: (+39) 06-49917647 Fax: (+39) 06-49917711

e-mail: [email protected]

Authors’ e-mail address Aurora D’Atri: [email protected] Luana Novelli: [email protected] Michele Ferrara: [email protected] Oliviero Bruni: [email protected] 1

ACCEPTED MANUSCRIPT Luigi De Gennaro: [email protected] Author Contributions LDG, OB, MF, and ADA designed the research. LN performed all experiments. Statistical analyses were done by ADA and LDG. All authors discussed the results and

submitted version and approve of the submission.

Conflict of interest All funding sources supporting this work are acknowledged.

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Aurora D’Atri has no conflict of interest to declare

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Luana Novelli has no conflict of interest to declare

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Michele Ferrara has no conflict of interest to declare

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Oliviero Bruni has no conflict of interest to declare

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Luigi De Gennaro has no conflict of interest to declare

Acknowledgments

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commented on the manuscript. LDG, OB and MF wrote the paper. All authors have seen the

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This work was supported by a grant of “Sapienza” University of Rome.

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ACCEPTED MANUSCRIPT Abstract Objective/Background: Massive changes in brain morphology and function in the first years of life reveal a postero-anterior trajectory of cortical maturation accompanied by regional modifications of NREM sleep. One of the most sensible marker of this maturation process is represented by electroencephalographic (EEG)

reflect maturational modifications of fast and slow spindles still lacks. Our study aimed at answering the following questions:

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1. Do cortical changes at 11.50 Hz frequency correspond to slow spindles?

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activity within the frequency range of sleep spindles. However, direct evidence that these changes actually

2. Do fast and slow spindles show different age trajectories and different topographical distributions?

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3. Do changes in peak frequency explain age changes of slow and fast spindles?

Patients/Methods: We measured the antero-posterior changes of slow and fast spindles in the first 60 min of nightly sleep of 39 infants and children (0-48 mo.).

Results: We found that (A) changes of slow spindles from birth to childhood mostly affect frontal areas, (B) variations of fast and slow spindles across age groups go in opposite direction, the latter progressively

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increasing across ages; (C) this process is not merely reducible to changes of spindle frequency. Conclusions: As a main finding, our cross-sectional study shows that the first form of mature spindle (i.e., corresponding to the adult phasic event of NREM sleep) is marked by the emergence of slow spindles on

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anterior regions around the age of 12 months.

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Keywords: Slow sleep spindles; Fast sleep spindles; Infants; Children; Brain Maturation; Local sleep.

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ACCEPTED MANUSCRIPT 1 INTRODUCTION Sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. Early childhood is a time of rapid maturation that is predictive of future functioning. We recently showed that electroencephalographic (EEG) sleep activity at 11.50 Hz exhibits specific regional changes

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during the first 48 months of life (Novelli et al., 2016). This EEG activity progressively moves along the antero-posterior axis as age progresses, with a positive relation between age and this EEG rhythm on the frontal cortex (Novelli et al., 2016; Chu et al., 2014). We hypothesized that EEG frequency at ~11 Hz may

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correspond to the so-called “slow spindles” (De Gennaro and Ferrara, 2003). In fact, several studies have revealed that there are two types of sleep spindles: slow (around 11.5 Hz) and fast (around 13.0 Hz), with

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different topographical distributions (Anderer et al., 2001; Jobert et al., 1992; Werth et al., 1997; Zeitlhofer et al., 1997). Density of slow spindles declines over consecutive NREM sleep episodes (Jobert et al., 1992), while density of fast spindles linearly increases across consecutive sleep cycles (Bódizs et al., 2009). Functional dissociations have been also reported as a function of age (Landolt et al., 1996), homeostatic and circadian factors (Aeschbach et al., 1997; Aeschbach and Borbély, 1993; Dijk and Czeisler, 1995; van

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Coevorden et al., 1991), menstrual cycle phase (Driver et al., 1996), pregnancy (Brunner et al., 1994) and pharmacological agents (Aeschbach et al., 1994; Dijk et al., 1995; Jobert et al., 1992; Scheuler et al., 1990). These observations on scalp recordings were substantially confirmed and strengthened by intracranial

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recordings (Andrillon et al., 2011; Peter-Derex et al., 2012) and fMRI (Schabus et al., 2007). A recent longitudinal study clearly showed that power in the range of sigma frequencies (11-15 Hz) become

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faster with age, linearly increasing from 6 to 18 age years (Campbell and Feinberg, 2016). However, analyses at a higher frequency resolution showed that EEG power at 11-12.8 Hz presents a declining maturational trajectory, while power at 13.4-14.4 Hz shows an increasing one (Campbell and Feinberg, 2016). This study, unfortunately, describes an opposite trend for different frequency ranges without a direct detection of spindles. In this direction, a second study across ages two, three, and five years directly measured sleep spindles, but did not discriminate between slow and fast spindles (McClain et al., 2016). Results indicate a temporal stability in all spindle characteristics between ages two and three years, while spindle frequency decreases by the age of five years, and spindle density did not show any change.

