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. Author manuscript; available in PMC: 2015 Dec 23.
Published in final edited form as: Neurobiol Dis. 2007 Jul 21;30(1):1–7. doi: 10.1016/j.nbd.2007.07.014

Sleep and EEG features in genetic models of Down syndrome

Damien Colas a,b, Janice S Valletta b, Ryoko Takimoto-Kimura b, Seiji Nishino a, Nobuhiro Fujiki a, William C Mobley b, Emmanuel Mignot a,*
PMCID: PMC4689324  NIHMSID: NIHMS116645  PMID: 18282758

Abstract

Down syndrome is characterized by a host of behavioral abnormalities including sleep disturbances. Sleep and EEG was studied at the age of 3 months in two mouse models of the condition, Ts65Dn and Ts1Cje, carrying one extra copy of partially overlapping segments of the mmu chromosome 16 (equivalent to the human chromosome 21). We found that the Ts65Dn mice showed increased waking amounts at the expense of non-REM sleep, increased theta power during sleep and a delayed sleep rebound after sleep deprivation. In contrast, Ts1Cje had limited sleep and EEG abnormalities, showing only a delayed sleep rebound after sleep deprivation and no difference in theta power. We previously found that mice over-expressing the human APPwt transgene, a gene triplicated in Ts65Dn but not Ts1Cje, also show increased wake and theta power during sleep. These results demonstrate abnormalities in sleep and EEG in Ts65Dn mice and underscore a possible correlation between App overexpression and hippocampal theta oscillations.

Keywords: Sleep, Wake, EEG, REM, Ts65Dn, Ts1Cje, Theta, App, Down syndrome, Mice

Introduction

Down syndrome (DS) is a common chromosomal abnormality and one of the most frequent cause of mental retardation (Epstein, 1995). DS results from the triplication of the whole or distal part of human chromosome 21 (chr 21) (Lejeune et al., 1959). Patients with DS suffer from many health problems (Epstein, 2002) and are known to have disturbed sleep, a common cause of difficulties for family members and a possible contributor to cognitive difficulties (Andreou et al., 2002). A major contributor to sleep abnormalities in DS is sleep apnea, a consequence of the unique craniofacial profile of these patients, obesity and possible abnormalities in upper airway muscle tone (Donnelly et al., 2004). Beyond sleep apnea, however, sleep has been shown to be independently disturbed. Core sleep abnormalities include prolonged sleep latency, increased sleep fragmentation and light sleep and a reduction in rapid eye movement (REM) sleep with abnormal occulomotor movement frequency during this stage (Grubar et al., 1986, Hamaguchi et al., 1989, Diomedi et al., 1999, Levanon et al., 1999, Andreou et al., 2002). Only one study inspected quantitatively the EEG of DS patients during sleep and found a specific decrease of alpha power during REM sleep (Smigielska-Kuzia et al., 2005). Abnormalities in the waking EEG have also been reported. These include a slower alpha band (8–12 Hz) and/or an increase in theta band power (4–8 Hz) (Hirai and Izawa, 1964, Schmid et al., 1985, Politoff et al., 1996, Katada et al., 2000) during wake. Waking EEG spectra abnormalities have been found to correlate with cognitive difficulties, suggesting clinical relevance (Politoff et al., 1996).

One difficulty in interpreting the mechanism and significance of these results is dictated by the complexity of the genetic defect involving triplication of hundreds of genes and multiple downstream phenotypes (Antonarakis et al., 2001) that could interfere with sleep. Several strategies have been used in mice to circumvent this problem. These involve models of transgenic overexpression of single genes or larger human genomic segments and creation of mouse models carrying partial triplication in regions of MMU16 syntenic to human chromosome 21 (segmental trisomy models or Ts) (Dierssen et al., 2001). Among these models, Ts65Dn and Ts1Cje are especially useful as they contain triplications of partially overlapping segments in the critical DS region and cover most of the region triplicated in humans (Epstein et al., 1985, Salehi et al., 2006).