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ACCEPTED MANUSCRIPT Since there are no studies evaluating the topographical evolution of slow and fast spindles in early ages, we analyzed our dataset of sleep recordings across 0-48 months (Novelli et al., 2016) to directly measure sleep spindles and their subtypes. Our study is aimed to respond to different questions: 1. Do the cortical changes of the 11.50 Hz EEG frequency [i.e., its anteriorization over the frontal areas

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(Novelli et al., 2016)] correspond to slow spindles? 2. Do fast and slow spindles change across different age groups and with different topographical distributions?

3. Do changes in peak frequency explain age changes of slow and fast spindles?

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According to our previous findings on the emergence of a specific 11.50 Hz frontal activity after 25 months of age (Novelli et al., 2016) and to the assumption that the postero-anterior gradient of cortical maturation is

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mirrored by parallel regional changes of sleep EEG, we hypothesize that: a) changes of slow spindles from birth to childhood will mostly affect the frontal areas; b) these spindles should have a maximum after 25 months of age, and c) this maturation process is not merely reducible to a change of spindle frequency,

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which occurs starting from four months of age.

2 MATERIAL AND METHODS 2.1 Subjects

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Our dataset (Novelli et al., 2016) consisted of 39 full-term neonates and infants aged between 0-48 months

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[14 F and 25 M; mean age = 15.0 months] enrolled from pediatric clinics at the University of Rome (N=4) and Padua (N=17), and through private sources (N=18). All infants and children had a regular birth course with uncomplicated postnatal adaptation, and a normal physical examination at birth. The inclusion criteria were: absence of a history of severe health problems; absence of epileptic seizures and mental retardation; normal psychomotor development; and no ongoing medication.No infants or children had a history of major sleep problems, as confirmed by polysomnography. Furthermore, no sleep disordered breathing or parasomnias or chronic insomnia of childhood (no complains of night wakings or difficulties falling asleep) have been reported by parents in a specific interview before enrolling for the study. More details can be found in the original study (Novelli et al., 2016).

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ACCEPTED MANUSCRIPT Subjects were divided into four groups, according to their age range (Table 1). [Please, insert Table 1 about here] All parents signed a consent form before performing the study. The study was approved by the local Institutional Ethics Committee and conducted in accordance with the Declaration of Helsinki.

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2.2 Procedure Each polysomnographic study was performed at home at habitual nightly bedtime, in the interval between 20.00 and 8.00 of the following morning. We chose the nocturnal time window due to the substantial differences in the amount and distribution of sleep within the 24-h period, depending on the age of children.

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All children were following the regular napping appropriate for age, and no attempt to modify the napping

Novelli et al. (2016)]. 2.3 Polysomnographic (PSG) recording

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schedule has been carried out [for details about the polysomnographic variables in the four age groups see

The unipolar PSG montage included: EEG (at least 12 channels with the electrodes placed according the 10– 20 International System: Fp1, Fp2, Fz, Cz, Pz, Oz, F3, F4, C3, C4, O1, O2); electrooculogram (EOG), chin

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electromyogram (EMG), bilateral tibialis EMG, electrocardiogram (ECG), thorax and abdominal effort, nasal cannula, peripheral oxygen saturation, pulse and position sensors. A linked-mastoid reference (A1-A2) was used for the EEG and EOG signals. Electrode impedance was below 10 kOhm at the start of each

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recording. An Embla® Titanium portable polygraph was used for recordings. Sleep EEG signals were highpass filtered at 0.15 Hz and low-pass filtered at 220 Hz by a 40 dB/decade anti-aliasing hardware input filter.

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Data were collected with 12 bit A/D resolution and with an analogue to digital conversion rate of 8192 Hz/channel. A further 40 dB/decade anti-aliasing digital filter was applied by digital signal processing, which low-pass filtered the data at 120 Hz. Afterwards, the digitized and filtered EEG was subsequently down-sampled at 256 Hz and then stored on hard disk in European Data Format (EDF). 2.4 Sleep stage scoring In accordance with standard guidelines, the scoring of sleep stages was performed according to GriggDamberger (2016) at age 0 - <3 months, and according to the American Academy of Sleep Medicine (AASM) (Iber et al., 2007) at ≥3 - 48 months. Sleep was visually scored for consecutive 20-s epochs as Quiet Sleep (QS) in infants aged < 3 months or NREM in infants ≥ 3 months, and as Active Sleep (AS) in

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ACCEPTED MANUSCRIPT infants < 3 months or REM in infants ≥ 3 months. NREM sleep of ≥ 3 months’ infants and children was subdivided into stages N1, N2 and N3. Sleep macrostructural measures are shortly summarized in Table 1. 2.5 Spindle detection Sleep signals, downsampled at 256 Hz, were bandpassed at 0.5–30 Hz. Artifacts were carefully assessed and

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rejected on the basis of the visual scoring, excluding all channels with a poor EEG signal. Then, data were re-referenced to the average values of the available channels.

Spindle detection was performed by means of a customized algorithm in MATLAB widely used in previous studies (Ferrarelli et al., 2007, 2010; Plante et al., 2013, 2015; Sarasso et al., 2014; Gorgoni et al., 2016).

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After the off-line artifact rejection, NREM epochs were band-pass filtered between 11 and 15 Hz (–3 dB at

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10 and 16 Hz) using a Chebyshev Type II filter. The amplitude of the filtered signal was used as a new time series for each channel. The detection of a spindle occurred when the amplitude fluctuation of each channel exceeded an upper threshold set at six times the mean single channel amplitude. The upper threshold was selected as six times the mean signal amplitude, instead of the eight times adopted in the original publication (Ferrarelli et al., 2007), according to the optimal detection parameters described in Warby et al. (2014) and

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recently tested on a children sample aged from two up to five years (McClain et al., 2016). The local amplitude maximum above the upper threshold was considered as the peak amplitude of the single spindle. The points at which the amplitude fell below a lower threshold (2 times the mean amplitude of each channel)

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occurring at least 0.25 s from the peak were considered as the beginning and the end of the spindle (maximum duration: 2.5 s). Spindles falling within the 11-13 Hz frequency range were considered as “slow”,

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while those falling in the 13-15 Hz range were considered as “fast”. Spindle density was calculated as the number of spindles divided by artifact-free QS/NREM sleep minutes. Figure 1 reports representative slow and fast spindles for each age group, while Figure 2 shows the distributions of the detected spindles on the antero-posterior midline axis and the corresponding power spectra over the same sites, respectively. [Please, insert Figures 1-2 about here]

2.6 Statistical analysis

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ACCEPTED MANUSCRIPT To account for differences in sleep episode durations, we considered the first 60 min of NREM sleep stages 2 and 3 (i.e., “quiet sleep” in the 0-3 months age). This criterion was chosen according to our previous study (Novelli et al., 2016) and to Kurth et al. (2010). As a preliminary statistical approach, we performed 12 × 4, Electrode × Age Group, Analyses of Variance

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(ANOVAs) on the measures of sleep spindles. As detailed in Table 2, differences between the four groups ever showed largely significant interactions with the different scalp locations. Therefore, the density and the frequency of sleep spindles for each scalp location were compared between groups by one-way analyses of

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variance (ANOVAs) with the factor Age group. [Please, insert Table 2 about here]

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The False Discovery Rate (FDR) was applied (Benjamini and Hochberg, 1995) to correct for multiple comparisons. According to the FDR, the significance level was set to p≤0.023. Post hoc t-tests were performed for direct comparisons between groups. This time, the significance level based on the FDR was set to p≤0.012. The relation between age and spindle density and frequency was then evaluated by Pearson’s correlations for each scalp location. The FDR was also applied to correct for multiple comparisons

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(significance level set to p≤0.0086).

In order to evaluate if slow spindles and fast spindles have different age trajectories, we replicated the between group analysis (ANOVAs and Post hoc t-tests) and the correlation analysis with age (Pearson’s r)

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for the two kinds of sleep spindles separately, according to their frequency (slow spindles: 11-13 Hz; fast spindles: 13-15 Hz). Also in this case, the FDR was applied to correct for multiple comparisons. The

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significance level based on the FDR was set to p≤0.016 for the ANOVAs, to p≤0.018 for the Post hoc t-tests and to p≤0.0072 for the correlations of age with the density of slow and fast spindles. To evaluate if maturation changes of slow sleep spindles are not merely reducible to changes of their frequency, we carried out multiple regressions considering slow spindle density (11-13 Hz) and spindle frequency within the whole range (11-15 Hz) as predictors, with age as a dependent variable (significance level based on the FDR set to p≤0.02).

3 RESULTS

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ACCEPTED MANUSCRIPT [Please, insert Figure 3 about here] 3.1 Whole range of sleep spindles (11-15 Hz) Figure 3A provides a picture of age changes in spindle density and frequency during the first four years. The incidence of sleep spindles shows variations across age groups with higher density and faster frequency in

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the age 4-12 months over most of the sites, except than the occipital areas. The statistical comparisons between groups indicate that spindle density is significantly higher in this age group as compared to all others (Fig. 3B). These differences mostly involve the central and frontal sites, with the exception of O2 site

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which was significantly different when comparing the age 4-12 months to the two older groups. Mean spindle frequency also significantly peaks at the age 4-12 months, being higher than the older groups over

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the central, frontal and parietal sites. Another significant difference, is that the age 0-3 months shows a higher mean frequency compared to the age 25-48 months, again over most of the available derivations (Fig. 3B).