In this study, we have used these genetic models of DS to explore whether sleep abnormalities could be identified and further map these changes to specific genomic subsegments. We believe this approach to be useful not only for DS research but also to the identification of potential sleep regulatory genes. Indeed, sleep in mice has been shown to be under strong genetic control, both in terms of quantitative aspects (e.g. sleep amounts), qualitative differences (e.g. spectral analysis) and homeostatic regulation (e.g. sleep rebound after sleep deprivation). Indeed, strong differences in sleep occurrence, EEG and regulation have been found across various inbred mice strains (Valatx et al., 1972, Franken et al., 1998, Franken et al., 2001), and genes of importance for sleep have been identified (Tafti et al., 2003, Maret et al., 2005).

Materials and methods

Animals

To minimize potential differences related to genetic background variability, comparisons were carried out between affected (Ts) and their corresponding age-matched control littermates (2N) for both animal models (Ts65Dn and Ts1Cje vs. 2N). The Ts65Dn mouse colony was maintained by crossing Ts65Dn females (originally obtained from Jackson Laboratory, Bar Harbor, ME) to C57BL/ 6JEi×C3H/HeSnJ (B6EiC3Sn) F1 males (Jackson Laboratory). Fibroblasts or lymphocytes were karyotyped to identify 2N and Ts65Dn mice in all offsprings. To generate Ts1Cje mice on a similar genetic background, Ts1Cje mice on the C57BL/6JEi background were crossed with C3H/HeSnJ mice and the resulting Ts1Cje mice were crossed to B6EiC3Sn F1 mice (Sago et al., 1998). Ts1Cje mice were genotyped using multiplex polymerase chain reaction (PCR) primers for neomycin (Neo) and for a glutamate receptor, ionotropic Kainite 1 (Grik1, as internal control) as previously described (Sago et al., 1998). Mouse breeding and experiments were approved by the Committee on Animal Research of Stanford University. Animals were housed individually and were placed in sound-isolated chambers (ambient temperature at 22 °C±1 °C; light–dark cycle 12 h–12 h, lights on at 08:00 h; water and food being available ad libitum).

Implantation of EEG and EMG electrodes

Three months old Ts and 2N control mice were chronically implanted with cortical (EEG) and muscular (EMG) electrodes under deep sodium pentobarbital anesthesia (60 mg/kg, i.p.) as previously described (Colas et al., 2005). Briefly, animals were mounted onto a stereotaxic frame and body temperature maintained at 36.5–37 °C using a homeothermic blanket. Four cortical electrodes (gold-layered screw, Ø 0.3 mm) and two muscle electrodes (fluorocarbon-coated, stainless steel wire, Ø 0.03 mm; Cooner Wire, Chatsworth, CA) were placed for recording EEG and EMG. Following placement, all electrodes were soldered to a multichannel electrical connector and insulated with a coating of heat-shrinkable polyester tubing. Cortical electrodes were inserted into the dura through holes made in the skull and located above the left and right frontal cortices (two electrodes, 1 mm lateral and anterior to the bregma) and above parietal cortices (two electrodes, 1 mm lateral to the midline and 1.5 mm caudal to the bregma). EMG electrodes were inserted into neck muscles. Finally, the entire assembly was anchored and fixed to the skull using Super Bond glue (Sun Medical Co., Shiga, Japan) and dental acrylic resin. At least 10 days of recovery from the surgery were allowed prior to connection of implants to light weight recording cables and commutators. Mice were then adapted for 10 additional days to the recording situation prior to actual EEG and sleep recording.

Experimental design and EEG/EMG data acquisition

Sets of eight mice (4 Ts and 4 2N) were recorded simultaneously for 3 consecutive 24 h periods, beginning at 20:00 h. Animals were left undisturbed and sleep recordings obtained for 48 h under baseline conditions. EEG and EMG signals were recorded continuously using a Grass-Telefactor amplifier (Astro-Med, Inc., RI, USA). All signals were amplified and analog-to-digital converted at a sampling rate of 128 Hz and digitally filtered (EEG: 0.5– 40 Hz; EMG: 10–60 Hz).