The correlations of age with spindle density and spindle frequency, respectively, are reported in Figure 4. While no correlation is significant for spindle density, mean frequency exhibits significant negative relations

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with age over all derivations except than Oz and O1. Although no linear correlation was significant for spindle density, it actually shows a non-linear fit. Figure 5 shows that the association between spindle density and age fits a third degree (cubic) polynomial function on the Fz scalp location. While the percentage

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of explained variance is relatively low, this non-linear relation is significant. Conversely, the evaluation of

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higher order polynomial does not explain additional percentages of variance. [Please, insert Figures 4-5 about here]

3.2 Slow and fast spindles

The independent detection of fast and slow spindles reveals a clear pattern that disambiguates results on the whole spindle frequency range. In fact, the two kinds of spindles show distinct and different changes across groups (Fig. 6A). The density of slow spindles progressively increases across age groups, with the increase being more pronounced at the frontal/prefrontal sites. However, the density of fast spindles is higher in the age 4-12 months, with larger values at the central sites. The statistical differences mostly affect the frontal areas for both slow and fast spindles, although the general pattern is largely dissimilar (Fig. 6B). The density of slow spindles on the frontal scalp locations progressively increase across age groups, and are significantly

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ACCEPTED MANUSCRIPT different at ages 13-24 and 25-48 months compared to younger ages. The density of fast spindles peaks at age 4-12 months at the frontal and central sites, with a significant reduction at older ages (Fig. 6B). Hence, the two kinds of spindles show maturational changes with different patterns of group differences on the frontal areas. Slow spindles progressively increase across age groups, and fast spindles progressively

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decrease and disappear after the age 4-12 months. The correlational analyses depicted in Figure 7 point to significant relations with age, which are limited to slow spindles. All scalp locations except C3 reveal significant correlations, with the midline electrodes showing a posterior-anterior gradient. No linear

correlation was significant for the density of fast spindles, which indeed shows a non-linear fit. Figure 8

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shows that the association between density of spindle and age fits a third degree (cubic) polynomial function on the scalp derivation where the linear association was the strongest for slow spindles (i.e., Fz).

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[Please, insert Figures 6-7-8 about here] 3.3 Multivariate analyses

According to our hypothesis, changes in both density of slow spindles and mean spindle frequency should covary across age groups. For this reason, the subsequent step was the multivariate examination of this

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covariation. The results of multiple regressions, which considered frequency of spindles and density of slow spindles as predictors and age as a dependent variable are shown in Figure 9 (supplementary videos show rotating 3D scatterplots of these relations). The density of slow spindles substantially maintains the same

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significant positive relations with age after partialing out the contribution of the mean frequency. Significant partial β values were found in correspondence of all frontal and occipital areas (Fig. 9A). The highest

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significant contribution was found at F3 derivation (Fig. 9B). On the other hand, mean spindle frequency remains negatively related to age only at Pz and O2 sites after partialing out the contribution of slow spindles. The highest contribution was at O2, where also the other variable entered the regression equation (Fig. 9C). [Please, insert Figure 9 about here]

4 DISCUSSION Our study was aimed to respond to three questions. The first concerned the topographic changes across ages of the 11.50 Hz frequency, spreading toward the frontal areas (Novelli et al., 2016), which correspond to slow spindles, particularly after two years of age. The second showed that the two kinds of spindles indeed

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ACCEPTED MANUSCRIPT have different age trajectories. Lastly, the observed changes in spindle peak frequency do not seem to explain the different trajectories of slow and fast spindles. 4.1 Changes in ~11 Hz frequency during sleep correspond to slow spindles The age-related changes of the 11.50 Hz frequency shown in our previous study (Novelli et al., 2016)

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actually correspond to changes of slow spindles. As hypothesized, the detection of spindles during quiet/NREM sleep in the first four years of life confirms that the frontal/prefrontal pattern of the upper alpha activity, expressed mainly by the ~11 Hz frequency, mostly corresponds to the so-called "slow spindles". Our previous results (Novelli et al., 2016) and the current finding suggest that the stable emergence of slow

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spindles around one year of age may be considered an early index of cortical maturation of frontal/prefrontal areas.

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In recent years, a higher density of slow spindles has been related to learning and memory processes. Interindividual differences in slow spindles of school age children age8-11 years are related to general cognitive abilities as assessed by WISC-IV (Geiger et al., 2011; Gruber et al., 2013; Hoedlmoser et al., 2014), and to declarative learning efficiency (Hoedlmoser et al., 2014). A sexually dimorphic association between both

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fast and slow spindles and cognitive ability was found in children age four to eight years. Raven Colored Progressive Matrices scores positively correlated with both slow and fast spindle amplitude, but this effect remained a tendency in males and vanished after correcting for the effects of age (Ujma et al., 2016).

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A stronger overnight enhancement in motor skill accuracy is associated with a higher density of slow spindles in children at 10.7 years age (Astill et al., 2014). Notably, although without a direct detection of

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spindles, low sigma power (10-13 Hz) is positively correlated with task processing speed in two to five yearold children (Doucette et al., 2015). According to these findings, and to the notion that sleep EEG reflects processes of brain maturation, the development of frontal slow spindles from the first year of life could be a very early measure of the future maturation in fundamental skills and cognitive abilities. 4.2 Different behaviour of slow and fast spindles across age groups Our analyses point to different age trajectories for fast and slow spindles. Both spindle types are correlated to age, but in different ways. Slow spindles progressively increase across age groups, while fast spindles decrease with age. However, a closer inspection of Figure 6 and of the post hoc comparisons (Fig. 6B) suggests that slow spindles appear at the frontal sites at around the first year and then peak after the age two

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ACCEPTED MANUSCRIPT years, while spindles with a fast frequency appear after the 4th month and then decrease after the 12th month. In other words, slow spindles seem to be a genuine maturational process, while this remains unconfirmed fast spindles. We question that these spindles actually correspond to the fast spindles of adults. Sleep exhibits substantial

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changes from birth to the first three to four months (Jenni and Carskadon, 2004), when massive changes in brain morphology and function occur. Spindles do not appear until two to four months [Lenard, 1970, as recorded by EEG; Wakai and Lutter, 2016, as recorded by magnetoencephalography (MEG) ] or at three months (Ellingson and Peters, 1980; Sankupellay et al., 2011) of age. Louis et al. (1992) showed a

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significant increase of sleep spindle frequency between one and three months of age. This increase reached a maximum between 6 and 13 weeks, but then rapidly decreased with age up to 23 weeks and then slowly

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decreased up to the end of the first year (Hughes, 1996). The two to three months age corresponds to the emergence of a peak in the sigma band (Jenni et al., 2004). At age three months post-term, only half of sleep spindle bursts are bilaterally synchronous and symmetrical, a situation that persists at six months (Ellingson, 1982). By 12 months there has been a significant improvement in bilateral synchrony, continuing into the

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second year (Kellaway, 1979). However, spindle characteristics are not uniform, being strongly influenced by the underlying cortical regions, particularly for spindle density and fundamental frequency (Piantoni et al., 2017).

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In our view, our fast spindles at the age 4-12 months seem an immature antecedent of the subsequent mature spindles, which have midline centroparietal maxima. The first form of mature spindle (i.e., corresponding to

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those detected in adults) is marked by the emergence of slow spindles on the anterior cortical regions. One possible explanation is that spindles, generated by thalamic reticular nucleus (Fuentealba and Steriade, 2005), are grouped and synchronized by cortical slow oscillations (Mölle et al., 2002). These <1Hz oscillations may appear just before the emergence of slow spindles. Coherent with this interpretation is the appearance of K complexes at five months over the frontal areas (Metcalf et al., 1971). According to this view, the timing of appearance of slow spindles may reflect a dependency on the maturation of K complexes and, slow oscillations. Jenni et al. (2004) first hypothesized that slow oscillations and sleep spindles in infants promote the formation of the thalamocortical network by providing an endogenous neural signal with repetitive and

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ACCEPTED MANUSCRIPT synchronized activity. In line with this hypothesis and with our results, the primary mature spindles are the slow ones, first appearing at the frontal/prefrontal regions around the first year of life (Tanguay et al., 1975). The appearance of mature fast spindles likely occurs later. To our best knowledge, no study has directly measured fast spindles as a function of age. The peak frequency of the sleep spindles increased with age in a

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cross-sectional (Jenni and Carskadon, 2004) and longitudinal (Tarokh and Carskadon, 2010) study in adolescents. A more recent longitudinal study by Campbell and Feinberg (2016) described an increasing maturational trajectory between ages 6 to 18 years for the EEG activity at 13.4-14.4 Hz, with a peak at 12 years (Campbell and Feinberg, 2016; Scholle et al., 2007). Two cross-sectional studies reported an increased

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peak frequency of centroparietal sigma activity around 13 years of (Shinomiya et al., 1999) and the

appearance of a centroparietal prevalence of sigma activity between ages 17-20 years (Kurth et al., 2010). In

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a prepuberal sample of children (age = 8-11 years), Hoedlmoser et al. (2014) found that the peak frequency of detected sleep spindles is restricted to the slow (11-13 Hz) spindle range, without any evidence of fast spindle peak.

Therefore, these sparse findings seem coherent in legitimating the hypothesis that the so called "fast

Ferrara, 2003).