Sleep state scoring and spectral analysis

Behavioral states were classified as wakefulness (W), non-rapid eye movement (NREM) sleep and REM sleep. Sleep states were determined for 8 s epochs by visual inspection of the EEG and EMG traces using Sleep Sign software (Kissei, Japan). Sleep amounts and episode transition numbers were evaluated across different time bouts: 1 h, 3 h, 12 h of light, 12 h of dark and over the entire 24 h period.

Spectral analysis of the EEG was performed on cumulated artifact-free 8s epochs of W, NREM and REM sleep using a fast Fourier transform (FFT) (sampling 512 Hz). This yielded a power spectra profile over a 0- to 50-Hz window with a 0.25-Hz resolution. A mean EEG spectrum profile was first obtained for every stage and every animal. To minimize non-specific differences in absolute power between individuals, EEG power densities in each frequency bin were then expressed as a percentage of mean total EEG power for each state. Individual power density spectra are expressed in % of total EEG power were for each behavioral state and were averaged within each animal genotype. The spectral profile comparisons across genotypes were achieved considering the overall power in the delta (1–4 Hz) and theta (6.5–8.5 Hz) range, as well as the REM sleep theta peak frequency (TPF), calculated as previously described (Colas et al., 2005).

Sleep deprivation

The integrity of homeostatic sleep regulation was explored through the study of sleep recovery after 4 h of sleep deprivation (SD) starting 4 h after lights on. SD was achieve blindly through environmental changes and by gently tapping cages or introducing novel objects whenever the animals appeared drowsy or exhibited slow waves on the EEG (Huber et al., 2000). This procedure did not differ between strains. Quantitative (sleep latency and amounts) and qualitative (EEG delta power) variables were calculated during the 4 h of recovery and normalized to the corresponding baseline value at the same Zeitgeber times for each animal. Results were finally expressed as percentage of change versus baseline for each animal and compared across genotype groups.

Statistical analysis

The overall profile of sleep and wake states distribution across the 24 h was compared across genotypes using ANOVAs on 3-h binned data values. Absolute amounts were also compared during the light, dark and overall 24-h periods. For the spectral analysis, mean band power (%) and peak frequency were also computed and compared across genotypes using one-way ANOVAs. p<0.05 (*) or p<0.01 (**).

Results

Baseline sleep–wake cycle and EEG in Ts65Dn mice versus 2N littermates

A nychthemeral distribution of sleep and wake typical of rodents was seen in animals of all genotypes, with sleep being predominantly found during the light period (Fig. 1, Table 1). Ts65Dn were characterized by increased W amounts due to increased mean duration of W bouts but a similar W bout number (Table 1A). Increased wakefulness was associated to decreased NREM sleep amounts and a decrease in NREM sleep bout duration (Table 1A). REM sleep was generally unchanged, although more REM sleep events were observed during the light period (Table 1A) and a slight increase in REM sleep amounts was seen at the beginning of the light period (Fig. 1C). The increased W and decreased NREM sleep were seen mainly during the dark period (Table 1A and Fig. 1, left panel).

Fig. 1.

Fig. 1

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Nychthemeral distribution of wake and sleep amounts in baseline. (A) Wake, (B) NREM and (C) REM sleep amounts per 3 h bouts in Ts mice versus 2N mice. Left panel Ts65Dn (bold line) versus 2N and right panel Ts1Cje (bold line) versus 2N. Ordinates: amounts in minutes; abscises: clock time in 3 h bouts. Values are expressed as mean ± SEM (n=7 per genotype). *p<0.05 and **p<0.01 (ANOVA).

Table 1.