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spindles" stably appear during adolescence, assuming the typical centroparietal prevalence (De Gennaro and

4.3 Changes in spindle frequency do not explain maturational differences of slow and fast spindles

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The observed age-related changes regarding spindle peak frequency do not explain the different trajectories of slow and fast spindles. In other words, the observed changes of slow and fast spindles across age groups

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are not merely explained by variations of the peak frequency of a unique type of spindles. The actual existence of two different types of sleep spindles with different mechanisms and correlates has been sometimes questioned (e.g., De Gennaro and Ferrara, 2003). Different variables (age and maturation, homeostatic and circadian factors) could differently affect the appearance of the two types of spindles and may directly modulate the frequency of sleep spindles (e.g., McClain et al., 2016) and also position may modulate the spindle density (Horne et al., 2003). According to this interpretation, the correlates of the two kinds of spindles could be re-interpreted in terms of an effect of age on spindle frequency. In other words, different factors, including age, may systematically affect spindle

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ACCEPTED MANUSCRIPT frequency. According to this interpretation, there are not two “different spindles” but systematic differences in the relation between spindle frequency and a long series of variables affecting it. While none cast doubts on the existence of two different frequency peaks in the range between 11 and 16 Hz, characterized by a long series of functional dissociations, we think that the issue of independent neural

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mechanisms still remains open. The main question is whether the functional dissociations imply different mechanisms, or the same mechanism at a different frequency. This issue is still more cogent for

developmental changes, since there is relatively solid evidence that maturation processes from infancy to adolescence are associated to changes in the frequency of sleep spindles (Campbell and Feinberg, 2016;

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Jenni and Carskadon, 2004; Scholle et al., 2007; Shinomiya et al., 1999; Tarokh and Carskadon, 2010). In other words, age changes co-vary with frequency changes of sleep spindles. Our results confirm the

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existence of this covariation even at early ages, indicating a progressively lower frequency of sleep spindles across the considered age range for most of the cortical areas. We confirm the recent observation by intracranial electrocorticography that the spindle band peak frequency was mostly dependent on the underlying cortical location (Piantoni et al., 2017). Although not conclusive, our multivariate analyses point

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to a partial independence of the two phenomena. Density of slow spindles remains significantly and positively associated with age even after partialing out the contribution to the correlation of the mean frequency. This association mainly involves the frontal and occipital areas. The covariation remains present

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in older ages, since a cross-sectional study between the ages of 4 and 24 years points to variations of frequency across ages and to different trends for centroparietal and frontal areas (Shinomiya et al., 1999).

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Notably, this study described four subjects, aged four to six years, with only a frontal low-frequency peak, and three teenage subjects with only a centroparietal peak (i.e., without any frontal peak). This finding, beyond confirming the existence of a covariation between age and spindle peak frequency differently affecting distinct cortical regions, also suggests that stable, "trait-like" differences in the frequency and topography of spindles/sigma activity in children (Geiger et al., 2011; Ujma et al., 2016), teens (Tarokh et al., 2011), and adults (De Gennaro et al., 2008, 2005; Tarokh et al., 2015) should be taken into account as a possible confounding factor. In our opinion, only longitudinal studies with large samples could completely disentangle the influence of these different factors. Our study, although with some intrinsic limitations (being cross-sectional with a

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ACCEPTED MANUSCRIPT relatively small sample size, and a limited record of the number of scalp locations, that precludes any conclusion about lateral, parietal or temporal sites), describes a possible independence of the maturation mechanisms of slow spindles, which covary with changes in mean frequency within the spindle range. With respect to the sample, the age ranges of the different groups strictly depended on an availability sampling.

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For this reason, the categories extended over a number of months. However, there was a general consistency of the between-group comparisons with those obtained with the regression analyses.

We are aware that our study has considered a relatively small number of electrodes. Although our electrodes array was adequately distributed to give a rough indication of antero-posterior differences, it does not

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completely warrant the use of planar topographic maps, since most of the data presented are the result of mathematical interpolation. However, we have maintained this graphical solution since it immediately

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conveys the main results.

Our methodological choice to perform sleep recordings at home at regular nightly bedtime (h: 8:00 pm – 8:00 am) depends on the substantial differences in the amount and distribution of sleep within the 24 hour period, showed by children in the age range under investigation. Moreover, in the current study, children

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were following regular napping schedules as appropriate for their age, and no attempt to modify such schedules was made. Nevertheless, as a consequence of this methodological choice, some of the current results may be potentially confounded by a circadian effect due to very young children, whose circadian

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rhythms are still not established. In other words, we had an experimental control for the circadian phase, but some age changes may potentially covary with changes in the establishment of a circadian pattern. Similarly,

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the lack of information regarding sleep duration and characteristics during the day does not allow to assess if potential differences in homeostatic pressure actually affect the manifestation of nocturnal sleep stages and spindles.

As a further limitation to the study, we considered the first 60 min of NREM sleep stages 2 and 3 (i.e., “quiet sleep” in the 0-3 months age). This criterion was chosen according to Kurth et al. (2010) aimed to account for differences in sleep episode durations. Nevertheless, this choice precludes any investigation on the evolution of spindles across the night. This could be not trivial since slow and fast spindles are characterized by different time courses across sleep cycles [i.e., fast spindles progressively increase across successive sleep cycles, while slow spindles decrease (De Gennaro and Ferrara, 2003)]. Finally, it will be of interest if future

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ACCEPTED MANUSCRIPT studies will consider empirically determined spindle frequency ranges (Ujma et al., 2016a; Ujma et al., 2016b; Horvath et al., 2017), instead of predefined ranges for slow and fast spindles.

5 CONCLUSIONS

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A growing body of studies has shown as sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. For this reason, the study of sleep changes in early childhood, that is a time of rapid maturation, which is predictive of future functioning, is becoming a privileged window to these processes.

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Here, we have shown that sleep spindles, a hallmark of NREM sleep firmly associated with learning mechanisms, are characterized by a specific maturational trajectory. The first form of mature spindle is

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marked by the emergence of slow spindles on anterior cortical regions around the first year. However, fast and slow spindles have age trajectories in opposite directions, with slow spindles at the frontal areas linearly increasing across ages.

The parallel maturation changes of sleep spindles and anterior cortical areas may be suggestive of a causal

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link. Only longitudinal studies could provide a direct contribution to this basic question, also investigating the potentially predictive relation between the timing of early sleep changes and later skill development during adolescence. Hence, the relations between the time course of development of functional brain

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neuroscience.

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specialization and that of cortical sleep physiology may become a fascinating challenge for developmental

Funding: This work was supported by a grant of “Sapienza” University of Rome (grant number: C26A1254AT).

Conflict of Interest: The authors declare that they have no conflict of interest. Ethical approval: “All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.” Informed consent: “Informed consent was obtained from all the parents of the participants included in the study.”

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ACCEPTED MANUSCRIPT LEGENDS TO THE FIGURESFigure 1. Representative slow and fast spindles in each of the 4 age groups. Slow (11–13 Hz) and fast (13–15 Hz) sleep spindles detected on Cz scalp derivation of one representative subject for each age group (2 mo.; 5 mo.; 13 mo.; 48 mo.). 3-sec EEG segments pass-band filtered between

Figure 2. Frequencies of slow and fast sleep spindles

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0.5 and 30 Hz are represented. The actual frequency of each spindle is also reported.

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Histograms of the frequencies of sleep spindles (bin resolution: 0.25 Hz) detected in correspondence of midline derivations (Fz, Cz, and Pz). The figure also reports the corresponding NREM power spectral

separation are indicated by shaded areas.

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density within the 8-16 Hz frequency range. The frequency ranges considered for the slow and fast spindles

Figure 3. Maturational changes in spindle density during the first four years of life

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A. Topographic maps of the spindle density (within the 11–15 Hz range), and mean spindle frequency in the four age groups. The maps are based on the 12 scalp derivations of the 10–20 system (electrodes positions indicated by black dots).

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B. Left side: Topographic maps of the statistical comparisons by one-way between groups ANOVAs (1st row: F values) and post hoc t-tests (2 - 7th rows: t values) on spindle density within the whole 11-15 Hz

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spindle frequency range, and mean spindle frequency. Right side: Topographic maps of probability values of the statistical comparisons.

The level of significance of the F (p ≤ 0.023 after the FDR correction) and t (p ≤ 0.012 after the FDR correction) coefficients is indicated by the arrow in correspondence of each p-values’ color bar. Maps of panels A and B are based on the 12 scalp derivations of the 10–20 system (electrodes positions indicated by black dots). Values are color-coded, plotted at the corresponding position on the planar projection of the hemispheric scalp model and interpolated between electrodes (biharmonic spline interpolation). The lateral parietal and temporal recordings are not available, and colors solely reflect the

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Figure 4. Correlations of age with spindles density and spindle frequency Topographic maps of the correlation coefficients (Pearson’s r) of age (mo.) with spindle density in the whole

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range (11–15 Hz) and mean spindle frequency, respectively (upper), and of the corresponding p values (bottom). The maps are based on the 12 scalp derivations of the 10–20 system (electrodes positions indicated by black dots). Values are color-coded, plotted at the corresponding position on the planar projection of the

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hemispheric scalp model and interpolated between electrodes (biharmonic spline interpolation). The lateral parietal and temporal recordings are not available, and colors solely reflect the procedure of interpolation.

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The level of significance (p ≤ 0.0086 after the FDR correction) is indicated by the arrow in correspondence of the p values color bar.

Figure 5. Linear and non-linear fit of the correlation between spindle measures (whole range) and age

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The figure shows that the association between spindle density and age fits a third degree (cubic) polynomial function on the Fz scalp location (panel A), and that the association over the same site between frequency

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peak of spindles and age fits a linear function (panel B).