Quantitative parameters in Ts65Dn and Ts1Cje mice versus 2N Mice

A-Ts65Dn vs. 2N Duration (min) Number of events
2N Ts65Dn 2N Ts65Dn
Wake 24 h 677±23 831±24** 302±31 292±35
Dark 412±16 533±18** 143±21 120±22
Light 265±12 298±15 159±15 172±18
NREM 24 h 653±23 493±27** 310±30 318±35
Dark 267±15 156±15** 146±20 123±22
Light 386±14 337±17 164±15 195±18
REM 24 h 99±7 104±9 98±14 122±16
Dark 38±4 28±6 42±7 35±6
Light 61±5 76±6 57±8 88±11*
B-Ts1Cje vs. 2N Duration (min) Number of events
2N Ts1Cje 2N Ts1Cje
Wake 24 h 543±25 568±17 236±26 241±23
Dark 339±20 360±14 108±15 113±10
Light 204±13 208±6 128±12 128±14
NREM 24 h 769±29 743±21 255±25 258±24
Dark 330±21 313±12 115±14 117±9
Light 440±13 429±10 140±12 141±15
REM 24 h 123±5 124±8 123±26 123±11
Dark 50±2 44±5 46±3 45±5
Light 74±5 80±4 77±5 79±7
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A-Ts65Dn vs. 2N mice and B-Ts1Cje vs. 2N mice. Total duration in minute and number of event are expressed as the mean ± SEM (n=7 per genotype) for the 24-h, dark and light periods for Wake, NREM and REM sleep. p<0.05

**

p<0.01 (ANOVA).

Spectral analysis profiles of W, NREM and REM sleep were characteristic of frequency distributions obtained using frontohippocampal EEG derivations (Fig. 2). Wake profiles did not discriminate across Ts65Dn versus controls (Fig. 2A, left panel). In contrast, large changes were observed in REM sleep (Fig. 2C, left panel) and to a lesser extent in NREM sleep (Fig. 2B, left panel). More specifically, REM sleep in Ts65Dn is characterized by a significant increase in the theta power frequency range (Fig. 2C, left panel). Using a theta band range of 6.5–8.5 Hz (frequencies defined on our experimental individual distribution), % theta power was significantly increased from 44.0 ± 1.8% to 53.1 ± 1.5% (p<0.01) (Fig. 2C, left panel). In NREM sleep, power was lower in the low frequency range including delta and higher in the theta range, as found during REM sleep (Fig. 2B, left panel). Peak frequency distribution for theta was also computed in both genotypes and found to be 7.5±0.1 Hz versus 7.0±0.2 (p<0.05) in Ts65Dn versus 2N, suggesting a slight increase in theta frequency.

Fig. 2.

Fig. 2

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Behavioral states spectral profiles in baseline. Left panel: Ts65Dn (marked line) versus 2N mice (plain line); right panel: Ts1Cje (marked line) versus 2N (plain line). (A) Wake, (B) NREM and (C) REM sleep. In ordinates, EEG power density are the mean % of total EEG power ± SEM (n = 7 per genotype) for 0.25 Hz frequency bins between 0.25 and 25 Hz (in abscises). The delta and theta ranges used for power band calculations are shown above the corresponding abscise. **p<0.01 (ANOVA).

Sleep deprivation in Ts65Dn mice compared to 2N littermates

All genotypes studied showed a characteristic rebound of sleep amounts and delta power after SD during the recovery period (Figs. 3B and C). Whereas qualitative measures of sleep (i.e. delta power) did not distinguish genotypes (Fig. 3C), quantitative differences were found in sleep amounts (Fig. 3A) and sleep latency (Fig. 3B) after SD. Ts65Dn showed a delayed rebound response as assessed by the increased latency time to the first sleep episode after the end of the deprivation period (Fig. 3A). Moreover, sleep rebound was quantitatively greater in Ts65Dn mice as assessed by a larger increase in NREM sleep amounts after SD (Fig. 3B).

Fig. 3.

Fig. 3

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Effects of SD over the 4-h subsequent rebound period versus baseline. Left: Ts65Dn (black bar) versus 2N mice (white bar); right: Ts1Cje (black bar) versus 2N (white bar). (A) Sleep latency after SD in mean min ± SEM (n=6 per genotype). (B) Sleep amounts variations after SD in mean % of variation versus individual base line values ± SEM (n=6 per genotypes). (C) NREM sleep delta power % of variation versus baseline values ± SEM (n=6 per genotypes). *p<0.05 and **p<0.01 (ANOVA).