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Figure 6. Maturational changes in spindle frequency during the first four years of life A. Topographic maps of slow (11–13 Hz) and fast (13–15 Hz) spindle density in the four age groups. B. Left side: Topographic maps of the statistical comparisons by one-way between groups ANOVAs (1st row: F values) and post hoc t-tests (2 - 7th rows: t values) on the density of slow (11–13 Hz) and fast (13–15 Hz) spindles. Right side: Topographic maps of p values corresponding to ANOVAs (1st row) and t-tests comparisons (2th - 7th rows). The level of significance of F (p ≤ 0.015 after the FDR correction) and t (p ≤ 0.018 after the FDR correction) coefficients is indicated by the arrow in correspondence of each p-values’ color bar. Maps of panels A and B are based on the 12 scalp derivations of the 10–20 system (electrodes positions

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procedure of interpolation.

Figure 7. Correlations between age and the density of slow and fast spindles

Topographic maps of the correlations coefficients (Pearson’s r) of age (mo.) with the density of slow (11–13

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Hz) and fast (13–15 Hz) spindles, respectively (upper), and of the corresponding p values (bottom). Values are color-coded and plotted at the corresponding position on the planar projection of the hemispheric scalp

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model. Values between electrodes were interpolated (biharmonic spline interpolation). The lateral parietal and temporal recordings are not available, and colors solely reflect the procedure of interpolation. The level of significance (p ≤ 0.007 after the FDR correction) is indicated by the arrow in correspondence of the p

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values color bar.

Figure 8. Linear and non-linear fit of the correlation between density of slow and fast spindles and age The figure shows that the association between density of slow spindles and age fits a linear function (panel

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A), while the correlation between fast spindles and age fits a third degree (cubic) polynomial function (panel

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B). These fits have been reported for the Fz scalp location, where the linear association was the strongest).

Figure 9. Partial correlations of age with the density of slow spindles and spindle frequency A. Topographic maps of the partial β coefficients (upper) and of the corresponding p values (bottom) resulting from the multiple regressions for each scalp derivation, considering frequency of spindles and density of slow spindles as predictors and age as a dependent variable. Values are color-coded, plotted at the corresponding position on the planar projection of the hemispheric scalp model and interpolated between electrodes (biharmonic spline interpolation). The lateral parietal and temporal recordings are not available, and colors solely reflect the procedure of interpolation. The level of significance (p ≤ 0.02 after the FDR

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predicted Age values (blue = 0 mo., red = 48 mo.). C. 3-D scatterplot of the Age as function of Slow spindle density and Spindle frequency at O2 site, as depicted in panel B.

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Table 1. Means (and SE) of polysomnographic variables in the four age groups.

TSP=Total Sleep Period; TST=Total Sleep Time; QS=Quiet Sleep; AS=Active Sleep 4-12 mo. (n=7) Mean (SE)

13-24 mo. (n=11) Mean (SE)

25-48 mo. (n=7) Mean (SE)

Age (mo.)

2.05 (1.69)

5.70 (1.30)

20.74 (6.05)

41.32 (7.50)

Gender

9 M/ 5 F

4 M/ 3 F

7 M/ 4 F

5 M/ 2 F

TSP (min)

268.4 (72.0)

349.6 (93.8)

349.2 (109.7)

359.7 (74.7)

TST (min)

216.2 (52.1)

307.4 (79.9)

327.2 (100.6)

325.4 (69.1)

QS/NREM (%)

64.5 (3.6)

76.2 (5.2)

81.7 (3.8)

85.2 (3.4)

35.5 (3.6)

23.8 (5.2)

18.3 (3.8)

14.8 (3.4)

10.4 (3.3)

7.7 (3.1)

3.6 (1.8)

8.0 (2.8)

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WASO (% SPT)

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AS/REM (%)

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0-3 mo. (n=14) Mean (SE)

Table 2. Results of the Electrode X Age Group Analyses of Variance (ANOVAs) on the measures of sleep spindles.

Dependent variables

Electrode (E)

Age Group (A)

ExA interaction

F11,385 (p)

F3,35 (p)

F33,385 (p)

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1.82 (p= 0.1620)

3.42 (p= 4.2 x 10-09)

Spindle Frequency

5.29 (p= 8.25 x 10-08)

10.65 (p= 4.03 x 10-05)

2.49 (p= 1.91 x 10-05)

Slow Spindle Density (11-13 Hz)

11.84 (p< 10-15)

11.30 (p= 2.46 x 10-05)

3.79 (p= 1.35 x 10-10)

Fast Spindle Density (13-15 Hz)

12.43 (p< 10-15)

5.01 (p= 0.005)

2.99 (p= 2.15 x 10-07)

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Spindle Density (11-15 Hz)

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Mean spindle frequency linearly decreases from birth to childhood Unlike fast spindles, slow spindles progressively increase across the age range Maturational changes of slow spindles mostly affect the frontal areas This maturation process is not merely reducible to changes of spindle frequency Frontal slow spindles emerging at one year represent the first form of mature spindle

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