Quantitative and qualitative sleep analysis of Ts1Cje versus 2N littermates

In contrast to Ts65Dn, Ts1Cje showed no changes in the overall daily occurrence of W, NREM and REM sleep (Table 1B and Fig. 1, right panel). Further, the spectral analysis profile of each individual state was similar across genotypes (Fig. 2, right panel). Interestingly, however, as in Ts65Dn, sleep latency after SD was significantly increased (although this effect was smaller). Sleep amounts and delta power after SD were similar across genotypes (Fig. 3A).

Discussion

In this study, we have used two mouse models of DS and explored sleep and EEG abnormalities that could be relevant to the human pathology. One of these strains, Ts65Dn, was characterized by major sleep abnormalities, including increased wakefulness, decreased NREM sleep, increased theta power during NREM and REM sleep and a delayed but increased sleep rebound after sleep deprivation. In contrast, Ts1Cje only showed a moderately delayed sleep rebound after SD but no other abnormalities. These findings were made at 3 months of age, at a time where synaptic abnormalities are established (Belichenko et al., 2004, Kleschevnikov et al., 2004). Additionally, while there are no changes in the size or number of basal forebrain cholinergic neurons (BFCNs), there were changes in the transport of NGF to BFCNs and in the pattern of cholinergic innervation of hippocampus in Ts65Dn mice at age of 6 months (Cooper et al., 2001, Salehi et al., 2006).

These results illustrate the power of using segmental trisomic models to dissect the contribution of specific genomic segments to a complex phenotype. Indeed, whereas both Ts65Dn and Ts1Cje carry one extra copy of an MMU chromosome 16 segment (equivalent to the human chromosome 21), the triplication involves a different set of overlapping genes. Ts65Dn and Ts1Cje triplicated gene region include 132 and 85 known genes, respectively. The Ts65Dn triplication spans from Znf295 to Mrpl39 whereas the Ts1Cje triplication spans from Znf295 to Sod1 and is therefore shorter of by approximately 46 genes. The finding that theta power changes and increased wakefulness are found only in Ts65Dn but not in Ts1Cje suggests that gene(s) or regulatory region(s) located between Sod1 and Mrp139 are involved in the expression of the majority of the sleep phenotype. Of notable additional interest was the comparison with our previous findings (Colas et al., 2003, Colas et al., 2004) describing the sleep and EEG phenotype in mice overexpressing Sod-1 and human wild-type App.

Whereas overexpressing Sod-1 led to increased REM sleep latency and decreased REM sleep occurrence, most striking was the finding of increased theta power in human wild-type App overexpressing mice at a similar age and using a similar protocol (Colas et al., 2004). Further, like the Ts65Dn, these mice were also found to have increased wake at the expense of NREM sleep. App is one of the 46 genes differentiating Ts65Dn from Ts1Cje. Even though the genetic background and expression levels of hAPPwt transgene differ from those of Ts65Dn mice, we hypothesize that this candidate could be involved in the generation of increased theta power and increased wakefulness in Ts65Dn mice. Other authors have found changes in EEG theta power in Tg2576, mice overexpressing App with the Swedish mutation (Wang et al., 2002, Wisor et al., 2005, Zhang et al., 2005). Of note, however, those studies have been using different experimental protocol (EEG derivations and behavioral assessment) and have been focusing on older age. Even though they do not provide directly comparable data for the age we used, they illustrate the fact that modulating App may have an effect on theta power.

Whereas changes in sleep amounts such as increased wakefulness may be difficult to interpret, more is known regarding the generation of theta oscillations. Theta oscillations are prominently expressed in the hippocampus network and influenced by a balance of inhibitory and excitatory septohippocampal inputs (Buzsaki, 2002). In this context, increased EEG theta power could emerge as the result of increased synaptic connectivity within the hippocampus network or through the recruitment of additional oscillating networks, either directly or via septohippocampal influences. As App is expressed ubiquitously in neurons, these hypotheses are all viable. Cholinergic and GABAergic influences on the network are well established (Buzsaki, 2002), and a role for App in regulating synaptic function and vesicular axonal transport is emerging (Kamal et al., 2001, Kamenetz et al., 2003). Of notable interest is the recent finding that App overexpression causes disruption of the retrograde transport of NGF, within septal cholinergic neurons in the Ts65Dn model (Salehi et al., 2006). This defect already is seen at 3 months, at the same age in which increased theta power is demonstrated. Cholinergic neuron size and number are still intact at this age, but there are notable differences in the pattern of cholinergic innervation; i.e., P75NTR immunoreactivity at 6 months in Ts65Dn (Salehi et al., 2006). At later ages, this pattern persists in Ts65Dn and was apparent in Ts1Cje. Another possibility may be dysregulated GABAergic neurotransmission within the hippocampus, as was detected (Kleschevnikov et al., 2004). However, this abnormality persists in Ts1Cje mice (Belichenko, unpublished observations). Together with the present study, those set of data suggest a possible correlation between the App gene and the regulation of hippocampal theta oscillations and will motivate additional studies e.g. the use of hippocampal slices and other genetic models (Ts65Dn, App+/+/−) to delineate the cellular and molecular substratum of this effect.

Beside theta power abnormalities during sleep, increased wake is a notable feature of Ts65Dn mice and, based on the genetic data, could also be linked to APP overexpression. A normal distribution of sleep and wake across the day and night suggests a normal circadian regulation, although not directly tested in this study. The major behavioral characteristic so far described in Ts65Dn mice is increased motor activity, particularly increased exploratory behavior, a behavior occurring mainly during the dark period (Coussons-Read and Crnic, 1996, Martinez-Cue et al., 2002). These data are in agreement with the present study showing increased wake amounts, mainly during the dark period. Increased wakefulness in these mice may be related to increased exploratory behavior. Importantly, however, the quality of the increased wake episodes is normal in these mice, as assessed by EEG on the total waking time. The dissociation between increased exploratory behavior and cognitive impairment is notable, suggesting an inability to process information. A possible hypothesis to conciliate these data may be that increased wake is a compensatory mechanism to defective online information processing but also offline information processing during sleep, as reflected by increased theta power. Alternatively, App may affect other networks involved directly in memory processing and cognition occurring during the different sleep stages (Hobson and PaceSchott, 2002). Electrophysiological studies in Ts65Dn brain slices have shown specific neuronal defects such as an impaired LTP and increased LTD (Siarey et al., 1999, Kleschevnikov et al., 2004). Hence, decreased sleep in this model could interfere with cognition (Hobson and Pace-Schott, 2002) and strategies aimed at normalizing the sleep–wake cycle in Ts65Dn might be beneficial.

We have also detected a slight increase in REM sleep occurring during the light period only. A number of studies have shown that sleep stage distribution and EEG power can vary depending of the prior previous waking experience. For instance, a learning task (whether through an experimental manipulation per se or as a side effect) or stress subsequently increases REM sleep (Maquet, 2001, Koehl et al., 2002). In Ts65Dn mice, increased REM in the light period might thus be secondary to increased sensorimotor inputs and increased processing secondary to changes in behavior during prior waking periods.

Whether or not the findings reported in our mice model parallel human data is unknown. There are a few sleep studies in DS patients, technically difficult because of the inability of those patients to be fully cooperative and the presence of sleep apnea. Nevertheless, the report of increased wakefulness and hyperactivity parallel increased wake amounts found in Ts65Dn. To our knowledge, increased theta power during sleep has not been reported in DS patients. The only relevant study found decreased alpha power in a small number of subjects during REM sleep but importantly did not discuss whether sleep apnea, a frequent co-occurrence, was controlled. More human studies, with appropriate age-matched controls and careful spectral analysis across different brain regions, are needed. These will allow examining whether abnormalities are corroborated with clinical evolution, especially with cognitive performance in the young and adults, and development of early dementia in the elderly.

Acknowledgments

Emmanuel Mignot and Damien Colas were supported by Foundation Jerome Lejeune, Paris. E. Mignot is an Howard Hughes Medical Institute Investigator. We thank Drs. Ahmad Salehi, Alexander M. Kleschevnikov and Pavel Belichenko from the Down syndrome Research and Treatment Foundation at Stanford for useful comments and discussion.

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